Wind power has emerged as a crucial substitute for conventional fossil fuels. The combination of advanced technologies such as the internet of things (IoT) and machine learning (ML) has given rise to a new generation of energy systems that are intelligent, reliable, and efficient. The wind en-ergy sector utilizes IoT devices to gather vital data, subsequently converting them into practical insights. The aforementioned information aids among others in the enhancement of wind turbine efficiency, precise anticipation of energy production, optimization of maintenance approaches, and detection of potential risks. In this context, the main goal of this work is to combine the IoT with ML in the wind energy sector by processing weather data acquired from sensors to forecast wind power generation. To this end, three different regression models are evaluated. The models under comparison include Linear Regression, Random Forest, and Lasso Regression, evaluated us-ing metrics such as coefficient of determination (R²), adjusted R², mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). After examining a dataset from IoT devices that included weather data, the models provided substantial insights regarding their ca-pabilities and responses to preprocessing, as well as each model's reaction in terms of statistical performance deviation indicators. Ultimately, the preprocessing and the data analysis show that Random Forest regression is more suitable for weather condition datasets than the other two re-gression models. Both the advantages and shortcomings of the three regression models indicate that their integration with IoT devices will facilitate successful energy forecasting.

This numerical study investigates the impact of reaction temperature, molar ratio of CH4:CO2, and catalyst porosity (p) on H2 yield and H2 selectivity during biogas dry reforming over a Ni/Cr catalyst. Using COMSOL Multiphysics, we conducted detailed simulations to elucidate the underlying reaction characteristics. Our findings reveal that increasing p significantly enhances H2 production and H2 selectivity, while simultaneously reducing CO selectivity. This effect is attributed to the improved mass transfer within the catalyst bed, leading to more efficient reactant conversion and product formation. Additionally, we observed a strong correlation between higher reaction temperatures and increased H2 yield and H2 selectivity, confirming the endothermic nature of the reforming process. By optimizing these operational parameters, our results suggest that Ni/Cr catalysts can be effectively employed for the sustainable production of H2 from biogas.

In the fourth industrial revolution, artificial intelligence (AI) and machine learning (ML) have increasingly been applied to manufacturing, particularly additive manufacturing (AM), to enhance processes and production. This study provides a comprehensive review of the state-of-the-art achievements in this domain, highlighting not only the widely discussed supervised learning but also the emerging applications of semi-supervised learning and reinforcement learning (RL). These advanced ML techniques have recently garnered significant attention due to their potential to further optimize and automate AM processes. The review aims to offer insights into various ML technologies employed in current research projects and to promote the diverse applications of ML in AM. By exploring the latest advancements and trends, this study seeks to foster a deeper understanding of ML’s transformative role in AM, paving the way for future innovations and improvements in manufacturing practices.

The position accuracy of polishing tool affects the surface quality of the polished aspheric surface. The contact deformation among the polishing tool, abrasives and aspheric part can cause a displacement, in turn will cause a position error of polishing tool, this will lead to a significant change of the polishing force. In order to resolve this error, this paper proposed a method of normal displacement compensation for NC polishing system by controlling the polishing force. Firstly, the coupling principle between polishing force and position of polishing tool are expounded, the relationship between normal displacement and deformation is analyzed, based on Hertz theory, a model of normal displacement is established. Then, on the basis of decoupled polishing system developed, a normal displacement compensation method was proposed. Finally, a group of comparative experiments was carried out to verify the effectiveness of the proposed method. Compared with no displacement compensation, when the part was polished with normal displacement compensation method, the value of roughness decreases from 0.4µm to 0.21µm, and the unevenness coefficients of surface roughness decreases from 112.5% to 19%. The experimental results show that the polishing quality is improved greatly and the aspheric surfaces can be polished more uniformly with method proposed in this paper.

This paper examines the control challenges faced by underactuated Autonomous Underwater Vehicles (AUVs) under ocean current disturbances. It proposes a Backstepping Integral Sliding Mode Control (BISMC) strategy to enhance their adaptability and robustness. The BISMC strategy integrates the system decomposition capability of the Backstepping Control method with the rapid response and robustness advantages of the Sliding Mode Control method, enabling the design of a heading controller and a double closed-loop depth controller. By introducing an integral component, the strategy eliminates steady-state errors caused by ocean currents, accelerating system convergence and improving accuracy. Furthermore, a saturation function is employed to mitigate output chattering issues. Simulation results demonstrate that the BISMC controller significantly enhances the control precision and anti-disturbance capabilities of AUVs under low-frequency ocean current disturbances, showcasing exceptional adaptive and self-disturbance rejection performance.

The transformative influence of digital engineering on photonic technologies is significant, es-pecially regarding advancements in laser processing through digital models, artificial intelli-gence (AI), and freeform optics. It presents a comprehensive review of how these technologies enhance efficiency, precision, and control in manufacturing processes. Digital models are pivotal for predicting and optimizing thermal effects in laser processing, thereby reducing material de-formation and defects. The integration of AI further refines these models, improving productiv-ity and quality in applications such as micro-machining and cladding. Additionally, the combi-nation of AI with freeform optics advances laser technology by enabling real-time adjustments and customizable beam profiles, which enhance processing versatility and reduce material damage. The use of digital twins is also examined as a key development in laser-based manu-facturing, offering significant improvements in process optimization, defect reduction, and sys-tem efficiency. By incorporating real-time monitoring, machine learning, and physics-based modeling, digital twins facilitate precise simulations and predictions, leading to more effective and reliable manufacturing practices. Overall, the integration of digital twins, AI, and freeform optics into laser processing marks a significant progression in manufacturing technology. These advancements collectively enhance precision, efficiency, and adaptability, resulting in improved product quality and reduced operational costs. The continued evolution of these technologies is expected to drive further advancements in manufacturing practices, offering more robust solu-tions for complex production environments.

The application of joint sensing and communications (JSACs) technology in air-ground networks, which include unmanned aerial vehicles (UAVs), offers unique opportunities for improving both sensing and communication performances. However, this type of networks are also sensitive to the peculiar characteristics of the aerial communications environment, which include shadowing and scattering caused by man-made structures. This paper investigates an aerial JSAC network and proposes a UAV-selection strategy that is shown to improve the communication performance. We first derive analytical expressions for the received signal-to-interference ratio for both communication and sensing functions. These expressions are then used to analyze the outage and coverage probability of the communication part, as well as the ergodic radar estimation information rate and the detection probability of the sensing part. Moreover, a performance trade-off is investigated under the assumption of a total bandwidth constraint. The presented results reveal the impact of shadowing severity and interference on the system’s performance.

This paper proposes a design for a platform-based Multi-phase Interleaved Synchronous Buck Converter (MISBC) using Silicon (Si) based Metal-Oxide-Semiconductor Field-Effect Transistors (MOSFETs). A custom platform was developed to compare the theoretical performance of a MISBC circuit simulated with Multisim and compared it with a prototype that was built at Western Sydney University. The work disclosed in this manuscript describes some steps adopted during the selection of each component and technical considerations taken during the design of the Printed Circuit Board (PCB). The platform designed has a maximum power output of 260 Watts, with a buck reduction of the nominal voltage from 97 Volts to 24 Volts at a maximum switching frequency of 50 kHz. This switching frequency is achieved with an open-loop circuit configuration coupled with synchronized signal generators, used to validate the dead band required between the activation of each MOSFET implemented in a half bridge configuration. A summary of the results based on the duty cycle required to achieve the buck voltage desired highlights the advantages of each operating mode of the MISBC circuit, where the theoretical performance is compared against the data acquired during functional evaluations of the prototype, allowing further interpretations towards the optimal control algorithm required to maximize the performance output of MISBC circuits.

This study aims to investigate the effectiveness of sustainable urban regeneration projects in mitigating Urban Heat Island (UHI). A place-based approach utilizing Geographic Information Systems (GIS) integrates spatial data and satellite imagery on the urban environment, human ac-tivity, demographics, and climate conditions, facilitating a deeper understanding of UHI dy-namics. Data-driven modelling with Machine Learning algorithms were developed to identify key variables influencing UHI effects, such as building density, greening extent, and surfaces’ albedo. A comparative pre- and post-intervention analysis in Turin (Italy), focusing on the Teksid ex-industrial area, was investigated. This analysis reveals a reduction in surface UHI intensity of -0.94 in summer and -0.54 in winter, highlighting the positive impact of mitigation strategies employed in the regeneration plan. The implications for urban and territorial policies were in-vestigated, focusing on urban planning tools for UHI mitigation. Specifically, the Local Climate Zones method was examined and compared with actual urban morphologies to find more effective and tailored solutions for policy makers and urban planners. The combined approach of geo-graphical and satellite information, Machine Learning models and Local Climate Zones joins quantitative and qualitative data, allowing to find more tailored, effective and alternative solu-tions in critical contexts such as the urban ones.

HTA introduced by Annett and Duncan is widely recognized for breaking down complex tasks into manageable subtasks, data, and quantitative analysis confirming its effectiveness are limited. By combining the HTA with the NASA Workload Index (NASA-TLX), a subjective workload assessment tool, this study aims to provide a comprehensive assessment of task performance and workload. The project involved assembling a three-jaw chuck with HTA for process definition and subtasks, while NASA-TLX was used for workload determination. Data were collected through questionnaires and observation forms and analyzed to determine the impact of HTA on task performance and workload distribution. The results show that HTA effectively identifies task complexity and bottlenecks, which facilitates task optimization and improves performance. The NASA-TLX integration provides critical insights into individuals' subjective workload and targets areas where task performance and resource allocation can be improved. This study shows that combining HTA and NASA-TLX provides a holistic approach to understanding and improving task performance, ultimately leading to better user experience and productivity.

Technological advancements and evolving government regulations are driving the adoption of construction equipment powered by renewable energy sources, aiming to significantly reduce emissions. Electrifying non-road mobile machinery (NRMM), particularly self-propelled Rough-Terrain Variable Reach Trucks (RTVRT) with telescopic booms, presents stability challenges. Replacing diesel engines with electric motors and battery packs shifts the centre of gravity, necessitating precise load capacity determinations via load charts to ensure safe operation. This paper introduces a virtual model developed using multi-physic modelling with Bond Graph methodology. The model encompasses crucial components, including the chassis, rear axle, telescopic boom, attachment fork, and wheels, requiring three-dimensional treatment for spatial dynamics resolution. An illustrative example, supported by experimental data, demonstrates the model's capabilities. Special focus is placed on calculating ground wheel reaction forces and hydraulic self-leveling of the attachment fork. Numerical results validate the BG-3D simulation model of the telehandler with 20-Sim as a promising tool for estimating stability limits with satisfactory precision and predicting dynamic behavior across various operating conditions. Moreover, the paper explores potential extensions of the model, such as integrating the virtual vehicle model with a platform of variable inclination in subsequent phases which aims to evaluate telehandler longitudinal and lateral stability, aligning with the ISO 22915 standard and operator safety.

Inconel 718 is a widely popular aerospace superalloy known for its high-temperature performance and resistance to oxidation, creep, and corrosion. Traditional manufacturing methods, like casting and powder metallurgy, face challenges with intricate shapes that can result in porosity and uniformity issues. On the other hand, Additive Manufacturing (AM) techniques such as Powder Bed Fusion (PBF) and Direct Energy Deposition (DED) can allow the creation of intricate single-part components to reduce weight and maintain structural integrity. However, AM- parts often exhibit directional solidification, leading to anisotropic properties and potential crack propagation sites. To address this, post-processing treatments like HIP and heat treatment are necessary. This study explores the effects of the raster and stochastic spot melt scanning strategies on the microstructural and mechanical properties of IN718 parts fabricated using Electron Beam Powder Bed Fusion (EB-PBF). The research demonstrates that raster scanning produces columnar grains with higher aspect ratios. In contrast, stochastic spot melt scanning promotes the formation of equiaxed grains, enhancing microstructural refinement and reducing anisotropy. Microhardness values also show variation in strength in build direction, denoting changes in mechanical performance due to scanning strategies. These findings emphasize the critical role of scanning strategies in optimizing the EB-PBF process for improved material properties.

This paper proposes a methodology for multi-criteria calibration of a thermo-mechanical model for numerical simulation of welding in vacuum. A finite element model of a steel plate is created. Experimental and computational data are obtained. An inverse problem is formulated for the vector identification of five calibration parameters from the heat flow model. They are evaluated for adequacy with controlled accuracy according to four criteria. An optimization problem is solved using a two-step interactive procedure. The parameter space studying method (PSI) has been applied to the study of multidimensional regions by means of quasi-uniform sounding. A Pareto-optimal set is defined. It is used to determine reduced ranked Pareto subsets by -selection. Salukvadze optimum is also determined.

