Search Results (2,196)

Screen defect detection has become a crucial research domain, propelled by the growing necessity of precise and effective quality control in mobile device production. This study presents the FusionScratchNet (FS-Net), a novel algorithm developed to overcome the challenges of noise interference and to characterize indistinct defects and subtle scratches on mobile phone screens. By integrating the transformer and convolutional neural network (CNN) architectures, FS-Net effectively captures both global and local features, thereby enhancing feature representation. The global–local feature integrator (GLFI) module effectively fuses global and local information through unique channel splitting, feature dependency characterization, and attention mechanisms, thereby enhancing target features and suppressing noise. The bridge attention (BA) module calculates an attention feature map based on the multi-layer fused features, precisely focusing on scratch characteristics and recovering details lost during downsampling. Evaluations using the PKU-Market-Phone dataset demonstrated an overall accuracy of 98.04%, an extended intersection over union (EIoU) of 88.03%, and an F1-score of 65.13%. In comparison to established methods like you only look once (YOLO) and retina network (RetinaNet), FS-Net demonstrated enhanced detection accuracy, computational efficiency, and resilience against noise. The experimental results demonstrated that the proposed method effectively enhances the accuracy of scratch segmentation. Full article

Treatment options for viral infections are limited and viruses have proven adept at evolving resistance to many existing therapies, highlighting a significant vulnerability in our defenses. In response to this challenge, we explored the modulation of cellular RNA metabolic processes as an alternative paradigm to antiviral development. Previously, the small molecule 5342191 was identified as a potent inhibitor of HIV-1 replication by altering viral RNA accumulation at doses that minimally affect host gene expression. In this report, we document 5342191 as a potent inhibitor of adenovirus, coronavirus, and influenza replication. In each case, 5342191-mediated reduction in virus replication was associated with altered viral RNA accumulation and loss of viral structural protein expression. Interestingly, while resistant viruses were rapidly isolated for compounds targeting either virus-encoded proteases or polymerases, we have not yet isolated 5342191-resistant variants of coronavirus or influenza. As with HIV-1, 5342191’s inhibition of coronaviruses and influenza is mediated through the activation of specific cell signaling networks, including GPCR and/or MAPK signaling pathways that ultimately affect SR kinase expression. Together, these studies highlight the therapeutic potential of compounds that target cellular processes essential for the replication of multiple viruses. Not only do these compounds hold promise as broad-spectrum antivirals, but they also offer the potential of greater resilience in combating viral infections. Full article

With the growing emphasis on sustainability, awareness of the environmental impacts and negative potential inherent in current business systems has increased. The circular economy (CE) represents an innovative approach that transforms the traditional linear economy into a restorative system, focussing on extending the life cycle of materials through continuous circulation. The Circular B project aims to develop an international framework that considers multiple facets of the CE, including material and asset management and the use of components in the built environment throughout the entire life cycle of the value chain. The primary objective of the CE is to eliminate waste and pollution (e.g., carbon reduction) and strengthen the resilience of the value chain. However, the current implementation of circular strategies has not yet been found to be effective, with several challenges that cause adverse impacts. This study focuses on investigating and analyzing these challenges, particularly in the cultural and societal domains, using both qualitative and quantitative approaches. The scope of the questionnaire was to identify (1) awareness and understanding, (2) cultural attitude, (3) barriers to adoption, (4) incentives and motivations, (5) participation and engagement, and (6) education and training. A questionnaire was distributed to 270 respondents, with anonymous responses collected. The survey included eight questions specifically designed to address cultural and societal challenges. The survey was conducted with participants from various sectors, including academia, local authorities, industry professionals, consultants, and others collected from all over the world, ensuring diverse perspectives. The main weaknesses found based on this survey are related to (1) budget constraints due to high costs of reintegrating in the loop of materials or components due to the complexity of circular processes, (2) applicability on the market remains still limited, (3) the importance of planning and design in the initial phases, (4) the importance of establishing a comprehensive network to enhance collaboration among stakeholders, and (5) inadequate policies. The insights gained from this study will help stakeholders, such as constructors, maintainers, engineers, designers, and consultants, across various organizations in the value chain to develop practical solutions to mitigate these challenges and improve the overall business system. Full article

