Search Results (758)

Emerging fungal plant pathogens are significant biotic stresses to crops that threaten global food security, biodiversity, and agricultural sustainability. Historically, these pathogens cause devastating crop losses and continue to evolve rapidly due to climate change, international trade, and intensified farming practices. Recent advancements in diagnostic technologies, including remote sensing, sensor-based detection, and molecular techniques, are transforming disease monitoring and detection. These tools, when combined with data mining and big data analysis, facilitate real-time surveillance and early intervention strategies. There is a need for extension and digital advisory services to empower farmers with actionable insights for effective disease management. This manuscript presents an inclusive review of the socioeconomic and historical impacts of fungal plant diseases, the mechanisms driving the emergence of these pathogens, and the pressing need for global surveillance and reporting systems. By analyzing recent advancements and the challenges in the surveillance and diagnosis of fungal pathogens, this review advocates for an integrated, multidisciplinary approach to address the growing threats posed by these emerging fungal diseases. Fostering innovation, enhancing accessibility, and promoting collaboration at both national and international levels are crucial for the agricultural community to protect crops from these emerging biotic stresses, ensuring food security and supporting sustainable farming practices. Full article

This study addresses the challenge of accurately detecting agricultural land-use changes in bi-temporal remote sensing imagery, which is hindered by cross-temporal interference, multi-scale feature modeling limitations, and poor large-area scalability. The study proposes the Semantic Change Detection (SCD) with Single-Head Dual-Attention Vision Transformer (SHDAViT) and Multidimensional Collaborative Attention (MCA) Block-Based Network (SMBNet). The SHDAViT module enhances local-global feature aggregation through a single-head self-attention mechanism combined with channel–spatial dual attention. The MCA module mitigates cross-temporal style discrepancies by modeling cross-dimensional feature interactions, fusing bi-temporal information to accentuate true change regions. SHDAViT extracts discriminative features from each phase image, MCA aligns and fuses these features to suppress noise and amplify effective change signals. Evaluated on the newly developed AgriCD dataset and the JL1 benchmark, SMBNet outperforms five mainstream methods (BiSRNet, Bi-SRUNet++, HRSCD.str3, HRSCD.str4, and CDSC), achieving state-of-the-art performance, with F1 scores of 91.18% (AgriCD) and 86.44% (JL1), demonstrating superior accuracy in detecting subtle farmland transitions. Experimental results confirm the framework’s robustness against label imbalance and environmental variations, offering a practical solution for agricultural monitoring. Full article

Surface ozone pollution poses a significant threat to human health and ecosystems. However, its highly variable spatiotemporal distribution, especially at hourly scales across China, complicates effective risk management. This variability presents substantial challenges for accurate estimation and forecasting, underscoring the importance of evaluating current hourly surface ozone estimation methods. Therefore, this study collaboratively evaluated the performance of chemical transport model simulations and satellite-based estimates of hourly surface ozone concentrations over mainland China in 2019. Using data from 3185 ground monitoring stations operated by the Ministry of Ecology and Environment, as well as six independent observation sites in Hong Kong, Xianghe, Nam Co, Akedala, Longfengshan, and Waliguan, this study found that both datasets exhibited systematic biases and lacked spatiotemporal consistency. The Community Atmosphere Model with Chemistry simulation results exhibited an average relative bias of 23.17%, generally overestimated ozone concentrations in high-altitude regions, but outperformed the satellite-based estimates at the independent sites, while consistently underestimating ozone concentrations in densely populated urban areas. In contrast, the satellite-based estimates performed better in regions with dense monitoring sites, with mean biases typically within 10% of observations, but their accuracy was limited in remote areas due to sparse ground-based calibration. It is particularly noteworthy that both datasets showed deficiencies in capturing extremely high-value events, nighttime ozone variations, and dynamic transport processes, underscoring challenges in the representation of photochemical processes in the model and in the design of satellite estimation algorithms. The results highlight the importance of optimizing model parameterization schemes, improving satellite estimation algorithms, and integrating multi-source data to enhance the accuracy and stability of hourly ozone estimates. This study provides multi-scale quantitative insights into the relative strengths and limitations of different ozone estimation methods, laying a solid scientific foundation for future data integration, regional air quality management, and policy development. Full article

