Search Results (44,711)

In recent years, shrubland is an important land cover type in alpine regions, while accurate segmentation of shrubs using remote sensing data remain challenging. To address these issues, this study proposes an alpine shrub segmentation method based on an improved lightweight DeepLabV3+ network, in which MobileNetV2 is used to replace the original backbone to reduce model complexity while maintaining feature representation capability, a channel squeeze-and-excitation (cSE) attention module is introduced to enhance the response to key shrub features and boundary details, and Ghost convolution is incorporated to reduce computational redundancy while preserving segmentation accuracy. Experimental results from both ablation and comparative studies demonstrate that the proposed model achieves a mean intersection over union (MIoU) of 88.47%, mean pixel accuracy (mPA) of 92.93%, F1-score of 91.80%, and overall accuracy of 94.52%, representing improvements of 3.53%, 2.64%, 2.96%, and 1.69%, respectively, over the original DeepLabV3+ model, while also significantly reducing the number of parameters and model size. In addition, independent cross-year validation using unmanned aerial vehicle (UAV) imagery acquired in 2025 suggests that the proposed model has good applicability under similar UAV sensor and acquisition conditions. Overall, this study provides an effective lightweight semantic segmentation approach for alpine shrub segmentation from high-resolution UAV imagery and offers useful technical support for vegetation monitoring in alpine regions such as the Qinghai–Tibet Plateau. Full article

Introduction: Elasmobranchs play a vital role in marine food webs through top-down control and the structuring of ecosystem stability, yet more than one-third of species face extinction. The Maldives, a recognised Indian Ocean hotspot for shark and batoid diversity, designated its EEZ as a shark sanctuary in 2010, but multispecies elasmobranch occurrence patterns and environmental drivers remain poorly characterised in Lhaviyani Atoll in the central Maldives, which hosts two Important Shark and Ray Areas (ISRAs). Recreational SCUBA networks can turn routine dive activity into long-term conservation evidence, already informing nearly 10% of the western Indian Ocean ISRAs. Objective: To characterise spatiotemporal patterns of elasmobranch assemblages in Lhaviyani Atoll (2017–2024), quantify how environmental and geomorphic drivers shape relative abundance, diversity, and hotspots, and provide evidence for targeted elasmobranch conservation. Methodology: A seven-year opportunistic dive-log dataset of 12,732 SCUBA surveys and 142,994 elasmobranch records across 94 dive sites was analysed. Effort-standardised relative abundance and community metrics (Shannon diversity, Pielou’s evenness) were modelled against sea surface temperature (SST), salinity, dissolved oxygen, chlorophyll-a, zonal current velocity, substrate type, and reef geomorphology using generalised additive models (GAMs). Spatial analyses identified persistent northern-rim aggregation areas aligned with ISRAs. Results: Twenty-eight species (14 sharks, 14 batoids) were recorded, including 23 threatened on the IUCN Red List (4 Critically Endangered, 12 Endangered, 7 Vulnerable). Relative abundance and diversity peaked during the late southwest monsoon (August–September) and declined during the northeast monsoon (December–March). After 2021, diversity and evenness increased while overall abundance declined. Relative abundance was primarily driven by SST, salinity, and current velocity; for sharks, dissolved oxygen and chlorophyll-a were additionally significant, whereas batoid abundance was driven mainly by temperature, oxygen, and current velocity. Four persistent hotspots along the northern atoll rim were identified, with sharks concentrated along exposed slopes and channels, and batoids distributed broadly within lagoonal habitats. Conclusions: Long-term citizen science dive-log monitoring is cost-effective for elasmobranch conservation in remote tropical seascapes. These results show how dive-industry partnerships can inform conservation governance over a decade after sanctuary designation, supporting targeted, habitat-focused management as shark and batoid conservation frameworks continue to evolve. Full article