Against the backdrop of globalization, the complexity of supply chains has been increasing, making supply chain resilience a critical factor in ensuring the stable operation of enterprises, national economies, and international trade. This paper adopts a network modeling perspective to systematically review the theoretical foundations and research progress in supply chain resilience, focusing on the application of network modeling methods. First, the concept of supply chain resilience is defined, and its developmental trajectory is reviewed. Through literature visualization analysis, this study delves into the current state of research on supply chain resilience, addressing challenges and risk management, highlighting the importance of network modeling techniques in this field. Subsequently, it explores supply chain network modeling based on complex networks and agent-based modeling, analyzing their strengths and limitations in simulating the overall evolution of supply chains and the dynamic behavior of individual entities. By integrating network structural characteristics with resilience evaluation methods, this paper suggests potential directions for future research. These include enhancing the description of individual firm behavior, analyzing the dynamics of information networks, and emphasizing task-oriented model design, thereby offering new perspectives and pathways for managing supply chain resilience in a way that can generate significant positive externalities for global economies. This research also indicates that the enhanced resilience of supply chains can produce a multiplier effect, benefiting not only individual firms but also promoting economic stability and growth across multiple countries. Full article

The growing integration of renewable energy sources and the electrification of multiple sectors have heightened the need for optimized planning and operation of modern electrical distribution systems. A critical challenge for distribution network operators is enhancing the resilience and reliability of their grids by identifying effective solutions. One promising approach to achieving this is through the deployment of battery energy storage systems, which can rapidly inject power to mitigate the impacts of network disturbances or outages. This study investigates the use of advanced genetic algorithms as a practical methodology for the optimal siting of batteries in modern distribution networks. By incorporating historical data on demand and network failures, the algorithm generates statistical models that inform the optimization process. The model integrates both the technical and economic aspects of battery systems to identify locations that minimize reliability indices such as SAIDI and SAIFI, while also reducing investment costs. Tested on a real distribution system comprising 1837 nodes, the proposed approach demonstrates the ability of genetic optimization to deliver efficient solutions compared with traditional methods, providing a high likelihood of identifying strategic battery locations that respond to variable demand, system failures, and technical constraints. Full article

Nowadays, traffic management challenges in the era of digital transport are rising, as the interactions of various stakeholders providing such technologies play a pivotal role in shaping traffic dynamics. The objective of this paper was to present a game-theory-based framework for modeling and optimizing urban traffic in road networks, considering the co-existence and interactions of different players composed of drivers of conventional vehicles, central governing authorities with traffic management capabilities, and competitive or cooperative connected mobility private service providers. The scope of this work was to explore and present the outcomes of diverse mixed equilibrium conditions in the road network of the city of Thessaloniki (Greece), integrating the principles of user equilibrium, system optimum, and Cournot oligopoly. The impacts of varying network attributes were systematically analyzed to provide quantitative indicators representing the overall network performance. Analysis of the results provided insights into the sensitivity and the resilience of the road network under various prevalence schemes of drivers of conventional vehicles, representing the user equilibrium characteristics, or drivers relying on traffic guidance provided by a central governing authority, representing the system optimum principles as well as the cooperation and competition schemes of private connected mobility providers with certain market shares in the network. Full article

Land management strategies play a pivotal role in the sustainable development of a region. Integrating space syntax into the ecological–social perspective to assess habitat services and optimize multi-scenario simulations and evaluations is crucial for developing resilient strategies for the future. This study takes Lanzhou, a semi-arid region, as a case study, combining multi-model analysis to explore the relationship between habitat quality and spatial accessibility and to conduct habitat service zoning. The findings indicate that under four development scenarios, the ecological network generally shows a three-segment distribution. The factors that have the most significant impacts on cultivated land, forests, shrubs, construction land, and bare land are GDP, precipitation, temperature, population density, and NDVI, respectively. The ecological priority scenario features the most corridors, while the cultivated land protection scenario incurs the lowest construction costs. Across various analysis radii of space syntax, except for MED at a 6000 m radius, the ecological priority scenario exhibits excellent network accessibility. The coupling coordination degree of the four scenarios generally lies within a mild imbalance level, with a spatial distribution pattern characterized by “high in the west and low in the east”. Based on 10 types of habitat services, a priority management sequence for land and key governance towns was established, leading to the proposal of a “dual coordination” multi-center compact network layout model. This research not only enriches the theory of land ecology but also overcomes the shortcomings in land spatial planning, addresses the practical problems of land development transformation in Lanzhou, and offers new data support and ideas for the construction of ecological cities in semi-arid regions. Full article

