Search Results (2,989)

Accurate land cover mapping is essential for environmental monitoring and agricultural management. Sentinel-2 imagery, with high spatial resolution and open access, provides valuable opportunities for operational classification. Convolutional neural networks (CNNs) have demonstrated state-of-the-art results, yet their adoption is limited by high computational demands and limited methodological transparency. This study proposes a lightweight CNN for soil–vegetation classification, in Dolj County, Romania. The architecture integrates three convolutional blocks, global average pooling, and dropout, with fewer than 150,000 trainable parameters. A fully documented workflow was implemented, covering preprocessing, patch extraction, training, and evaluation, addressing reproducibility challenges common in deep leaning studies. Experiments on Sentinel-2 imagery achieved 91.2% overall accuracy and a Cohen’s kappa of 0.82. These results are competitive with larger CNNs while reducing computational requirements by over 90%. Comparative analyses showed improvements over an NDVI baseline and a favorable efficiency–accuracy balance relative to heavier CNNs reported in the literature. A complementary ablation analysis confirmed that the adopted three-block architecture provides the optimal trade-off between accuracy and efficiency, empirically validating the robustness of the proposed design. These findings highlight the potential of lightweight, transparent deep learning for scalable and reproducible land cover monitoring, with prospects for extension to multi-class mapping, multi-temporal analysis, and fusion with complementary data such as SAR. This work provides a methodological basis for operational applications in resource-constrained environments. Full article

Across a diverse landscape of anonymity designs, the dominant paradigms—onion routing (e.g., Tor) and mix networks (e.g., Loopix)—carry intrinsic constraints: long-lived circuits invite traffic correlation, and mixnets often rely on a network-wide state, making it hard to reconcile anonymity and scalability. This paper presents Draughts, a fully decentralized system in which each packet follows an independent and dynamically determined transmission path. Built upon Jump routing, Draughts introduces three key innovations: (i) replacing global state $O\left(N\right)$ with local two-hop neighborhood knowledge $O\left(k^2\right)$; (ii) supporting anonymous replies to enable real-time bidirectional communication; and (iii) proposing a path-length control mechanism that balances anonymity and transmission efficiency. Evaluation results show that Draughts achieves strong sender anonymity, resists predecessor and traffic analysis attacks, and reduces receiver buffer maintenance overhead, achieving a favorable trade-off between anonymity and performance. Full article

The accurate and timely extraction of water bodies from high-resolution remote sensing imagery is vital for environmental monitoring, yet segmenting small, scattered, and irregularly shaped water bodies in complex rural landscapes remains a persistent challenge. While state-of-the-art deep learning models have advanced segmentation accuracy, they often achieve this at the cost of substantial computational overhead, limiting their practical application for large-scale monitoring. To address this trade-off between precision and efficiency, this paper introduces HWBENet, a novel hybrid network for water body extraction. HWBENet is built upon a lightweight MobileNetV3 encoder to ensure computational efficiency while preserving strong feature extraction capabilities. Its core innovation lies in two specifically designed modules. First, the Contextual Information Mining Module (CIMM) is proposed to enhance the network’s ability to learn and fuse both global scene-level context and fine-grained local details, which is crucial for identifying fragmented water bodies. Second, an Edge Refinement Module (ERM) is integrated into the decoder, which uniquely leverages transformer mechanisms to sharpen boundary details by effectively fusing prior feature information with up-sampled features. Extensive experiments on challenging rural water body datasets demonstrate that HWBENet strikes a superior balance between accuracy and computational cost. The experimental results validate the finding that HWBENet is an efficient, accurate, and scalable solution, offering significant practical value for large-scale hydrological mapping in complex rural environments. Full article