Additive Manufacturing (AM) methods are increasingly beign abopted in the industry as an alternative for mass production. In particular, Fused Deposition Modeling (FDM) technology is leading the way in this field. However, the adhesion of layers in products produced using FDM technology is an important issue. These products are particularly vulnerable to forces acting parallel to the layers and specially to impact strength. This study aims to optimize the impact strength against loads acting parallel to the layers (ZX orientation) of PA6, one of the most widely used materials in the industry. This orientation is critical in term of mechanical properties, and the mechanical characteristics are significantly lower compared to other orientation. In this study, filaments containing pure PA6 with 15% short carbon fiber and 30% glass fiber were utilized. Additionally, printing temperature, layer thickness and heat treatment duration were used as independent variables. An L9 orthogonal array was employed in this study, and each experiment was repeated three times to conduct impact strength tests. Characterization, Taguchi optimization, a factor analyses were performed, followed by fracture surface characterization by SEM. As a result, the highest impact strength was achieved with pure PA6 at 8.9 kJ/m2, followed by PA6 GF30 at 8.1 kJ/m2 and the lowest impact strength was obtained with PA6 CF15 at 6.258 kJ/m2. When compared to the literature and manufacturer datasheets, it was concluded that the impact strength values have significantly increased, and the chosen experimental factors and their levels, particularly nozzle temperature, were effective.

This study aims to evaluate the potential of machine learning algorithms (Random Forest and Support Vector Machine) in predicting the open porosity of a general-use industrial mortar applied to different substrates based on the characteristics of both the mortar and substrates. For this purpose, an experimental database comprising 1592 samples of industrial mortar applied to five different substrates (hollowed ceramic brick, solid ceramic brick, concrete block, concrete slab, and lightweight concrete block) was generated by an experimental program. The samples were characterized by bulk density, open porosity, capillary water absorption coefficient, drying index, and compressive strength. This database was then used to train and test the machine learning algorithms to predict the open porosity of the mortar. The results indicate that it is possible to predict the open porosity of the mortar with good prediction accuracy and that both Random Forest (RF) and Support Vector Machine (SVM) algorithms (RF =0.880; SVM = 0.896) are suitable for this task. Regarding the main characteristics that influence the open porosity of the mortar, bulk density and the open porosity of the substrate are significant factors. Furthermore, the study employs a straightforward methodology with a machine learning no-code platform, enhancing the replicability of its findings for future research and practical implementations.

This study examines the rationale and perceptions surrounding AI in military technologies, focusing on its application in autonomous weapons systems (AWS), intelligence, surveillance, and reconnaissance (ISR), and decision-support systems (DSS). AI models, such as unmanned aerial and ground platforms, showed high mission success rates, with 92% mission accomplishment, 95% true positive threat identification, and low false alarms. AI's effectiveness surpassed human operators, particularly in response time and threat detection. A global survey revealed mixed opinions: two-thirds believed AI enhances national security, yet 70% opposed autonomous weapons due to ethical concerns, particularly civilian casualties. The study highlights AI's advantages in improving decision-making speed and accuracy but acknowledges challenges such as ethical considerations and public trust in autonomous systems. Future research should address ethical AI models, international humanitarian law (IHL) standards in military operations, human-AI interaction in hazardous environments, and policies related to military robotics and accountability. Understanding and enhancing public awareness of AI in military contexts is also essential.

This study outlines the development and optimization of a Long Short-Term Memory (LSTM) network designed to analyze and classify time-series data from tribological experiments, with a particular emphasis on identifying stationary phases. The process of fine-tuning key hyperparameters was systematically optimized through Bayesian optimization, coupled with K-fold cross-validation, to minimize the inherent randomness associated with training neural networks. The refined LSTM network achieved a weighted average accuracy of 84%, demonstrating a high level of agreement between the network's identified stationary phases and those manually determined by researchers. This result suggests that LSTM networks can reliably mimic manual identification processes in tribological data, providing a promising avenue for automating data analysis. The study underscores the potential of neural networks to transcend their traditional role in predictive modeling within tribology, opening up new possibilities for their application across a broader spectrum of tasks within the field.

This study investigates the effect of different variables in the optimization of a hybrid air charging systems for marine diesel engines, exploring the potential for energy recovery and assistance using electric machines. The optimization parameters include turbine speed, compressor power, start of injection, turbomachine size, and valve timing. At low engine speeds, the larger improvements come from increasing compressor power, which increases air-to-fuel ratio compared with the baseline turbocharging. As free-floating turbocharged engines are known to have low air-to-fuel ratio at low engine speeds, the relative improvement of the hybrid architecture is large at low engine speeds, being near to 10% less BSFC. At high engine speeds, the largest influence is marked by the SOI, an early injection obtains a faster combustion, which improves gross efficiency. However, the relative improvement compared with baseline turbocharger is less than 1% at high engine speeds. Due to the distribution of relative improvement of BSFC, this architecture has a deeper impact on fuel consumption in vessel operating cycles that focus on low engine speeds and loads, such as those of tugboats.

The retrofitting of existing reinforced concrete (RC) buildings with Cross-Laminated Timber (CLT) panels presents a promising approach for enhancing seismic performance and overall structural resilience. However, effective integration of CLT with existing RC structures poses significant challenges, particularly concerning the design of connections between CLT panels and the RC structure. This paper introduces a novel connection that addresses these challenges by focusing on both structural and architectural considerations. Structurally, the connection is engineered to provide optimal stiffness, strength, and deformation capacity, ensuring robust performance under seismic and dynamic loads. Architecturally, the design incorporates a pre-defined weak component that facilitates easy access and rapid replacement of damaged parts, thereby reducing downtime and maintenance efforts. The proposed connection was evaluated through a series of monotonic and cyclic loading tests, demonstrating its structural efficiency and reliability. The results indicate that the new connection system not only meets the necessary structural requirements but also offers practical benefits for maintenance and repair, contributing to the overall sustainability and resilience of retrofitted RC buildings. This innovative approach represents a significant advancement in the field of structural retrofitting, providing a viable solution for integrating CLT panels into existing RC frameworks.

This paper presents the development and testing of the passive Almaty Ankle Exoskeleton V.2. The device features a new design with three electric linear actuators, and the shin and foot platforms are connected by links with spherical and universal joints. Functional testing demonstrates the prototype's ability to operate effectively within the natural range of ankle joint movements. Preliminary results indicate that the exoskeleton can reduce calf muscle activation in the work limb where the device is applied. In this the passive exoskeleton is designed for dorsiflexion and plantarflexion, whose operation with numerical results is highlighted by focusing on parameters such as angular velocity, linear acceleration, and angles. These measurements provide valuable information about the exoskeleton's performance and efficiency.

The development of innovative materials, based on the modern technologies and processes, is the key factor to improve the energetic sustainability and reduce the environmental impact of the electrical equipment. In particular, modeling of magnetic hysteresis is crucial for the design and construction of electrical and electronic devices. In recent years, additive manufacturing techniques are playing a decisive role in the project and production of magnetic elements and circuits for applications in various engineering fields. To this aim, the use of Deep learning paradigm, integrated with the most common models of the magnetic hysteresis process, has become increasingly present in recent years. Particularly in the paper, different Neural networks used in scientific literature, integrated with various hysteretic mathematical models, including the well-known Preisach model, are compared. It is shown that this hybrid approach not only improves the modelling of hysteresis by significantly reducing computational time and efforts, but also offers new perspectives for analysis and prediction of the behavior of magnetic materials, with significant implications for the production of advanced devices.

This paper presents RMSPowerSims.jl, an open-source Julia package for time-domain simulation of power systems. The package is designed to be used in conjunction with PowerModels.jl, a widely used Julia package for power system optimization. RMSPowerSims.jl provides a framework for the simulation of power systems in the time domain, allowing for the study of transient stability, fault analysis, and other dynamic phenomena. The package is designed to be intuitive and flexible, allowing users to easily define custom models for network components and disturbances. The package has been verified against DIgSILENT PowerFactory and has produced results that are in close agreement with those obtained using PowerFactory. RMSPowerSims.jl is available under an open-source license and can be downloaded from the Julia package registry.

A large amount of sea ice exists in the polar regions, which is prone to ship-ice collisions leading to ship breakage and sinking events. Complex environmental factors pose challenges to navigation safety, and it is particularly important to perceive the polar environment and make decisions in advance. In this paper, You only look once (YOLOv5) is targeted to be optimized according to the characteristics of ice remote sensing images, which includes adding Squeeze-and-Excitation (SE) attention mechanism, improving spatial pyramid pooling, and replacing Flexible Rectified Linear Unit (FReLU) activation function. In order to verify the effect of the optimized algorithm, ablation experiments are set up, and the accuracy of the optimized algorithm is improved by 3.5% com-pared with the original YOLOv5. Comparison experiments with other target identification algo-rithms are conducted, and the results show that the optimized YOLOv5 has higher accuracy and better robustness, and can identify small target sea ice that is difficult to identify in large-size re-mote sensing images. Polar ship path planning based on the identification results. The identifica-tion output of remote sensing ice images is used as the input for the construction of path planning maps, and raster maps corresponding to the actual polar scenes are built. Taking the path length, the number of ship turns, and the sea ice risk value as the objective function, the Any-Angle Path Planning on Grids (Theta*) algorithm is used to simulate the path planning under different sea conditions with different sea ice concentration, and a path with a shorter length, a smoother path, and a higher safety coefficient is found.

For advancing manufacturing, arising AM, with an inverse philosophical approach compared to conventional procedures, has benefits that include intricate fabrication, reduced material waste, flexible design, and more. Regardless of its potential, AM must overcome several challenges due to multi-physical processes with miscellaneous physical stimuli in diverse materials systems and situations, such as anisotropic microstructure and mechanical properties, a restricted choice of materials, defects, and high cost. Unlike conventional experimental work that requires extensive trial and error resources and FEM, which generally consumes substantial computational power, the analytical approach based on physics is an exceptional choice. Understanding the relationship between the microstructure and material properties of the fabricated parts is a crucial focus in AM research. Texture is a vital factor in almost every modern industry. This study first proposed a physics-based model to foreshadow the multi-phase crystallographic orientation distribution in Ti-6Al-4V LPBF while considering the part boundary conditions due to the importance of part geometry in real industry. The thermal distribution obtained from this function operates as the information for the single-phase crystallographic texture model. In this model, we forerun and validate the orientations of single-phase materials utilizing three Euler Angles with the principles of CET and thermodynamics, as well as the intensity of the texture by approximating them with published results. Then, we transform the single-phase texture into a dual-phase texture in Bunge calculation, illustrating visualized by pole figures of both BCC and HCP phases. This texture evolution model provides a new paradigm for future researchers to model the texture or microstructure evolution semi-analytically and save many computational resources in a real-world perspective. Others have not yet done this work about simulating the multi-phase texture in an analytical approach, so this work bridges the gap in this field. Furthermore, this paper establishes the foundation for future research on materials properties affected by microstructure or texture in academic and industrial environments. The precision and dependability of the results obtained through this method make it a valuable tool for ongoing research and advancement.

Reliable prediction of building-level energy demand is crucial for the building managers to optimize and regulate energy consumption. Conventional prediction models omit the uncertainties associated with demand over time; hence, they are mostly inaccurate and unreliable. In this study, a Bayesian neural network (BNN)-based probabilistic prediction model is proposed to tackle this challenge. By quantifying the uncertainty, BNNs provide probabilistic predictions that capture the variations in the energy demand. The proposed model is trained and evaluated on a subset of the building operations dataset of Lawrence Berkeley National Laboratory (LBNL), Berkeley, California, which includes diverse attributes related to climate and key building performance indicators. We have performed thorough hyperparameter tuning and used fixed-horizon validation to evaluate trained models on various test data to assess generalization ability. To validate the results, quantile random forest (QRF) was used as a benchmark. The study compared BNN with LSTM, showing that BNN outperformed LSTM in uncertainty quantification.

Robotic exoskeletons are being actively applied to support the activities of daily living (ADL) for patients with hand motion impairments. In terms of actuation, soft materials and sensors have opened new alternatives upon conventional rigid body structures. In this arena, biomimetic soft systems are playing an important role for modeling and controlling the human hand kinematics without the restrictions of rigid mechanical joints, while having an entire deformable body with limitless points of actuation. In this paper, we address the computational limitations for modeling large-scale articulated systems for soft robotic exoskeletons, by integrating a parallel architecture to compute the exoskeleton’s dynamics equations of motion (EoM), obtaining a computation with O(log2n) complexity for the highly-articulated n degrees of freedom.

The transport sector is heavily relying on the use of fossil fuels that are causing major environmental concerns. Solutions relying on the direct or indirect use of electricity, through e-fuels production, are emerging to power the transport sector. To ensure environmental benefits over this transition, an accurate estimation of the impact of the use of electricity is needed. This work presents a temporal analysis of carbon intensity of e-fuels using grid electricity in countries members of the European Network of Transmission System Operators (ENTSO-E). It also provides an estimation of the potential load factor for to produce low-carbon e-fuels according to the European Union legislative framework. This was achieved, building on top of the existing EcoDynElec tool, by developing EcoDynElec_xr a python tool enabling, with an hourly time resolution, the calculation, visualisation and the analysis of the historical time-series of electricity mix from the ENTSO-E. The results highlight that, in 2023, a minority of European countries were reaching low carbon intensity of electricity that enables the use of grid electricity for green electrolytic hydrogen production. The methodological hypothesis to consider the consumed electricity mix instead of the production mix, and the considered time step, are of paramount importance and drastically impact the potential load factor of green hydrogen production. The developed tool and calculated data are released under an open-source license to ensure transparency, results reproducibility, reuse on newer data, or for other purposes.