This study details the design and implementation of a high-gain Yagi-Uda antenna network for the transmission of real-time monitoring data from the Misti Volcano to the city of Arequipa. As Misti is classified as a high-risk volcano due to its active volcanic nature and the close proximity of nearly one million inhabitants, the current monitoring infrastructure is insufficient to meet the demands of effective surveillance. In response, this project integrates Internet of Things (IoT) technology, the LoRa (Long Range) network, and an optimized seven-element Yagi-Uda antenna, developed using advanced optimization algorithms to enhance transmission efficiency. The primary objective is to facilitate the reliable collection and transmission of critical sensor data for subsequent analysis by volcanological experts, thereby supporting improved prediction and mitigation of potential volcanic hazards. Field tests have demonstrated that the Yagi-Uda antenna, when coupled with LoRa technology, achieved uninterrupted data transmission over a distance of 16 km. The integration of IoT, LoRa, and the optimized antenna design offers a scalable and resilient solution for the continuous monitoring and risk assessment of Misti, enabling the incorporation of advanced high-precision sensors for enhanced surveillance capabilities. Full article

The ever-growing number of cyber attacks in today’s digitally interconnected world requires highly efficient intrusion detection systems (IDSs), which accurately identify both frequent and rare network intrusions. One of the most important challenges in IDSs is the class imbalance problem of network traffic flow data, where benign traffic flow significantly outweighs attack instances. This directly affects the ability of machine learning models to identify minority class threats. This paper is intended to evaluate various machine learning algorithms under different levels of class imbalances, using resampling as a strategy for this problem. The paper will provide an experimental comparison by combining various algorithms for classification and class imbalance learning, assessing the performance through the F1-score and geometric mean (G-mean). The work will contribute to creating robust and adaptive IDS through the judicious integration of resampling with machine learning models, thus helping the domain of cybersecurity to become resilient. Full article

The integration of Full Electric Vehicles (FEVs) into the smart city ecosystem is an essential step towards achieving sustainable urban mobility. This study presents a comprehensive mobility network model designed to predict and optimize the energy supply for FEVs within smart cities. The model integrates advanced components such as a Charge Station Control Center (CSCC), smart charging infrastructure, and a dynamic user interface. Important aspects include analyzing power consumption, forecasting urban energy demand, and monitoring the State of Charge (SoC) of FEV batteries using innovative algorithms validated through real-world applications in Valencia (Spain) and Ljubljana (Slovenia). Results indicate high accuracies in SoC tracking (error < 0.05%) and energy demand forecasting (MSE ~6 × 10⁻⁴), demonstrating the model’s reliability and adaptability across diverse urban environments. This research contributes to the development of resilient, efficient, and sustainable smart city frameworks, emphasizing real-time data-driven decision-making in energy and mobility management. Full article

The major task of a wireless sensor network (WSN) is data collection. Key predistribution (KP) is to establish pairwise keys for secure communication in a WSN, such that all collected data could be securely sent to a backend database. Most research on KP-like schemes is dedicated to enhancing resiliency against node capture attack (NA) and retaining the link connectivity in the meantime. For large-scale wireless sensor networks, a more common approach is to use a multiple-sink WSN (MWSN) to support a large number of sensor nodes. In MWSNs, there are different clusters (referred to as groups). We took the lead in studying KP in the MWSN environment. Based on the new MWSN environment, we present intragroup and intergroup KP (I²KP) to fulfill both requirements of security and energy efficiency when gathering data via various sink nodes in a large-scale WSN. Three types of I²KP with respective pros and cons are proposed. Theoretical analysis and numerical simulation demonstrate their effectiveness. Full article

Wild fish are often considered more nutritionally valuable than cultured fish. This study aimed to elucidate the relationship between the gut microbiota and meat quality through the gut–muscle axis. Therefore, cultured and wild bighead carp (Hypophthalmichthys nobilis, Richardson 1845) from the Yangtze River were investigated to compare the differences in the meat quality and gut microbiota composition. Cultured bighead carp were collected from four intensive ponds along the Yangtze River area, while wild bighead carp were obtained from three different sites in the Yangtze River. The results showed that wild bighead carp muscle had significantly higher total saturated fatty acid (∑SFA) and total ω − 3 polyunsaturated fatty acid (∑n − 3 PUFA) content and water-holding capacity and lower lipid, histidine, and total ω − 6 polyunsaturated fatty acid (∑n − 6 PUFA) content than cultured bighead carp, while the muscle texture was not significantly different between the two groups, with the exception of the resilience. Moreover, the hindgut microbiota was analyzed using 16S rRNA high-throughput sequencing. The alpha and beta diversity differences between the cultured and wild groups were significant. The LEfSe analysis revealed Mycobacterium, Longivirga, and Acetobacteroides as biomarkers in cultured bighead carp, while Clostridium_T and other Firmicutes-associated genera were predominant in wild bighead carp. Regarding the relationship between the hindgut microbiota and meat quality, Mycobacterium exhibited a positive correlation with the muscle n-6 PUFA content and a negative correlation with muscle n − 3 PUFAs, while Clostridium_T exhibited the opposite pattern. According to the ecological network, the abundance of Actinobacteria could serve as a significant indicator of variations in the abundance of Mycobacterium and Clostridium_T. Consequently, differences in meat quality, particularly in the fatty acid composition, were observed between wild and cultured bighead carp. These differences may be associated with variations in the hindgut microbiota, shedding light on the gut–muscle axis. Full article

