Search Results (17,878)

The three-leg 50-Years Roundabout at King Abdulaziz University (KAU) is known for its vibrance and important location as it is located at the center of several major buildings and hospitals. In recent years, the roundabout is witnessing a huge demand that influences the university road networks’ level of service, “LOS”, which in return, has negative impacts on students and faculties in terms of delay and travel time. Several treatments can be implemented along the roundabout. One of those treatments is applying restrictions during morning peak hours such as blocking and restricting specific lanes. This treatment has the advantage of reducing conflict points that cause sudden and frequent stops at the roundabout; as a result, delay and congestion occur. By reducing conflict points, traffic flow can be improved, in addition to enhancing safety and promoting sustainability. This paper examines the base condition of the 50-Years Roundabout in terms of traffic flow, LOS, delay, capacity, and toxic emissions, and proposes traffic system management (TSM) strategies through applying restricted and designated lanes to improve traffic condition. The study employs PTV Vissim, SIDRA Intersection, and Surrogate Safety Assessment Model “SSAM” to examine the base and proposed conditions. The results show a significant improvement through the reduction in conflict points, so that reflects the positive impacts on sustainability, congestion, delay, travel time, LOS, and overall toxic emissions. Full article

Türkiye’s forest governance exhibits a persistent policy–implementation gap rooted in a governance paradox: while the Ecosystem-Based Functional Planning (EBFP) system promotes ecological integrity and adaptive management, the foundational Forest Law No. 6831 (1956) still legitimizes extractive uses under a broad “public interest” doctrine. This contradiction has enabled 94,148 permits covering 654,833 ha of forest conversion, while marginalizing nearly seven million forest-dependent villagers from decision-making. The study applies a doctrinal and qualitative document-analysis approach, integrating legal, institutional, and socio-economic dimensions. It employs a comparative design with five EU transition countries—Poland, Romania, Bulgaria, Czechia, and Greece—selected for their shared post-socialist administrative legacies and diverse pathways of forest-governance reform. The analysis synthesizes legal norms, policy instruments, and institutional practices to identify drivers of reform inertia and regulatory capture. Findings reveal three interlinked failures: (1) institutional and ministerial conflicts that entrench centralized decision-making and weaken environmental oversight—illustrated by the fact that only 0.97% of Environmental Impact Assessments receive negative opinions; (2) economic and ecological losses, with foregone ecosystem-service values exceeding EUR 200 million annually and limited access to carbon markets; and (3) participatory deficits and social contestation, exemplified by local forest conflicts such as the Akbelen case. A comparative SWOT analysis indicates that Poland’s confrontational policy reforms triggered EU infringement penalties, Romania’s fragmented legal restitution fostered illegal logging networks, and Greece’s recent modernization offers lessons for gradual legal harmonization. Drawing on these insights, the paper recommends comprehensive Forest Law reform that integrates ecosystem-service valuation, climate adaptation, and transparent participatory mechanisms. Alignment with the EU Nature Restoration Regulation (2024/1991) and Biodiversity Strategy 2030 is proposed as a phased transition pathway for Türkiye’s candidate-country obligations. The study concludes that partial reforms reproduce systemic contradictions: bridging the policy–law divide requires confronting entrenched political-economy dynamics where state actors and extractive-industry interests remain institutionally intertwined. Full article

To mitigate the impacts of urbanisation and the attendant surface sealing, appropriate measures are required when adapting urban spaces and drainage infrastructure. In this context, the deployment of Sustainable Drainage Systems (SuDSs) has emerged as a viable alternative, delivering highly positive outcomes by enhancing hydrological, hydraulic and landscape performance while restoring ecosystem services to the community. This study evaluates the relative performance of five SuDS typologies, green roofs, bioretention cells, infiltration trenches, permeable pavements, and rain barrels, implemented in a 64 ha subbasin of the metropolitan area of Barcelona, Spain. Using Giswater integrated with the SWMM, the stormwater drainage network was modelled under multiple rainfall scenarios. Performance was assessed using two qualitative indicators, the junction index (I_j) and the conduit index (I_c), which measure surcharge levels in manholes and pipes, respectively. The results show that SuDS implementation affecting 42.8% of the drained area can enhance network performance by 35.6% and reduce flooded junctions by 67%. Among the typologies, rain barrels and bioretention cells were the most effective. The study concludes that SuDS construction, supported by open-source tools and performance-based indicators, constitutes a replicable and technically robust strategy for mitigating the effects of surface sealing and increasing urban resilience. Full article