The increasing global demand for biodiesel is due to the urgent need to replace mineral diesel with a fuel based on renewable energy sources. In this scenario, biocatalysis stands out as an efficient and environmentally friendly alternative to chemical catalysis. When associated with the use of immobilized enzymes, which allow their reuse in the process of obtaining biofuel, this approach makes production sustainable and reduces costs. In this sense, we investigated the use of the lipase produced by an Amazonian endophytic fungus in immobilized form as a biocatalyst in the transesterification of waste cooking oil for biodiesel production. The fungus Endomelanconiopsis endophytica QAT_7AC demonstrated high production of lipase. The lipolytic extract was precipitated in ethanol, which increased the specific enzyme activity by 28%. The lipolytic extract and the precipitated lipolytic extract were immobilized in calcium alginate beads. Immobilization efficiency was over 89%. The immobilized biocatalysts showed thermal stability (30-50 °C) and were used in the production of biodiesel using waste cooking oil and ethanol. It was possible to reuse them for up to four reaction cycles, with yields greater than 70%. These results prove the efficiency of immobilized biocatalysts in the production of ethyl esters from waste oils.

The connection between a prefabricated reinforced concrete column and a pocket foundation is a case treated from a general perspective in the European Standard named EN 1992-1-1, and when the structural engineer deals with the dimensioning or verification of the connection, he tackles several unknowns. The present work aims to fill in the missing information by presenting detailed calculation models based on the strut-and-tie method for 4 of the widely used pocket foundations: pedestal pocket foundation with smooth, rough or keyed internal walls, and pad foundation with pocket possessing keyed internal walls. Additionally, detailed design prescriptions applicable to seismic areas are given. Afterwards, as a case study, a pocket foundation is designed in all 4 variations, having the structural design particularities, similitudes and differences pointed out. Finally, to conclude the advantages and disadvantages are mentioned for pocket foundations in respect to the type of internal wall surface used. Quantifiable data based on the case study undertaken is available.

Anaerobic digestion has the potential to convert organic waste materials into valuable energy. At the same time, using press water from biomass materials for energy generation, while taking advantage of the resulting cake for other purposes is an emerging approach. Therefore, this study aimed to investigate the residual potential expected from a typical biogas feedstock in the West African context after it has been mechanically separated into liquid and solid phases. Hence, in this study, the rumen contents of ruminants (cow, goat and sheep) and their proportionate ratios were obtained from an abattoir in Ghana. Resource characterisation of the waste samples were carried out in the Central laboratory of the HFR, Germany. Anaerobic batch tests for biogas (biomethane) yield determination were set-up using the Hohenheim Biogas Yield Test (HBT). Inoculum used was obtained from an inoculum production unit at the Hohenheim University biogas laboratory. The trial involved two different forms of the sample: mixture of rumen contents, press water, and inoculum, each in four (4) replicates. The trial was carried out at a mesophilic temperature of 37°C. Results obtained over a seventy (70) day period were transformed into biogas yields. Overall, the results show that the current contents are suitable for biogas generation as an option, as opposed to the current form of disposal at a refuse dump. However, using these mixtures in their original forms is more technically viable than using press water without further treatment.

This research seeks to provide local governments with a comprehensive alternative for the proper maintenance of roads, focusing on the surface and functional evaluation of pavements. It com-pares conventional methods of the International Roughness Index (IRI) and the Pavement Condi-tion Index (PCI) with novel methodologies that employ smart technologies. Road maintenance is essential for economic development, facilitating the circulation of services and resources. This re-search analyzes the efficiency of smart technologies in the maintenance of local roads in Arequipa, taking as a case study a 2 km section of the AR-780 highway in Polobaya. The International Roughness Indices (IRI) obtained through the Merlin Roughness Meter and the Roadroid applica-tion were compared, finding a minimum variation of 4.007% in the left lane and 8.5% in the right lane. Roadroid turned out to be 60 times faster than the conventional method, with a cost differ-ence of 220.11 soles. Both methods classified the road condition (PSI) as GOOD, validating the accuracy of Roadroid. In addition, the Pavement Condition Index (PCI) was evaluated with con-ventional methods and a DJI Mavic 2 Pro drone, finding a variation of 6.89%. The cost difference between the methodologies was 1,047.73 soles, and the use of the drone proved to be 10 times faster than visual inspection. Finally, it can be said that this study contributes to closing the knowledge gap regarding the use of smart technologies for better pavement management on local roads, so that the actors in charge of the roads can make decisions based on science and thus con-tribute to the well-being of the population.

In the DC distribution networks, DC bus voltage is maintained by the grid-connected converter, the controllability and reliability of the grid-connected converter significantly affects the bus voltage characteristic. To address the problem of limited stability and frequent oscillations, this paper proposes a state feedback control method for the AC/DC converter. Conventional AC/DC converter adopts the voltage-current double-closed-loop control structure with the proportional integral (PI) controllers, which is the equivalent of the typical type II control system, but the typical type II control system cannot fully settle the stability and immunity problems. In contrast, the state feedback control strategy not only achieves the control objectives of the traditional double-closed-loop control, but also reduce the AC/DC converter system model to a typical I-type system, which improves the stability, thus enhances the oscillation suppression capability of the bus voltage. By selecting multiple state variables and designing the converter pole configuration range, the proposed single-loop state feedback control method manages to optimize both the dynamic and steady-state performances of the grid-connected AC/DC converter. Finally, the effectiveness of the proposed single-loop state feedback control strategy has been verified by both the simulation and experiment results.

Assessing handling qualities is crucial for ensuring the safety and operational efficiency of aircraft control characteristics. The growing interest in Urban Air Mobility (UAM) has increased the focus on electric Vertical Take-Off and Landing (eVTOL) aircraft; however, a comprehensive assessment of eVTOL handling qualities remains a challenge. This paper proposed a handling qualities framework to assess eVTOL handling qualities, integrating pilot compensation, task performance, and qualitative comments. An experiment was conducted, where eye-tracking data and subjective ratings from 23 participants as they performed various Mission Task Elements (MTEs) in an eVTOL simulator were recorded. The relationship between pilot compensation and task workload was investigated based on eye metrics. Data mining results revealed that pilots' eye movement patterns and workload perception change when performing Mission Task Elements (MTEs) that involve aircraft deficiencies. Additionally, pupil size, pupil diameter, iris diameter, interpupillary distance, iris-to-pupil ratio, and gaze entropy are found to be correlated with both handling qualities and task workload. Furthermore, a handling qualities and pilot workload recognition model is developed based on Long-Short Term Memory (LSTM), which is subsequently trained and evaluated with experimental data, achieving an accuracy of 97%. A case study was conducted to validate the effectiveness of the proposed framework. Overall, the proposed framework addresses the limitations of the existing Handling Qualities Rating Method (HQRM), offering a more comprehensive approach to handling qualities assessment.

Anaerobic digestion followed by microalgal cultivation is considered a promising renewable alternative for the production of biomethane with reduced effluent generation, thus lowering the environmental impact. In this arrangement, in addition to generating energy, the microalgae act by potentiating the refinement of the effluents generated via anaerobic digestion (digestates). In this study, the microalga Tetradesmus obliquus was cultivated in photobioreactors with the final digestate resulting from the codigestion of Arundo donax L. plant biomass and cattle wastewater. The biotechnological route used was efficient, and biogas production ranged from 50.20 to 94.69 mL gVS-1. The first-order kinetic model with variable dependence (FOMT) provided the best fit for the biogas production data. In the microalgal posttreatment, the removal values ranged from 81.5 to 93.8% for chemical oxygen demand, 92.0 to 95.3% for N-NH4+, and 41.7 to 83.3% for PO43- after 26 days. The macromolecular composition of the algal biomass reached lipid contents ranging from 33.4 to 42.7%. Thus, the proposed process mediated by microalgae can be considered promising for the bioremediation and recovery of effluents produced by agriculture through the use of microalgal biomass for bioproduct production.

Ventricular Assist Devices (VADs) are designed to provide adequate blood flow to patients suffering from heart failure. Once implanted, the patient becomes dependent on the VAD, making it unacceptable to have situations with the potential to cause harm to patients on circulatory support. The VADs can be categorized as Critical Systems (CS), as device malfunction or Adverse Events (AEs) can result in harm to the patient, including the risk of death. Undesirable situations can occur in two ways: deviations in the functionality of the VAD linked to failures or defects or deviations in the patient's physiological responses associated with AEs. The AEs can result in hospitalization, causing permanent harm to the patient, including the risk of death. Patient care is provided through in-person consultations, and reports of incidents are submitted through Medical Device Reports (MDRs), monitored by the Food and Drug Administration (FDA). However, the crucial issue is that there is no real-time monitoring of AEs or supervision of patients. Therefore, an Open System for Supervising Critical Adverse Processes in Patients with Implanted Ventricular Assist Devices (OSCVAD) is proposed based on horizontal and vertical integration architecture. A method for structuring the processes in this system is proposed to address its complexity, involving different domains of operation: device, patient, and medical team, as well as the possible interactions between the entities that comprise the OSCVAD. In this context, the formalism of Interpreted Petri Nets was employed to synthesize diagnostic models for AEs and intervention models based on Intervention Protocols, utilizing the medical team's knowledge.

An analysis of the current state of development in the agro-industrial complex (AIC) has established that freeze-drying is the most effective method for processing raw materials entering the drying stage. However, this drying method is hindered by the lack of modern, high-performance, energy-efficient drying technologies and equipment. The production of freeze-dried agricultural products will stimulate the development of AIC sectors. The implementation scheme of an automatic control system for a vacuum freeze-drying unit is considered, representing a promising and environmentally friendly direction in the processing of agricultural products. The drying equipment includes the following components and systems: a drying chamber, a cooling system, a vacuum pump, a control system, and a heating system. The product is placed on shelves in the drying chamber and frozen to a temperature that ensures the removal of 75-80% of the moisture contained in the product. The vacuum pump creates a pressure of 10-30 Pa and evacuates the spent air from the sublimator. Water vapor condensation occurs in the desublimator, and at the final stage of the drying process, the heating elements are activated to remove the remaining moisture. To validate the reliability of controlling the operating modes of the sublimator, experimental studies were conducted on the temperature dependence of the product on the surface and in the center over time. The graphs show temperature changes on the surface and in the center of the product throughout the drying process. The research results indicate that at a pressure of 20±5 Pa, the desublimator temperature should be below -20°C. The surface temperature of the product during the sublimation stage should be within the range of -25±5°C. Drying is completed when the chamber temperature reaches no more than 40°C, and the surface temperature of the product is around 30°C.

A basic limitation of the periodogram as frequency estimator is that any of its significant peaks may be produced by a diffuse (or spread) frequency component instead of a pure one. Diffuse components are common in applications such as channel estimation in which a given periodogram peak reveals the presence of a complex multipath distribution (unresolvable propagation paths or diffuse scattering, for example). We present a method to detect the presence of a diffuse component in a given peak based on analyzing the projection of the data vector onto the span of the signature’s derivatives up to a given order. Fundamentally, a diffuse component is detected if the energy in the derivatives’ subspace is too high at the peak’s frequency, and its spread is estimated as the ratio between this last energy and the peak’s energy. The method is based on exploiting the signature’s Vandermonde structure through the properties of discrete Chebyshev polynomials. We also present an efficient numerical procedure for computing the data component in the derivatives’ span based on barycentric interpolation. The paper contains a numerical assessment of the proposed estimator and detector.

Geological storage of carbon dioxide (CO2) is a crucial technology for mitigating global temperature rise. Near-depleted edge-bottom water reservoirs are attractive targets for CO2 storage, as they can not only enhance oil recovery (EOR) but also provide important potential candidates for geological storage. This study investigated CO2 enhanced oil recovery and storage for a typical near-depleted edge-bottom water reservoir that had been developed for a long time with a recovery factor of 51.93%. To improve the oil recovery and CO2 storage, new production scenarios are explored. At the near-depleted stage, by comparing the four different scenarios of water injection, gas injection, water alternating gas injection, and bi-directional injection, the highest additional recovery of 8.64% is achieved by the bi-directional injection scenario. Increasing the injection pressure leads to a higher gas-oil ratio and liquid production rate. After shifting from the near-depleted to depleted stage, the most effective approach to improve CO2 storage capacity is to increase reservoir pressure. At 1.4 times the initial reservoir pressure, the maximum storage capacity is 6.52×108 m3. However, excessive pressures boosting poses potential storage and leakage risks. Therefore, lower injection rates and longer intermittent injections are expected to achieve a larger amount of long-term CO2 storage. Through the numerical simulation study, a gas injection rate of 80,000 m3/day and a schedule of 4-6 years injection with 1 year shut-in was shown to be effective for the case considered. During 31 years of CO2 injection, the percentage dissolved CO2 increased from 5.46% to 6.23% during the near-depleted period, and to 7.76% during the depleted period. This study acts as a guide for the CO2 geological storage of typical near-depleted edge-bottom water reservoirs.