Structural seismic resilience is influenced by both the structural performance loss (loss) and the repair path (path). Some studies ensure the reality of path but lack accuracy of loss. Others ensure the accuracy of loss but lack the reality of path. Therefore, this paper proposes a new resilience evaluation method for frame-core tube structure that considers both loss and path. Firstly, the complex network method is used to establish structural network performance and its loss index. Next, the repair path is determined by updating the network performance after component repairs. Then, a resilience index that considers both network performance loss and repair path is proposed. Finally, the resilience of the case structures is evaluated and compared with methods that do not reasonably consider loss or path. The results indicate that the proposed method can comprehensively consider the impact of loss and path on resilience. Loss has a greater impact on resilience when the structure is less than slight loss or is in the late stage of moderate loss. Path has a greater impact on resilience when the structure is slight loss or is in the early stage of moderate loss. When only considering loss, the fixed linear path underestimates the resilience of a convex-shaped path and overestimates the resilience of a concave-shaped path. When only considering path, underestimating or overestimating loss can lead to a higher or lower resilience. Particularly when the structure is in the late stage of moderate loss, resilience dropped from 89.16% to 42.04% due to overestimation of loss. Full article

Vegetation cover in urban and peri-urban areas is threatened by urban sprawl, through habitat fragmentation, loss of green space, biodiversity reduction, and the urban heat island effect intensifying. The intrusion of cities into natural landscapes reduces vital ecosystem services provided by vegetation. Hence, sustainable and integrated urban planning practices are required. Our study aims to investigate the dynamics of the urban and peri-urban fabric by exploring the relationship between the green fabric distribution and recent trends in urban expansion, focusing specifically on the peri-urban areas of Iași Municipality, Romania. We designed a mixed-method approach combining a multivariate analysis of four critical indicators (vegetation cover, built-up space, land surface temperature, and population density), emerging hot-spots, and space-time cubes in a GIS environment to achieve our research aims. Our results demonstrate that uncontrolled urban expansion has manifested in diverse patterns, impacting territories next to road transport networks and with construction-suitable topography. Concurrently, the development of green spaces prevails in forests and unexpected locations such as brownfields, railway corridors, and old industrial zones, through the growth of urban greenery. This approach provides a comprehensive understanding of how urban sprawl impacts the environment and how different land types are prone to this transformation, creating a path towards sustainability, resilience, and equitable development. Full article

The proteinogenic amino acid proline plays crucial roles in both plant development and stress responses, far exceeding its role in protein synthesis. However, the molecular mechanisms and the relative importance of these additional functions of proline remain under study. It is well documented that both stress responses and developmental processes are associated with proline accumulation. Under stress conditions, proline is believed to confer stress tolerance, while under physiological conditions, it assists in developmental processes, particularly during the reproductive phase. Due to proline’s properties as a compatible osmolyte and potential reactive oxygen species (ROS) scavenger, most of its beneficial effects have historically been attributed to the physicochemical consequences of its accumulation in plants. However, emerging evidence points to proline metabolism as the primary driver of these beneficial effects. Recent reports have shown that proline metabolism, in addition to supporting reproductive development, can modulate root meristem size by controlling ROS accumulation and distribution in the root meristem. The dynamic interplay between proline and ROS highlights a sophisticated regulatory network essential for plant resilience and survival. This fine-tuning mechanism, enabled by the pro-oxidant and antioxidant properties of compartmentalized proline metabolism, can modulate redox balance and ROS homeostasis, potentially explaining many of the multiple roles attributed to proline. This review uniquely integrates recent findings on the dual role of proline in both ROS scavenging and signaling, provides an updated overview of the most recent research published to date, and proposes a unified mechanism that could account for many of the multiple roles assigned to proline in plant development and stress defense. By focusing on the interplay between proline and ROS, we aim to provide a comprehensive understanding of this proposed mechanism and highlight the potential applications in improving crop resilience to environmental stress. Additionally, we address current gaps in understanding and suggest future research directions to further elucidate the complex roles of proline in plant biology. Full article