Accurate building instance segmentation from high-resolution remote sensing images remains challenging due to complex urban scenes featuring occlusions, irregular building shapes, and heterogeneous textures. To address these issues, we propose a novel Multi-Scale Hybrid Dual-Attention Network (MS-HDAN), which integrates a dual-stream encoder, multi-scale feature extraction, and a hybrid attention mechanism. Specifically, the encoder is designed with a Local Feature Extraction Pathway (LFEP) and a Global Context Modeling Pathway (GCMP), enabling simultaneous capture of structural details and long-range semantic dependencies. A Local-Global Collaborative Perception Enhancement Module (LG-CPEM) is introduced to fuse the outputs from both streams, enhancing contextual representation. The decoder adopts a hierarchical up-sampling structure with skip connections and incorporates a dual-attention module to refine boundary-level details and suppress background noise. Extensive experiments on benchmark urban building datasets demonstrate that MS-HDAN significantly outperforms existing state-of-the-art methods, particularly in handling densely distributed and structurally complex buildings. The proposed framework offers a robust and scalable solution for real-world applications, such as urban planning, where precise building segmentation is crucial. Full article

Online laboratories have emerged as a viable alternative for providing hands-on experience to engineering students, especially in fields related to computer, software, and electrical engineering. In particular, remote laboratories enable users to interact in real time with physical hardware via the internet. However, current remote laboratory systems often restrict access to a single user per session, limiting broader participation. Embedded systems laboratory activities have traditionally relied on in-person instruction and direct interaction with hardware, requiring significant time for code development, compilation, and hardware testing. Students typically spend an important portion of each session coding and compiling programs, with the remaining time dedicated to hardware implementation, data collection, and report preparation. This paper proposes a remote laboratory implementation that optimizes remote laboratory stations’ availability, allowing users to lock the system only during the project debugging and testing phases while freeing the remote laboratory station for other users during the code development phase. The implementation presented here was developed for a microprocessor laboratory course. It enables users to code the solution in their preferred local or remote environments, then upload the resulting source code to the remote laboratory hardware for cross-compiling, execution, and testing. This approach enhances usability, scalability, and accessibility while preserving the core benefits of hands-on experimentation and collaboration in online embedded systems education. Full article

Land use/cover (LUCC) change in arid oasis–desert ecotones has significant implications for spatial governance in ecologically fragile regions. To better capture the temporal and spatial complexity of land transitions, this study developed a vector tracing method by integrating time-series remote sensing data with vector-based transfer pathways. Analysis of northern Oman from 1995 to 2020 revealed the following: (1) Arable land and impervious surfaces expanded from 0.51% to 1.09% and from 0.31% to 0.98%, respectively, while sand declined from 99.03% to 97.01%. Spatially, arable land was concentrated in piedmont irrigation zones, impervious surfaces near coastal cities, and shrubland and grassland along the Al-Hajar Mountains, forming a complementary land use mosaic. (2) Human activities were the dominant driver, with typical one-way chains accounting for 69.76% of total change. Sand was mainly transformed into arable land (7_C1, 7_D1, 7_E1; where the first part denotes the original type, the letter denotes the year of change, and the last digit denotes the new type), impervious surfaces (7_C6, 7_D6, 7_E6), and shrubland (7_E4). (3) Water scarcity and an arid climate remained primary constraints, manifested in typical reciprocating chains in the oasis–desert interface (7_D1_E7, 7_A1_B7, 7_C1_D7) and in the arid vegetation zone along the Al-Hajar Mountain foothills (7_D3_E7, 7_C3_D7), together accounting for 24.50% of total change. (4) The region exhibited coordinated transitions among oasis, urban, and ecological land, avoiding the common conflict of cropland loss to urbanization. During the study period, transitions among arable land, impervious surfaces, forest, shrubland, and wetland were rare (Type 16: 3.31%, Type 82: 2.89%, Type 12: 0.04%, Type 18: 0.01%). The case of northern Oman provides a valuable reference for collaborative spatial governance in ecologically fragile arid zones. Future research should integrate socio-economic drivers, climate change projections, and higher-temporal-resolution data to enhance the applicability of the chain-spectrum method in other arid regions. Full article

