Search Results (778)

This paper presents a method of choosing a single solution in the Pareto Optimal Front of the multi-objective problem of the spectral and energy efficiency trade-off in Massive MIMO (Multiple Input, Multiple Output) systems. It proposes the transformation of the group of non-dominated alternatives using the Box–Cox transformation with values of λ < 1 so that the graph with a complex shape is transformed into a concave graph. The Box–Cox transformation solves the selection bias shown by the decision-making algorithms in the non-concave part of the Pareto Front. After the transformation, four different MCDM (Multi-Criteria Decision-Making) algorithms were implemented and compared: SAW (Simple Additive Weighting), TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), PROMITHEE (Preference Ranking Organization Method for Enrichment Evaluations) and VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje). The simulations showed that the best value of the λ parameter is 0, and the MCDM algorithms which explore the Pareto Front completely for different values of weights of the objectives are VIKOR as well as SAW and TOPSIS when they include the Max–Min normalization technique. Full article

Dynamic workflow scheduling in cloud environments is a challenging task due to task dependencies, fluctuating workloads, resource variability, and the need to balance makespan and energy consumption. This study presents a novel scheduling framework that integrates Graph Neural Networks (GNNs) with Deep Reinforcement Learning (DRL) using the Proximal Policy Optimization (PPO) algorithm to achieve multi-objective optimization, focusing on minimizing makespan and reducing energy consumption. By leveraging GNNs to model task dependencies within workflows, the framework enables adaptive and informed resource allocation. The agent was evaluated within a CloudSim-based simulation environment using synthetic datasets. Experimental results across benchmark datasets demonstrate the proposed framework’s effectiveness, achieving consistent improvements in makespan and energy consumption over traditional heuristic methods. The framework achieved a minimum makespan of 689.22 s against the second best of 800.72 s in moderate-sized datasets, reducing makespan significantly with improvements up to 13.92% over baseline methods such as HEFT, Min–Min, and Max–Min, while maintaining competitive energy consumption of 10,964.45 J. These findings highlight the potential of combining GNNs and DRL for dynamic task scheduling in cloud environments, effectively balancing multiple objectives. Full article

Correlation analysis is one of the most prolific statistical methods used in data analysis problems, mining of knowledge focused on relationships of attributes in large datasets, and in various predictive tasks utilizing statistical, machine learning, and deep learning models. This approach to the analysis of functional relationships in multidimensional datasets is commonly used in conjunction with visual analysis approaches, which offer novel context for the relationships in data and clarify the results presented in large correlation matrices. One of such visualization methods uses graphical models called correlation graphs and chains, which visualize individual direct and indirect relationships between pairs of attributes in a dataset of interest as a graph structure, where vertices of the graph represent attributes of the dataset and edges between vertices represent the correlation of these attributes. This work focuses on the definition, identification, and exploration of so-called correlation cycles, which can be—through their deconstruction—used as an approach to lower error values in regression tasks. After the implementation of the correlation cycle identification and deconstruction, the proposed concept is evaluated on predictive analysis tasks in the context of three benchmarking datasets from the engineering field—the Sensor dataset, Superconductivity dataset, and Energy Farm dataset. The results obtained in this study show that when using simple, explainable regressors, the method utilizing deconstructed correlation cycles reaches a lower error rate in 83.3% of regression cases compared to the same regression models without the cycle incorporation. Full article