Accurate and automated extraction of solid waste sites from remote-sensing imagery constitutes a pivotal demand for contemporary environmental regulation and risk mitigation. However, in high-resolution remote-sensing imagery, solid waste sites are typically represented as a single semantic image object (SIO), which is composed of multiple physical image parcels (PIPs) exhibiting significant variations in scale, morphology, and spectral properties. This intrinsic heterogeneity substantially increases the complexity and uncertainty of multi-class site identification. To address this challenge, this paper proposes WasteMOE Net (WMN), which is developed based on the core concept of modeling the SIO–PIP relationship. WMN adopts a heterogeneous expert selection mechanism combined with a nested mixture-of-experts architecture. It thus enables adaptive perception of complex PIPs across diverse scenarios and their integrated discrimination at the SIO level. In addition, by incorporating the explicit nonlinear representation capability of the KAN network, WMN effectively improves multi-class recognition accuracy while maintaining computational efficiency. Furthermore, this study constructs a high-resolution solid waste site dataset in accordance with the SIO–PIP-aware annotation principle, encompassing five representative categories: tailings ponds (TP), construction spoil sites (CSS), landfill sites (LS), garbage dump sites (GDS), and excavation sites (ES). Experimental results show that WMN achieves mAP50 values of 74.2% (GDS), 63.5% (CSS), 80.9% (ES), 85.4% (TP), and 83.1% (LS) in detection tasks, and 75.4%, 64.1%, 83.0%, 86.7%, and 84.1% for the corresponding categories in segmentation tasks. It achieves competitive performance compared with state-of-the-art methods in both tasks. Further, in a real-world application over Loudi City, China, WMN completed the processing of a 490.67 km² area within 1.34 h. The recognition accuracies for GDS and ES reached 54.8% and 65.3%, respectively. Finally, the proposed method has been successfully integrated into a GIS-based solid waste pollution risk prevention system, which markedly boosts the overall efficiency of environmental monitoring and on-site inspections. Full article

Background: Wearable cardioverter-defibrillators (WCDs) are equipped with the TRENDS remote-monitoring system, enabling continuous assessment of arrhythmias, physiological parameters, and patient-reported outcomes. This study evaluated the clinical utility of TRENDS-integrated WCD management and compared it with a historical control. Methods: We prospectively analyzed 36 consecutive patients who received a WCD with TRENDS between 2019 and 2024 and compared them with 30 historical controls treated before the implementation of TRENDS. Results: The WCD indications were heart failure as primary prevention (64%) and acute coronary syndrome with ventricular arrhythmias (28%). Among 18 patients who met the criteria for an implantable cardioverter-defibrillator (ICD), including 1 patient with WCD shock, 9 ultimately underwent ICD implantation. The mean daily WCD wear-time was 21.3 h and did not differ significantly from that of the historical control. The response rate to health-related questionnaires was 89%. TRENDS detected symptom exacerbation in 31% of patients, weight gain in 19% of patients, and missed medication in 19% of patients. Daily step-count was significantly lower in patients with ICD indications than in those without (5012 ± 2980 steps vs. 7977 ± 3584 steps, p = 0.01). TRENDS data also aided in initiating anticoagulation therapy and optimizing beta-blocker therapy. Conclusions: TRENDS provided clinically actionable physiologic and patient-reported information that supported individualized cardiovascular management. Full article

The increased use of technology-assisted distance counseling practices is one result of COVID’s impact on behavioral health, including in counselor education and the delivery of supervision. First, technology-assisted distance supervision needed for “real time” communication grew. Furthermore, there is an emergence of artificial intelligence (AI) technologies that have the potential to contribute to aspects of supervision; however, current evidence remains emerging, context-dependent, and at times mixed, warranting cautious interpretation of their effectiveness. The article offers an overview of using AI in clinical supervision, examines the benefits and potential concerns of AI from different perspectives, and considers the significance of using AI in counseling supervision. The role of AI is discussed as applied to counseling supervision including the use of AI tools, such as chatbots and reasoning AI, to detect and track sessions, note behavioral and emotional cues, aid/monitor communication and feedback, while also attending to ethical and legal consideration for its use. The article will report a range of benefits for supervisors and trainees using AI—for example, by enhancing data-driven supervision decisions, analyzing feedback trends, providing more efficient administrative monitoring, flexible/remote support, skill development, and promoting ethical decisions and self-reflection. Special attention is given to the challenges of using AI in supervision, including risks of undervaluing intuition and qualitative insights, potential for algorithms to reinforce systemic biases, risks of replacing human interaction, as well as non-compliance with HIPAA, FERPA, and ethical guidelines in data storage and privacy. The article will discuss privacy concerns, depersonalized feedback, and increased judgment-driven anxiety despite needed empathy when using AI as a tool for clinical supervision. Recommendations will also be offered for effective, ethical integration of AI in counseling supervision. Full article

