Abstract: A one-dimensional plug-flow reactor modeling procedure was developed and used to investigate the performance of a membrane reactor (MR) for hydrogen separation from syngas. A feed syngas enters from one side, while a sweep gas of nitrogen enters from the opposite side. The model treats the membrane reactor as a series of 200 segments with a constant cross section and temperature. The adopted spatial resolution was verified to be accurate based on a conducted resolution sensitivity analysis. Permeation is modeled as happening through thin palladium membranes that are selectively permeable to hydrogen, depending on the temperature and membrane thickness. After analyzing the hydrogen permeation profile in a base case corresponding to reference operational temperature and pressures, the temperature of the module, the retentate-side pressure, and the permeate-side pressure were varied individually and their influence on the permeation performance was investigated. In all the simulation cases, fixed targets of 95% hydrogen recovery and 40% mole-fraction of hydrogen at the permeate exit were demanded. The module length is allowed to change to satisfy these targets, with a shorter module requiring less space and reflecting better hydrogen permeation mass flux. Other dependent permeation-performance variables that were investigated include the logarithmic mean pressure-square-root difference, the hydrogen apparent permeance, and the efficiency factor. Various linear and nonlinear regression models were proposed based on the obtained results. This work gives general insights about hydrogen permeation via palladium membranes.

Abstract: The integration of Artificial Intelligence (AI) into manufacturing processes is revolutionizing the industry, significantly enhancing operational efficiency and productivity. This comprehensive review explores the multifaceted impact of AI on manufacturing efficiency, analyzing various AI technologies such as machine learning, robotics, computer vision, and natural language processing. By automating production processes and enabling predictive maintenance, AI minimizes downtime and reduces human error, leading to streamlined workflows and optimized resource allocation. The review highlights advancements in quality control through real-time defect detection and improved supply chain management facilitated by AI-driven demand forecasting and inventory optimization.Case studies from diverse industries, including automotive, electronics, and aerospace, illustrate successful AI implementations, showcasing measurable efficiency gains and enhanced competitiveness. However, the adoption of AI in manufacturing is not without challenges. Issues such as data quality, resistance to organizational change, workforce skills gaps, and ethical considerations pose significant barriers to effective implementation. The review also addresses future directions for AI in manufacturing, emphasizing emerging technologies and their potential to further transform the industry.Overall, this review underscores the critical role of AI in reshaping manufacturing efficiency, offering insights for practitioners and researchers alike. It concludes with recommendations for future research to address existing challenges and leverage AI's full potential in the manufacturing sector.

Abstract: The integration of Artificial Intelligence (AI) in supply chain management represents a transformative shift aimed at enhancing operational efficiency and precision. This study investigates the role of AI in optimizing supply chain processes, particularly focusing on its impact on Enterprise Resource Planning (ERP) systems and the reduction of errors inherent in traditional management methods.Supply chains are complex networks that require real-time data processing and accurate forecasting to function effectively. ERP systems serve as the backbone of these networks, facilitating seamless integration across various business functions. However, many organizations face challenges such as data entry errors, integration issues, and inefficiencies that hinder performance. This research delineates how AI technologies—such as machine learning, predictive analytics, and natural language processing—can address these challenges by improving data accuracy, enhancing demand forecasting, and optimizing inventory management.The paper presents a comprehensive literature review to contextualize the current state of AI applications in supply chain optimization. It further examines case studies where organizations have successfully implemented AI solutions to minimize errors in ERP systems, illustrating the tangible benefits of increased precision and operational agility. Key findings highlight that AI not only reduces human error through automation but also empowers decision-makers with actionable insights derived from real-time data analysis.Despite the promising potential of AI, the study also discusses significant challenges, including implementation costs, organizational resistance, and data privacy concerns. These barriers necessitate a strategic approach to AI adoption, emphasizing the importance of training, change management, and robust data governance frameworks.In conclusion, this research underscores the critical need for organizations to embrace AI-driven strategies within their supply chain operations. By leveraging advanced technologies, companies can enhance their ERP systems, reduce operational errors, and ultimately achieve a competitive advantage in an increasingly dynamic market landscape. The findings contribute to the growing body of knowledge in supply chain management and provide actionable recommendations for practitioners seeking to navigate the complexities of AI integration.