With many Americans eager to resume travel after prolonged restrictions from the coronavirus pandemic, car rentals are on the rise. According to Auto Rental News, “2021 U.S. Car Rental Revenue Climbs 21% Year-Over-Year” (Brown, 2021). In conjunction with increased car rental, and associated travel, we can expect to see an increase in cyber security and data privacy attacks. In the digital age, enterprises have a responsibility to protect consumers’ personally identifiable information (PII). As it pertains to rental vehicles, when consumers connect their mobile devices to a rental vehicle’s Entertainment and Infotainment System or utilize the built-in navigation feature, they do so, unbeknownst to themselves that the information from their devices is transferring to the vehicle, where it will remain, unless properly expunged from the system. A large majority of consumers are blissfully unaware that their PII is being stored, and how easy it would be for an adversary to potentially breach their data security and privacy. To expand upon this hypothesis, I conducted a further investigation on the subject, by partaking in the rental of three different vehicles, each belonging to a different manufacturer (Nissan, Chrysler, and Maserati), from three different vehicle rental companies (Enterprise, Hertz and Turo). I then, as a consumer, tested my hypothesis to determine if, I was able to easily breach the prior consumers’ data security and privacy by viewing devices that had previously connected to the vehicle, as well as any of the previous consumers’ personal information. The results were undeniable; the PII of the previous consumers is not protected.

The aim of this research work consisted of assessing the effects of the COVID-19 pandemic on the interurban public transport system in a rural region with a sparse population density, considering the number of tickets sold and passengers in each locality, as well as the different connecting lines. From a methodological point of view and with the intention of identifying patterns to explain the behaviour of both routes and passengers, a series of variables were selected, becoming determining factors that sought to offer a solution to the search for a common trend. Additionally, data processing by means of statistical analysis and the application of Geographic Information Systems (GIS) tools complemented the procedure. The results obtained in the investigation were provided both by municipality and by interurban route. An interesting finding of the research was the uneven recovery of the municipalities. The localities closest to the attractor nucleus have recovered more quickly to pre-pandemic mobility levels due to their geographical proximity, larger population, higher income per household and the need to access certain public services. In terms of routes, all lines showed significant decreases in ticket sales, although with variations. Although passenger numbers have shown a gradual recovery, the initial loss was considerable, and pre-pandemic normality has not been completely achieved. The authors consider that future research should include other alternative means of transport in these interurban areas, incorporating variables to characterise passengers, such as age, gender...etc.

Wind energy is one of the main drivers of the renewable energy market. The wind turbine gearbox, used as a multiplier - is one of the main components directly related to wind turbine efficiency and lifespan. Therefore, the strict control of the gearbox project and its manufacturing processes or even minor improvements in this component heavily and positively impact energy production/generation over time. This work diagnoses a 1.6 MW gearbox fracture mechanics to identify its main reason and how the chain of events took place, leading to the catastrophic failure. The diagnoses underwent numerical simulation (Finite Element Analysis - FEA) and further analysis of the lubrication system, bearings, planetary stage gears, helical stage gears, and high-speed shaft. In conclusion, although the numerical simulation showed high contact stresses between the teeth, the region with the crack nucleated was the top land (tip of the tooth). The most likely factors that led to the premature failure were the missed lubrication for the planetary bearings, lack of gear raw material cleanliness, and excessive case depth at the tip of the sun shaft gear teeth. With the sun gear, planet bearings, and planet gears broken in pieces, those small and large pieces dropped into the oil, between the gears, and into the tooth ring, ending up as a premature gearbox failure.

This paper proposes an omnidirectional conveyor as a novel alternative to existing omnidirectional conveyors. With symmetric and compact layout, this new structure ensures consistent kinematics and enhanced flexibility in trajectory planning. The kinematic model of the proposed omnidirectional conveyor is developed and verified through simulation in CoppeliaSim. Four typical classes of trajectories are generated and verified in the simulation environment. Using PID control, the actual trajectories of a package on the conveyor closely match the desired trajectories. In addition, this paper outlines the workspace and corresponding wheel patterns for the conveyor, demonstrating how different supported wheel patterns emerge when packages move across various areas of the conveyor. The discussion extends to fault tolerance and obstacle avoidance, examining the workspace and wheel patterns with one or two omni wheels failed. Furthermore, this paper provides a comprehensive analysis on the feasible desired movements for the packages on the conveyor under the constrained wheel speed. This provides insights and guidance on trajectory planning and design of the conveyor.

The effect of zirconium modified with silica dioxide in the conductivity of modified nanocomposite membrane in proton exchange membrane fuel cells (PEMFCs) application. In this study silica-zirconia nanoparticles were successful synthesized by precipitation method. The surface area and surface morphology of Si-ZrO2 nanoparticles were observed under BET analysis, X-ray diffraction (XRD), scanning electron microscope (SEM), and transmission electron microscope (HRTEM). SEM and XRD results show that silica was well incorporated into ZrO2 nanoparticles with the mixed shape of nanorods and nanosphere. HRTEM shows a crystallised small nanoparticle with average diameter about 5–8 nm. The element analysis (EDX) confirms the presence of silica and zirconia nanoparticles as well as oxygen on the surface. The addition of silica has impact on the surface area and pore size of Si-ZrO2 nanoparticles, with Si-ZrO2 nanoparticles having a higher surface area of 185 m2/g when calcinated at 600 °C. It was shown that the Si-ZrO2 can be used as an impregnating membrane to enhance the conductivity as observed by cyclic voltammetry (CV), with a rectangular shape that exhibiting the best capacitance behaviour.

In this study, the structural behavior of a short threaded anchor with a 20 mm diameter and an embedment length of 50 mm (2.5Ø) in UHPFRC is investigated using non-linear Finite Element models. UHPFRC is assumed to exhibit tensile strain-hardening behavior, with tensile strengths of 7 MPa and 11 MPa, respectively. The modelled anchor was subjected to a continuously increasing uniaxial pull-out force. The results indicate that the fracture mechanism of threaded anchors in UHPFRC is primarily characterized by the formation of a tensile membrane within the UHPFRC, which acts as the main resisting element against the pull-out force. Additionally, the influence of UHPFRC's tensile properties on the pull-out behavior and ultimate resistance of the threaded anchors was determined.

As interest in renewable fuels grows, biomass has gained increasing attention for its potential applications. Understanding the kinetic and thermodynamic parameters of biomass pyrolysis and combustion are crucial for optimizing its valorization. In this study, TGA was employed to examine bamboo powder's pyrolysis and combustion behaviors under different temperature ramps in nitrogen and air environments. The TG and DTG curves reveal that bamboo pyrolysis occurs in three distinct stages: drying, devolatilization, and carbonization. Similarly, combustion also proceeds through three stages: drying, devolatilization, and char combustion. Notable differences in the temperature ranges of the key stages were observed between pyrolysis and combustion. Moreover, kinetic parameters were evaluated using the KAS, FWO, and STR model-free approaches. The findings indicate that, the activation energy during the oxidative devolatilization stage of combustion are notably lower compared to those during pyrolysis devolatilization. The disparity in activation energy is even more pronounced in the third stage. Thermodynamic analysis shows that the pyrolysis and combustion of bamboo are endothermic and non-spontaneous. It can be stably converted into value-added energy through pyrolysis or combustion process. This study provides essential data to aid in designing and scaling up the thermochemical conversion processes for bamboo.

In order to study the influence factors of vertical bearing capacity of pile foundation under the special address condition of multi-layer karst cave, the finite element model of foundation pile in double-layer karst area was established by using a commercial general finite element software Ansys, the bearing capacity of concrete pile foundation with different rock height, different ec-centricity and different elastic modulus under the gradually increasing load was studied. The results show that: (1) Under the condition of constant external load, the maximum displacement of pile foundation increases with the increase of eccentricity; (2) With the increase of concrete strength, the maximum displacement decreases gradually, but the corresponding maximum stress is roughly the same; (3) The height of the rock cave has a great influence on the bearing capacity of the pile foundation. In the construction process, it is necessary to minimize or avoid passing through the area with higher karst caves.

Hybrid Power Switches (HPS) combine the advantages of SiC unipolar and Si bipolar devices and therefore can bridge the gap between these technologies. In this paper, the performance of a hybrid power switch configuration based on the latest SiC MOSFET and Si IGBT technologies is presented. The device is evaluated through experimental measurements of its characteristics under various conditions. The results show the HPS can achieve switching losses as low as a SiC MOSFET while offering the high current capability of the IGBT without significant increase in costs.

Modern methods of designing and constructing hydraulic pumps can no longer be imagined without the use of appropriate mathematical models, phenomena and processes that take place in concrete constructions. A mathematical model of a process represents an analytical interpretation with certain assumptions. Obtaining a mathematical model requires detailed theoretical research, based on knowledge of the laws of fundamental sciences and scientific disciplines in order to fully understand and interpret the process, based on which assumptions are adopted and model equations are defined. As mobile machines continue to evolve, so do the demands on their hydraulic systems. Efficiency, automation and other major industry trends present ongoing development opportunities for hydraulic systems used in mobile applications. Hydraulic systems remain an important component in a range of mobile applications, from construction and agricultural equipment to heavy trucks because of the power density they can provide for a range of work functions. The power density of hydraulics remains unmatched in many applications. The pump, as an essential hydraulic component, converts mechanical energy into hydraulic energy with a relatively small amount and speed of fluid.

The aim of this work is to propose an angular positioning solution for the round cutting insert of an indexable insert torus milling cutter, dedicated to the multi-axis milling process under HSM conditions. The solution was based on the development of a wear model using a nonlinear estimation method with an eigenfunction, the development of a tool life model taking into account parameter of the cutter blade working angle and the method of reference marking and insert mounting, as well as the measurement methodology. The own function adopted to approximate the wear of the tool over its useful life is adequate and provides a prediction accuracy of 90%. The developed procedure for angular positioning and measurement enables the entire cutting edge of the cutting blade to be fully used, so that the total lifespan of the tool as an indexable insert tool is significantly extended. In addition, the measured largest defect values between the worn cutting edge and the nominal outline of the insert indicate the location of “notching” type wear.

As an important civil and military infrastructure, airport runway pavement is faced up with threats from cluster munitions, since it is vulnerable to projectile impact with internal explosion. Aiming at the damage assessment of island airport runway pavement under impact, this work dealt with discrete modeling of rigid projectile penetration into concrete pavement and calcareous sand subgrade multi-layer structure. First, Discrete Element Method (DEM) is introduced to model concrete and calcareous sand granular material features like cohesive fracture and strain hardening due to compression, with mesoscale constitutive laws governing the normal and shear interactions between adjacent particles. Second, sequent DEM simulations of uniaxial and triaxial compression were performed to calibrate the DEM parameters for pavement concrete as well as subgrade calcareous sand. Piror to multi-layer structure investigations, penetration into sole concrete or calcareous sand is validated in terms of projectile deceleration and depth of penetration (DOP) with relative error

This study presents a comprehensive investigation on an innovative mechanical system inspired by recent advancements in metamaterials, more specifically the work is focused on Origami-type structures for their intriguing mechanical properties. Originating from the specific fields such as aerospace for their lightweight and foldable characteristics, Origami mechanical devices exhibit unique non-linear stiffness, in particular, when suitably designed, they show Quasi-Zero Stiffness (QZS) characteristics within a specific working range. The QZS property, aligned with the High Static Low Dynamic (HSLD) stiffness concept, suggests promising applications such as a low-frequency mechanical passive vibration isolator. The study explores the vibration isolation characteristics of Origami-type suspensions, with a particular emphasis on their potential application as low-frequency passive vibration isolators. The Kresling Origami Module (KOM) has been selected for its compactness and compatibility with 3D printers. A detailed analysis using 3D CAD, Finite Element Analysis, and experimental testing have been carried out. The investigation includes the analysis of the influence of geometric parameters on the non-linear Force-Displacement curve. Multibody simulations validate the low-frequency isolation properties within the QZS region, and disparities in dynamic properties beyond the QZS range. The study underscores the transformative potential of Origami-type metamaterials in enhancing low-frequency vibration isolation technology. It also highlights challenges related to material properties and loading mass variations, providing valuable insights for future developments in this promising field.

