Search Results (3,464)

To date, the only probiotic yeast with evidence of health-promoting effects is Saccharomyces cerevisiae var. boulardii. The expanded market including dietary supplements and functional foods supplemented with Saccharomyces cerevisiae var. boulardii creates an environment conductive to food adulterations, necessitating rapid testing to verify product probiotic status. Herein, qPCR-HRM analysis was tested for probiotic yeast identification. The effectiveness of the primer pairs’ set was examined, designed to amplify heterogeneous regions in (a) rDNA sequences previously designed to identify food-derived yeast and (b) genes associated with physiological and genotypic divergence of Saccharomyces cerevisiae var. boulardii. Preliminary tests of amplicons’ differentiation power enabled the selection of interspecies sequences for 18SrRNA and ITS and genus-specific sequences HO, RPB2, HXT9 and MAL11. The multi-fragment qPCR-HRM analysis was sufficient for culture-dependent Saccharomyces cerevisiae var. boulardii identification and proved effective in the authentication of dietary supplements’ probiotic composition. The identification of S. cerevisiae var. boulardii in complex microbial mixtures of kefir succeeded with more specific intragenus sequences HO and RPB2. The predominance of S. cerevisiae var. boulardii in the tested matrices, quantitatively corresponded to the probiotic-enriched food, was crucial for identification with qPCR–HRM analysis. Considering the reported assumptions, qPCR-HRM analysis is an appropriate tool for verifying probiotic-enriched food. Full article

Generative models such as generative adversarial networks and variational autoencoders are widely used for design synthesis. A diffusion model is another generative model that outperforms GANs and VAEs in image processing. It has also been applied in design synthesis, but was limited to only shape generation. It is important in design synthesis to generate shapes that satisfy the required performance. For such aims, a conditional diffusion model has to be used, but has not been studied. In this study, we applied a conditional diffusion model to the design synthesis and showed that the output of this diffusion model contains noisy data caused by Gaussian noise. We show that we can conduct flow analysis on the generated data by using smoothing filters. Full article

This paper presents the design process and resulting technical concept for an integrated hyperloop system, aimed at realizing efficient high-speed ground transportation. This study integrates various functions into a coherent and technically feasible solution, with key design decisions that optimize performance and cost-efficiency. An iterative design process with domain-specific experts, regular reviews, and a dataset with a single source of truth were employed to ensure continuous and collective progress. The proposed hyperloop system features a maximum speed of 600 $\frac{\mathrm{km}}{h}$ and a capacity of 21 passengers per pod (vehicle). It employs air docks for efficient boarding, electromagnetic suspension (EMS) combined with electrodynamic suspension (EDS) for high-speed lane switching, and short stator motor technology for propulsion. Cooling is managed through water evaporation at an operating pressure of 10 $\mathrm{m}$$\mathrm{bar}$, while a 300 $\mathrm{k}$$\mathrm{W}$ inductive power supply (IPS) provides onboard power. The design includes a safety system that avoids emergency exits along the track and utilizes separated safety-critical and high-bandwidth communication. With prefabricated concrete parts used for the tube, construction costs can be reduced and scalability improved. A dimensioned cross-sectional drawing, as well as a preliminary pod mass budget and station layout, are provided, highlighting critical technical systems and their interactions. Calculations of energy consumption per passenger kilometer, accounting for all functions, demonstrate a distinct advantage over existing modes of transportation, achieving greater efficiency even at high speeds and with smaller vehicle sizes. This work demonstrates the potential of a well-integrated hyperloop system to significantly enhance transportation efficiency and sustainability, positioning it as a promising extension to existing modes of travel. The findings offer a solid framework for future hyperloop development, encouraging further research, standardization efforts, and public dissemination for continued advancements. Full article

This paper proposes a method to investigate the design properties of geotechnical seismic isolation (GSI). This technique has been the object of many research contributions, both experimental and numerical. However, methods that may be used by practitioners for design procedures are still unavailable. The formulation presented herein may be used for preliminary assessments of two important properties: the thickness and the shear wave velocity. Three-dimensional advanced numerical simulations were performed with the state-of-the-art platform OpenSees in order to verify the analytical formulation on a benchmark case study. The elongation ratio has been taken as the relevant parameter to discuss the efficiency of GSI in decoupling the soil from the structure. The main findings consist of assessing the dependency of the elongation ratio on two parameters: the thickness and the shear velocity of the GSI layer. In this regard, a novel formulation was proposed in order to make preliminary design assessments that can be used by practitioners for practical applications. Full article

