Search Results (88,097)

This study focuses on improving the wear resistance of cutting tools and extending their service life under intense mechanical, thermal, and radiation loads in nuclear power plant environments. This research investigates the potential of electrospark alloying (ESA) using W–Zr–B system electrodes obtained from disks synthesised by spark plasma sintering (SPS). The novelty of this work lies in the use of SPS-synthesised W–Zr–B ceramics, which are promising for nuclear applications due to their high thermal stability, radiation resistance and neutron absorption, as ESA electrodes. This work also establishes the relationship between discharge energy, coating microstructure and performance. The alloying electrode material exhibited a heterogeneous microstructure containing WB₂, ZrB₂, and minor zirconium oxides, with high hardness (26.6 ± 1.8 GPa) and density (8.88 g/cm³, porosity <10%). ESA coatings formed on HS6-5-2 steel showed a hardened layer up to 30 µm thick and microhardness up to 1492 HV, nearly twice that of the substrate (~850 HV). Elemental analysis revealed enrichment of the surface with W, Zr, and B, which gradually decreased toward the substrate, confirming diffusion bonding. XRD analysis revealed a multiphase structure comprising WB₂, ZrB₂, WB₄, and BCC/FCC solid solutions, indicating the formation of complex boride phases during the ESA process. Tribological tests demonstrated significantly enhanced wear resistance of ESA coatings. The results confirm the efficiency of ESA as a simple, low-cost, and energy-efficient method for local strengthening and restoration of cutting tools. Full article

Anodic aluminum oxide (AAO) is a well-known nanomaterial template formed under specific electrochemical conditions. By adjusting voltage, temperature, electrolyte type, and concentration, various microstructural modifications of AAO can be achieved within its hexagonally arranged pore array. To enable broader applications or enhance performance, post-treatment is often employed to further modify its nanostructure after anodization. Among these post-treatment techniques, AAO membrane detachment methods have been widely studied and can be categorized into traditional etching methods, voltage reduction methods, reverse bias voltage detachment methods, pulse voltage detachment methods, and further anodization techniques. Among various delamination processes, the mechanism is highly related to the selectivity of wet etching, as well as the Joule heating and stress generated during the process. Each of these detachment methods has its own advantages and drawbacks, including processing time, complexity, film integrity, and the toxicity of the solutions used. Consequently, researchers have devoted significant effort to optimizing and improving these techniques. Furthermore, through-hole AAO membranes have been applied in various fields, such as humidity sensors, nanomaterial synthesis, filtration, surface-enhanced Raman scattering (SERS), and tribo-electrical nano-generators (TENG). In particular, the rough and porous structures formed at the bottom of AAO films significantly enhance sensor performance. Depending on specific application requirements, selecting or refining the appropriate processing method is crucial to achieving optimal results. As a versatile nanomaterial template, AAO itself is expected to play a key role in future advancements in environmental safety, bio-applications, energy technologies, and food safety. Full article

Recently, the live commerce market has experienced rapid growth, accompanied by increasingly intense competition. To improve business performance in this dynamic environment, it is essential to foster competent streamers and create effective commerce environments. Therefore, this study developed a research model based on the stimulus–organism–response (S-O-R) framework, focusing on streamer competencies and the commerce environment, to explore ways to effectively enhance live commerce business performance. Data for this study were collected through a questionnaire and analyzed using statistical techniques with 390 respondents. The results revealed that streamers’ competencies (expertise, demonstration skills, and interactive ability) significantly influence consumers’ internal states (perceived functional value of products and perceived trust in product recommendations), which in turn significantly influence purchase intentions. Moreover, the physical surroundings of the studio and the social surroundings, including peers’ perceptions of live commerce, were found to moderate the relationships between consumers’ internal states and their purchase intentions. This study holds academic significance in that it presents a model that effectively understands the mechanisms influencing viewers’ purchase decisions in live commerce contexts. The findings and practical implications discussed in this study are expected to provide valuable insights for developing strategies to enhance the performance of live commerce. Full article