Oceans are a major source of clean energy, harnessing the vast and consistent power of waves to generate electricity. Today, they are seen as a vital renewable and clean solution for transitioning to a complete fossil fuel-free future world. To get the most out of ocean wave potential, Wave Energy Converters (WECs) are being used to harness the power of ocean waves into usable electrical energy. To this end, to maximize the power generated from the WECs, two strategies for WEC design improvement and optimal coordination can be considered. Among these, optimal coordination is the more straightforward method to implement. However, most of the recently developed coordination strategies are dynamic-based, encountering challenges as the system’s scale expands and grows larger. Consequently, a novel Graph Isomorphic Network (GIN)-based model is presented in this paper. The proposed model consists of the following five layers: the input graph, two GIN convolutional layers (GIN Conv.1, and 2), a mean pooling layer, and the output layer. The target of total generated power is predicted based on the features of the generated power from each WEC and the related spatial coordinates $\{x_i,y_i\}$. Subsequently, based on the anticipated total power considered by the model, the system enables maximum generation. The model performs spatial coordination analyses to present the optimal coordination for each WEC to achieve the objective of maximizing total generated power. The proposed model is evaluated through several Key Performance Indicators (KPIs), achieving the least number of errors in prediction and optimal coordination performances. Full article

Camels, with the ability to survive under drought and chronic hunger, developed exceptional efficient lipid reserves and energy substance metabolic characteristics. Fibroblast growth factor (FGF) 21 is a hormone that regulates important metabolic pathways and energy homeostasis. However, the absence of a specific detection method for camel FGF21 impacts research on camels’ metabolic regulation. This study established a direct competition ELISA assay for detecting camel FGF21. Camel FGF21 antigen was expressed and purified through prokaryotic expression system. Polyclonal antibody was produced and purified via immunizing guinea pigs and affinity chromatography assay. Biotin-labeled FGF21 was synthesized artificially as the competitive antigen. After the determination of optimal conditions, including the working concentrations of the antibody and antigen, blocking solution, dilution buffer, and the competition reaction time, the standard curve with a typical “S” shape was generated using GraphPad Prism. The regression equation was Y = 0.1111 + (X^−0.7894) × (2.162 − 0.1111)/(X^−0.7894 + 15.76^−0.7894), with the IC₅₀ 15.59 ng/mL, the limit of detection (LOD) 0.024 ng/mL, the limit of quantification (LOQ) 1.861 ng/mL, and the linear range IC₂₀~IC₈₀ 2.0~119.22 ng/mL. The verification test showed that the recovery rate ranged from 91.34% to 98.9%, and the coefficients of variation for the intra- and inter-plate both were less than 10%, indicating that the ELISA method had high accuracy, good repeatability, and high stability. In addition, this ELISA method had the potential to detect FGF21 secretion levels in other species such as mouse, human, and pig. This study provided a rapid quantitative tool for conducting research on the FGF21 factor in camels. Full article

Within smart homes, consumers could generate a vast amount of data that, if analyzed effectively, can improve the convenience of consumers and reduce energy consumption. In this paper, we propose to organize household appliance data into a knowledge graph by using the consumers’ usage habits, the periods of usage, and the location information for graph modeling. A framework, ‘DARK’ (Device Action Recommendation with Knowledge graphs), is proposed that includes three parts for enabling demand response. Firstly, a household device action recommendation algorithm is proposed that improves the knowledge graph attention algorithm to make accurate household appliance recommendations. Secondly, graph interpretable characteristics are developed in the DARK using trained graph embeddings. Finally, with the recommendation expectation, the consumers’ comfort level and appliances’ average power load are modeled as a multi-objective optimization problem in the DARK to participate in demand response. The results demonstrate that the proposed system can generate appliances’ action recommendations with an average of 93.4% accuracy and reduce power load by up to 20% while providing reasonable interpretations for the device action recommendation results on the customized UK-DALE dataset. Full article