For decades, fossil fuels have been the backbone of reliable energy systems, offering unmatched energy density and flexibility. However, as the world shifts toward renewable energy, overcoming the limitations of intermittent power sources requires a bold reimagining of energy storage and integration. Power-to-X (PtX) technologies, which convert excess renewable electricity into storable energy carriers, offer a promising solution for long-term energy storage and sector coupling. Recent advancements in machine learning (ML) have revolutionized PtX systems by enhancing efficiency, scalability, and sustainability. This review provides a detailed analysis of how ML techniques, such as deep reinforcement learning, data-driven optimization, and predictive diagnostics, are driving innovation in Power-to-Gas, Power-to-Liquid, and Power-to-Heat systems. While ML applications have shown great potential in optimizing operational decisions and managing uncertainties in renewable energy integration, challenges such as data quality, real-time processing, and scalability remain. Addressing these gaps presents important future research opportunities. These advancements are critical to decarbonizing hard-to-electrify sectors such as heavy industry, transportation, and aviation, aligning with global sustainability goals.

As motor capacity and rated voltage increase, the demand for motor insulation also increases. Additionally, the electric field distribution at the end of a large-scale hydro generator is extremely non-uniform, and corona discharge readily occurs. This destroys the main insulation, which significantly affects the service life of electrical machinery. Thus, the regulation of the electric field concentration at the end of a large-scale hydro generator needs to be addressed. There are many problems in resistance–capacitance chain model research. Particularly, these models cannot accurately reflect the insulation, corona structure, and electric field distribution in electrical machinery. Additionally, the corner electric field and loss distributions cannot be obtained, leading to a higher calculation error. The electric field distribution at the motor end and the loss distribution were analyzed using the finite-element method. First, by applying the basic principles of electric circuits and electromagnetic field theory, we calculated the electric field distribution at the stator bar end. Second, a 3D model of the stator bar was built using Creo Parametric, yielding a model that is more closely related to the prototype wirebar model. Then, the potential, electric field, and loss distributions around the stator bar were simulated by using COMSOL simulation software. The effects of the circular arc corner, nonlinearity coefficient, anti-corona coating length, and resistivity of the stator bar on the electric field distribution at the motor end were explored.

This study aims to enhance the detection and assessment of safety hazards in small-scale reservoir engineering using advanced image processing and deep learning techniques. Given the critical importance of small reservoirs in flood management, water supply, and ecological balance, the effective monitoring of their structural integrity is crucial. This paper developed a fully convolutional semantic segmentation method for hidden danger images of small reservoirs using an encoding-decoding structure, utilizing a deep learning framework of convolutional neural networks (CNNs) to process and analyze high-resolution images captured by UAVs. The method incorporated data augmentation and adaptive learning techniques to improve model accuracy under diverse environmental conditions. Finally, the quantification data of hidden dangers (length, width, area, etc.) was obtained by converting the image pixels to the actual size. Results demonstrate significant improvements in detecting structural deficiencies, such as cracks and seepage areas, with increased precision and recall rates compared to conventional methods, and the HHSN-25 network (Hidden Hazard Segmentation Network with 25 layers)proposed in this paper outperforms other methods. The main evaluation indicator mIoU of HHSN-25 is higher than other methods, reaching 87.00%, and Unet is 85.50%, Unet++is 85.55%. The proposed model achieves reliable real-time performance, allowing for early warning and effective management of potential risks. This study contributes to the development of more efficient monitoring systems for small-scale reservoirs, enhancing their safety and operational sustainability.

The numerical algorithm used for the original multi-dimensional non-linear optimization analysis of the marine fixed pitch (screw) propulsion presented herewith, after its invention and publication has been widely used for the analysis and best fit (most probable / less uncertain) solution of over-determined non-linear mathematical problems, where the points of observations (measured data) are more than the unknown parameters in the mathematical formulation expressing the significant aspects of the observed phenomena. Any “local” attainable solution of this formidable and ingenious algorithm, may rely to the set of initial values of the unknown parameters, while not all possible solutions are expected to be physically significant for a specific problem, whereas judgment, bounds and testing are to be practiced for making sure that the attained solution is the best fit (most probable / less uncertain) one, and not necessarily the “global minimum” one. In the present paper the above algorithm is applied for the original analysis of standard ships propulsion, and particularly for the original combined application of thermo-fluid and frictional models of main engines, as well as of fixed pitch propulsion hydrodynamic models, necessary for resolving in an integrated manner the combined marine propulsion problem for standard ships under seagoing conditions.

The construction industry, characterized by high energy use and carbon emissions, necessitates a thorough and accurate life cycle assessment (LCA). This review investigates how building information modeling (BIM) software can streamline the LCA process to improve both efficiency and precision. Although BIM has considerable potential, challenges remain, such as issues with software interoperability and a lack of standardized options for BIM-integrated LCA tools. The review also evaluates the strengths and weaknesses of various BIM software, LCA tools, and energy consumption tools, and highlights case studies of BIM-LCA integration. It provides a critical analysis of methods and techniques for BIM-LCA integration and data exchange, including the import of bills of quantities, Industry Foundation Classes (IFC), the use of BIM viewers, direct LCA calculations with BIM plugins, and calculations using LCA plugins. The study concludes with future outlooks, aiming to direct the development of improved LCA tools that offer better integration with BIM software, which is crucial for advancing sustainable construction practices.

Humanity is shifting its power generation to renewable energy, notably wind power. As more and more wind power plants are built, it has become important to improve the efficiency of wind power generation and reduce the manufacturing costs of wind power equipment. The mechanical multiplier gearbox is one of the most important parts of the wind power turbine, and it is now being built as a planetary gear train as these gearboxes offer a compact build capable of high power ratings in a relatively small package with coaxial input and output shafts. It is very important to select the right kinematic scheme and the other relevant parameters in the early design stages as the wrong kinematic scheme will result in a suboptimal solution. This article deals with the selection of the optimal gearbox solution for a 700 kW wind turbine application, taking into account both two-carrier and three-carrier gearbox solutions. As the optimal solution may be deducted only by systematic analysis, the proprietary 2-SPEED software has been developed for this purpose. The results of the analysis have been presented in this article, and optimal gear train solutions have been suggested for both two-carrier and three-carrier multiplier configurations.

This work aims to integrate the circular economy (CE) into learning experiences to enhance learning outcome development in Higher Education (HE). Building on existing active learning research, this work aligns CE initiatives—particularly waste reduction and elimination— with the Sustainable Development Goals (SDGs). The study proposes an experiential learning framework to cultivate citizenship commitment, sustainability impact, and engineering learning outcomes through realistic problem-solving in the supply chains of small and medium enterprises (SMEs). Using the ADDIE instructional design model and Kolb’s experiential learning cycle, the approach is illustrated in a case study within an undergraduate industrial engineering module. Based on student opinion surveys and achievement metrics, results indicate that the experience significantly enhanced student engagement and motivation. This research contributes to the broader dissem-ination of CE concepts in HE and supports the advancement of SDG #4 Quality Education and #12 Sustainable Consumption and Production. Future research will explore the application of this learning experience across diverse industries and assess its impact on student learning outcomes in various educational contexts.

Exoskeleton robots have become a promising tool in neurorehabilitation, offering effective physical therapy and recovery monitoring. The success of these therapies relies on precise motion control systems. Although computed torque control based on inverse dynamics provides a robust theoretical foundation, its practical application in rehabilitation is limited by its sensitivity to model accuracy, making it less effective when dealing with unpredictable payloads. To overcome these limitations, this study introduces a novel model reference computed torque controller that accounts for parametric uncertainties while optimizing computational efficiency. A dynamic model of a seven-degree-of-freedom human lower limb exoskeleton is developed, incorporating a realistic joint friction model to accurately reflect the physical behavior of the robot. To reduce computational demands, the control system is split into two loops: a slower loop that predicts joint torque requirements based on input trajectories and robot dynamics, and a faster PID loop that corrects trajectory tracking errors. Coriolis and centrifugal forces are excluded from the model due to their minimal impact on system dynamics relative to their computational cost. Experimental results show high accuracy in trajectory tracking, and statistical analyses confirm the controller’s robustness and effectiveness in handling parametric uncertainties. This approach presents a promising advancement for improving the stability and performance of exoskeleton-based neurorehabilitation.

Objective: Fludeoxyglucose F18 positron emission tomography (FDG PET) can give early clues for diagnosing Alzheimer’s disease (AD). Our objective was to use the same scan to predict Apolipoprotein E4 (ApoE4), known to be a risk factor. A second objective was to determine the brain regions and imaging features associated with this gene allele that can potentially help elucidate the mechanisms of the disease and better comprehend the images. Method: We employed data from 112 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). PET and structural MRI data were co-registered with ANTx, and the Freesurfer tool delineated 95 whole brain, 19 hippocampal, and 9 amygdala regions. Using PyRadiomics we extracted 120 radiomic features from these segments. We employed a hybrid feature selection strategy. The HistGradientBoosting (HGB) classifier was evaluated using stratified five-fold cross-validation. Results: The proposed radiomics model predicted the homozygous ApoE4 genotype, with an AUC of 0.945 an accuracy of 0.875, a sensitivity of 0.914, and a specificity of 0.838. Reducing the feature set to seven key features resulted in a slightly decreased performance (AUC: 0.889). The mean values of the features showed statistically significant (p<0.05) incremental deterioration between different groups of homozygous ApoE4 carriers. Conclusion: Our findings underscore the critical role of specific brain regions and associated features in differentiating ApoE4 carriers. The hippocampus, entorhinal cortex, amygdala, thalamus, and pars orbitalis exhibited significant associations with the ApoE4 genotype. By employing SHAP analysis, we identified key features driving model predictions, enhancing interpretability, and providing insights into the underlying pathophysiology.

Serpentine (Mg₃Si₂O₅(OH)₄), like quartz, dolomite and magnesite minerals, is a versatile mineral group characterized by silica and magnesium silicate contents with multiple polymorphic phases. Among the phases composed of antigorite, lizardite, and chrysotile, lizardite and chrysotile are the most prevalent phases in the serpentinites studied here. The ΣLREE/ΣHREE ratios of the samples are between 0.16 and 4 ppm. While Ce shows a strong negative anomaly (0.1–12), Eu shows a weak positive anomaly (0.1–0.3). This indicates that fluid interacts significantly with rock during serpentinization, and Highly Incompatible Elements (HIEs) gradually become involved in the serpentinization process. The large differential thermal analysis (DTA) peak at 810–830 °C is an important sign of dehydration, transformation reactions and thermal decomposition, and is com-patible with H₂O phyllosilicates in the mineral structure losing water at this temperature. In SEM images, chrysotile, which has a fibrous structure, and lizardite, which has a flat appearance, trans-form into talc as a result of dehydration with increasing temperature. Therefore, the sudden tem-perature drop observed in DTA graphs is an indicator of crystal form transformation and CO₂ loss. In this study, the mineralogical and structural properties and the formation of serpentinites were examined for the first time using thermo-gravimetric analysis methods. In addition, the mineralogical and physical properties of serpentinites can be recommended for industrial use as additives in polymers or in the adsorption of organic pollutants. As a result, the high refractory nature of examined serpentine suggests that it is well-suited for applications involving high temperatures. This includes industries such as metallurgy and steel production, glass manufacturing, ceramic production, and the chemical industry.

This paper presents a hybrid probability distribution known as the FCD, which is based on the hazard functions of the Fréchet and Chen distributions. A notable characteristic of the FCD is its ability to model monotonic (increasing and decreasing) and non-monotonic (bathtub-shaped) behaviors, commonly linked to electrical and electronic device failure times in reliability analysis. The paper explores the statistical properties relevant to the reliability analysis of these devices. It proposes a Maximum Likelihood Estimator (MLE) and Bayesian Analysis equations for estimating the parameters of the FCD. Three case studies of electronic device lifetimes with varying behaviors were analyzed to validate the proposed methodology. The FCD was compared to other distributions with similar characteristics, and the results from each case study demonstrated that the proposed distribution yields competitive outcomes, closely reflecting the behavior of the devices analyzed, making it a valuable option for reliability engineering practitioners in warranty and maintenance time evaluations

This study investigates the compressive strength and workability of concrete incorporating coconut shells and bamboo strips as alternative aggregates. The research compares traditional concrete to concrete mixtures with varying percentages of coconut shells and bamboo strips. Specifically, the study examines the effects of adding 10% coconut shell, 10% bamboo strip, and a combination of 5% coconut shell and 5% bamboo strip by volume to the concrete mix, tested in both C20 and C30 grades. To prepare the materials, coconut shells and bamboo strips were first crushed and then sieved. The sieving process retained particles that passed through a 10mm sieve but were retained on a 4.7mm sieve. The primary focus of the experimental tests was on measuring the compressive strength of the resulting concrete mixtures. The findings indicate that concrete incorporating coconut shells showed moderate workability. In contrast, concrete with bamboo strips exhibited lower workability. Regarding compressive strength, the concrete mix containing a combination of 5% coconut shell and 5% bamboo strips demonstrated a reduction in strength compared to the control group of normal concrete. This research highlights the potential of using coconut shells and bamboo strips as sustainable alternatives in concrete construction. While the workability varies between the different materials, the results provide valuable insights into their suitability for specific applications. This encourages further exploration of eco-conscious building practices and the development of sustainable construction materials.