Leakage problems occur from time to time due to the large number of equipment and complex processes during oil and gas production and transportation. The traditional detection methods highly depend on manpower with large workload and are prone to missed and false alarms, which seriously affects the efficiency and safety of oil and gas production and transportation. With the continuous improvement of information technology and the rapid advancement of artificial intelligence (AI), the research on leakage detection technology based on AI methods has attracted more and more attention. This paper discusses the application scenarios of an AI agent based on the recently emerged large language model (LLM) technology in oil and gas production leakage detection: (1) Compared with the traditional leakage detection methods, this paper innovatively employs a combination of AI-based diagnostics and infrared temperature measurement technologies to develop a specialized small model for oil and gas leakage detection, which has been proven to significantly improve the accuracy of detecting industrial venting events in natural gas valve chambers; (2) By employing retrieval-augmented generation (RAG) technology, a knowledge vector library is constructed, utilizing a series of leakage-related documents, assisting the LLM to carry out knowledge questioning and inference. Compared with the traditional SimBERT, the accuracy can be improved by about 15% in the Q&A search ability test. The correct rate is about 70% higher than the SimBERT in the Chinese complex reasoning quiz. Also, it can still remain stable under high load conditions, with the interruption rate of retrieval closing to zero. (3) By combining the specialized small model and the knowledge Q&A tool, the natural gas valve chambers’ leakage detection AI agent based on the open-source LLM model was designed and developed, which preliminarily achieved the leakage detection based on the specialized small model, and the automatic processing of the retrieval reasoning process based on the knowledge Q&A tool and the intelligent generation of corresponding leakage disposal scheme. The effectiveness of the method has been verified by actual project data. This article conducts preliminary explorations into the in-depth applications of AI agents based on LLMs in the oil and gas energy industry, demonstrating certain positive outcomes. Full article

A diffractive solar sail is an elegant concept for a propellantless spacecraft propulsion system that uses a large, thin, lightweight surface covered with a metamaterial film to convert solar radiation pressure into a net propulsive acceleration. The latter can be used to perform a typical orbit transfer both in a heliocentric and in a planetocentric mission scenario. In this sense, the diffractive sail, proposed by Swartzlander a few years ago, can be considered a sort of evolution of the more conventional reflective solar sail, which generally uses a metallized film to reflect the incident photons, studied in the scientific literature starting from the pioneering works of Tsander and Tsiolkovsky in the first decades of the last century. In the context of a diffractive sail, the use of a metamaterial film with a Littrow transmission grating allows for the propulsive acceleration magnitude to be reduced to zero (and then, the spacecraft to be inserted in a coasting arc during the transfer) without resorting to a sail attitude that is almost edgewise to the Sun, as in the case of a classical reflective solar sail. The aim of this work is to study the optimal (i.e., the rapid) transfer performance of a spacecraft propelled by a diffractive sail with a Littrow transmission grating (DSLT) in a three-dimensional heliocentric mission scenario, in which the space vehicle transfers between two assigned Keplerian orbits. Accordingly, this paper extends and generalizes the results recently obtained by the author in the context of a simplified, two-dimensional, heliocentric mission scenario. In particular, this work illustrates an analytical model of the thrust vector that can be used to study the performance of a DSLT-based spacecraft in a three-dimensional optimization context. The simplified thrust model is employed to simulate the rapid transfer in a set of heliocentric mission scenarios as a typical interplanetary transfer toward a terrestrial planet and a rendezvous with a periodic comet. Full article

Exer-games are interactive experiences in which participants engage in physical exercises to achieve specific goals. Some of these games have a collaborative nature, wherein the actions and achievements of one participant produce immediate effects on the experiences of others. Music serves as a stimulus that can be integrated into these games to influence players’ emotions and, consequently, their actions. In this paper, a framework of music services designed to enhance collaborative exer-games is presented. These services provide the necessary functionality to generate personalized musical stimuli that regulate players’ affective states, induce changes in their physical performance, and improve the game experience. The solution requires to determine the emotions that each song may evoke in players. These emotions are considered when recommending the songs that are used as part of stimuli. Personalization seeds based on players’ listening histories are also integrated in the recommendations in order to foster the effects of those stimuli. Emotions and seeds are computed from the information available in Spotify data services, one of the most popular commercial music providers. Two small-scale experiments present promising preliminary results on how the players’ emotional responses match the affective information included in the musical elements of the solution. The added value of these affective services is that they are integrated into an ecosystem of Internet of Things (IoT) devices and cloud computing resources to support the development of a new generation of emotion-based exer-games. Full article