In 5G cellular networks, end-to-end data transmission delay is a key metric for evaluating network performance. High-frequency signal fading and complex transmission links often lead to increased delays. Pinching-antenna optimizes signal propagation through directional transmission, enhancing signal quality and reducing delay. Therefore, this paper analyzes the end-to-end transmission delay performance of 5G cellular networks assisted by pinching-antenna. Specifically, the data transmission process is modeled as a two-hop link, where data is first transmitted from the base station to the relay station (RS) via a 5G high-frequency transmission link, and then from the RS to the user equipment via a dielectric waveguide-based pinching-antenna link. We derive the statistical characteristics of the service processes for both the 5G high-frequency transmission link and the dielectric waveguide link. Considering traffic arrivals and service capabilities, we then precisely define the network’s end-to-end delay using stochastic network calculus. Through numerical experiments, we initially evaluate the impact of various network parameters on the performance upper bound and provide system performance. The experimental results show that the pinching-antenna-assisted 5G cellular network significantly reduces end-to-end delay compared with the traditional decode and forward relay, further confirming the substantial advantage of pinching-antenna in optimizing delay performance. Full article

Proper functioning of the endoplasmic reticulum (ER) plays a key role in maintaining the internal homeostasis of the cell. A common feature of many common diseases (such as diabetes and inflammatory bowel diseases) is the induction of ER stress in cells. While some ER stress is beneficial for cellular survival, high levels of stress can lead to cell death. For this reason, many studies are focused on understanding the exact mechanism of the ER stress response. There are a variety of well-established stressors on the market that can be used to induce ER stress under laboratory conditions (i.e., thapsigargin and tunicamycin). However, new scientific results suggest that these ER stressors act very differently on the stress response mechanism and, therefore, cannot always be used reliably. By using various mathematical methods, our systems biology approach presented here seeks to answer how the well-known ER stressors affect the dynamic characteristic of the control network, specifically highlighting how we can delay the negative impact of ER stress. Furthermore, using mathematical models, we make suggestions on which ER stressors may be useful in which therapeutic treatment. Full article

Urban CO₂ mitigation strategies typically aim at particular zones or sectors but do not account for spatial interdependencies among different components within the city. Understanding how land uses emit within and across districts can reveal systemic leverage points for climate-resilient urban planning. This study applies a bipartite spatial network approach using high-resolution Urban Atlas land-use data and a hierarchical spatial framework for emissions and sequestration estimation. The approach links urban land uses to their emissions profiles, offering a structural view of how different areas interconnect within urban carbon dynamics, moving beyond fragmented emission accounting. Using the Reggio Calabria Functional Urban Area in Italy as a case study, the analysis identifies influential areas and emission-intensive land uses. Subsequently, using centrality metrics highlights the spatial units with strong connections to emission-dense land uses, marking them as points of intervention. Results show that although 53% of districts act as net carbon sinks, their sequestration capacity is outweighed by the intensity of a smaller group of emitter districts. Among these, five central districts (IDs 94, 82, 107, 108, and 72) emit over 500 million kg CO₂ per year, making them leverage points for systemic mitigation. The integration of bipartite spatial network and multiscale territorial analysis provides a replicable, data-driven framework for urban CO₂ mitigation. Ultimately, the study demonstrates that mapping emissions through spatial interdependencies enables planners to target interventions where localized action yields the greatest network-wide climate impact. Full article

Text-based CAPTCHAs remain a widely deployed mechanism for mitigating automated attacks across web platforms. However, the increasing effectiveness of convolutional neural networks (CNNs) and advanced computer vision models poses significant challenges to their reliability as a security measure. This study presents a comprehensive forensic and security-oriented analysis of text-based CAPTCHA systems, focusing on how individual and combined visual distortion features affect human usability and machine solvability. A real-world dataset comprising 45,166 CAPTCHA samples was generated under controlled conditions, integrating diverse anti-recognition, anti-segmentation, and anti-classification features. Recognition performance was systematically evaluated using both a CNN-based solver and actual human interaction data collected through an online exam platform. Results reveal that while traditional features such as warping and distortion still degrade machine accuracy to some extent, newer features like the hollow scheme and multi-layer structures offer better resistance against CNN-based attacks while maintaining human readability. Correlation and SHAP-based analyses were employed to quantify feature influence and identify configurations that optimize human–machine separability. This work contributes a publicly available dataset and a feature-impact framework, enabling deeper investigations into adversarial robustness, CAPTCHA resistance modeling, and security-aware human interaction systems. The findings underscore the need for adaptive CAPTCHA mechanisms that are both human-centric and resilient against evolving AI-based attacks. Full article

