Search Results (7,271)

Methodological opacity and the omission of environmental variables in forest masks can generate biased estimates. The objective of this study was to validate a reproducible workflow for quantifying forest cover change in the area adjacent to Nevado de Colima over the 2019–2025 period, subdivided into nine assessment areas with standardized sampling based on 3 × 3 pixel kernels (900 m²). An enriched Random Forest model with slope and spectral indices (NDVI, NBR, NDWI-Gao, and BSI) classified six spectral combinations derived from Sentinel-2 L2A bands B2, B3, B4, B8, B11, and B12, together with a new index proposed in this study, Red-Enhanced Normalized Burn Ratio (RE-NBR), used as a conservative classifier and auxiliary classifier output in the probabilistic cross-check estimation. Validation employed thematic and areal metrics. All combinations reached OA values between 89.44% and 92.53% and Kappa values between 0.79 and 0.85, with Shortwave Infrared (B12, B8, B4) as the most consistent configuration across dates. Allocation disagreement systematically exceeded quantity disagreement on all dates. The Seasonal Stability Index increased from 0.73 in 2019 to 0.77 in 2025, with persistent positive asymmetry between February and April. The probabilistic cross-check adjustment produced an adjusted forest loss of 1594.74 ha and an adjusted gain of 802.65 ha over 120,289.70 ha. Within the protected natural areas, expected change was distributed unevenly among vegetation types, with pine–oak forest showing the highest total expected loss, whereas high-mountain meadow showed the highest expected gain and also remained among the covers with the highest expected loss, indicating active spatial reconfiguration in the upper ecological domain where Pinus hartwegii Lindl. is the dominant species, though no species-level classification was performed. These results provide spatial evidence to support field verification, forest-health monitoring, and management decisions in the protected high-mountain study area. Full article

Urban reservoirs are increasingly exposed to multiple interacting pressures associated with eutrophication, pollutant inflow, ageing sewerage and stormwater infrastructure, and climate-related hydrological instability. This issue is of growing concern because municipalities often possess fragmented monitoring and planning evidence without an operational framework for translating it into remediation action. This study develops an integrated decision-support framework for the Ternopil Reservoir based primarily on recent hydrochemical monitoring data, complemented by historical targeted sampling and local environmental and planning materials. The analysis focuses on the most informative indicators of ecological deterioration in an urban reservoir, including oxygen regime, organic pollution, nutrient-related parameters, suspended solids, and selected pollution markers. The available evidence indicates that the Ternopil Reservoir is among the most environmentally stressed water bodies within the local reservoir system, with recurrent eutrophication symptoms, seasonal water blooming, and spatially differentiated exceedances of selected water-quality indicators. The results further indicate persistent nutrient-related and organic pressure, pronounced hydrochemical tension in 2022, and hotspot vulnerability in hydraulically weak sectors of the reservoir. To address these pressures, the study proposes a four-stage monitoring-to-remediation framework that links environmental diagnosis with the identification of vulnerable zones, the prioritisation of hydraulic and hydrobiological measures, and post-remediation control. The proposed framework is intended as an operational planning tool for translating fragmented local evidence into a coherent remediation pathway for urban reservoirs under multiple environmental stressors. Full article

As sea ice recedes, the strategic importance of Arctic shipping routes has intensified, yet the complex polar environment poses severe challenges to navigational safety. Through a systematic search of the Scopus database, relevant key studies in both English and Chinese were identified and selected based on predefined inclusion criteria for in-depth review. The present study establishes a systematic categorization framework to parse existing research on Arctic navigational risk assessment. It structurally analyzes the literature across three core dimensions: sea ice characteristics, accident statistical analysis, and risk modeling methodologies. Addressing current limitations in data sparsity, factor coupling, and dynamic forecasting, this study proposes that future research should focus on the construction of structural models for risk interdependencies, multi-source data-driven environmental risk learning, and intelligent small-sample assessment based on Case-Based Reasoning (CBR), which extracts effective risk solutions from limited historical samples by interpreting past navigational successes and failures to improve decision quality. This review aims to provide a comprehensive reference for developing a systematic and intelligent risk assessment architecture for Arctic shipping. Full article