Abstract: The cross-border port serves as a crucial cross-border travel connecting mainland China with Hong Kong and Macau, directly impacting the overall satisfaction of cross-border travel. While previous studies on neighborhoods, communities, and other areas have thoroughly examined the heterogeneity and asymmetry in satisfaction, research on the satisfaction of cross-border travel at ports remains notably limited. This paper explores the heterogeneity and asymmetry of cross-border travel satisfaction using gradient boosted decision trees (GBDT) and k-means cluster analysis under the framework of three-factor theory, aiming to demonstrate the latest scientific research results on the fundamental theories and applications of artificial intelligence. The results show that prevalent asymmetric relationships between factors and cross-border travel satisfaction, with the factor structure exhibiting heterogeneity across different groups. High-income individuals were more likely to prioritize the reliability of cross-border travel, whereas low-income individuals tended to emphasize the convenience of travel. Finally, this paper proposes improvement priorities for different types of passengers, reflecting the practical application of advanced mathematical methods in artificial intelligence to drive intelligent decision-making.

Abstract: To reduce pollutants generated by automotive painting processes and improve coating efficiency, this study proposes a clean production method for automotive body painting based on an improved whale optimization algorithm from the perspective of "low-carbon consumption and emission-reduced production." A multi-level, multi-objective decision-making model is developed by integrating three dimensions of clean production: material flow (optimizing material costs), energy flow (minimizing painting energy consumption), and environmental emission flow (reducing carbon emissions and processing time). The whale optimization algorithm is enhanced through three key modifications: the incorporation of nonlinear convergence factors, elite opposition-based learning, and dynamic parameter self-adaptation, which are then applied to optimize the automotive painting model. Experimental validation using the painting processes of TJ Corporation's New Energy Vehicles (NEVs) demonstrates the superiority of the proposed algorithm over MHWOA, WOA-RBF, and WOA-VMD. Results show that the method achieves a 42.1% increase in coating production efficiency, over 98% exhaust gas purification rate, 18.2% average energy-saving improvement, and 17.9% reduction in manufacturing costs. This green transformation of low-carbon emission-reduction infrastructure in painting processes delivers significant economic and social benefits, positioning it as a sustainable solution for the automotive industry.

Abstract: This paper presents a combined theoretical and experimental investigation of an innovative building material known as PVA Fiber Reinforced Composite - an advanced class of cement-based materials renowned for their exceptional flexibility and superior crack control, overcoming the limitations of traditional concrete. The study aimed to develop new compositions and conduct a comparative performance analysis. The proposed composites utilize locally available cement at conventional dosage levels, incorporating high volumes of fly ash, silica fume, PVA fibers, and a superplasticizer, while entirely omitting stone and sand aggregates. A total of nine compositions were designed and tested: four with uncoated PVA fibers at 1%, 1.5%, 2%, and 2.5%, four with oil-coated PVA fibers in the same proportions, one control mix containing sand aggregates. The research evaluated key experimental and design parameters, including bulk density, compressive strength and strain, secant modulus of elasticity, flexural tensile strength and deflection, fracture energy, and structural design applicability. As a novel building material aimed at enhancing both environmental sustainability and structural performance, the studied fiber-reinforced composites exhibit promising characteristics, positioning them as viable alternatives for practical construction applications.

Abstract: This paper presents an adaptive strategy to solve the formation control problem for a set of second-order agents with parametric uncertainty and nonlinearity. The strategy regards a group of agents where the nolinearities and uncertainties are represented by a linearly parametrised term, which allows us to consider non-identical agents. In order to ensure the collision-free motion of agents, we propose the use of a repulsive vector field component that is applied only when a pair of agents become nearer than a predefined minimum bound. Numerical simulations were carried out to show the effectiveness of the proposed scheme, first with a simplified example to verify the key features of the control law and a general case to illustrate the performance of the algorithm in a more complex scenario.