Background: Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor symptoms like bradykinesia, tremor, rigidity, and postural instability. Additionally, PD severely impacts physical abilities and independence. Chronic pain, affecting 67.6% of PD patients, varies in form and presentation, and it is often underdiagnosed. Objectives: This study investigated the association between chronic pain and motor symptom severity in PD patients. Methods: This analysis used data from a cross-sectional study on 52 Parkinson’s disease (PD) patients conducted at Jena University Hospital, Germany. The dataset, available on Dryad, included demographics; clinical reports; and assessments of coping strategies, quality of life, and pain. Descriptive statistics, a bivariate analysis, and an ordinal logistic regression model were executed to explore the association between pain and motor symptom severity (MSS). A direct acyclic graph was used to represent the relationship between variables and identify potential confounders, and an outcomes definition sensitivity analysis was used to assess the impact of using pain intensity as an outcome. The E-value was calculated to evaluate the strength of association needed by an unmeasured confounder to nullify the observed association. Results: A total of 50 Parkinson’s disease (PD) patients were included, with 64% being male, with an average age of 76.1 years. The sample included 20 patients without pain and 30 with chronic pain. The bivariate analysis did not identify significant differences in disease duration, cognitive function, and non-motor symptoms between pain and no-pain groups. However, significant differences (p-value < 0.05) emerged in motor symptom severity, coping strategies, and several SF-36 domains (Physical and Social Functioning, Role Functioning, Energy/Fatigue, Pain, General Health, and Health Change). The ordinal logistic regression showed a substantial association between chronic pain and MSS: patients with chronic pain had 3.52 times higher odds (95% CI: 1.40–8.84, effect size d ≈ 0.70, p = 0.02) of low to medium MSS and 5.44 times higher odds (95% CI: 2.03–14.60, effect size d ≈ 0.94, p = 0.01) of medium to severe MSS, indicating a dose–response relationship. Additionally, male patients had increased odds of higher MSS (OR 4.63, 95% CI: 1.15–18.58, effect size d ≈ 0.85, p = 0.03). Conclusions: Chronic pain is strongly associated with MSS in PD patients, with a more pronounced effect as MSS progresses from medium to severe, supporting a dose–response relationship. Effect sizes suggest a robust association, emphasizing the need for pain assessment in managing motor symptoms in PD. Full article

Numerous studies have explored the placement of solar panels on the facades or roofs of buildings. This study investigates a new approach to estimating energy generation from transparent, double-sided solar panels integrated into the facade of an existing building, focusing on how the façade’s color influences panel performance. The most significant advantages of integrating double-sided and transparent solar panels on the sides of a building are the natural lighting provided by the sunlight entering the building and the additional energy generated when the radiation returns to the back of the panel. The light beam strikes the front panel, allowing some radiation to pass through the transparent panel to the back side, where it hits the surface. Part of the beam is then reflected toward the rear panel. The fraction of light reflected (albedo) depends on the surface’s color. We first constructed a double-sided, transparent solar panel and integrated it with MATLAB software 2024 code. The model was verified by comparing the simulation results, specifically the I–V and P–V graphs, with data from the manufacturer’s specifications. We conducted an extensive investigation into panels installed on surfaces made of different materials during each installation. This investigation aimed to understand the behavior and performance of the panels when installed on the surfaces of various materials. Full article

Software-defined networking (SDN) represents a transformative approach to network management, enabling the centralized and programmable control of network infrastructure. This paradigm facilitates enhanced scalability, flexibility, and security in managing complex systems. When integrated with the Internet of Things (IoT), SDN addresses critical challenges such as security and efficient network management, positioning the SDN-IoT paradigm as an emerging and impactful technology in modern networking. The rapid proliferation of IoT applications has led to a significant increase in security threats, posing challenges to the safe operation of IoT systems. Consequently, SDN-IoT-based applications and services have been widely adopted to address these issues and challenges. However, this platform faces critical limitations in ensuring scalability, optimizing energy consumption, and addressing persistent security vulnerabilities. To overcome these issues, we proposed a secure SDN-IoT environment for intrusion detection and prevention using virtual blockchain (V-Block). Initially, IoT users are registered and authenticated to the shadow blockchain nodes using a picture-based authentication mechanism. After that, authenticated user flows validation was provided by considering effective metrics utilizing the Trading-based Evolutionary Game Theory (TEGT) approach. Then, we performed a local risk assessment based on evaluated malicious flows severity and then the attack graph was constructed using an Isomorphism-based Graph Neural Network (IGNN) model. Further, multi-controllers were placed optimally using fox optimization algorithm. The generated global paths were securely stored in the virtual blockchain Finally, the two agents in the multi-controllers were responsible for validating and classifying the incoming suspicious flow packets into normal and malicious packets by considering the operative metrics using the Dueling Deep Q Network (DDQN) algorithm. The presented work was conducted by Network Simulator-3.26 and the different performance matrices were used to itemize the suggested V-Block model based on its malicious traffic, attack detection rate, link failure rate, anomaly detection rate, and scalability. Full article