Rapid detection of Pseudomonas aeruginosa and its virulence factor ExoU is essential for improving patient outcomes. In this study, a CRISPR–Cas13a-based diagnostic assay combined with recombinase polymerase amplification (RPA) was developed to detect P. aeruginosa and the exoU gene in blood samples. The assay demonstrated robust amplification, with detection limits of 6 log₁₀ and 8 log₁₀ CFU/mL in Luria–Bertani medium and blood, respectively, and a 100% specificity, without cross-reactivity against four Gram-negative bacilli and Staphylococcus aureus reference strains. The utilisation of a fluorescence-based readout facilitated unambiguous discrimination between P. aeruginosa and P. aeruginosa/exoU⁺ isolates vs. negative controls. In conclusion, these results support the potential of RPA/CRISPR-Cas13a diagnostics for the rapid identification of P. aeruginosa and its ExoU virulence factor. Further optimisation and clinical validation are required to confirm its utility as a bedside diagnostic test, where its application would speed up clinical decisions in the treatment of these infections. Full article

The Oncotype DX^® 21-gene recurrence score (RS) guides adjuvant chemotherapy decisions in estrogen receptor-positive, human epidermal growth factor receptor 2-negative (ER+/HER2−) breast cancer, yet requires invasive tissue sampling and involves substantial costs. This study evaluated intratumoral tumor ecological diversity (iTED), a habitat imaging approach, as a non-invasive complement for predicting Oncotype DX^® high-risk classification (RS > 25). This retrospective multi-center study included 312 patients with ER+/HER2− invasive breast cancer who underwent Oncotype DX^® testing (development: n = 168; external validation: n = 144). The iTED framework employed superpixel-based habitat determination using Gaussian mixture models on pretreatment dynamic contrast-enhanced MRI. Four predictive models were compared: clinical, conventional whole-tumor radiomics (C-radiomics), iTED, and combined (Clinical + iTED). The iTED model achieved higher discriminative performance compared with C-radiomics in both development (area under the curve [AUC]: 0.868 ± 0.068 vs. 0.730 ± 0.112) and external validation (AUC: 0.811 vs. 0.587) sets. The combined model further improved performance (development AUC: 0.908 ± 0.043; external AUC: 0.889). Habitat imaging-based iTED features achieved numerically higher performance than conventional radiomics in predicting Oncotype DX^® high-risk classification. These findings suggest the potential of iTED as a non-invasive imaging biomarker to support molecular testing in clinical decision-making. Full article

The diagnostic field of prostate cancer (PCa) has undergone a significant evolution with the widespread integration of multiparametric magnetic resonance imaging (mpMRI) and mpMRI-targeted biopsies (TBx). This approach has been shown to improve the detection of clinically significant prostate cancer (csPCa) while reducing the overdiagnosis of low-risk disease. However, a conceptual and clinical challenge, which can be referred to as the “Precision Paradox,” has emerged. By directing biopsy cores almost exclusively into the most suspicious MRI lesions, clinicians may inadvertently overrepresent the biological significance of a limited high-grade component. This can lead to grade migration and pathological downgrading at the time of radical prostatectomy (RP). Although downgrading does not automatically equate to clinical overtreatment, it introduces prognostic uncertainty that complicates risk stratification for active surveillance (AS) and focal therapy. This conceptual commentary provides a critical perspective on this diagnostic issue. We synthesize recent meta-analyses to evaluate the true rates of grade mismatch associated with TBx and combined biopsy approaches. Furthermore, we discuss the spatial limitations of biopsy sampling, the pathological mechanisms driving grade discordance, and the clinical relevance of minor high-grade components such as cribriform architecture. Finally, we highlight the role of multi-omics and validated genomic biomarkers in risk models, ultimately fostering improved shared decision-making in the modern mpMRI era. Full article