Structural integrity of buried pipelines is threatened by the effects of Permanent Ground Deformation (PGD), resulting from seismic-induced landslides and lateral spreading due to liquefaction parallel to the pipeline axis, requiring accurate analysis of the system performance. Analytical fragility functions allow to estimate the likelihood of seismic damage along the pipeline, supporting design engineers and network operators in prioritizing resource allocation for mitigative or remedial measures in spatially distributed lifeline systems. To efficiently and accurately evaluate the seismic fragility of buried operating steel pipeline under longitudinal PGD, this study develops an analytical model considering the asymmetric pipeline material behavior in tension and compression under varying operational loads. The validated model is further implemented within a fragility function calculation framework based on Monte Carlo Simulation (MCS), allowing to assess the probability of exceedance of the pipeline performance limit states conditioned to the intensity of PGD. The evaluated fragility surfaces showed that the pipeline probability of exceeding the performance criteria increases for larger soil displacement and lengths, as well as cover depths, because of the greater mobilized soil reaction counteracting pipeline deformation. Additionally, the performed Global Sensitivity Analysis (GSA) highlighted the influence of the PGD and soil-pipeline interaction parameters, as well as the effect of the service loads on structural performance, requiring proper consideration in pipeline system modeling and design. Finally, the proposed analytical fragility function calculation framework provides a useful methodology for effectively assessing the performance of operating pipelines under longitudinal PGD, quantifying the effect of the uncertain parameters impacting structural response.

This paper presents the validation of a numerical method for quantifying the physical quality parameters of apples through a comparative analysis with a traditional measurement method. The numerical method was applied to determine the parameters of batches of Kazakh apples according to standard requirements, using image analysis of apples. Five common varieties of Kazakh apples were selected: Aport Alexander, Ainur, Sinap Almatynski, Nursat and Kazakhski Yubileinyi. The geometric parameters of the apples and the percentage of red in the images were determined. The parameters of the 5 apple varieties were processed and measured both manually and digitally, revealing a close agreement between the obtained values. The developed digital method achieved high accuracy in determining the (diameters (d) and (D) in two perpendicular planes and height (h) of each apple), with maximum relative errors of 2.99% for (d), 3.03%, and 4.12% for the (h), and (D) parameters, respectively. Regression models were developed to determine and predict the mass and volume of apples via the digital method. The best results for the apple weight prediction were obtained for Sinap Almatynski variety by stepwise linear regression, and for the apple volume prediction were obtained for Nursat variety by linear regression. Regression equations for mass, volume and geometric dimensions constitute the basis for the development of a small instrument for automatically sorting apples by commercial variety.

The increasing demand for agricultural automation within the precision agriculture sector necessitates the development of advanced robotic systems to enhance efficiency in fruit harvesting. This study presents the Computer-Aided Design (CAD) and modeling of a 4-degree-of-freedom (DOF) robotic arm specifically designed for automated fruit harvesting applications. Utilizing Fusion 360 software, a comprehensive model has been created, encompassing material selection, stress analysis, and motion simulations to verify both the functionality and durability of the robotic system. Design methodologies are articulated, alongside simulation tests that evaluate the arm’s operational performance. Proposed enhancements aim to optimize harvesting efficiency while minimizing potential damage to crops. The robotic arm is equipped with an adaptive gripper, engineered to adjust to various fruit sizes, ensuring delicate and precise manipulation during the harvesting process. This work establishes a robust foundation for the advancement of robotic systems in agricultural contexts, contributing to improved productivity and sustainability in fruit harvesting operations.

Some vessels do not comply with maritime rules, particularly in port and safety zones; they must be detected to avoid incidents. Due to background noise and congestion interference at the sea surface, it is difficult to accurately detect these vessels when it is getting dark, especially smaller ones. For these reasons, this paper proposes and develops an improved multi-structural mor-phology (IMSM) approach to eliminate all this noise and interference. The vessel target is sepa-rated from the sea surface backdrop using weighted morphological filtering of many groups of structural components. Then, the neighborhood-based adaptive fast median filtering is used to filter out impulse noise. Finally, the characteristic morphological model of the vessel target is established using the connected domain; this permits eliminating the sea surface congestion and locating vessels movement in real-time. Multiple tests were carried out in a small and discon-tinuous area of the vessel’s inter-frame motion. The results of many groups of data collected show that the proposed approach can effectively eliminate the background noise and congestion in-terference in the monitoring video. The detection accuracy rate and the processing time have increased by approximately 3.91% and 1.14s, respectively.

Additive Manufacturing (AM) plays a vital role in rapid prototyping. Polylactic acid (PLA), a biomass thermoplastic monomer made from corn starch is the most used filament material in AM. Due to the strength requirement of haptic device prototypes, there is a need to carefully explore all parameters that could enhance the performance of printed parts. This study considered three printing parameters: printing speed, infill density, and layer thickness on three levels each. A tensile test according to ASTM D638 was performed on each. Statistical analysis was done to investigate the influence of each of the parameters on maximum tensile stress, load at maximum tensile stress, and Modulus respectively for each sample. Subsequently, a regression analysis was performed. The sample with an Infill density of 15%, printing speed of 70mm/s, and 0.1mm layer thickness exhibited the highest tensile strength. However, it was discovered further that the infill density has the highest influence on the mechanical properties of the 3D printed PLA material in tensile testing. The chart of the mechanical properties and the parameter configurations were also presented to assist in selecting printing parameters for desired mechanical properties.

The increasing demand for cost-effective floating offshore wind turbines (FOWT) necessitates streamlined mass production and efficient assembly strategies. This research investigates the assembly and integration of 15 MW FOWT floaters, utilising a semi-submersible floater equipped with a 15 MW wind turbine. The infrastructure and existing port facilities of Taiwan are used as an example. The effectiveness of various assembly and integration strategies has been evaluated. The study outlines equipment and infrastructure requirements for on-quay floater and turbine assembly, comparing on-quay assembly versus construction at remote locations and subsequent towing. Detailed analyses of port operations, crane specifications, and assembly procedures are presented, emphasizing the critical role of crane selection and configuration. The findings indicate that on-quay assembly at one major port is feasible and cost-effective, provided that port infrastructure and operational logistics are optimised. This research offers insights and recommendations for implementing large-scale FOWT projects, contributing to advancing offshore wind energy deployment.

To enhance the peak-shaving capability of the boiler, a mathematical model of hydrodynamic and wall temperature characteristics for the water-cooled wall of a 1000 MW boiler was established. The attributes pertaining to the mass flow distribution, the temperature of the effluent working fluid, pressure drop, and the thermal distribution across the wall surfaces at 30% of the boiler's maximum continuous rating (BMCR) were meticulously computed and subjected to a thorough analytical review. The findings suggest that, under the operation at 30% BMCR load, there is a substantial equilibrium in the flow distribution across the quartet of walls that constitute the water-cooled wall assembly. The maximum mass flow rate deviations in the helical and vertical sections are 1.95% and 3.47%, respectively, showing small flow deviations and reasonable distribution. The temperature deviation in the helical section is 0.3 °C, reflecting the characteristic low thermal deviation in helical tubes. While the temperature deviation at the outlet of the vertical section is higher, it remains within safe limits. The pressure loss across the water-cooled wall system amounts to 0.4 MPa. The peak wall temperature reaches 337.5°C, remaining within the material's permissible safety limits. In-depth performance analysis ensures the safe operating conditions of the water-cooled wall during low load conditions.

Background: In the field of brain-computer interfaces, neural decoding plays a crucial role in translating neural signals into meaningful physical actions. These signals are transmitted to processors for decoding via wired or wireless channels, often encountering data loss, commonly referred to as "packet loss." Despite its importance, the impact of different types and degrees of packet loss on neural decoding has yet to be comprehensively studied. Understanding this impact is critical for advancing neural signal processing and data filtering. Methods: This paper addresses this gap by constructing four distinct packet loss models, simulating congestion packet loss, distributed packet loss, and burst packet loss scenarios. Using macaque superior arm-movement decoding experiments, we evaluate the effects of these packet loss types on decoding performance. Results: Our findings reveal that continuous burst packet loss significantly degrades decoding performance at the same packet loss probability, and decoding parameters that are sensitive to data continuity are more heavily impacted by packet loss. Conclusions: This study provides an analysis of the relationship between packet loss and neural decoding results, offering valuable insights for future strategies aimed at preventing and handling packet loss in neural signal acquisition.

This study investigates the optimization of boron coating parameters for medium carbon steels, specifically AISI 1137, and their subsequent effects on mechanical properties, which are crucial for industrial applications. Despite extensive research on boronizing processes, an understanding of the optimal conditions that enhance wear resistance and hardness while maintaining structural integrity is still lacking. To address this gap, we systematically examined the impact of boronizing temperatures (850 °C and 950 °C) and durations (2, 4, and 8 hours) on the structural and mechanical properties of AISI 1137 steel. Our findings indicate the need for improved surface properties in medium carbon steels used in demanding environments, such as automotive and machinery components. The boronizing process was carried out using Ekabor 1 powder, with characterization performed through optical microscopy, pin-on-disk wear tests, and Vickers hardness analysis. Results showed that the thickness of the boronized layer ranged from 50.6 μm to 64.8 μm, with wear resistance increasing by 1.8 to 3.9 times at 950 °C compared to 850 °C. The measured hardness of the boronized surface layers varied between 1963.7 HV and 219.3 HV, decreasing from the boronized layer toward the base material. The optimal parameters for wear resistance and hardness were found to be a temperature of 950 °C and a duration of 8 hours, facilitating the formation of FeB and Fe2B phases, which significantly enhanced the steel's mechanical properties. This research provides valuable insights into the boronizing process and establishes a foundation for optimizing surface treatments to extend the lifespan and performance of medium carbon steels in industrial use.

The external bonding system using CFRP composite was widely used for strengthening of different structures worldwide. However, premature debonding in this strengthening technique is the critical failure that leads to the fiber not reaching its ultimate capacity. In order to enhance the capacity of the externally bonded (EB) FRP and to slow the premature debonding failure mechanism, numerous anchoring techniques have been applied to improve the bonding capacity. The externally-bonded reinforcement on grooves (EBROG) technology is one of the strategies that have been recently developed to postponing the debonding issue. Although the extensive studies have been conducted in literature on (EBROG) method, most of these studies have been focused on the bonding characteristics of longitudinal grooves direction, and there is lack in studies about effect of different design and configurations (width, height, and spacing) of transverse grooves direction. In present study, an experimental investigation was carried out to study the bond behavior of the externally bonded reinforcement on transverse grooves (EBROTG) technique on the CFRP-to-concrete joints involving different parameters, including groove width, depth, spaces between grooves, strain values, and bond stress-slip relationships. 24 concrete prisms, divided into 8 groups of three specimens, were tested using a single-lap shear test set-up. The result of testing proved that the EBROTG method furnished a proper anchor system and highly enhanced the bonding force of the tests. The increasing range in bond strength with the specimens reinforced by the transverse grooving method was ranging from 11 to 86 % compared with the externally bonded reinforcement (EBR) reflecting the effect of different width, depth and distance between grooves

This paper presents an 8x8 channel optoelectronic readout array (ORA) realized in a 180-nm CMOS process for the applications of short-range light detection and ranging (LiDAR) sensors. We propose several circuit techniques in this work including an amplitude-to-voltage (A2V) converter that reduces the notorious walk-errors by intensity compensation, and a time-to-voltage (T2V) converter that acquires the linear slope of the output signals by exploiting a charging circuit, thus extending the input dynamic range significantly from 5 uApp to 1.1 mApp, i.e. 46.8 dB. These results correspond to the maximum detection range of 8.2 meters via the action of the A2V converter and the minimum detection range of 56 centimeters with the aid of the proposed T2V converter. Optical measurements utilizing an 850-nm laser diode confirm that the proposed 8 x 8 ORA with 64 on-chip avalanche photodiodes (APDs) can successfully recover the narrow 5-ns light pulses even at the shortest distance of 56 centimeters. Hence, this work provides a potential CMOS solution for low-cost, low-power short-range LiDAR sensors.

At the beginning of any electric arc welding process, a regime called transient regime will always occur, characterized by the continuous variation of the energy flow, also resulting in the variation of the cooling rate of the welded joint. Consequently, the weld bead hardness varies from a maximum value to an approximately constant value, and this phenomenon can be represented by an asymptotic exponential function. For several welded specimens, it would be possible to fit a non-linear regression for each specimen, however, it would be a difficult task to combine different regressions in a single analysis. The objective of this study is to evaluate the applicability of mixed-effects models and artificial neural networks to predict the longitudinal hardness of the weld bead in the initial transient regime. To this end, linearized and nonlinear mixed-effects models and artificial neural networks with different architectures were fitted. Finally, valid models were obtained with all approaches, with artificial neural network model slightly better fitted. However, the explainability capacity of the linearized mixed-effects model was superior, showing that the relative thickness, one of the predictor variables, was responsible for 48% of the variation in longitudinal hardness of weld bead.