Nystagmus, the involuntary rhythmic oscillation of the eyes, is a critical diagnostic marker in vestibular medicine, distinguishing life-threatening central disorders such as stroke from benign peripheral conditions including Benign Paroxysmal Positional Vertigo (BPPV). Despite its clinical importance, accurate nystagmus assessment has long been constrained by expensive infrared video-oculography equipment such as videonystagmography, specialist dependency, and the episodic nature of vestibular symptoms that are often resolved before a clinical encounter. This review synthesizes approximately 50 papers published between 1952 and 2026 across four thematic domains: AI-driven nystagmus analysis, clinical medicine, smartphone and portable hardware innovations, and telemedicine and remote monitoring. On the AI front, classical machine learning models achieve up to 98.77% nystagmus recognition accuracy using ensemble methods, while deep learning frameworks spanning CNNs, U-Nets, LSTMs, and optical flow networks demonstrate clinical-grade slow-phase velocity measurement equivalent to gold standard video-oculography on standard smartphone RGB video. Large language and vision models including GPT-4V and Gemini 2.0 show early-stage promise as zero-shot triage tools but currently fall well below specialist-level diagnostic accuracy. Concurrently, portable hardware innovations ranging from 3D-printed goggle systems to ARKit-based smartphone applications are narrowing the accessibility gap, while telemedicine frameworks enable ictal recording and cloud-based specialist review outside the clinic. Across all domains, the common barriers to clinical translation are dataset scarcity for rare BPPV subtypes, sensitivity to ambient conditions, and the absence of explainable AI mechanisms. This review maps the current state of the field and identifies multimodal data fusion, prospective clinical validation, and interpretable AI as the critical next steps toward equitable, specialist independent vestibular diagnostics. Full article

Background/Objectives: Digital, remote, and ecological tools may complement clinic-based assessment in multiple sclerosis (MS), but the distribution of evidence across fatigue/fatigability, mobility, and real-world functional activity remains unclear. This scoping review mapped tools, metrics, constructs, contexts of use, and reported clinical utility in adults with MS, with attention given to whether the evidence was balanced across domains. Methods: Following Joanna Briggs Institute guidance and PRISMA-ScR/PRISMA-S reporting standards, five databases were searched on 14 March 2026. After deduplication, title/abstract screening, full-text assessment, and manual extraction and verification, the findings were synthesized descriptively without formal critical appraisal. Results: Of 3100 records identified, 1433 unique records were screened and 125 sources were included. Gait was the most frequently assessed domain (105/125), followed by fatigue/fatigability (33/125), physical activity (29/125), and sleep (2/125). The most frequent technologies were wearable devices (60/125), accelerometry (54/125), remote/home-based/telemonitoring modalities (52/125), and inertial measurement units (42/125). Conclusions: The evidence is predominantly gait- and mobility-focused, while fatigue/fatigability and broader real-world functional activity are less consistently represented. Reported clinical utility was usually framed around functional assessment, longitudinal/remote monitoring, rehabilitation planning, patient stratification, and decision support, but these characteristics were extracted as reported and were not independently appraised. Full article