The use of digital tools in agroecological crop production can help mitigate current farming challenges such as labour shortage and climate change. The aim of this study was to map digital tools used in crop production, assess their impacts across economic, environmental, and social dimensions, and determine their potential as enablers of agroecology. A systematic search and screening process, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses methodology, identified 453 relevant studies. The results showed that most digital tools are applied for crop monitoring (83.4%), with unmanned aerial vehicles (37.7%) and camera sensors (75.2% combined) being the most frequently used technologies. Farm Management Information Systems (57.6%) and Decision Support Systems (25.2%) dominated the tool categories, while platforms for market access, social networking, and collaborative learning were rare. Most tools addressed the first tier of agroecology, which refers to input reduction, highlighting a strong focus on efficiency improvements rather than systemic redesign. Although digital tools demonstrated positive contributions to social, environmental, and economic dimensions, studies concentrated mainly on economic benefits. Future research should investigate the potential role of digital technologies in advancing higher tiers of agroecology, emphasising participatory design, agroecosystem services, and broader coverage of the agricultural value chain. Full article

Yellow leaf disease (YLD) has been the most severe disease threatening areca palm, commonly known in areca palm cultivation. However, it has not yet been systematically studied in terms of the relationship between infected plants and the structure of rhizosphere microbial communities. In order to systematically study the impact of YLD on the rhizosphere fungi of the areca palm, we implemented high-throughput sequencing technology to analyze the microbial community structure and diversity under different disease conditions. The results indicate that as the severity of the disease increases, the diversity of the fungal community diminishes, with species abundance and richness initially decreasing before subsequently increasing, while phylogenetic diversity increases, and significant changes occur in the structure of the soil fungal community. At the phylum level, the dominant fungal phyla in the rhizosphere of areca palm are Ascomycota and Basidiomycota. At the genus level, the dominant genera are Sarocladium, Roussoella, Penicillium, etc., and their relative abundance increases with the severity of the disease. LEfSe analysis revealed that Archaeorhizomyces, Codinaea, and Albifimbria serve as indicator species for healthy areca palms, with their relative abundance trends consistent with changes in Alpha diversity. FUNGuild prediction results indicated that the fungal nutrient type structures of the three rhizosphere samples were highly similar, with saprotrophs being the absolutely dominant type. With the increase in the severity of the disease, the number of harmful fungi in the soil (such as Plectosphaerella, Fusarium, etc.) increases, thereby limiting the sustainable development of the soil. Network analysis indicates that beneficial microbial communities such as Stachybotrys and Roussoella exhibit extensive negative interactions. Therefore, the YLD of areca palm significantly alters the structure and diversity of the rhizosphere fungal community. Simultaneously, some beneficial microorganisms may be recruited by the areca rhizosphere to resist the invasion of YLD by improving the rhizosphere environment and enhancing plant immunity, such as Trechispora, Saitozyma, and Marasmiellus. This experiment is expected to provide a theoretical basis for the study of the rhizosphere microecology of the areca palm, the exploration of excellent biocontrol resources, and the green control of YLD in the areca palm. Full article

Promoting breakthrough innovation is a critical strategy for overcoming technological bottlenecks and addressing “chokepoint” challenges, especially for emerging economies. This paper constructs a two-tier innovation network comprising collaborative R&D and technology transaction subnetworks. Using panel data from Chinese A-share listed companies between 2008 and 2022, we empirically examine the impact of network embeddedness on firm breakthrough innovation in the artificial intelligence industry and explore the moderating effect of enterprise digitalization. The results reveal a U-shaped relationship between embeddedness breadth and breakthrough innovation, and an inverted U-shaped relationship between embeddedness depth and breakthrough innovation. The heterogeneous resource acquisition mediates these nonlinear relationships. As a firm’s digitalization intensity increases, the U-shaped and inverted U-shaped relationships between embeddedness dimensions and breakthrough innovation are significantly amplified. This study deepens our understanding of the mechanisms and boundary conditions by which network embeddedness affects firm innovation and provides new theoretical insights for fostering breakthrough innovation in emerging economies. Full article

Consumers and businesses are often connected online in today’s digitally connected world. Fast and barrier-free communication, easier and faster operation, and automation and networking of robots and production offer many competitive advantages. Recognizing the limiting factors of new technology, such as the significant dependency on technology and the vulnerability of IT devices, is crucial. As digitalization might increase the competitiveness of companies and have an impact on both the supply and value chains, we need to consider and assess their vulnerability from an information security perspective. Consequently, competitive advantage is not only about creating value more cost-efficiently and with higher quality but also about extracting the correct information from big data, interpreting and integrating it into business operations, and protecting it. This study proposes a fishbone model to help identify and overcome these challenges. It allows companies to identify the root cause of each information security incident. Full article