The reinforcement and retrofit of damaged steel buildings has emerged as a primary focus in civil engineering. It should be noted that completing the reasonable strengthening design for avoiding the sudden collapse of a structure in extreme engineering conditions was an urgent task, while the existing method required a long time which significantly influenced the reinforcing practice. In the present study, an improved explainable machine learning (ML) framework was developed for the rapid assessment of the bending property of repaired laminated channel beams. Firstly, a comprehensive database of 192 samples combining experimental and finite element data was established. The Mahalanobis distance analysis and Pearson correlation analysis were sequentially performed to evaluate the singularity of the samples and the dependencies between the variables. Secondly, the adversarial tests were conducted on the randomly selected 10 pairs of training and testing sets to determine the database with the best distribution consistency. Then, three machine-learning models of artificial neural networks (ANN), random forest (RF), and extreme gradient boosting tree (XGBoost) were respectively trained and validated. Finally, the explainability analysis of the XGBoost model was carried out in the global and local perspectives based on the SHAP method. The prediction accuracy (R²) of all ML models exceeded 90%, demonstrating good accuracy and providing a useful reference within the current database for the reinforcement design of damaged steel beams in emergency situations. In addition, the XGBoost model achieved superior prediction accuracy (R² = 97.98%) and stability (CoV = 0.82%) compared to ANN and RF. The explainability analysis revealed that boundary conditions and load type had the most significant influence on bending capacity. The proposed ML approach enabled efficient and reliable bending capacity estimation, supporting rapid decision-making in emergency reinforcement scenarios for damaged steel structures. Full article

Pediatric septic shock remains a major cause of morbidity and mortality in critically ill children and is increasingly recognized as a syndrome of profound immunometabolic dysregulation. This narrative review synthesizes current clinical, translational, and mechanistic evidence on the glutamate–glutamine axis in pediatric septic shock. The review focuses on how glutamine and glutamate metabolism may interact with immune-cell function, mitochondrial substrate handling, redox defense, and intestinal barrier integrity, while distinguishing biological plausibility from validated clinical utility. Current evidence supports the glutamate–glutamine axis as a mechanistically relevant pathway and a source of candidate biomarkers, but pediatric-specific data remain limited and do not yet justify routine biomarker use or glutamine-based intervention in unselected children with septic shock. Future studies should use standardized sampling, reproducible analytical methods, pediatric validation cohorts, and phenotype-guided trial designs before this axis can be translated into clinical decision making. Full article

As the Industrial Internet of Things becomes increasingly interconnected with critical infrastructure, intrusion traffic exhibits characteristics such as high-dimensional redundancy, class imbalance, and temporal correlation, posing challenges for detection systems in terms of feature representation, model complexity control, and real-time performance. To address the aforementioned issues, this paper proposes an intrusion detection algorithm based on feature optimization and asynchronous advantage actor-critic learning (APA3CID). First, the raw dataset was preprocessed using methods such as label encoding and normalization. Feature selection was performed using the improved Whale Optimization Algorithm (WOA) to reduce data redundancy and eliminate irrelevant features. The samples were then serialized based on the order in which they were collected. Second, we model the detection process as a Markov decision process, use a sliding window to construct states that capture recent temporal features, and, building upon the Asynchronous Advantage Actor-Critic (A3C) framework, we incorporate an adaptive exploration mechanism to address the issues of insufficient exploration in the early training phase and unstable convergence in the later phase. Additionally, we introduce an asynchronous lag correction strategy that utilizes truncated importance weights to mitigate the bias caused by policy lag in asynchronous parallel training, thereby enhancing the stability and robustness of policy updates. Finally, experimental results show that on the X-IIoTID dataset, APA3CID achieves a 3.51% increase in detection rate and a 4.26% increase in F1-score compared to the traditional A3C algorithm. On the WUSTL-IIoT-2021 dataset, single-sample prediction takes as little as 11.56 microseconds, with Acc, DR, and F1-score all exceeding 90%. This outperforms comparison models such as LR, XGBoost, CNN, and the baseline A3C, meeting the requirements of industrial IoT scenarios for low false-negative rates and high real-time performance. Full article