In this study, the composition and mechanical properties of composites designed for shipbuilding are described. Four different composites were designed and fabricated by the research team, using quadriaxial glass fiber fabric (eight layers in all composites), two different resins (the epoxy resin SikaBiresin^® CR82 with the hardener CH80-2 or the polyester resin Enydyne H 68372 TA with Metox-50 W as the accelerator), and a middle layer of Coremat Xi 3 (only applied in some composites). The experimental results of low-velocity impact tests are also discussed, including the graphics force (displacement) and absorbed energy (displacement and velocity). The displacement and composite quality were evaluated through several parameters, such as maximum force, absorbed energy, and maximum displacement. Impact tests were carried out using four impact energy values (50–200 J), with an average impact velocity in the range of 4.37 ± 0.05 m/s. Only partial penetrations were obtained for all tested composites. For the low energy tests (50 J), the four composite materials were not well differentiated by graph shapes and parameter values, but for the higher energy tests, the composites containing Coremat Xi 3 displayed better behavior, having F_max reduced with 10.8% to 29.08%. The higher absorbed energy of these composites can be explained by the plateau generated by the force from a longer impactor displacement in contact with the composite. The results generated in this study confirm the suitability of the designed composites for shipbuilding applications. Still, the composites have light differences in terms of energy absorption in low-velocity impact and a significant reduction in maximum force. Full article

With increasing power system complexity and distributed energy penetration, traditional voltage control methods struggle with dynamic changes and complex conditions. While existing deep reinforcement learning (DRL) methods have advanced grid control, challenges persist in leveraging topological features and ensuring computational efficiency. To address these issues, this paper proposes a DRL method combining Graph Convolutional Networks (GCNs) and soft actor-critic (SAC) for voltage control through load shedding. The method uses GCNs to extract higher-order topological features of the power grid, enhancing the state representation capability, while the SAC optimizes the load shedding strategy in continuous action space, dynamically adjusting the control scheme to balance load shedding costs and voltage stability. Results from the simulation of the IEEE 39-bus system indicate that the proposed method significantly reduces the amount of load shedding, improves voltage recovery levels, and demonstrates strong control performance and robustness when dealing with complex disturbances and topological changes. This study provides an innovative solution to voltage control problems in smart grids. Full article

In this work, we focused on the characterization of closed-cell Al foams and aluminum honeycomb panels, in particular their energy absorption capacity under conditions of static compressive stress. Through experimental tests, the specific energy absorbed by different samples was evaluated: in the honeycomb panels the mechanical behavior was analyzed both for large assemblies and for structures with a reduced number of cells, and the effect of the number of cells was studied too. Furthermore, for larger structures, the specific energy absorbed was calculated from stress–strain compressive graphs. For the closed-cell Al foams, manufactured in the laboratory using the powder compaction method with different percentages of SiC and TiH₂ and characterized by different relative densities, the specific energy absorbed was evaluated too. The experimental results showed that the specific energy absorbed by the Al honeycomb was always higher than that of the different types of closed-cell foams. However, when selecting the material for each specific application, it is necessary to take into account numerous parameters such as the relative density, absorbed energy, peak stress, plateau stress, plateau extension, densification strain and so on. Consequently, the overall performance must be evaluated from time to time based on the type of application in which the best compromise between strength, stiffness and lightness can be achieved. Full article