We modelled, for the first time, the seasonal dynamics and long-term trends of Abies marocana forests (Rif Mountains, northern Morocco) using remote-sensing-derived vegetation indices. Using the MODIS Terra Vegetation Indices product MOD13Q1 (enhanced vegetation index, EVI; 16-day frequency; 250 m spatial resolution) from 2000 to 2024 (575 images over 25 years), we applied a robust seasonal trend analysis (RSTA) workflow, representing an inferential extension of classical seasonal trend analysis (STA) through the explicit control of Type I error under serial and spatial correlation. This approach combined: (i) harmonic regression to capture the annual and semi-annual cycles of A. marocana forests, estimating seasonal amplitudes and phases while filtering out low-frequency noise; (ii) an iterative trend-free prewhitening (TFPW) procedure following Wang and Swail, applied only to time series with significant serial autocorrelation according to the Durbin–Watson test; (iii) the Theil–Sen slope (TS) estimator, a robust non-parametric method, to quantify the magnitude and direction of seasonality trends; (iv) the contextual Mann–Kendall (CMK) test to assess the statistical significance of seasonality trends, while correcting for spatial autocorrelation and accounting for cross-correlation among neighbouring pixels; (v) the Benjamini–Hochberg (BH) procedure to control the false discovery rate (FDR), ensuring that only statistically robust seasonality trends were retained; and (vi) reconstruction of seasonal curves representing the beginning and end of the study period and derivation of phenological metrics from the statistically significant seasonal trends retained after inferential filtering. After applying the complete analytical workflow, statistically significant trends were detected in 79.2% of pixels within A. marocana forests, compared with 86.4% when prewhitening and false discovery rate control were not applied. All Theil–Sen slopes retained by the RSTA workflow were positive, with a mean slope of approximately 0.00175 EVI year⁻¹, corresponding to an average annual increase of roughly 0.7% and an overall increase of approximately 15% over the 2000–2024 study period relative to the initial mean EVI conditions. Browning trends identified by classical STA were not supported after inferential filtering and FDR control, indicating that all these patterns were spurious or only marginal, and confined to limited areas and edge zones. The reconstructed seasonal trend curves were consistent with a longer growing season, although this inference is based on land-surface vegetation dynamics rather than direct phenological observations. The long-term ecological consequences of these changes in seasonal vegetation activity will hinge on the interactions among warming, rising water demand, and potential disturbance regimes under future climatic conditions. Full article

Soil particle size fractions (PSFs)—sand, silt, and clay—are fundamental determinants of soil hydrological behavior, nutrient retention, and erodibility, yet their spatial prediction remains challenging due to the compositional nature of the data, unquantified prediction uncertainty, and limited interpretability of machine learning models. This study develops an integrated compositional mapping framework incorporating multi-source Sentinel-1/2 and topographic covariates, coupling the isometric log-ratio (ILR) transformation with Quantile Regression Forests (QRFs), a Monte Carlo simulation (MCS)-based latent-to-physical space uncertainty propagation strategy, and a Wrapper-SHAP attribution method to jointly address these challenges. The framework was evaluated across regional croplands in the central Shandong mountain-hilly region of China, using an elevation-stratified spatial cross-validation. Validations achieved R² values of 0.72, 0.61, and 0.59 for sand, silt, and clay, respectively, and a global Aitchison distance of 0.34. Critically, the MCS error propagation strategy effectively compensated for the probability distribution shift introduced by non-linear ILR back-transformation. This ensured that all predicted compositions strictly satisfied compositional closure and the [0, 100%] constraint, while aligning the prediction interval coverage probability (PICP) of each fraction closely with the 90% nominal level. Wrapper-SHAP overcame direct attribution limitations in compositional models, revealing the predictive associations of these multi-source covariates: high remote sensing-derived Bare Soil Index (BSI) and Moisture Stress Index (MSI) values primarily exhibited strong predictive associations with sand enrichment, whereas their lower values, combined with elevated Normalized Difference Moisture Index (NDMI), Enhanced Vegetation Index (EVI), and anthropogenic indicators, favored silt and clay accumulation. The proposed framework provides a transferable methodological reference for remote sensing-integrated compositional soil mapping with reliable uncertainty estimates and interpretable driver identification at regional scales. Full article

Remote supervision of agricultural robots depends on continuous interpretation of robot status and wireless link quality. In smart greenhouses, crop canopies, metallic frames, cultivation rows, and non-line-of-sight propagation can cause intermittent packet loss and RSSI attenuation. Treating such transient degradation as immediate communication failure can interrupt robot operation unnecessarily, whereas delayed recognition of persistent loss can compromise safety. This study proposes a probabilistic communication-state inference method for remotely supervised agricultural robots. The robot-to-gateway wireless link is represented by three states: normal, degraded, and failure. The degraded state acts as an uncertainty buffer that preserves recoverable degradation before failure escalation. Packet reception ratio, received signal strength, and trajectory-derived context are used to update state probabilities through a bounded transition mechanism. Field experiments with a mobile agricultural robot in a smart greenhouse showed an accuracy of $0.915\pm 0.007$ and a macro F1-score of $0.907\pm 0.008$, while reducing the premature failure rate to $18.0\pm 1.4\%$. Comparisons with threshold-based, moving-average, and adapted WSN fault-detection baselines, including a FedLSTM-inspired baseline, showed that binary fault-detection logic cannot explicitly preserve recoverable degraded communication intervals. The results indicate that probabilistic degradation modeling supports communication-aware remote supervision by distinguishing transient degradation from failure-level communication loss. Full article