There is an extensive body of research in the literature focusing on predicting the mechanical properties of concrete, such as compressive strength. Summarizing the current studies following the valuable contributions of researchers will serve as a guide for future studies and researchers. To this end, this study aims to identify the key authors, sources, institutions, and countries that have contributed to the prediction of concrete compressive strength. Additionally, it aims to provide researchers with comprehensive information on prominent research themes, trends, and gaps in the literature related to the prediction of concrete compressive strength. For this purpose, 2319 articles on the prediction of concrete compressive strength published from 2000 to 19th August 2024 were identified through the Scopus Database. The scientific measurement analyses were conducted using VOSviewer software. Upon reviewing the relevant research, it was found that machine learning methods are frequently used in predicting concrete compressive strength. In this context, the study will make significant contributions to the literature by examining leading institutions, countries, authors, and sources in the field, synthesizing data, and highlighting research areas, gaps, and trends related to concrete compressive strength prediction.

This paper introduces an open-source active high-precision signal amplifier designed for enhancing biodata acquisition, specifically focusing on EEG signals. The compact device, with a diameter of 18 mm, seamlessly integrates electronics, signal processing, and meticulous design considerations. Incorporated within the single board are circuits for low-noise amplification, an analog interface equipped with bandpass filters, and an integrated ADC for precise digitization. A comprehensive evaluation of signal quality has been conducted, encompassing aspects such as noise levels, electrode bias tolerance, and common mode rejection ratio. To facilitate this evaluation, the widely recognized Silver Chloride electrode (Ag/AgCl) has been employed. Additionally, this paper delves into an extensive discussion concerning diverse sources of noise, along with strategies to curtail input noise and minimize noise originating from electrodes and amplifiers. Leveraging the substantial advancements achieved in the design of dry contact electrodes, this specific device is poised to carve a notable niche in the realm of brain-computer interface.

Trajectory planning determines whether a UAV can successfully complete its mission, and the reasonable planning of UAV trajectory in 3D environment is a complex global optimization problem, which needs to take into account many constraints such as urban environment, mountain terrain, obstacles, no-fly zones, flight boundaries, flight distances, and trajectory change rates. In view of the shortcomings of the whale optimization algorithm in 3D trajectory planning, such as slow convergence speed, low accuracy and easy to fall into the local optimum, this paper increases the diversity of the initial population through the introduction of the reverse learning mechanism, and optimizes the coordination of the global and local search ability by integrating the nonlinear convergence factor and the random number generating mechanism, so as to realize the improved whale optimization algorithm that can cope with the higher degree of freedom and the more complex constraints. The simulation results show that the optimization algorithm has improved the convergence accuracy by 22.1% and reduced the standard deviation by 74.1%, which can effectively deal with the 3D UAV path planning problem.

— An extreme bandgap Al0.64Ga0.36N quantum channel HEMT with Al0.87Ga0.13N top and back barriers grown by MOCVD over bulk AlN substrate demonstrated a critical breakdown field of 11.37 MV/cm, higher than 9.8 MV/cm expected for the channel Al0.64Ga0.36N material. We show that the fraction of this increase is due to the quantization of the 2D electron gas. The polarization field maintains electron quantization in the quantum channel even at low sheet densities in contrast to conventional HEMT designs. An additional increase in the breakdown field is due to quantum-enabled real space transfer of energetic electrons into high-Al barrier layers in high electric fields. These results show the advantages of quantum channel design for achieving record high breakdown voltages and enabling superior power HEMT devices.

Waveform generation as part of an on-chip built-in self-test (BIST) circuitry often necessitates sufficient linearity without expensive hardware overhead. Achieving high linearity is critical for accurate signal generation, especially in applications requiring high precision, such as biomedical and instrumentation. Currently, achieving the high linearity and precision required in signal generators often relies on costly hardware such as automated test equipment (ATE). This paper presents a DAC based hardware synthesizable arbitrary waveform generator. We use a low-cost DAC and a fully digital on-chip testing and calibration approach to nullify the effect of the DAC’s non-linearity on the generated waveform. The ultra-low cost and high linearity benefit of the proposed waveform generator makes it highly suitable for integration into resource-constrained systems. The proposed approach is validated using simulation results of the small-area DAC designed in TSMC 0.18um technology and the testing and calibration algorithms implemented in MATLAB. The DAC, designed with a matching accuracy at only the 5-bit level, is able to generate a signal with an ENOB of 12 bits alongside a SFDR and THD surpassing 100 dB. This high level of signal purity is consistently maintained across 100 Monte-Carlo simulations, demonstrating the robustness of the architecture against PVT variations as well as random mismatches.

This study examines the degradation of agglomerate particles in treated produced water used for hydraulic fracturing in the Bakken Formation. By analyzing d90 values and pump speeds from different manufacturers, the research quantifies particle size reduction rates, revealing how degradation contributes to pore throat blocking in tight formations. The study found that agglomerate particles in hydraulic fracturing fluids ranged from 9 to 85 microns in size, with degradation occurring most rapidly within the first five minutes of pump action. As time progresses, degradation slows, indicating that pore clogging is most likely at the beginning of the fracturing process. The research emphasizes that pump speed is a critical factor in determining the rate of particle degradation. To minimize formation damage, it is recommended to scale up particle size and use coarser particles in suspension. However, both large and small particles pose risks: larger particles can reduce porosity, while smaller particles may penetrate pore throats and form internal filter cakes. The study highlights the importance of selecting the appropriate pump speed and particle size distribution to optimize fracturing fluid design, maintain formation permeability, and reduce the risk of formation damage. Lower pump speeds and fewer coarse particles are advised to avoid pore clogging during the fracturing process.

With the rising awareness of environmental issues, concerns about pollution and global warming have gained importance, and electricity generation costs continue to increase. As a result, research on energy recovery using thermoelectric modules has attracted attention in recent years. Given the lack of significant breakthroughs in thermoelectric materials, improving the structure of thermoelectric modules has become a key direction for enhancing efficiency. Common thermoelectric module types include flat plate (Flat Plate TEG), annular (Annular TEG), flexible (Flexibility TEG), segmented, and cascaded designs. Factors influencing thermoelectric performance include the number, shape, height, contact area of thermoelectric elements, temperature difference, copper plate length, contact resistance, and the geometric arrangement of the elements. This study is based on the commercially available TGM1-127-1.0-0.8 thermoelectric module. By simulating the output performance of both flat plate and annular thermoelectric module structures under identical conditions—such as total volume of thermoelectric material, shape, and height of the thermoelectric legs, the total contact area with the heat source, temperature differential, and total volume of the connecting copper plate—we observed that the number of thermoelectric elements not only determines the open-circuit voltage but also influences the output power. The trend of output power remains consistent under varying temperature differences, regardless of changes in the load resistance.

This paper investigates the flexural behavior of unfired and fired one-way slabs constructed from reinforced ferronickel slag-based alkali-activated concrete. The study aims to expand the limited knowledge of large-scale testing of alkali-activated concrete structural elements, with a particular emphasis on their post-fire performance. Four slabs, each measuring 2.1 × 0.9 × 0.18 m³, were produced—two made of alkali-activated concrete and two of conventional concrete incorporating ordinary Portland cement. Both types of concrete exhibited similar compressive strengths. In each group, one slab served as a reference (control specimen—tested under monotonic four-point bending in its original state), while the other was subjected to a standard fire curve on the tensioned side before mechanical testing. The fired slabs were then tested in a cooled condition under ambient conditions. The effect of fire on the alkali-activated concrete was further examined using mercury intrusion porosity and scanning electron microscopy. The results reveal that the load capacity of structural members cast with alkali-activated concrete is only slightly lower than that of their conventional concrete counterparts (6% lower). However, the alkaliactivated slabs retain more of their stiffness and ductility after fire exposure.

The simultaneous progress of integrated op- tical frequency comb (OFC) and radio frequency (RF) photonic signal processing technique have promoted the rapid development of real-time signal processing. Integrated optical frequency comb offer multiple wave- lengths as a powerful source for RF photonic sig- nal transversal filter. Here, we review development of real-time signal processing system consisting of inte- grated OFC and RF photonic signal transversal filter in chronological order, and focus on the applications of this system such as differentiator, integrator, Hilbert transformer, and image processor. We also discuss and present our outlook on more parallel functions and fur- ther integration of real-time signal processing system.

With the escalating proliferation of wind power plants, the imperative focus on system robustness and stability intensifies. Doubly fed induction machines (DFIMs) are extensively employed as generating units in land-based wind power plants due to their performance advantages. While the stator windings of DFIMs are directly connected to the power system, the three-phase rotor windings are connected via power converters, rendering these units highly vulnerable to voltage disturbances. During faults, voltage drops at the stator terminals lead to elevated voltages and currents on the rotor side due to electromagnetic coupling between stator and rotor, potentially damaging rotor insulation and costly power electronics. Historically, to safeguard equipment, wind power plants employing distributed energy resources such as DFIMs were disconnected from the grid during faults—an unsatisfactory solution given the burgeoning number of these installations. Consequently, grid operators and IEEE standard 2800 mandate fault ride-through or low voltage ride-through capabilities to maintain supply adequacy during recovery from disturbances and voltage fluctuations. This paper provides an overview of diverse techniques devised to enable fault ride-through for DFIMs. Additionally, it scrutinizes existing approaches in modeling DFIM behavior during faults and introduces an approach tailored specifically for DFIMs, detailing its comparative advantages and limitations vis-à-vis established models. A conclusion about the necessity for more precise models in comparison to the existing ones is provided.

In this paper, we present an improved 100CMM regenerative thermal oxidizer (RTO) designed for low-emission combustion, capable of processing over 95% of volatile organic compound (VOC) gas emissions at generating sites. The existing RTO system features a cylindrical drum structure that cyclically introduces and discharges VOC gas into a rotating disk, achieving excellent energy efficiency with a heat recovery rate exceeding 95%. However, the driving force exerted on the rotating disk in the RTO combustion chamber causes increased wear around the rotating shaft, leading to untreated gas being released through the outlet due to the channeling phenomenon. Additionally, prolonged use can result in operational issues such as rotation stoppages or explosions caused by pollutant discharge, dust accumulation, and thermal expansion at high temperatures of up to 800°C. To address these challenges, we designed an enhanced rotating plate drive unit while ensuring the stable operation of the RTO combustion chamber. Considering that the combustion chamber can reach temperatures of up to 920°C when processing high-concentration VOC gas, the gas burner has been designed with a diameter of 125 mm, and the outlet dimensions are set to 650 mm x 650 mm to effectively discharge high-temperature waste heat. The heat storage material in the combustion chamber is configured with a ceramic block thickness, and the outer diameter and height of the combustion chamber are set at 250 mm, 2,530 mm, and 1,875 mm, respectively, to optimize the gas residence time and the insulation material thickness. Empirical experiments were conducted to analyze changes in VOC removal efficiency, nitrogen oxide emission concentration, and total hydrocarbon (THC) concentration, ultimately evaluating the economic performance of the 100CMM RTO.

This paper presents the theory, design, and application of a dual-branch series-diode analog pre-distortion (APD) linearizer to improve the linearity and efficiency of a K-band high power amplifier (HPA). A first-of-its-kind, frequency-dependent large-signal APD model is presented. This model is used to evaluate different phase relationships between the linear and nonlinear branches, suggesting independent gain, and phase expansion characteristics with this topology. This model is used to assess the impact of diode resistance, capacitance, and ideality factor on the APD characteristics. This feature is showcased with two similar GaAs diodes to find the best fit for the considered HPA. The selected diode is characterized and modeled between 1-26.5 GHz. A comprehensive APD design and simulation workflow is reported. Before fabrication, the simulated APD is evaluated with the measured HPA to verify linearity improvements. The APD prototype achieves a large signal bandwidth of 6 GHz with 3 dB gain expansion and 8∘ phase rotation. This linearizer is demonstrated with a 17-21 GHz GaN HPA with 41 dBm output power and 35 % efficiency. Using a wideband 750 MHz signal, this APD improves the noise power ratio (NPR) by 6.5-8.2 dB over the whole HPA bandwidth. Next, the HPA output power is swept to compare APD vs. power back off for the same NPR. APD improves the HPA output power by 1-2 W and efficiency by approximately 5-9 % at 19 GHz. This efficiency improvement decreases by only 1-2 % when including the APD post-amplifier consumption, thus suggesting overall efficiency and output power improvements with APD at K-band frequencies.