In contemporary organizations, digital learning environments and AI-supported instructional modalities play an increasingly important role in workforce upskilling and operational efficiency. Despite growing investments in video-based learning and AI-generated instructional agents, empirical evidence on their effectiveness remains inconclusive. This study examines whether different digital learning modalities influence skill acquisition, task performance, retention, and user perceptions in a simulated work-related context. An experimental study was conducted with 65 participants assigned to one of three learning conditions: static instructional material, video-based instruction with human narration, and video-based instruction with an AI-generated avatar. Performance was assessed through a pretest–posttest design, a practical task simulating a typical data-processing activity, and a delayed retention test after seven days. Participants also evaluated the learning experience in terms of clarity, engagement, and overall effectiveness. The results revealed no statistically significant differences between instructional modalities in knowledge acquisition, task performance, or retention. Similarly, no statistically significant differences were observed in participants’ self-reported ratings. However, qualitative findings suggested that some participants perceived the AI-generated avatar as somewhat distracting, despite generally positive evaluations of the video-based formats. These findings did not provide evidence that more technologically advanced and resource-intensive learning formats led to superior performance outcomes in the present sample. The findings highlight the importance of instructional design quality over technological complexity and point to a potential mismatch between user preferences and actual performance. From a management perspective, the results raise relevant questions regarding the cost-effectiveness of AI-supported learning solutions and provide evidence-based insights for decision-making in organizational learning and digital transformation strategies. Full article

In this paper, we focus on online stochastic optimization problems in which random parameters follow time-varying distributions. In each round t, a decision is obtained from solving the current optimization problem. Then samples are drawn from distributions which are updated after obtaining the decision. The objective and constraint are updated in this process, and the updated problem is used to obtain the next decision. To solve the online stochastic optimization problem, we propose a model-based stochastic augmented Lagrangian method, which is referred to as the MSALM. In each round, we construct model functions for the sample objective and constraint functions based on their properties, which reduce computational complexity. The step size is designed in a dynamic way and decreases as t increases to accelerate convergence. Due to the setting of the online stochastic problem, we use stochastic dynamic regret and constraint violation to measure the performance of our algorithm. Under certain assumptions, we prove that our algorithm’s stochastic dynamic regret and constraint violation have a sublinear bound in terms of the total number of slots T. We design simulation experiments to verify the efficiency of our online algorithm. Its performance is evaluated on a range of information and system engineering problems, including adaptive filtering, online logistic regression, time-varying smart grid energy dispatch, online network resource allocation, and path planning. In addition, in the context of the path planning problem, we integrate our algorithm with supervised learning to demonstrate its enhanced capabilities. The experimental results validate the performance of our new algorithm in practical applications. Full article

Background: Complete blood count (CBC) phenotypes are routinely assigned using deterministic rule-based thresholds. While operationally efficient, such rules may lead to unstable phenotype assignments for results close to clinical cutoffs in the presence of analytical variability. Methods: We analyzed routine CBC data from a tertiary care hospital laboratory. Rule-based phenotypes for anemia subtype, white blood cell (WBC) status, and platelet (PLT) status were assigned using established laboratory thresholds. A patient-independent development and holdout split was applied. A multi-output gradient boosting model was trained to reproduce rule-based labels and provide probabilistic outputs. Phenotype stability was assessed by perturbing CBC parameters under realistic analytical noise. Instability was defined as any change in phenotype assignment across perturbations. Distances to decision boundaries were grouped into quantile-based bins. Model uncertainty was evaluated for the triage of unstable cases. Results: Phenotype instability was strongly concentrated near decision boundaries. Under medium analytical variability, samples closest to hemoglobin cutoffs exhibited the highest instability, with the highest instability in the bin closest to the cutoff, a sharp decrease in the adjacent bin, and lower instability across more distant bins. Model uncertainty was enriched among unstable cases, enabling prioritization of borderline samples while reviewing only a subset of all cases. Conclusions: Rule-based CBC phenotyping exhibits intrinsic instability near decision thresholds. Uncertainty-aware machine learning supports a practical framework to identify and prioritize borderline cases without replacing existing laboratory rules, supporting workload-controlled post-analytical decision support. Full article