In this study, a method is presented to determine the matching media parameters that maximize the electromagnetic energy penetrating into the human arm modeled as a radially stratified cylinder. In this context, first, the electromagnetic scattering problem related to the layered cylindrical model in question was solved analytically using cylindrical harmonics. Then, based on this solution, a frequency-dependent functional in terms of the electromagnetic parameters of the matching medium was defined, and the parameters that minimize this functional were determined through the graphs of this functional. In this functional, which depends on the permittivity, conductivity and frequency of the matching medium, one parameter was kept constant at every turn while the other two parameters were optimized. The accuracy of the approach was demonstrated by calculating the electric field amplitudes inside and outside the layers for the parameters determined by the proposed method. The numerical results given in this context demonstrate that if a matching medium is used, the penetrating field increases between 1.3 to 13.96 times compared to the case where the matching medium is absent. Full article

To improve the operational efficiency of electric vehicle (EV) charging infrastructure, this paper proposes a multi-stage hybrid planning method for charging stations (CSs) based on graph auto-encoder (GAE). First, the network topology and dynamic interaction process of the coupled “Vehicle-Station-Network” system are characterized as a graph-structured model. Second, in the first stage, a GAE-based deep neural network is used to learn the graph-structured model and identify and classify different charging station (CS) types for the network nodes of the coupled system topology. The candidate CS set is screened out, including fast-charging stations (FCSs), fast-medium-charging stations, medium-charging stations, and slow-charging stations. Then, in the second stage, the candidate CS set is re-optimized using a traditional swarm intelligence algorithm, considering the interests of multiple parties in CS construction. The optimal CS locations and charging pile configurations are determined. Finally, case studies are conducted within a practical traffic zone in Hong Kong, China. The existing CS planning methods rely on simulation topology, which makes it difficult to realize efficient collaboration of charging networks. However, the proposed scheme is based on the realistic geographical space and large-scale traffic topology. The scheme determines the station and pile configuration through multi-stage planning. With the help of an artificial intelligence (AI) algorithm, the user behavior characteristics are captured adaptively, and the distribution rule of established CSs is extracted to provide support for the planning of new CSs. The research results will help the power and transportation departments to reasonably plan charging facilities and promote the coordinated development of EV industry, energy, and transportation systems. Full article

The industrial application of high hydrostatic pressure (HHP) can be traced back to the late 19th century in the fields of mechanical and chemical engineering. Its growth as a food preservation technique has developed and massified in certain countries in the last 30 years. However, there is no global overview of the research conducted on this topic. The aim of this study was to recognize global trends in the scientific population on the subject of HHP over time at the main levels of analysis: sources, authors, and publications. This article provides a summary of research related to the use of HHP through a bibliometric analysis using information obtained from the Web of Science (WoS) database between the years 1975–2023, using the terms “pascalization”,“high-pressure processing”, and “high hydrostatic pressure” as input keywords. The results are shown in tables, graphs, and relationship diagrams. The countries most influential and productive in high hydrostatic pressure are the People’s R China, the USA, and Spain, with 1578, 1340, and 1003 articles, respectively. Conversely, the authors with the highest metrics are Saraiva, J. (Universidade Aveiro-Portugal), Hendrickx, M. (Katholieke Universiteit Leuven-Belgium), and Wang, T. (China Agricultural University-China). The most productive journals are Innovative Food Science & Emerging Technologies, Food Chemistry, and LWT-Food Science and Technology, all belonging to Elsevier, with 457, 281, and 264 documents, respectively. In relation to the connection between the documents under study and the United Nations Sustainable Development Goals (SDGs), most documents in the period 1975–2023 are linked to SDG 03 (good health and well-being), followed by SDG 02 (zero hunger), and SDG 07 (affordable and clean energy). Finally, the information presented in this work may give valuable key insights for those interested in the development of this interesting topic in non-thermal food preservation. Additionally, it serves as a strategic resource for stakeholders, such as food industry leaders, policymakers, and research funding bodies, by providing a clear understanding of the current state of knowledge and innovation trends. This enables informed decision-making regarding research priorities, investment opportunities, and the development of regulatory frameworks to support the adoption and advancement of non-thermal preservation technologies, ultimately contributing to safer and more sustainable food systems. Full article