Water scarcity has become a significant constraint to agricultural development in China. In this study, we employed bibliometric methods to systematically review the current research on crop water use efficiency (WUE) and the development trends in the North China Plain (NCP) and Northwest China (NWC). We analyzed 1569 articles (NCP = 788; NWC = 781) from the Web of Science Core Collection (1995–2025) using visualization tools such as CiteSpace and VOSviewer to investigate annual numbers of publications, leading scholars and research institutions, and then to map keyword co-occurrence and co-citation structures. Our results showed that keyword clustering exhibited high structural quality (NCP: Q = 0.7345, S = 0.8634; NWC: Q = 0.758, S = 0.8912), supporting reliable thematic interpretation. The bibliometric analysis indicates a steady growth in annual publications since 1995, with the Chinese Academy of Sciences and China Agricultural University as leading contributors. From 1995 to 2005, studies centered on irrigation, yield and field-scale WUE, emphasizing the optimization of irrigation strategies and crop productivity. During 2006–2015, the thematic focus has broadened to encompass nitrogen use efficiency, crop quality and eco-environmental performance, thereby moving toward integrated evaluation frameworks that capture ecological synergies. Since 2016, the literature now emphasizes system integration, regional adaptability, climate-response mechanisms and the ecological co-benefits of agricultural practices. Future studies are expected to incorporate indicators such as crop quality, water footprint and carbon isotope indicators to support the sustainable development of agricultural water use. This study offers insights and recommendations for developing a comprehensive crop WUE evaluation framework in China, which will support sustainable agricultural water management and the realization of national “dual carbon” targets. Full article

Collagen is well-known as an essential and structural protein in the body and is classified into many types, with different roles. Type I collagen is the most abundant, offering firmness, elasticity, and resistance to the skin. Starting from natural resources such as calf, American buffalo hide, turkey, and perch skin, this research aims to develop a comparative study between the porous matrices obtained from collagen, extracted in the form of gel, with potential medical use. The extracted collagen gels were analyzed for their proximate analysis. The structural conformation of the gels was confirmed using circular dichroism measurements. The extracted collagen gels were dried using a freeze dryer in the form of porous matrices, and structural analyses were performed using FT-IR. Further, the collagen scaffolds were assessed for biocompatibility using an XTT assay. The water swelling behavior, the morphology, and the thermal stability of the collagen matrices were determined. The collagen porous matrices presented good antimicrobial activity, especially COLL_P, which presented the highest inhibition zone, making them suitable for biomedical uses. Overall, this study provides a method for producing collagen matrices from various sources for biomedical applications. Full article

The central purpose of this study is to develop and validate an advanced numerical model capable of simulating the vibrational behavior of the operator’s seat in a tractor-type agricultural vehicle designed for operation in protected horticultural environments, such as vegetable greenhouses. The three-dimensional (3D) model of the seat was created using SolidWorks 2023, while its dynamic response was investigated through Finite Element Analysis (FEA) in Altair SimSolid, enabling a detailed evaluation of the natural vibration modes within the 0–80 Hz frequency range. Within this interval, eight significant natural frequencies were identified and correlated with the real structural behavior of the seat assembly. For experimental validation, direct time-domain measurements were performed at a constant speed of 5 km/h on an uneven, grass-covered dirt track within the research infrastructure of INMA Bucharest, using the TE-0 self-propelled electric tractor prototype. At the operator’s seat level, vibration data were collected considering the average anthropometric characteristics of a homogeneous group of subjects representative of typical tractor operators. The sample of participating operators, consisting exclusively of males aged between 27 and 50 years, was selected to ensure representative anthropometric characteristics and ergonomic consistency for typical agricultural tractor operators. Triaxial accelerometer sensors (NexGen Ergonomics, Pointe-Claire, Canada, and Biometrics Ltd., Gwent, UK) were strategically positioned on the seat cushion and backrest to record accelerations along the X, Y, and Z spatial axes. The recorded acceleration data were processed and converted into the frequency domain using Fast Fourier Transform (FFT), allowing the assessment of vibration transmissibility and resonance amplification between the floor and seat. The combined numerical–experimental approach provided high-fidelity validation of the seat’s dynamic model, confirming the structural modes most responsible for vibration transmission in the 4–8 Hz range—a critical sensitivity band for human comfort and health as established in previous studies on whole-body vibration exposure. Beyond validating the model, this integrated methodology offers a predictive framework for assessing different seat suspension configurations under controlled conditions, reducing experimental costs and enabling optimization of ergonomic design before physical prototyping. The correlation between FEA-based modal results and field measurements allows a deeper understanding of vibration propagation mechanisms within the operator–seat system, supporting efforts to mitigate whole-body vibration exposure and improve long-term operator safety in horticultural mechanization. Full article