Maritime transport plays a pivotal role in global trade, where efficiency and accuracy in port operations are crucial. Among the various tasks carried out in ports, container code recognition is essential for tracking and handling cargo. Manual inspections of container codes are becoming increasingly impractical, as they induce delays and raise the risk of human error. To address these issues, this work proposes a hybrid Optical Character Recognition system that integrates YOLOv7 for text detection with the transformer-based TrOCR for recognition of the container codes, enabling accurate and efficient automated recognition. This design addresses the real-world challenges, such as varying light, distortions, and multi-orientation of container codes. To evaluate the system, we conducted a comprehensive evaluation on datasets that simulate the conditions found in port environments. The results demonstrate that the proposed hybrid model delivers significant improvements in detection and recognition accuracy and robustness compared to traditional OCR methods. In particular, the reliability in recognizing multi-oriented codes marks a notable advancement compared to existing solutions. Overall, this study presents an approach to automating container code recognition, contributing to the efficiency and modernization of port operations, with the potential to streamline port operations, reduce human error, and enhance the overall logistics workflow. Full article

In the context of escalating geopolitical tensions, recurring aircraft safety incidents, and frequent unforeseen events, the security of aircraft supply faces significant challenges. This research employs complex network theory to analyze the evolutionary characteristics of three global aircraft trade network (GATN) systems from 2015 to 2024. It then applies the entropy-weighted TOPSIS method to assess node importance within the network and finally conducts a robustness analysis based on the node importance ranking. The results indicate that the number of participating countries has declined post-pandemic, while trade concentration has increased. Analysis of the node’s importance reveals that the United States holds the most critical role in the GATN. The global medium aircraft trade network is characterized by one dominant player alongside several strong competitors, whereas the global large aircraft trade network features multiple major players coexisting. Regarding network robustness, targeted node attacks cause significantly more disruption than random node attacks. After removing 10% of key nodes, the global small aircraft trade network’s average connectivity fell to 0.6, and efficiency dropped to 0.1. Similar patterns were observed in the medium and large aircraft networks, with connectivity decreasing to 0.4 and efficiency to 0.05. Under targeted attacks, the global small aircraft trade network is more robust than the medium and large ones. This study provides quantitative insights to help optimize aircraft trade strategies. Full article

Against the global rollout of Carbon Border Adjustment Mechanisms (CBAMs), carbon tariffs have emerged as a core tool for developed economies to internalize environmental externalities—especially for energy-intensive imports that dominate cross-border carbon flows. However, emission information asymmetry, a critical barrier to implementing cross-border energy and environmental policies, undermines the design of optimal carbon tariffs, as it distorts the link between tariff levels and actual fossil energy-related emissions. This study develops a two-country analytical model to examine how biased assessments of exporters’ carbon intensity influence optimal tariff settings, exporters’ strategic behavior, and aggregate carbon emissions—with a focus on energy-intensive production contexts. The results show that underestimating carbon intensity reduces exporters’ compliance costs, incentivizing emission concealment; this weakens tariffs’ environmental stringency and may raise global emissions. Overestimation, by contrast, inflates exporters’ marginal costs, discouraging green investment and causing emission displacement rather than reduction. The analysis highlights a policy feedback loop wherein misjudged emission information distorts both trade competitiveness and environmental performance. This study concludes that a transparent, accurate, and internationally verifiable carbon accounting system is essential: it not only facilitates the effective implementation of CBAM but also aligns optimal carbon tariffs with CBAM’s dual goals of climate action and trade equity, while supporting global energy transition efforts. Full article