Abstract: This paper proposes a novel maximum power point tracking (MPPT) strategy for renewable energy systems using Input Impedance Control (I²C) in power electronic converters, combined with an adaptive nonlinear controller. Unlike conventional voltage- or current-based methods, the I²C-MPPT approach leverages the maximum power transfer theorem by dynamically matching the converter’s equivalent input impedance to the source’s internal impedance. The adaptive nonlinear controller, designed using the Lyapunov stability theory, estimates system uncertainties and provides superior performance compared to traditional PI controllers. The proposed approach is validated through both simulations in MATLAB and experimental implementation using a DSP-based controller. Practical results confirm the controller’s effectiveness in maintaining maximum power transfer under dynamic variations in source parameters, demonstrating improved settling time and robust operation. These findings underscore the potential of the I²C approach for enhancing the efficiency and reliability of renewable energy systems.

Abstract: To meet diverse industrial needs, temperature sensors with a wide measurement range have become an indispensable key element. In this paper, we propose an asymmetric Mach-Zehnder Interferometer (MZI) temperature sensor based on polymer optical waveguides. Experimental results show that the output interference signal exhibits periodic changes with temperature variations. The device exhibits a temperature measurement range of 120°C, and the sensitivity of 0.27 rad/°C. This study provides an effective new approach for developing high-performance, low-cost temperature sensors suitable for extended temperature measurement range.

Abstract: This paper provides a comprehensive analysis of photovoltaic (PV) systems, their development and security issues in the past decade in Europe and Romania. It begins with the presentation of the PV systems development in the two regions, and proceeds with the critical risk evaluation of PV systems as essential components of the energy infrastructure of Romania. The article presents the authors' arguments in support of the proposal to include PV systems in the critical infrastructure category, reflecting their strategic importance to national energy resilience. This is achieved through a comprehensive assessment of the current levels of safety, security, cybersecurity, and physical protection of PV systems, highlighting potential vulnerabilities that may compromise operational continuity. The evaluation of cybersecurity risks leads to the conclusion that PV systems face increasing exposure to digital threats, reinforcing the urgent need for robust cyber defense mechanisms in this rapidly evolving sector.This study aims to create an entire set of guidelines for enhancing the security and resilience of PV systems as they increasingly form a critical component of sustainable energy infrastructure.

Abstract: (1) Background: Blood pressure (BP) variability is an important risk factor for cardiovascular diseases. Still, existing BP monitoring methods often require periodic cuff-based measurements, raising concerns about accuracy and convenience. This study aims to develop a subject-independent, cuff-less BP estimation method using finger and toe photoplethysmography (PPG) signals combined with an electrocardiogram (ECG) without the need for an initial cuff-based measurement. (2) Methods: A customized measurement system was used to record 80 readings from human subjects. Fifteen features with the highest dependency on the reference BP, including time and morphological characteristics of PPG and subject information, were analyzed. A multivariate regression model was employed to estimate BP. (3) Results: The results showed that incorporating toe PPG signals improved the accuracy of BP estimation, reducing the mean absolute error (MAE). Using both finger and toe PPG signals resulted in an MAE of 9.63±12.54 mmHg for systolic BP and 6.76±8.38 mmHg for diastolic BP, providing the lowest MAE compared to previous methods. (4) Conclusions: This study is the first to integrate toe PPG for more accurate BP estimation and proposes a method that does not require an initial cuff-based BP measurement, offering a promising approach for non-invasive, continuous BP monitoring. conclusions.