Extreme weather events, such as storms, pose significant challenges to the reliability and efficiency of urban road networks, making intersection design and management critical to maintaining mobility. This paper addresses the dual objectives of traffic efficiency and resilience by evaluating the performance of roundabouts, signalized, and two-way stop-controlled (TWSC) intersections under normal and storm-disrupted conditions. A mixed-method approach was adopted, combining a heuristic framework from the Highway Capacity Manual with microsimulations in AIMSUN Next. Three Polish case studies were examined; each was modeled under alternative control strategies. The findings demonstrate the superior robustness of roundabouts, which retain functionality during power outages, while signalized intersections reveal vulnerabilities when control systems fail, reverting to less efficient TWSC behavior. TWSC intersections consistently exhibited the weakest performance, particularly under high or uneven traffic demand. Despite methodological differences in delay estimation, the convergence of results through Level of Service categories strengthens the reliability of findings. Beyond technical evaluation, the study underscores the importance of resilient intersection design in climate-vulnerable regions and the value of integrating analytical and simulation-based methods. By situating intersection performance within urban resilience, this research provides actionable insights for policymakers, planners, and engineers seeking to balance efficiency with adaptability in infrastructure planning. Full article

Biofouling remains a critical challenge in bioelectrochemical cells (BECs), hindering their efficiency and performance. This research article reviews advances in biofouling mitigation techniques within BEC systems during the period from 2019 to 2025, focusing on membrane modifications and electro-assisted membrane technologies. Through comprehensive analysis, it is revealed that Nafion alternatives, including ceramic membranes and recycled nonwoven fabrics like polypropylene, have emerged as significant contenders due to their combination of low cost and high performance. Additionally, the incorporation of silver, zeolite, and graphene oxide onto membranes has demonstrated efficacy in mitigating biofouling under laboratory conditions. Furthermore, the application of direct current electric fields has shown potential as a chemical-free preventative measure against biofouling in BECs. However, challenges related to long-term stability, scalability, and cost-effectiveness must be addressed for widespread adoption. Full article

Individuals post-stroke commonly experience impairments in upper extremity function that limit participation in valued activities. Task repetitive practice is an effective intervention strategy, but accurately monitoring adherence and movement quality in home programs remains a challenge. This pilot study investigates the reliability and validity of raw accelerometer data captured by a commercially available, wrist-worn activity monitor to assess upper extremity movement in healthy adults during task repetitive practice. Measures of duration, angular velocity, and acceleration were obtained from activity monitors worn by 25 healthy adults performing four functional tasks under varying conditions. Preliminary results indicate moderate to excellent within-session reliability in these three measures when compared across repeated trials of the same task, with one exception. Across all tasks, the duration measure consistently detected differences in exercise time between sets of 5, 10, and 20 repetitions at a comfortable pace. All three measures differentiated between 10 comfortable repetitions and 10 fast repetitions on three out of four tasks. These findings provide initial psychometric properties in a healthy population and further research is required to determine whether these properties remain robust in the presence of motor impairment. This work represents an early step towards developing approaches for monitoring home exercise programs that support stroke recovery. Full article

The growing demand for fast and sustainable urban deliveries has accelerated exploration of the use of Unmanned Aerial Vehicles as viable logistics solutions for the last mile. This study investigates the integration of a distributed multi-agent system with a structured three-dimensional Urban Aerial Network (3D-UAN) for drone delivery operations. The proposed architecture models each drone as an autonomous agent operating within predefined air corridors and communication protocols. Unlike traditional approaches, which rely on simplified 2D models or centralized control systems, this research exploits a multi-layered 3D network structure combined with decentralized decision-making for improving scalability, safety, and responsiveness in complex environments. Through agent-based simulations, this study evaluates the operational performance of the proposed system under varying fleet size conditions, focusing on travel times and system scalability. Preliminary results demonstrate that the potential of this approach in supporting efficient, adaptive, resilient logistics within Urban Air Mobility frameworks depends on both the size of the fleet operating in the 3D-UAN and constraints linked to the current regulations and technological properties, such as the maximum allowed operational height. These findings contribute to ongoing efforts to define robust operational architectures and simulation methodologies for next-generation urban freight transport systems. Full article

Rapid urbanization drives significant land use transformations, making the timely detection of newly constructed buildings a critical research focus. This study presents a novel unsupervised framework that integrates pixel-level change detection with object-level, mono-temporal building information to identify new constructions. Within this framework, we propose the Building Line Index (BLI) to capture structural characteristics from building edges. The BLI is then combined with spectral, textural, and the Morphological Building Index (MBI) to extract buildings. The fusion weight (φ) between the BLI and MBI was determined through experimental analysis to optimize performance. Experimental results on a case study in Wuhan, China, demonstrate the method’s effectiveness, achieving a pixel accuracy of 0.974, an average category accuracy of 0.836, and an Intersection over Union (IoU) of 0.515 for new buildings. Critically, at the object-level—which better reflects practical utility—the method achieved high precision of 0.942, recall of 0.881, and an F1-score of 0.91. Comparative experiments show that our approach performs favorably against existing unsupervised methods. While the single-case study design suggests the need for further validation across diverse regions, the proposed strategy offers a robust and promising unsupervised pathway for the automatic monitoring of urban expansion. Full article