As a critical raw material in the semiconductor and new energy sectors, antimony is a strategic mineral resource for nations to safeguard industrial chain security. However, the scarcity of its resources and the complexity of its trade pattern underscore the urgency of antimony-related research. This study aims to reveal the structural characteristics of the global antimony trade network and explore the external factors influencing trade. Based on global antimony trade data from 2007 to 2022, the characteristics of the antimony trade network were analyzed using the complex network analysis method, and the influencing factors of antimony trade were examined via the fixed effects model. The results show that the global antimony trade network maintains a density of 0.05–0.06, with an average path length of 2.4–2.7 and a network diameter that mainly fluctuates between 5 and 6. The average clustering coefficient fluctuates within the range of 0.35–0.45. Overall, the network exhibits the characteristics of stable transmission efficiency, loose overall connectivity, and local agglomeration without a consistent upward or downward trend. Countries such as Germany, China, and the United States occupy core positions in the network. The fixed effects model indicates that GDP and LOGISTICS development are key factors promoting trade, while TARIFFS and REGULATORY policies have a significant inhibitory effect on trade. Therefore, ① Focus on the High-End Development of the Antimony Industry Chain and Promote the In-Depth Integration of Antimony Trade with the Semiconductor and New Energy Industries; ② Improve the Cross-Border Logistics and Warehousing System for Antimony Trade to Ensure the Efficient Circulation of Strategic Resources; ③ Promote; Promote Tariff Liberalization in Antimony Trade and Eliminate Market Access Barriers; ④ Strengthen the Government’s Strategic Support for the Antimony Industry to Enhance Global Discourse Power in Antimony Trade; Trade; ⑤ Maintain Macroeconomic Stability and Flexibly Manage Exchange Rates to Safeguard the Resilience of Antimony Trade. Full article

As convolutional neural networks (CNNs) become gradually larger and deeper, their applicability in real-time and resource-constrained environments is significantly limited. Furthermore, while self-attention (SA) mechanisms excel at capturing global dependencies, they often emphasize low-frequency information and struggle to represent fine local details. To overcome these limitations, we propose a multi-scale adaptive modulation network (MAMN) for image super-resolution. The MAMN mainly consists of a series of multi-scale adaptive modulation blocks (MAMBs), each of which incorporates a multi-scale adaptive modulation layer (MAML), a local detail extraction layer (LDEL), and two Swin Transformer Layers (STLs). The MAML is designed to capture multi-scale non-local representations, while the LDEL complements this by extracting high-frequency local features. Additionally, the STLs enhance long-range dependency modeling, effectively expanding the receptive field and integrating global contextual information. Extensive experiments demonstrate that the proposed method achieves an optimal trade-off between computational efficiency and reconstruction performance across five benchmark datasets. Full article

Person Re-Identification (Re-ID), a critical component of intelligent surveillance and security systems, seeks to match individuals across disjoint camera networks under complex real-world conditions. While deep learning has revolutionized Re-ID through enhanced feature representation and domain adaptation, a holistic synthesis of its advancements, unresolved challenges, and ethical implications remains imperative. This survey offers a structured and critical examination of Re-ID in the deep learning era, organized into three pillars: technological innovations, persistent barriers, and future frontiers. We systematically analyze breakthroughs in deep architectures (e.g., transformer-based models, hybrid global-local networks), optimization paradigms (contrastive, adversarial, and self-supervised learning), and robustness strategies for occlusion, pose variation, and cross-domain generalization. Critically, we identify underexplored limitations such as annotation bias, scalability-accuracy trade-offs, and privacy-utility conflicts in real-world deployment. Beyond technical analysis, we propose emerging directions, including causal reasoning for interpretable Re-ID, federated learning for decentralized data governance, open-world lifelong adaptation frameworks, and human-AI collaboration to reduce annotation costs. By integrating technical rigor with societal responsibility, this review aims to bridge the gap between algorithmic advancements and ethical deployment, fostering transparent, sustainable, and human-centric Re-ID systems. Full article

Forest diseases threaten tree health, biodiversity, and ecosystem services, with impacts amplified by climate change and global trade. Understanding and managing these threats is difficult due to the longevity of trees, the size and inaccessibility of forests, and the often cryptic or delayed expression of symptoms. This review first introduces the field of forest pathology and the key challenges it faces, including multifactorial declines, root and vascular diseases, and emerging invasive pathogens. We then examine how artificial intelligence (AI) can be applied to biotic, abiotic, and decline-related diseases, integrating remote sensing, imaging, genomics, and ecological data across spatial and temporal scales. Lessons from agricultural systems are discussed, highlighting potential tools and pitfalls for forestry. Finally, we outline future directions, emphasizing the need for interpretable models, incorporation of ecological context, cross-species validation, and coordinated data infrastructures to ensure AI delivers actionable, scalable solutions for complex forest ecosystems. Full article