In this paper, a leader motion mechanism is studied for the finite time achievement of any desired formation of a multi-agent system. The approach adopted in this paper exploits a recent technique based on leader motion to the formation control problem of second-order systems, with a special effort to networks of mobile devices and teams of vehicles. After a thorough description of the problem framework, the leader motion mechanism is designed to accomplish the prescribed formation attainment in finite time. Both asymptotic and transient behavior are thoroughly analyzed, to derive the appropriate analytical conditions for the controller design. The overall algorithm is then finalized by two procedures that allow the exploitation of local data only, and the leader motion mechanism is performed based on data collected by the leader during a preliminary experimental stage. A final section of simulation results closes the paper, confirming the effectiveness of the proposed strategy for formation control of a multi-agent system. Full article

(1) Background: A preliminary investigation of Protected Designation of Origin (PDO) wines (red and rosé) produced from Negroamaro grapes—a native Salento (Apulia, Southern Italy) vine that is part of the Salice s.no PDO area—was performed in this work. (2) Methods: ¹H-NMR spectroscopy, in combination with multivariate statistical analysis (MVA), was employed to characterize the metabolic profiles of 39 wine samples. Spectrophotometric methods were used to obtain preliminary information on the phenolic composition of wines and the associated antioxidant activity. The HepG2 liver cell line was used to assess the biological activity (effect on cell viability and genotoxicity activity) of wine samples. (3) Results: The NMR spectra analysis revealed the presence of signals ascribable to phenolic compounds such as gallic, hydroxycinnamic, and syringic acids. Relative content of these metabolites has been shown to be higher in red than in rosés wines and related to the wine producers. Interestingly, a similar pattern was observed in biological analyses. Red wines compared to the rosé wines display great variations in antioxidant capacity when evaluated as fresh samples using the DPPH and ORAC methods. Furthermore, all red wines exhibited a concentration-dependent decrease in cellular viability and live cells; this phenomenon is much less pronounced in rosé wines. (4) Conclusions: The resulting findings from this study reveal that winemaking operations could lead to final products with different chemical compositions and related properties. Even when starting from the same crop variety and cultivation region, significant differences were observed in the wine samples NMR-metabolic profiles and in vitro biotoxicological activity. Full article

In this study, the design of mixture experiments was used to find empirical models that could predict, for a first approximation, the relative density, flexural strength, Vickers hardness and fracture toughness of sintered composites in order to identify further areas of research in the Al₂O₃-TiB₂-TiC ternary system. The composites were obtained by spark plasma sintering (SPS) of these mixtures at 1700 °C, 80 MPa and a dwell of 3 min. The obtained experimental results were analyzed in the statistical analysis software Minitab 17, and then, different regression models were obtained for each property. Based on the selected models, contour plots were made in the Al₂O₃–TiB₂–TiC simplex for a visual representation of the predicted results. By combining these plots, it was possible to obtain one common zone in the Al₂O₃–TiB₂–TiC simplex, which shows the following combination of physical and mechanical properties for sintered samples: relative densities, flexural strength, Vickers hardness, and fracture toughness of than 99%, 500 MPa, 18 GPa, and 7.0 МPa·m^1/2, respectively. For a first approximation in determining the further area of research, the obtained models describe well the behavior of the studied properties. The results of the analysis showed that the design of mixture experiments allows us to identify the most promising compositions in terms of mechanical properties without resorting to labor-intensive and financially expensive full-scale experiments. Our work shows that 10 different compositions were required for preliminary analysis. Full article

Racial/ethnic minorities, including the Black community, experience stigma as a significant barrier to mental health care, with fears of being devalued or discriminated against deterring individuals from seeking help. Racial stigma further exacerbates mental health issues and negatively influences perceptions of service utilization. To address this, our research team partnered with a national non-profit storytelling organization to develop and evaluate a virtual narrative storytelling intervention series that amplifies the voices and experiences of Black Americans living with mental illness and addiction. We randomly assigned 193 participants to either the intervention (n = 102) or an active control condition (n = 91) and used a pre–post survey design to assess the changes in the outcome variables. Contrary to our hypothesis, there were no race-based interactions; instead, the results show significant reductions in public stigma and perceived discrimination and increased positive attitudes toward seeking treatment universally among all the intervention participants. This study provided preliminary evidence that a virtual storytelling intervention is instrumental across varied demographic cohorts, transcending potential cultural barriers in the discourse and understanding of mental health to effectively mitigate stigma and improve attitudes toward mental health treatment. Full article