Abstract: Fiber optic sensors (FOSs) have developed as a transformative technology in healthcare, often offering unparalleled accuracy and sensitivity in monitoring various physiological and biochemical parameters. Their applications range from tracking vital signs to guiding minimally invasive surgeries, enabling advancements in medical diagnostics and treatment. However, the integration of FOSs into biomedical applications faces numerous challenges. This article describes some of the challenges for adopting FOSs for biomedical purposes, exploring technical and practical obstacles, and examining innovative solutions. Major challenges include biocompatibility, miniaturization and addressing signal processing complexities as well as meeting regulatory standards. Through outlining solutions to the stated challenges, it is intended that this article will therefore provide a better understanding of FOSs technology in biomedical settings and their implementation. A wider appreciation of the technology provided in this article will ultimately lead to enhancing patient care and improved medical outcomes.

Abstract: Timely detection and treatment of moisture anomalous regions in grain storage facilities is crucial for preventing mold growth, germination, and pest infestation. To locate these regions, this paper presents a novel anomalous moisture region localization algorithm based on the delay-multiply-and-sum (DMAS) beamforming techniques, including the design of an effective spatial arrangement of electromagnetic wave transmitters and receivers, along with comprehensive testing of detectable regions and experimental validation of anomaly localization across varying moisture levels and positions within grain piles. Following initial localization using the proposed algorithm, the study introduces a reliability assessment method for unknown samples based on comparing the region of maximum response intensity with that of maximum connected domain volume. The system demonstrated successful localization of a 7 cm × 7 cm × 7 cm region with 15.4% moisture content within a cubic experimental bin containing 10.5% moisture content long-grained rice, achieving an average recall accuracy exceeding 50%. The proposed method presents rapid detection capabilities and precise localization, showing potential for moisture content evaluation of anomalous regions and practical applications in grain storage monitoring systems.

Abstract: Large amounts of high-quality data is required for the training of Artificial Intelligence (AI) models, which are indeed cumbersome to curate and perform quality assurance via human intervention. Moreover, models trained using erroneous data (human errors, data faults) can cause significant problems in real-world applications. This paper proposes an automated cleaning framework and quality assurance strategy for 2D object detection datasets. The proposed cleaning method was designed according to the ISO/IEC 25012 data quality standards, and uses multiple AI models to filter anomalies and missing data. In addition, it balances out the statistical unevenness in the dataset, such as the class distribution and object size distribution. Thereby ensuring the quality of the training dataset and examining the relationship between the amount of data required for enhanced performance in terms of detection. The experiments were conducted using popular datasets for autonomous driving, including KITTI, Waymo, nuScenes and publicly available datasets from South Korea. An automated data cleaning framework was employed to remove anomalous and redundant data, resulting in a reliable dataset for training. The automated data pruning and assurance system demonstrated the ability to substantially decrease the time and resources needed for manual data inspection.

Abstract: The deployment of utility-scale hybrid wind-solar PV power plants is gaining global attention due to their enhanced performance in power systems with high renewable energy penetration. To assess their potential, accurate estimations must be derived from available data, addressing key challenges such as: (1) spatial and temporal resolution requirements, particularly for renewable resource characterization; (2) energy balances aligned with various business models; (3) regulatory constraints (environmental, technical, etc.); and (4) cost dependencies of different components and system characteristics. When conducting such analyses at regional or national scales, a trade-off must be achieved to balance accuracy with computational efficiency. This study reviews existing experiences in hybrid plant deployment, with a focus on Spain, and proposes a simplified methodology for country-level analysis.

Abstract: As conventional oil resources decline, optimizing the development of tight reservoirs has become critical for sustaining production. Horizontal wells with artificial fractures offer a promising solution, but improper water injection often leads to uneven waterflooding, particularly in irregular horizontal-vertical well systems—a common challenge in fields like China’s Fuxian oilfield. This study tackles this issue by introducing a practical and effective method to optimize water injection flow rates, significantly enhancing oil recovery in such complex well patterns. Through advanced numerical modeling and three-dimensional flow visualization, we analyze sweep efficiency and water breakthrough risks, categorizing the horizontal well’s drainage area into three distinct regions, each requiring tailored injection rates. Using a representative model with one horizontal well and three vertical wells, we demonstrate that adjusting the flow rate ratio among injectors to 6:3:1 (instead of a uniform 1:1:1) boosts cumulative oil production by an additional 2997.6 m³. These findings provide field engineers with a actionable strategy to improve waterflooding efficiency, directly increasing recoverable reserves and economic viability in tight reservoirs. The proposed approach has immediate relevance for oilfield operations, offering a scalable solution to maximize recovery in similar unconventional reservoirs worldwide.