Africa has always been among the top geopolitical priorities for the EU due to the continent’s close geographical proximity and long-standing economic ties. The agro-food trade between the EU27 and Africa is extremely important for both subjects and not only in terms of food security, as it is also a useful tool to secure a long-term partnership between the two continents, making them true and reliable allies ready to give support to each other, especially in the current unstable global situation. The analyzed data were taken from the official publications of the Eurostat (ESTAT). The time frame under analysis is 23 time periods—from 2002 to 2024 inclusive. Such methods and tools of scientific research as textual and tabular methods, empirical, statistical and comparative analyses, as well as the logical method, comprising deductive and inductive reasoning, time series analysis, modelling and forecasting, methods of time series data decomposition, etc. were used while conducting the research presented in the given article. The results for the time series analysis, modelling and forecasting assume the projections for the next four time periods for the EU27 to Africa agro-exports to be around their last observed value, slightly fluctuating or increasing with a delicateslope. The EU27 from Africa agro imports for the next four time periods are projected to increase, with a rather sharp slope. The research and its results can be of great help for public administrators, decision makers, academic community representatives, statisticians, and data analysts. Full article

With the development of global maritime trade, ship emissions pose an increasing threat to the global atmospheric environment, especially in international navigation waters and important port areas, where their impact on coastal air quality and ecosystems is becoming increasingly significant. This study proposes a high-throughput gridding algorithm (H-Grid) based on spatial hashing to rapidly generate ship emission inventories, which overcomes the inefficiency of traditional methods caused by complex index building and maintenance. The H-Grid algorithm achieves a constant processing time per data point and possesses inherent parallelism. Based on the H-Grid algorithm, taking the Yellow Sea area between China and Republic of Korea as a case study, the emissions of atmospheric pollutants from ships in 2024 were calculated, and their spatiotemporal distribution characteristics were analyzed. In our empirical study, the algorithm’s computational efficiency for processing millions of AIS records was improved by over 10 times compared to traditional geometric calculations, and by more than 4 times when compared to mainstream database spatial queries. Our findings provide an efficient tool for large-scale maritime emission analysis, strongly supporting the green development of global shipping. Full article

Under the dual carbon goals, microgrids face significant challenges in managing multi-energy flow coupling and maintaining operational robustness with high renewable energy penetration. This paper proposes a novel dynamic pressure-aware spatiotemporal optimization dispatch strategy. The strategy is centered on intelligent energy storage and enables proactive energy allocation for critical pressure moments. We designed and validated the strategy under an ideal benchmark scenario with perfect foresight of the operational cycle. This approach demonstrates its maximum potential for spatiotemporal coordination. On this basis, we propose a Multi-Objective Self-Adaptive Hybrid Enzyme Optimization (MOSHEO) algorithm. The algorithm introduces segmented perturbation initialization, nonlinear search mechanisms, and multi-source fusion strategies. These enhancements improve the algorithm’s global exploration and convergence performance. Specifically, in the ZDT3 test, the IGD metric improved by 7.7% and the SP metric was optimized by 63.4%, while the best HV value of 0.28037 was achieved in the UF4 test. Comprehensive case studies validate the effectiveness of the proposed approach under this ideal setting. Under normal conditions, the strategy successfully eliminates power and thermal deficits of 1120.00 kW and 124.46 kW, respectively, at 19:00. It achieves this through optimal quota allocation, which involved allocating 468.19 kW of electricity at 13:00 and 65.78 kW of thermal energy at 18:00. Under extreme weather, the strategy effectively converts 95.87 kW of electricity to thermal energy at 18:00. This conversion addresses a 444.46 kW thermal deficit. Furthermore, the implementation reduces microgrid cluster trading imbalances from 1300 kW to zero for electricity and from 400 kW to 176.34 kW for thermal energy, significantly enhancing system economics and multi-energy coordination efficiency. This research provides valuable insights and methodological support for advanced microgrid optimization by establishing a performance benchmark, with future work focusing on integration with forecasting techniques. Full article