This study presents a multi-indicator remote sensing framework for assessing satellite-derived water-quality-related and trophic-state-related dynamics across four freshwater systems in Türkiye Egirdir Lake, Sapanca Lake, Catalan Dam, and Yuvacik Dam between the baseline (2015–2018) and recent (2023–2025) periods. Rather than providing a regulatory or use-specific satellite-based assessment of water-quality-related indicators, the study evaluates optically and thermally detectable surface water indicators derived from Sentinel-2 MSI and Landsat 8/9 imagery processed in Google Earth Engine. The Normalized Difference Chlorophyll Index (NDCI), the Normalized Difference Turbidity Index (NDTI), and land surface temperature (LST, applied to water surfaces) were used to detect change patterns through period-mean difference mapping (Δ-mask) and interannual time series analysis. Results reveal distinct spatial and temporal dynamics broadly consistent with the interplay of climatic, hydrological, and anthropogenic drivers. In the southern Mediterranean systems, positive ΔNDCI anomalies in littoral and inflow zones were associated with increasing summer LST, with Egirdir Lake exhibiting a statistically significant warming trend of +0.170 °C yr⁻¹ (Mann–Kendall τ = 0.53, p = 0.029), interpreted cautiously as a physically plausible signal consistent with regional climate trends, suggesting elevated thermally mediated eutrophication-related optical risk. In the northern Marmara systems, satellite-observed patterns were more strongly associated with anthropogenic nutrient loading and morphological constraints, with turbidity-related optical increases concentrated in western and marginal zones despite relatively stable thermal conditions. As concurrent in situ measurements were unavailable, cross-sensor consistency checks and literature-based benchmarking were applied as alternative validation strategies. Across all four systems, positive ΔNDCI anomalies were systematically concentrated in shallow marginal and inflow zones, while ΔNDTI patterns varied by system, underscoring the role of littoral dynamics as early indicators of optically detectable water-quality deterioration and trophic-state-related change. The proposed framework offers a scalable, cost-effective approach for freshwater quality surveillance in data-scarce environments and provides direct support for integrated water resource management under Türkiye’s National Water Plan (2026–2036). Full article

The prevention and treatment of cardiovascular disease (CVD) are undergoing a paradigm shift from a lipid-centric approach to a holistic metabolic perspective. Central to this evolution is the gut–fat–heart axis, a sophisticated three-dimensional communication network that integrates neural, endocrine, and immunometabolic signaling to regulate systemic lipid homeostasis. This manuscript systematically explores how the gut microbiota acts as a “metabolic organ” to remotely control host health through the production of bioactive metabolites and the modulation of molecular communication networks. At the physiological level, microbial products such as short-chain fatty acids (SCFAs) and modified bile acids regulate energy balance and lipid synthesis via the FXR-FGF15/19 axis and G protein-coupled receptors. Furthermore, gut hormones like GLP-1 and neuro-reflex pathways involving the vagus nerve provide rapid control over postprandial lipid clearance and feeding behavior. Conversely, pathological dysbiosis triggers the accumulation of harmful metabolites, such as trimethylamine N-oxide (TMAO) and lipopolysaccharides (LPS), which drive lipotoxicity, vascular inflammation, and “dysfunctional HDL” formation. These processes accelerate the progression of atherosclerosis, heart failure, and metabolic syndrome. Finally, the article outlines promising clinical translation strategies, including the development of TMA lyase inhibitors, next-generation probiotics, and the use of phytochemicals to reshape the microbial landscape. By decoding the molecular dialogues within the gut–fat–heart axis, this research provides a novel strategic vantage point for the integrated management of cardiovascular–kidney–metabolic (CKM) syndrome. Full article