This paper presents a comprehensive analytical–numerical algorithm constructed for proprotor performance evaluation, focusing on accommodating large inflow angles. The algorithm’s design, range, and analytical features are clarified, indicating its potential to improve performance analysis, particularly for blades with substantial pitch variations. The Stahlhut model has not been validated against the conventional BEMT small-inflow angle methodology. This paper implements a modified Stahlhut model, coupled with the conventional BEMT. Preliminary validations of the model demonstrate promising results, with deviations reduced to −3% to 4% compared to conventional BEMT methods exhibiting deviations as high as 20% to 88% against experimental data for a highly twisted proprotor. The reconsideration of the computational module carries considerable implications for the design and refinement of proprotors, providing alternative analysis methods that could improve operational effectiveness across a range of flight scenarios. Drawing upon the theoretical framework presented by Stahlhut, the algorithm enables a more complex understanding of proprotor dynamics, facilitating accurate predictions of the loads at each blade section. The introduced algorithm emerges as a valuable asset for evaluating proprotor performance during the early stages of design and certification, offering both low computational cost and medium to high reliability. Full article

In recent years, cybersecurity management has increasingly required advanced methodologies capable of handling complex, evolving threat landscapes. Scenario network-based approaches have emerged as effective strategies for managing uncertainty and adaptability in cybersecurity projects. This article introduces a scenario network-based approach for managing cybersecurity projects, utilizing fuzzy linguistic models and a Takagi–Sugeno–Kanga fuzzy neural network. Drawing upon L. Zadeh’s theory of linguistic variables, the methodology integrates expert analysis, linguistic variables, and a continuous genetic algorithm to predict membership function parameters. Fuzzy production rules are employed for decision-making, while the Mamdani fuzzy inference algorithm enhances interpretability. This approach enables multi-scenario planning and adaptability across multi-stage cybersecurity projects. Preliminary results from a research prototype of an intelligent expert system—designed to analyze project stages and adaptively construct project trajectories—suggest the proposed approach is effective. In computational experiments, the use of fuzzy procedures resulted in an over 25% reduction in errors compared to traditional methods, particularly in adjusting project scenarios from pessimistic to baseline projections. While promising, this approach requires further testing across diverse cybersecurity contexts. Future studies will aim to refine scenario adaptation and optimize system response in high-risk project environments. Full article

The objective of this paper was to establish a comprehensive methodology for the optimized design of propellers for ice-class vessels, aiming to enhance hydrodynamic efficiency while ensuring structural integrity. This paper begins by introducing a novel approach for calculating blade stress, which takes into account both extreme ice loads and hydrodynamic loads, to be utilized in the propeller strength design process. Subsequently, a backpropagation (BP) neural network model was developed based on the data obtained from B-series propeller charts and integrated with a genetic algorithm to achieve a preliminary optimized design of the propeller’s hydrodynamic performance. To illustrate the application of this methodology, a case study of an ice-breaking tug propeller design is presented, detailing the optimization design process, including the preliminary, intermediate, and final design stages. The study also addresses key aspects such as geometric parameterization, the selection of optimization variables, the implementation of optimization algorithms, and the balance of multi-objective trade-offs. The proposed design approach can serve as a valuable reference for the practical engineering design of propellers for ice-class vessels, providing a systematic framework for achieving optimal performance in challenging operating conditions. Full article

Iris is one of the most widely used biometric modalities because of its uniqueness, high matching performance, and inherently secure nature. Iris segmentation is an essential preliminary step for iris-based biometric authentication. The authentication accuracy is directly connected with the iris segmentation accuracy. In the last few years, deep-learning-based iris segmentation methodologies have increasingly been adopted because of their ability to handle challenging segmentation tasks and their advantages over traditional segmentation techniques. However, the biggest challenge to the biometric community is the scarcity of open-source resources for adoption for application and reproducibility. This review provides a comprehensive examination of available open-source iris segmentation resources, including datasets, algorithms, and tools. In the process, we designed three U-Net and U-Net++ architecture-influenced segmentation algorithms as standard benchmarks, trained them on a large composite dataset (>45K samples), and created 1K manually segmented ground truth masks. Overall, eleven state-of-the-art algorithms were benchmarked against five datasets encompassing multiple sensors, environmental conditions, demography, and illumination. This assessment highlights the strengths, limitations, and practical implications of each method and identifies gaps that future studies should address to improve segmentation accuracy and robustness. To foster future research, all resources developed during this work would be made publicly available. Full article