Urban rail transit projects situated in Quaternary deposits are progressively influenced by ultra-shallow gas. During the investigation and construction phases, this gas may instigate gas outbursts, combustion, explosions, stratum disturbances, and secondary ground deformations. To transparently and applicably identify the most crucial disaster-inducing factors in engineering practice, this research constructs a hierarchical risk factor evaluation framework for shallow gas hazards during the investigation stage of rail transit engineering. Initially, candidate indicators were screened via a literature review of shallow gas hazard studies and metro engineering reports. Subsequently, by employing the AHP, four first-level indicators and fifteen second-level indicators were compared and weighted. The findings indicate that shallow gas pressure, methane content per ton of soil, and the occurrence form of shallow gas are the three most influential factors, with comprehensive weights of 0.2735, 0.2319, and 0.1113 respectively. A metro tunnel case in Guangdong Province was then utilized to illustrate how the ranked indicators can guide the verification of suspected zones, section-based hazard discrimination, and the planning of controlled gas release. In comparison with existing studies that concentrate on descriptive disaster phenomena or single-factor analyses, the contributions of this study are threefold. Firstly, it offers a structured indicator system specifically tailored to Quaternary shallow gas in rail transit engineering. Secondly, it makes the expert-based weighting process explicit. Thirdly, it links the ranking results to practical investigation and prevention decisions. This framework is intended as a preliminary engineering decision support tool rather than a substitute for detailed predictive modeling or large-sample statistical validation. Full article

Spain’s extensive marine jurisdiction—comprising a continental shelf of approximately 100,000 km² and an Exclusive Economic Zone approaching one million km²—requires robust geospatial frameworks to support ecosystem assessment and marine policy implementation. This study presents GIS-based methodologies developed by the Spanish Oceanographic Institute (IEO-CSIC) within national initiatives such as LIFE IP INTEMARES project and the implementation of Marine Strategy Framework Directive (European Directive 2008/56/EC). The geospatial workflows developed for these initiatives integrates heterogeneous spatial datasets—such as multibeam bathymetry, acoustic backscatter, Remote Operated Vehicle (ROV) and towed-camera transects, sediment samples, oceanographic profiles, and species-habitat occurrence records—into a unified spatial analysis environment. Applied methods include digital terrain modeling, derivation of geomorphometric indices (e.g., slope, rugosity, curvature), image classification, and spatial statistics to quantify habitat extent, condition, and anthropogenic pressures. An integrated spatial analysis framework combining environmental and anthropogenic data is used to support zoning and management decisions within Marine Protected Areas (MPAs). Additionally, the deployment of WebGIS platforms facilitates data dissemination, iterative review, and stakeholder engagement, thereby enhancing transparency and accessibility. The resulting high-resolution maps, harmonized datasets, and computed spatial indicators—aligned with Marine Strategy Framework Directive (MSFD) descriptors such as habitat distribution (D1C4–C5) and seafloor integrity (D6C2–C3)—demonstrate how GIScience methods provide reproducible, decision-ready information to support the monitoring and management of Spain’s diverse marine ecosystems. Full article

Climate change continues to threaten global food security. Climate-smart agriculture (CSA) offers a solution to addressing this challenge in urban agriculture (UA). This paper addresses a gap in the empirical literature on decision-making about the adoption of CSA practices by examining the determinants of CSA adoption among small-scale urban crop (SSUC) farmers in eThekwini (ETH) Municipality, South Africa. Grounded in a utility theory framework, the paper draws on 412 respondents (Cochran-estimated) from a multi-stage sample design across four wards, providing reasonable coverage of SSUC farmers in ETH Municipality. While the sample size is statistically representative of SSUC farmers in ETH Municipality, it is a single metropolitan case rather than universal. The results show strong complementarities among these CSA practices, for example, between OM and CD (r ≈ 0.70, p < 0.001) and M and CD (r ≈ 0.61, p < 0.001). The multivariate probit (MVP) model predicts that the socio-economic and institutional factors age, gender, marital and employment status, education, credit access, extension contact, land tenure, and location (distance from home to farm plots) (p < 0.05) were significant determinants of adopting CSA practices by SSUC farmers. The findings contribute to the global literature on the UA–CSA nexus, demonstrating that socio-economic and institutional factors shape the adoption of bundled CSA practices. While the findings underscore the need for integrated, custom, and UA context-specific policy and extension interventions to strengthen urban food system resilience, UA farmers, practitioners, researchers, and policymakers should apply these insights elsewhere with caution. Full article