Industry Revolution Five (Industry 5.0) will shift the focus away from technology and rely more on to the collaboration between humans and AI-powered robots. This approach emphasizes a more human-centric perspective, enhanced resilience, optimized workplace processes, and a stronger commitment to sustainability. The humanoid robot market has experienced substantial growth, fueled by technological advancements and the increasing need for automation in industries such as service, customer support, and education. However, challenges like high costs, complex maintenance, and societal concerns about job displacement remain. Despite these issues, the market is expected to continue expanding, supported by innovations that enhance both accessibility and performance. Therefore, this article proposes the design and implementation of low-cost, remotely controlled humanoid robots via a mobile application for home-assistant applications. The humanoid robot boasts an advanced mechanical structure, high-performance actuators, and an array of sensors that empower it to execute a wide range of tasks with human-like dexterity and mobility. Incorporating sophisticated control algorithms and a user-friendly Graphical User Interface (GUI) provides precise and stable robot operation and control. Through an in-house developed code, our research contributes to the growing field of humanoid robotics and underscores the significance of advanced control systems in fully harnessing the capabilities of these human-like machines. The implications of our findings extend to the future development and deployment of humanoid robots across various industries and societal contexts, making this an ideal area for students and researchers to explore innovative solutions. Full article

The structural safety monitoring of industrial heritage is of great significance for global urban renewal and the preservation of cultural heritage. However, traditional tilt monitoring methods suffer from limited accuracy, low efficiency, poor global perception, and a lack of intelligence, making them inadequate for meeting the tilt monitoring requirements of super-high-rise industrial heritage chimneys. To address these issues, this study proposes a tilt monitoring method for super-high-rise industrial heritage chimneys based on LiDAR point clouds. Firstly, LiDAR point cloud data were acquired using a ground-based LiDAR measurement system. This system captures high-density point clouds and precise spatial attitude data, synchronizes multi-source timestamps, and transmits data remotely in real time via 5G, where a data preprocessing program generates valid high-precision point cloud data. Secondly, multiple cross-section slicing segmentation strategies are designed, and an automated tilt monitoring algorithm framework with adaptive slicing and collaborative optimization is constructed. This algorithm framework can adaptively extract slice contours and fit the central axes. By integrating adaptive slicing, residual feedback adjustment, and dynamic weight updating mechanisms, the intelligent extraction of the unit direction vector of the central axis is enabled. Finally, the unit direction vector is operated with the x- and z-axes through vector calculations to obtain the tilt-azimuth, tilt-angle, verticality, and verticality deviation of the central axis, followed by an accuracy evaluation. On-site experimental validation was conducted on a super-high-rise industrial heritage chimney. The results show that, compared with the results from the traditional method, the relative errors of the tilt angle, verticality, and verticality deviation of the industrial heritage chimney obtained by the proposed method are only 9.45%, while the relative error of the corresponding tilt-azimuth is only 0.004%. The proposed method enables high-precision, non-contact, and globally perceptive tilt monitoring of super-high-rise industrial heritage chimneys, providing a feasible technical approach for structural safety assessment and preservation. Full article

Volumetric video streaming holds transformative potential for telemedicine, enabling immersive remote consultations, surgical training, and real-time collaborative diagnostics. However, transmitting sensitive patient data (e.g., 3D medical scans, surgeon head/gaze movements) raises critical privacy risks, including exposure of biometric identifiers and protected health information (PHI). To address the above concerns, we propose SecureTeleMed, a dual-track encryption scheme tailored for volumetric video based telemedicine. SecureTeleMed combines viewport obfuscation and region of interest (ROI)-aware frame encryption to protect both patient data and clinician interactions while complying with healthcare privacy regulations (e.g., HIPAA, GDPR). Evaluations show SecureTeleMed reduces privacy leakage by 89% compared to baseline encryption methods, with sub-50 ms latency suitable for real-time telemedicine applications. Full article