Human immunodeficiency virus (HIV), comprising two distinct types, HIV-1 and HIV-2, remains one of the most significant global health challenges, originating from multiple cross-species transmissions of simian immunodeficiency viruses (SIVs) in the early 20th century. This review traces the evolutionary trajectory of HIV from zoonotic spillover to its establishment as a global pandemic. HIV-1, the principal strain responsible for AIDS, emerged from SIVcpz in Central African chimpanzees, with phylogenetic evidence indicating initial human transmission between the 1920s and 1940s in present day Democratic Republic of Congo. The virus disseminated through colonial trade networks, reaching the Caribbean by the 1960s before establishing endemic transmission in North America and Europe. HIV’s extraordinary genetic diversity—driven by high mutation rates (~10⁻⁵ mutations per base per replication cycle) and frequent recombination events—has generated multiple groups, subtypes, and circulating recombinant forms (CRFs) with distinct epidemiological patterns. HIV-1 Group M, comprising subtypes A through L, accounts for over 95% of global infections, with subtype C predominating in sub-Saharan Africa and Asia, while subtype B dominates in Western Europe and North America. The extensive genetic heterogeneity of HIV significantly impacts diagnostic accuracy, antiretroviral therapy efficacy, and vaccine development, as subtypes exhibit differential biological properties, transmission efficiencies, and drug resistance profiles. Contemporary advances, including next-generation sequencing (NGS) for surveillance, broadly neutralizing antibodies for cross-subtype prevention and therapy, and long-acting antiretroviral formulations to improve adherence, have transformed HIV management and prevention strategies. NGS enables near real-time surveillance of drug resistance mutations and inference of transmission networks where it is available, although access and routine application remain uneven across regions. Broadly neutralizing antibodies demonstrate cross-subtype efficacy, while long-acting formulations have the potential to improve treatment adherence. This review synthesizes recent evidence and offers actionable recommendations to optimize clinical and public health responses—including the routine use of genotypic resistance testing where feasible, targeted use of phylogenetic analysis for outbreak investigation, and the development of region-specific diagnostic and treatment algorithms informed by local subtype prevalence. While the understanding of HIV’s evolutionary dynamics has substantially improved and remains essential, translating this knowledge into universally implemented intervention strategies remains a key challenge for achieving the UNAIDS 95-95-95 targets and the goal of ending AIDS as a public health threat by 2030. Full article

Floating and offshore photovoltaic (FPV) installations present a promising solution for addressing land-use conflicts while enhancing renewable energy production. With an estimated global offshore PV potential of 4000 GW, FPV systems offer unique advantages, such as increased efficiency due to water cooling effects and synergy with other offshore technologies. However, challenges related to installation costs, durability, environmental impacts, and regulatory gaps remain. This review provides a comprehensive and critical analysis of FPV advancements, focusing on inland, nearshore, and offshore applications. A systematic evaluation of recent studies is conducted to assess technological innovations, including material improvements, mooring strategies, and integration with hybrid energy systems. Furthermore, the economic feasibility of FPVs is analysed, highlighting cost–benefit trade-offs, financing strategies, and policy frameworks necessary for large-scale deployment. Environmental concerns, such as biofouling, wave-induced stress, and impacts on aquatic ecosystems, are also examined. The findings indicate that while FPV technology has demonstrated significant potential in enhancing solar energy yield and water conservation, its scalability is hindered by high capital costs and the absence of standardised regulations. Future research should focus on developing robust offshore floating photovoltaic (OFPV) designs, optimising material durability, and establishing regulatory guidelines to facilitate widespread adoption. By addressing these challenges, FPVs can play a critical role in achieving global climate goals and accelerating the transition to sustainable energy systems. Full article