Accurate land use/land cover (LULC) mapping from remotely sensed data remains challenging in arid regions, particularly for spatial and seasonal generalization. This work proposes a novel exclude-one-city-out (EOCO) framework based on machine learning (ML) to achieve LULC generalization across summer and winter in arid environments. Four cities in Saudi Arabia witnessing rapid urban growth were selected: Riyadh, Madinah, Jeddah, and Dammam. The ML models were trained on three cities and tested on the unseen city. Sentinel-2 surface reflectance data for the visible (Blue, Green, and Red) and near-infrared bands (NIR, SWIR1, and SWIR2) were used. Spectral indices, texture features, and topographical data were used to form five feature sets, which were utilized as inputs for four ML algorithms: random forest, support vector machine, classification and regression trees, and K-nearest neighbors. Statistical tests (Friedman, Kendall’s W, and Wilcoxon signed rank) were conducted to assess differences across ML models, feature sets, and seasons. The random forest model consistently outperformed other models across the five feature sets, while the spectral texture and combined feature sets outperformed other feature combinations. Significant differences in feature importance were observed across cities and seasons for spectral texture during summer and winter (p-values: 1.25 × 10⁻⁴ and 9.2 × 10⁻⁵, respectively), with strong agreement (Kendall’s W = 0.9212 and 0.9424). The findings can support urban environmental monitoring in arid regions, contributing to sustainable urban development. Full article

Background: Telemedicine is increasingly used in inflammatory bowel disease (IBD), but its effects on quality of life (QoL) and psychological outcomes remain unclear. Objectives: This study aimed to evaluate the impact of 6-month telemonitoring on QoL, disease activity, treatment adherence, psychological well-being, patient satisfaction, and healthcare utilization. Methods: This randomized, open-label, single-center study conducted in Russia (July 2023–December 2024) included adults with ulcerative colitis or Crohn’s disease, who were assigned 1:1 to telemonitoring or standard care. The intervention involved monthly remote assessments and access to a web-based platform containing educational information, disease activity assessment, and a chat with a gastroenterologist. The primary outcome was health-related QoL (SIBDQ). Exploratory outcomes included general QoL (WHOQOL-26), psychological well-being (HADS), alexithymia (TAS-26), visceral sensitivity (VSI), treatment adherence (GMAS), patient satisfaction (PSQ-18), achievement of clinical remission, and healthcare utilization. Results: Sixty-eight patients completed the study (32 intervention, 36 control). Telemonitoring was associated with lower anxiety levels (β = −1.76, p = 0.021), reduced visceral sensitivity (β = −5.08, p = 0.039), and higher medication adherence (β = 1.75, p = 0.008). No significant associations were observed for SIBDQ, WHOQOL-26 domains, depressive symptoms, alexithymia, achievement of clinical remission, or patient satisfaction with care (p > 0.05). Patients in the telemonitoring group also required fewer outpatient visits (p < 0.001), with no difference in hospitalizations. Within-group analysis demonstrated improvements in QoL, treatment adherence, visceral sensitivity, and disease activity in the telemonitoring group, but not in the controls. Conclusions: Six-month telemonitoring in IBD was associated with lower anxiety, reduced visceral sensitivity, improved treatment adherence, and fewer outpatient visits. The health-related QoL assessed by the SIBDQ did not differ compared to standard care. No clear clinical disadvantage compared with standard care was detected during the study period. Full article

Accurate road-event detection and timely alert message dissemination are essential for the safety of connected and automated vehicles. In many scenarios, alert messages must reach not only nearby vehicles but also remote stakeholders, such as traffic management centers, cloud services, and infrastructure operators. This requirement motivates the adoption of cellular-based communication technologies in addition to short-range vehicle-to-everything (V2X) communications for data dissemination. In this work, we investigate vehicle-to-network-to-everything (V2N2X) communications for the dissemination of alert messages generated after the on-board detection of hazardous road events through machine learning (ML) algorithms. Although V2N2X connectivity is well suited for extending data dissemination beyond the local vehicular environment, its capability to guarantee prompt message delivery under strict latency constraints remains an open challenge, particularly when ML inference is integrated into the end-to-end processing pipeline. To address this issue, we develop and experimentally evaluate a proof-of-concept (PoC) platform that combines real-time road-event detection with relevant message dissemination towards both nearby and remote recipients. The proposed framework leverages 5G connectivity and publish/subscribe messaging protocols. The experimental results showcase that dissemination latency is highly influenced by both the adopted type of 5G deployment (private versus commercial networks) and the load conditions at the message broker. Full article