Abstract: Solar radiation forecasting is critical for optimizing renewable energy systems, particularly in regions with high solar potential like Kuwait. This paper presents a theoretical framework for a hybrid forecasting system that combines fuzzy logic and neural networks to predict solar radiation with high accuracy. The proposed system leverages the Adaptive Neuro-Fuzzy Inference System (ANFIS) to handle the inherent uncertainty and variability in meteorological data. While the study is primarily theoretical due to limitations in data and resources, it provides a comprehensive review of existing methods and highlights the potential of hybrid systems for improving solar radiation forecasting. The paper concludes with a discussion of the limitations and suggests future work involving experiments to validate the proposed framework.

Abstract: This study evaluates Lolium perenne press juice as a sustainable substrate for Single-Cell Protein (SCP) production using Kluyveromyces marxianus. Key fermentation parameters were systematically optimized, including microbial reduction, dilution ratios, temperature, and nutrient supplementation. Pasteurization at 75 °C preserved essential nutrients better than autoclaving, resulting in a 27.8% increase in biomass yield. A 1:2 dilution of press juice enhanced fermentation efficiency, achieving 20.2% higher biomass despite lower initial sugar content. Cultivation at 30 °C enabled sustained substrate utilization and outperformed 40 °C fermentation, increasing final biomass by 43.4%. Nutrient supplementation with yeast extract, peptone, and glucose led to the highest biomass yield, with a 71% increase compared to unsupplemented juice. Press juice from the tetraploid variety Explosion consistently outperformed the diploid Honroso, especially when harvested early, reaching up to 16.62 g·L⁻¹ biomass. Early harvests promoted faster growth, while late harvests exhibited higher biomass yield coefficients due to improved sugar-to-biomass conversion. Compared to conventional YM medium, fermentation with L. perenne press juice achieved up to a threefold increase in biomass yield. These findings highlight the potential of grass-based substrates for efficient SCP production and demonstrate how agricultural parameters like variety and harvest timing influence both quantity and quality. The approach supports circular bioeconomy strategies by valorizing underutilized biomass through microbial fermentation.

Abstract: Multi-key homomorphic encryption is widely applied into outsourced computing and privacy-preserving applications in multi-user scenarios. However, the existence of CRS weakens the ability of users to independently generate public keys, and it is difficult to implement in decentralized systems or scenarios with low trust requirements. In order to reduce excessive reliance on public parameters, a multi-key homomorphic encryption scheme without pre-setting CRS is proposed based on a distributed key generation protocol. The proposed scheme does not require the pre-generation and distribution of CRS, which enhances the security and decentralization of the scheme. Furthermore, in order to further protect the plaintext privacy from each user, by embedding the specified target user into the ciphertext, this paper proposes an enhanced multi-key homomorphic encryption scheme that only allows only the target user to decrypt. Finally, this paper applies the proposed lattice-based multi-key homomorphic encryption scheme into the data submission stage of the perceived users, and thereby proposes a crowd-sensing scheme with privacy preservation.

Abstract: The refurbishment of school buildings offers the opportunity to reduce energy consumption and carbon emissions, which positively influences the reduction of environmental impact. It is also important to remember to maintain or enhance the comfort of the users of such buildings. This paper presents a systematic review of the state of the art on current trends and low-carbon technical, operational and behavioural methods used in the refurbishment of school buildings in cool temperate climates. This subject matter is positioned at the interface of architecture and environmental engineering. This study identifies the most commonly used active and passive refurbishment methods, as well as the research gaps and problems of applied solutions, and demonstrates the most likely and cost-effective optimisation directions in existing schools.