Unsupervised Hebbian learning is a biologically inspired algorithm designed to extract representations from input images, which can subsequently support supervised learning. It presents a promising alternative to traditional artificial neural networks (ANNs). Many attempts have focused on enhancing Hebbian learning by incorporating more biologically plausible components. Contrarily, we draw inspiration from recent advances in ANNs to rethink and further improve Hebbian learning in three interconnected aspects. First, we investigate the issue of overfitting in Hebbian learning and emphasize the importance of selecting an optimal number of training epochs, even in unsupervised settings. In addition, we discuss the risks and benefits of anti-Hebbian learning in model performance, and our visualizations reveal that synapses resembling the input images sometimes do not necessarily reflect effective learning. Then, we explore the impact of different activation functions on Hebbian representations, highlighting the benefits of properly utilizing negative values. Furthermore, motivated by the success of large pre-trained language models, we propose a novel approach for leveraging unlabeled data from other datasets. Unlike conventional pre-training in ANNs, experimental results demonstrate that merging trained synapses from different datasets leads to improved performance. Overall, our findings offer fresh perspectives on enhancing the future design of Hebbian learning algorithms. Full article

Background/Objectives: Transportation is a critical social determinant of health with direct impacts on healthcare access and utilization. This study examines transportation challenges faced by low-income individuals with chronic conditions who participated in the Food as Medicine (FAM) program offered by their primary care provider and explores the strategies they employ to overcome those challenges, particularly during the COVID-19 pandemic. Methods: We conducted semi-structured interviews with 36 FAM participants from Grady Health System in Atlanta, Georgia between May 2022 and October 2023. Interviews explored their ability to access routine care, FAM, and healthy food as prescribed by their physicians and nutritionists, as well as how the COVID-19 pandemic affected their transportation challenges and solutions. Results: Participants reported various transportation barriers including long wait times, delays, cost burdens, unreliable services, and coordination failures, which contributed to missing doctor appointments and FAM attendance. To overcome those challenges, participants planned trips in advance, used multiple transportation options, relied on social networks, or reduced and sometimes forwent trips. The COVID-19 pandemic limited their accessibility to healthcare, FAM, and healthy food options by reducing business hours and disrupting transportation services. Alternatives such as telemedicine and online ordering were less utilized due to distrust, dissatisfaction, and limited digital literacy. Conclusions: Transportation barriers can substantially restrict healthcare and food access for low-income individuals managing chronic conditions, especially during public crises that may lead to service disruptions. Transportation assistance that accommodates individuals’ financial circumstances and health conditions, implemented through collaborative efforts of healthcare institutions, transportation agencies, and governments, is essential to facilitating chronic disease management and reducing health disparities. Full article

Accurate quality-of-transmission (QoT) estimation prior to lightpath deployment is essential for minimizing design margins in optical networks. Owing to their high precision and strong generalization capabilities, artificial neural networks (ANNs) have emerged as a promising approach for lightpath QoT estimation. However, focusing exclusively on prediction accuracy is inadequate for maximizing global network capacity. Conventional models employing symmetric loss functions apply identical penalties to both overestimation and underestimation errors, thereby precluding controlled bias in predictions and their impact on overall network capacity. This paper investigates the margin configuration for the whole network capacity and proposes a novel QoT estimation method with asymmetric loss functions, which jointly considers the assessment of global network capacity and gives different penalties for overestimation and underestimation. We further present an iterative search algorithm grounded in network capacity considerations to optimize the parameters of these asymmetric loss functions. Simulation results confirm that our ANN-based models facilitate efficient modulation format assignment, leading to corresponding increases in network capacity. Full article

Background/Purpose: Skilled motor performance depends on the action–observation networks (AONs), which supports the internal simulation of perceived movements. While expertise effects are well-documented in sports, neuroimaging evidence in golf is scarce, particularly on temporal dynamics across swing phases. This study examines how golf expertise modulates AON activation and functional connectivity during temporally distinct swing phases (pre-hitting vs. hitting) and assesses implications for predictive-coding models of motor skill. Methods: Fifty-seven participants (elite golfers: n = 28; controls: n = 29) underwent functional magnetic resonance imaging (fMRI) scanning while viewing golf swing videos segmented into pre-hitting and hitting phases. Data analysis employed generalized linear models (GLMs) with two-sample t-tests for group comparisons and generalized psychophysiological interaction (gPPI) to assess functional connectivity using GLM-identified activation clusters as seeds. Results: (1) Compared to controls, elite golfers showed stronger activation in right insula and posterior cingulate cortex during pre-hitting, and in right cerebellum and bilateral postcentral cortex during hitting phases. The hitting > pre-hitting contrast revealed enhanced bilateral postcentral gyrus activation in golfers. (2) gPPI analysis demonstrated significant group × phase interaction in functional connectivity between right postcentral gyrus and left precuneus. Conclusions: Elite golf expertise dynamically retunes AON across swing phases, shifting from anticipatory interoceptive processing to impact-centered sensorimotor–parietal circuitry. These findings refine predictive-coding models of motor skill and identify the postcentral–precuneus loop as a potential target for neurofeedback interventions aimed at optimizing golf performance. Full article