The acquisition of nighttime remote-sensing visible-light images is often accompanied by low-illumination effects and haze interference, resulting in significant image quality degradation and greatly affecting subsequent applications. Existing low-light enhancement and dehazing algorithms can handle each problem individually, but their simple cascade cannot effectively address unknown real-world degradations. Therefore, we design a joint processing framework, WFDiff, which fully exploits the advantages of Fourier–wavelet dual-domain features and innovatively integrates the inverse diffusion process through differentiable operators to construct a multi-scale degradation collaborative correction system. Specifically, in the reverse diffusion process, a dual-domain feature interaction module is designed, and the joint probability distribution of the generated image and real data is constrained through differentiable operators: on the one hand, a global frequency-domain prior is established by jointly constraining Fourier amplitude and phase, effectively maintaining the radiometric consistency of the image; on the other hand, wavelets are used to capture high-frequency details and edge structures in the spatial domain to improve the prediction process. On this basis, a cross-overlapping-block adaptive smoothing estimation algorithm is proposed, which achieves dynamic fusion of multi-scale features through a differentiable weighting strategy, effectively solving the problem of restoring images of different sizes and avoiding local inconsistencies. In view of the current lack of remote-sensing data for low-light haze scenarios, we constructed the Hazy-Dark dataset. Physical experiments and ablation experiments show that the proposed method outperforms existing single-task or simple cascade methods in terms of image fidelity, detail recovery capability, and visual naturalness, providing a new paradigm for remote-sensing image processing under coupled degradations. Full article

Background: Greek islands face mounting pressures on their marine water resources due to tourism growth, agricultural runoff, climate change, and emerging pollutants. Safeguarding seawater quality is critical for ecosystem integrity, public health, and the sustainability of tourism-based economies. Objectives: This scoping review synthesizes and evaluates the existing research on seawater quality in the Greek islands, with emphasis on pollution sources, monitoring methodologies, and socio-environmental impacts, while highlighting the gaps in addressing emerging contaminants and aligning with sustainable development goals. Methods: A systematic literature search was conducted in Scopus, Google Scholar, ResearchGate, Web of Science, and PubMed for English- and Greek-language studies published over the last two to three decades. The search terms covered physical, chemical, and biological aspects of seawater quality, as well as emerging pollutants. The PRISMA-ScR guidelines were followed, resulting in the inclusion of 178 studies. The data were categorized by pollutant type, location, water quality indicators, monitoring methods, and environmental, health, and tourism implications. Results: This review identifies agricultural runoff, untreated wastewater, maritime traffic emissions, and microplastics as key pollution sources. Emerging contaminants such as pharmaceuticals, PFASs, and nanomaterials have been insufficiently studied. While monitoring technologies such as remote sensing, fuzzy logic, and Artificial Neural Networks (ANNs) are increasingly applied, these efforts remain fragmented and geographically uneven. Notable gaps exist in the quantification of socio-economic impact, source apportionment, and epidemiological assessments. Conclusions: The current monitoring and management strategies in the Greek islands have produced high bathing water quality in many areas, as reflected in the Blue Flag program, yet they do not fully address the spatial, temporal, and technological challenges posed by climate change and emerging pollutants. Achieving long-term sustainability requires integrated, region-specific water governance linked to the UN SDGs, with stronger emphasis on preventive measures, advanced monitoring, and cross-sector collaboration. Full article

The creation of the LitUCA lithotheque represents a significant methodological advance in geoarchaeological research in the southwest of Spain. This article presents a systematic framework for the conservation, documentation, and digital integration of lithic collections, with particular emphasis on data traceability, reproducibility, and interoperability. The methodology adopted is inspired by international standards, adapted to the regional context, and incorporates rigorous protocols for sampling, analytical documentation, and a relational database system. The collection comprises over 5000 items, all of which are catalogued, photographed, and characterised both petrographically and morphometrically, with metadata being progressively aligned with FAIR principles, aiming for full compliance in the future. Preliminary analysis demonstrates the collection’s capacity to facilitate comparative studies of procurement, mobility, and lithic technological organisation. Furthermore, the digital infrastructure developed promotes remote access and fosters both academic and societal collaboration. Despite ongoing challenges regarding sample representativeness and interoperability, LitUCA stands as a scalable and versatile model for the management of lithotheques. This study highlights the importance of integrated lithotheques for scientific progress, heritage management, and interdisciplinary education. Full article