Today, a number of works are being carried out all over the world to develop data processing and management systems, as well as to apply artificial intelligence and information technologies in the fields of production, science, education, and healthcare. The optimization of the management of socio-economic process systems, and the management and reliability of databases of the digital payment information-based information systems of enterprises and organizations are relevant. This research work investigates the issue of increasing the reliability of information in information systems working with payment information. The characteristics of ambiguous suspicious transactions in payment systems are analyzed, and based on the analysis, preliminary data preparation stages are carried out for the intelligent detection of ambiguous suspicious transactions. Traditional and neural network models of machine learning for the detection of suspicious transactions in payment information management systems are developed, and a comparative analysis is carried out. Furthermore, to enhance the performance of the core LSTM model, an Artificial Bee Colony (ABC) optimization algorithm was integrated for automated hyperparameter tuning, which improved the model’s accuracy and efficiency in identifying complex fraudulent patterns. Full article

Segmentation of white blood cells is critical for a wide range of applications. It aims to identify and isolate individual white blood cells from medical images, enabling accurate diagnosis and monitoring of diseases. In the last decade, many researchers have focused on this task using U-Net, one of the most used deep learning architectures. To further enhance segmentation accuracy and robustness, recent advances have explored the combination of U-Net with other techniques, such as attention mechanisms and aggregation techniques. However, a common challenge in white blood cell image segmentation is the similarity between the cells’ cytoplasm and other surrounding blood components, which often leads to inaccurate or incomplete segmentation due to difficulties in distinguishing low-contrast or subtle boundaries, leaving a significant gap for improvement. In this paper, we propose GAAD-U-Net, a novel architecture that integrates attention-augmented convolutions to better capture ambiguous boundaries and complex structures such as overlapping cells and low-contrast regions, followed by a gating mechanism to further suppress irrelevant feature information. These two key components are integrated in the Double U-Net base architecture. Our model achieves state-of-the-art performance on white blood cell benchmark datasets, with a 3.4% Dice score coefficient (DSC) improvement specifically on the SegPC-2021 dataset. The proposed model achieves superior performance as measured by mean the intersection over union (IoU) and DSC, with notably strong segmentation performance even for difficult images. Full article

Electroencephalography (EEG) provides insights into the neural mechanisms underlying attention, response inhibition, and distraction in cognitive tasks. This dataset was collected to examine neural activity in young drivers and non-drivers performing Go/No-Go tasks under visual and auditory distraction conditions. A total of 40 university students (20 drivers, 20 non-drivers; balanced by sex) completed eight experimental blocks combining visual or auditory stimuli with realistic distractions, such as text message notifications and phone call simulations. EEG was recorded using a 16-channel BrainAccess MIDI system at 250 Hz. Experiments 1, 3, 5, and 7 served as transitional blocks without participant responses and were excluded from behavioral and event-related potential analyses; however, their EEG recordings and event markers are included for baseline or exploratory analyses. The dataset comprises raw EEG files, event markers for Go/No-Go stimuli and distractions, and metadata on participant demographics and mobile phone usage. This resource enables studies of attentional control, inhibitory processes, and distraction-related neural dynamics, supporting research in cognitive neuroscience, brain–computer interfaces, and transportation safety. Full article

Collaborative clustering is an ensemble technique that enhances clustering performance by simultaneously and synergistically processing multiple data dimensions or tasks. This is an active research area in artificial intelligence, machine learning, and data mining. A common approach to co-clustering is based on non-negative matrix factorization (NMF). While widely used, NMF-based co-clustering is limited by its bilinear nature and fails to capture the multilinear structure of data. With the objective of enhancing the effectiveness of non-negative Tucker decomposition (NTD) in image clustering tasks, in this paper, we propose a dual-graph constrained sparse non-negative Tucker decomposition NTD (GDSNTD) model for co-clustering. It integrates graph regularization, the Frobenius norm, and an $l_1$ norm constraint to simultaneously optimize the objective function. The GDSNTD mode, featuring graph regularization on both factor matrices, more effectively discovers meaningful latent structures in high-order data. The addition of the $l_1$ regularization constraint on the factor matrices may help identify the most critical original features, and the use of the Frobenius norm may produce a more highly stable and accurate solution to the optimization problem. Then, the convergence of the proposed method is proven, and the detailed derivation is provided. Finally, experimental results on public datasets demonstrate that the proposed model outperforms state-of-the-art methods in image clustering, achieving superior scores in accuracy and Normalized Mutual Information. Full article