Structural Health Monitoring (SHM) plays a vital role in ensuring the health status of a wide range of structures, such as bridges, buildings and large infrastructure in general. The advantages of this process can be further enhanced by incorporating more numerical and statistical approaches into traditional methods, such as Finite Element Analysis and Machine Learning. In this study a bridge structure is examined, and neural networks are trained with data derived from finite element analyses under static loads and dynamic excitations. Initially, a binary classification problem is addressed, where numerically trained classifiers are tasked with identifying whether the structure is in a healthy state or not. This category is further divided into two subcategories, depending on the extent of the damage present in the structure. Subsequently, a multi-class classification problem is defined, where three different damage classes of the same extent are considered, and the trained network is required to distinguish between them. Finally, conclusions are drawn from the results of the study regarding the model error parameter, the impact of the damage size, as well as the types of neural networks and training data used.

A broadband 28 nm complementary metal-oxide-semiconductor (CMOS) power amplifier was implemented using a distributed amplification design. To develop a model library for high-frequency design, various test patterns for active and passive elements were fabricated and compared through measurements. As a result, an n-channel field-effect transistor (NFET) with a symmetrical layout was adopted as the active device, and a co-planar waveguide (CPW) structure with floating first and second metal layers at the bottom was selected as the transmission line for the passive element. These choices demonstrated superior radio frequency (RF) characteristics at high frequencies compared to other device candidates. Furthermore, to address the low breakdown voltage of CMOS, a triple-stacked FET structure was designed as the gain cell of the distributed power amplifier (DPA). The fabricated DPA showed a maximum small-signal gain of 22 dB and a minimum of 10 dB from DC to 56 GHz, with a maximum saturated Pout of 20 dBm and a minimum of 13 dBm from 1 to 39 GHz. Notably, these results were achieved on the first attempt by designing solely based on measurement data from the test patterns.

This study presents the development and implementation of a remote monitoring system for a dual-axis solar tracker, integrating Industry 4.0 technologies such as IoT, SCADA, and MQTT. The system, which combines a Siemens S7-1200 PLC, an IoT gateway, and a SCADA platform based on Ignition, demonstrated significant increases in solar capture efficiency, with production gains of 25-30% compared to fixed systems, reaching up to 67.65% under ideal conditions. In addition to improving operational management through predictive monitoring and early fault detection, the study identified areas for improvement, such as the need for real-world testing and the integration of weather predictions. In conclusion, the research validates the feasibility and effectiveness of automated IoT and SCADA-based systems to optimize solar trackers, significantly contributing to improved energy efficiency in photovoltaic installations.

This study assesses the performance of calcium aluminate (CAC) and calcium sulfoaluminate (CSA) cement mortars, particularly when blended with fly ash (FA) and limestone powder (LP). The effects of incorporating 20% FA, 15% LP, and their combination (FA 20% and LP 15%), using a water-binder ratio of 0.40 were explored. The study meticulously evaluates the performance of the mortar mixtures in terms of setting time and mechanical properties, including compressive strength, flexural strength, and direct tensile strength. Additionally, the study investigated the alkali-silica reaction (ASR) and autogenous shrinkage. Advanced analytical methods such as X-ray diffraction (XRD), Thermogravimetric Analysis (TGA), and Scanning Electron Microscopy (SEM) were employed to further understand the microstructural changes. The study reveals a significant correlation between compressive, flexural, and direct tensile strengths of the mortar mixtures. Notably, CSA and CAC mortar mixtures with SCMs showed an ASR value lower than the recommended 0.10%, while the autogenous shrinkage for both CAC and CSA mixtures was less compared to Ordinary Portland Cement (OPC). The influence of SCMs, though not substantial, was clearly observed. TGA results corroborated the phases identified in XRD and those observed in SEM, through the thermal decomposition of the phases present in the mixtures. This study contributes valuable insights into the use of SCMs in improving the performance and sustainability of CAC and CSA cement mortars.

This review article explores the innovative application of software sensors for monitoring and controlling biosystems, emphasizing their advantage in managing the complexities of various biological processes. Biosystems—from cellular interactions to ecological dynamics—are characterized by intrinsic nonlinearity, temporal variability, and uncertainty, posing significant challenges for traditional monitoring approaches. A critical challenge highlighted is that what is typically measurable may not align with what needs to be monitored. Software sensors bridge this gap by combining hardware sensor data with advanced computational modelling techniques to indirectly infer hard-to-measure target variables, such as stress level, animal and human health indicators, and chemical soil properties. The article outlines advancements in sensor technologies and their integration into model-based monitoring and control systems, leveraging the capabilities of Internet of Things (IoT) devices, wearables, remote sensing, and smart sensors. It provides an overview of common methodologies for designing software sensors, focusing on the modelling process. The discussion contrasts hypothetico-deductive (mechanistic) models with inductive (data-driven) models, illustrating the trade-offs between model accuracy and interpretability. Specific case studies are presented, showcasing software sensor applications such as the use of Kalman filter in greenhouse control, remote detection of soil organic matter, and sound recognition algorithms for early detection of respiratory infections in animals. Key challenges in designing software sensors, including the complexity of biological systems, inherent temporal and individual variabilities, and the trade-offs between model simplicity and predictive performance, are also discussed. This article positions software sensor as remarkable tool for advancing biosystem management, driving forward the potential for sustainable practices in various sectors such as agriculture, healthcare, and environmental monitoring.

In the last two decades, the application of composite structures in many industrial sectors has been made progressively. For the current major scientific and technological challenge, the energy transition with sustainable and eco-friendly initiatives, the polymer matrix laminated composites have been revealed as an excellent alternative for the renewable energy sector. In this context, the knowledge about mechanical behavior of multiphase materials is required. Then, researches related to macro, mesomechanical and multiscale approaches are very important to improve the accuracy of the models to predict effective mechanical properties and to develop a better understanding about complex phenomena such as progressive damage, creep and fatigue. Due to theses necessities, this research is a synthesis of fundamentals and advanced topics concerning to global modeling of laminated composite structures, layerwise analyzes and micro-macromechanical models, in which mainly fiber, matrix, interface and damage characteristics are studied and used in the macroscale approach. A deeper physical and mathematical description of the models is proposed, intead of general and vague reviews typically stated in the specific literature. At the end of this work, some final remarks and a trend analysis are made based on articles consulted, in other words, expectations for the future works are done by the authors of this study.

Transition zones are in the places on the track, with a change in the basic composition of the railway body. There are many sections with a sudden change in the stiffness of the structures built. When the trains are running, a longitudinal shock wave is created from the wheels, which hits these building objects with a higher stiffness and deforms the surroundings of these zones. The greatest attention should be paid mainly to the transition points from the fixed track to the classic track with a track bed, including objects of the railway substructure such as bridges, portals of tunnels, etc. As part of the research sections on the main corridor lines, a long-term inspection and monitoring was carried out with a continuous measurement system using the KRAB trolley. Height changes in the deflections of rails are evidence of their behavior. The measurements took place on a fixed track and a track with a ballast. Changes in the height jumps between the fixed railway track and the track with a gravel bed are significant and have been recorded since the tracks were put into operation. These certain height deflections allow designers to develop new, more durable construction designs.

Conventional approaches in Structural Health Monitoring (SHM) tend to be complex, destructive, and time-intensive. Additionally, they often require a large number of sensors to thoroughly assess structural integrity. In this study, we present a novel, non-destructive SHM framework based on machine learning (ML) for the accurate detection and localization of structural cracks. This approach leverages a minimal number of strain gauge sensors linked via Bluetooth communication. The framework is validated through empirical data collected from 3D carbon fiber-reinforced composites, including three distinct specimens, ranging from crack-free samples to specimens with up to ten cracks of varying lengths and depths. Strain data from five sensors were analyzed using a combination of Shewhart charts, Grubbs Test (GT), and a hierarchical clustering algorithm, specifically designed to evaluate and classify fractures. Our ML-based framework offers a streamlined and efficient alternative to traditional laboratory procedures, delivering precise crack detection with significant potential for applications in the composites industry.

This article presents a kinematic analysis of a 4-degree-of-freedom (DOF) robotic arm equipped with a 3-finger gripper, specifically designed for harvesting fruits of varying sizes. The kinematic modeling is thoroughly detailed, encompassing the equations of motion, homogeneous transformation matrices, and kinematic solutions required for the efficient operation of the robotic arm. This analysis is crucial for optimizing both the design and programming of agricultural robots, aiming to enhance harvesting efficiency and reduce labor costs. Additionally, an in-depth examination of the 3-finger gripper’s movement is provided, illustrating how MATLAB facilitates the simulation and visualization of its dynamic behavior during the opening and closing processes. The benefits of using MATLAB for this purpose are emphasized, including its ease of implementation, advanced simulation capabilities, and seamless integration with other tools to optimize the performance of robotic systems in agricultural settings. This study makes a valuable contribution to the field of agricultural robotics, offering technical insights that can be applied in the development of sophisticated robotic solutions for the modern agricultural industry.

Due to satisfactory mechanical properties, low specific weight and other advantages, a substantial utilization of laminated structural composites in many components and equipments of several industry sectors, especially in the past two decades, has been made. This class of materials has a good potential to be an important ally to support scientists and engineers with the main current world challenge: an energy transition process that must occur with sustainability, eco-friendly materials and based on an efficient reduction of greenhouse gas emissions. In this context, a microanalysis (scale length: 10μm to 1mm) of mechanical behavior that considers the multiphase characteristics, at leastwise a matrix and a reinforcement phase, of this kind of composites must be explored, i. e., a heterogeneous material is assumed to predict, for example, the effective tensors of stiffness or compliance by a homogenization process, modeling to damage analyses, studies about progressive delamination and other specific aspects of a composite can be previously investigated using micromechanical approaches. Therefore, the understanding of fundamental micromechanics as well as some advanced models are essential to apply these materials in structural industry components. In this sense, this literature review is designed to investigate and analize intrinsic physical hypothesis and theories behind the main basic and advanced models of polymer laminated composite micromechanics expanding possibilities to the development of more accurate approximations and showing future perspectives to the topic.

The high thrust linear motor used for electromagnetic launch is switched with the anti-shunt thyristor as the switch. During the switching process, different current path states will appear, which will lead to changes in motor parameters and current fluctuation. Aiming at the air-core synchronous linear motor with parallel and series power supply, this paper analyzes the change of current path caused by the change of trigger signal of anti-parallel thyristor in the process of sectionalized stator switching. The motor circuit model of the switching process is derived. A new sectionalized stator switching method is proposed to realize the smooth switching of the sectionalized stator powered linear motor. Finally, the correctness of the analysis and modeling is verified by simulation, and the effectiveness of the sectionalized stator switching method is tested by using the test prototype.

Following the economic and political transformations after 1991, including the autonomy of the Kurdistan Region and the establishment of Iraq's new federal government in 2003, extensive construction, including mosques, has reshaped the region. This requires examinations of evolving architectural features in new mosques and questioning their alignment with religious and aesthetic design objectives. This research focuses on the transformative impact of modern architectural styles on mosque evolution in Sulaymaniyah Governorate. Acknowledging the interplay of cultural evolution, technology, and artistic innovation, the study employs an in-depth case study approach to analyze selected mosques in Sulaymaniyah Governorate that blend traditional Islamic architecture with modern design principles. This synthesis of traditional and modern elements raises questions about religious legitimacy, historical symbolism, and architecture's role in reflecting and shaping religious values. Using quantitative methods including indicator-based impact assessment, the study identifies modern architectural indicators, determines religious objectives of mosque design through Islamic legitimacy references, and analyzes the case study data. By employing person coefficient correlation, the research aims to identify the causal relationship between modern architectural indicators and achieved design objectives. The results reveal that the overall influence of the modern architecture style tends to be more negative on the evol¬ution of mosques.

This research explores a novel approach to integrating offshore wind farms into smart microgrids through the development of an advanced model predictive control (MPC) strategy for offshore wind turbines. The MPC strategy comprehensively incorporates complex wind dynamics, such as wind speed, direction, and turbulence, ensuring an accurate representation of turbine behavior. Additionally, the research includes models addressing wind farm layout, wake effects, and electrical infrastructure, enhancing the modeling framework's fidelity and applicability to real-world offshore wind energy systems within a smart microgrid environment. The inherent variability of wind power, transmission technology limitations, and the need for seamless transitions between grid-connected and islanded operation modes in smart microgrids necessitate robust control mechanisms. These mechanisms ensure efficient power capture, grid stability, and optimal energy utilization. To tackle these challenges, the research proposes a smart microgrid design incorporating both AC-DC and DC-DC conversion technologies. This design leverages renewable energy sources like solar and wind with AC-DC conversion for efficient power capture, integrates DC energy storage systems to store excess energy during low demand periods, and utilizes power control mechanisms to manage energy flow effectively within the smart microgrid. The study emphasizes the importance of modeling and control strategies for both AC-DC and DC-DC converters within the smart microgrid framework. To enhance the microgrid's cost-effectiveness, priority is given to the utilization of solar PV energy sources. It also highlights the integration of protection systems for DC faults and coordinated control of AC/DC breakers and converters. This comprehensive approach aligns with the core principles of adaptability, reliability, and sustainability in smart grids, aiming to advance smart microgrid technologies and pave the way for a more resilient, efficient, and secure energy future.