Amid accelerating climate change, climate-related hazards—such as floods, wildfires, hurricanes, and sea-level rise—increasingly drive land transformations and pose growing risks to housing markets by affecting property valuations, insurance availability, mortgage performance, and broader financial stability. This review synthesizes recent progress in two distinct domains and their linkage: (1) assessing climate-related financial risks in housing markets, and (2) applying AI-driven remote sensing for hazard detection and land transformation monitoring. While both areas have advanced significantly, important limitations remain. Existing housing finance studies often rely on static models and coarse spatial data, lacking integration with real-time environmental information, thereby reducing their predictive power and policy relevance. In parallel, remote sensing studies using AI primarily focus on detecting physical hazards and land surface changes, yet rarely connect these spatial transformations to financial outcomes. To address these gaps, this review proposes an integrative framework that combines AI-enhanced remote sensing technologies with financial econometric modeling to improve the accuracy, timeliness, and policy relevance of climate-related risk assessment in housing markets. By bridging environmental hazard data—including land-based indicators of exposure and damage—with financial indicators, the framework enables more granular, dynamic, and equitable assessments than conventional approaches. Nonetheless, its implementation faces technical and institutional barriers, including spatial and temporal mismatches between datasets, fragmented regulatory and behavioral inputs, and the limitations of current single-task AI models, which often lack transparency. Overcoming these challenges will require innovation in AI modeling, improved data-sharing infrastructures, and stronger cross-disciplinary collaboration. Full article

Heart failure, a significant global health burden, is divided into heart failure with reduced ejection fraction (HFrEF) and preserved ejection fraction (HFpEF), characterized by systolic dysfunction and diastolic stiffness, respectively. While HFrEF benefits from pharmacological and device-based therapies, HFpEF lacks effective treatments, with both conditions leading to high rehospitalization rates and reduced quality of life, especially in older adults with comorbidities. This review explores the role of artificial intelligence (AI) in advancing autonomic neuromodulation for heart failure management. AI enhances patient selection, optimizes stimulation strategies, and enables adaptive, closed-loop systems. In HFrEF, vagus nerve stimulation and baroreflex activation therapy improve functional status and biomarkers, while AI-driven models adjust stimulation dynamically based on physiological feedback. In HFpEF, AI aids in deep phenotyping to identify responsive subgroups for neuromodulatory interventions. Clinical tools support remote monitoring, risk assessment, and symptom detection. However, challenges like data integration, ethical oversight, and clinical adoption limit real-world application. Algorithm transparency, bias minimization, and equitable access are critical for success. Interdisciplinary collaboration and ethical innovation are essential to develop personalized, data-driven, patient-centered heart failure treatment strategies through AI-guided neuromodulation. Full article

With the advancement of precision agriculture, Unmanned Aerial Vehicle (UAV)-based remote sensing has been increasingly employed for monitoring crop water and nutrient status due to its high flexibility, fine spatial resolution, and rapid data acquisition capabilities. This review systematically examines recent research progress and key technological pathways in UAV-based remote sensing for crop water and nutrient monitoring. It provides an in-depth analysis of UAV platforms, sensor configurations, and their suitability across diverse agricultural applications. The review also highlights critical data processing steps—including radiometric correction, image stitching, segmentation, and data fusion—and compares three major modeling approaches for parameter inversion: vegetation index-based, data-driven, and physically based methods. Representative application cases across various crops and spatiotemporal scales are summarized. Furthermore, the review explores factors affecting monitoring performance, such as crop growth stages, spatial resolution, illumination and meteorological conditions, and model generalization. Despite significant advancements, current limitations include insufficient sensor versatility, labor-intensive data processing chains, and limited model scalability. Finally, the review outlines future directions, including the integration of edge intelligence, hybrid physical–data modeling, and multi-source, three-dimensional collaborative sensing. This work aims to provide theoretical insights and technical support for advancing UAV-based remote sensing in precision agriculture. Full article