Search Results (430)

Permafrost warming is reshaping cold region surface water systems, where thermokarst lake drainage can abruptly alter lake abundance, hydrological connectivity and exposed thaw terrain. Yet future drainage risk trajectories remain poorly constrained for thermokarst lakes on the Mongolian Plateau (MP), a mid-latitude permafrost region undergoing strong climatic and cryospheric change. Here, we developed a future-compatible drainage risk assessment framework that combines Landsat-derived drainage mapping, environmental predictors, eXtreme Gradient Boosting (XGBoost) modelling, and fixed 2020 baseline risk thresholds with climate projections. The model was trained with 2003–2020 annual lake observations and applied to 31,786 undrained candidate lakes. Independent validation using 2021–2025 drainage events showed that 987 of 1188 events (83.1%) exceeded the fixed 2020 top 10% risk threshold, corresponding to an enrichment ratio of 8.31. Future projections showed a substantial upward shift in drainage risk levels relative to the 2020 baseline. These outputs represent relative risk levels referenced to the fixed 2020 distribution rather than calibrated probabilities of drainage within a specified future period. Under the Shared Socioeconomic Pathway 5-8.5 (SSP5-8.5) scenario, 12,448 lakes (39.16%) exceeded the fixed top 10% threshold by 2081–2100, while 17,980 lakes (56.57%) exceeded the fixed top 25% threshold. Among the top 10% high-risk lakes, 9597 were consistently identified by all six global climate models (GCMs), indicating strong cross-model agreement. These GCM-supported high-risk lakes formed spatially coherent clusters in the northern, western and northeastern MP. The Stefan-type active layer sensitivity test retained high spatial overlap with the main projection (Jaccard = 0.917). Environmental association analysis showed that mean annual ground temperature (MAGT) was the most closely related factor, with consistently negative Spearman correlations across scenarios and projection periods (−0.45 to −0.43), followed by thawing degree days (TDD), freezing degree days (FDD), June–September precipitation and river network distance. These findings identify areas with elevated relative drainage risk levels as warming continues. Full article

This study investigated the elevated-temperature deterioration of loess-containing concrete with different target strengths and evaluated ultrasonic-pulse-velocity (UPV)-based models for predicting residual compressive strength. Six mixtures combining target strengths of 30 and 45 MPa with loess replacement levels of 0, 15, and 30% were exposed to 23, 100, 200, 300, 500, and 700 °C. The dataset comprised 108 individual measurements representing 36 mixture-temperature conditions. Bulk density, UPV, and compressive strength were measured after natural cooling. Two normalization schemes were distinguished: a normal-concrete-based relative performance index, which retains both the initial penalty caused by loess replacement and subsequent thermal deterioration, and a mixture-specific residual ratio referenced to the initial value of each mixture. Experimental variability was quantified using standard deviations, coefficients of variation, and 95% confidence intervals. The effects of target strength, loess replacement, and temperature were examined using three-way ANOVA and Kruskal–Wallis tests. In addition, leakage-free condition-wise group cross-validation was performed so that the three replicates from each mixture-temperature condition were never divided between training and validation sets. UPV and compressive strength decreased markedly between 300 and 500 °C. Absolute compressive strength was significantly affected by all three factors, whereas exposure temperature was the dominant main effect for the mixture-specific residual strength ratio. Under condition-wise cross-validation, the normal-concrete-based relative model retained R² = 0.906, MAPE = 10.81%, and MPE = 0.60%, while the mixture-specific residual-ratio model achieved R² = 0.957 and MAPE = 7.94%. The proposed models are therefore suitable as preliminary screening-level tools within the investigated material and temperature ranges, but not as stand-alone bases for final structural safety decisions. Full article

Correct packet delivery does not guarantee correct semantic interpretation when endpoint meanings for the same learned codeword diverge. CoSMA DAI is proposed for mismatch detection, protected confirmation, task preservation and semantic repair. Channel-conditioned global evidence, semantic class local evidence, temporal dynamics, channel context and protected probe evidence are fused by a causal machine-learned mismatch belief. A transmitter-derived task belief preserves the downstream decision while repair is pending and BDIx agents select guarded intentions for probing, fallback and resynchronisation. Evaluation uses 30 held-out drift seeds, 300 matched null streams and 300 degrading channel controls. Six referenced sequential monitors receive the same conditioned payload score. CoSMA DAI obtains 100.00 percent balanced accuracy, precision, recall, F1 score and Matthews correlation coefficient with zero observed matched null false alarms. Its aggregate delay is 5.62 slots, compared with 11.58 slots for the other zero false alarm method. The task-preservation belief maintains 94.73 percent task accuracy through every divergence scenario, above the quantised accuracy ceiling of 0.919 of the semantic path, because it is derived from the unquantised transmitter latent. A task-label-only control confirms that this accuracy is secured by the preservation belief alone, independently of the detector, so task preservation and mismatch detection are decoupled by design and detectors are compared on residual functional semantic outage, outage duration and semantic reconstruction fidelity, which measure the restoration of the semantic representation itself. Without repair, the residual semantic outage is 73.69 percent at 15.97 dB reconstruction fidelity, whereas CoSMA DAI reduces it to 0.73 percent over 6.62 slots at 21.74 dB. Under five declared parity tiers, in which multivariate and supervised baselines receive the identical features, training seeds, protected probe and candidate budget, the protected confirmation stage reduces false repair for every detector to which it is attached. Zero-shot evaluation over 7 unseen mismatch families and 5 unseen link models retains full detection with zero observed false repair in 6 of the 7 families and on every link and identifies receiver-side decoder drift as a condition the present observation model cannot detect. The learned belief is validated at slot level with an area under the receiver operating characteristic curve of 0.99997 and a class overlap of 0.00039, leave-one-mechanism-out and cross-channel retraining are reported, behaviour is characterised down to the practical detection boundary and scaling to 64-dimensional representations with 2048-entry codebooks is demonstrated. The task-belief mechanism is shown to be economical only for small closed-set output spaces and the channel-conditioning tables are shown to reduce to 6 cells without loss. Every comparator is additionally retuned on the same development budget, paired bootstrap intervals and signed-rank tests are reported over the shared streams, auxiliary traffic and radio energy are normalised per correct decision, authentication of the task belief is specified and charged and transfer to MNIST, Fashion-MNIST, CIFAR-10 and CIFAR-100 is demonstrated without retraining, including on a convolutional VQ-VAE representation with a jointly learned 512-entry codebook, where foreground segmentation and localisation are restored to within the quantisation limit while a class decision cannot serve either task. The control traffic share is 23.59 percent, which is 12.62 percent lower than the monitor value. The additional semantic side information increases radio energy to 0.393 mJ per stream and reduces control-adjusted resource efficiency to 6.203 source-equivalent bits per channel use. The results therefore establish reliable detection and semantic repair within the principal comparison, with comparator-specific delay advantages and without claiming task-accuracy, semantic-rate or energy superiority. Full article

Large unmanned cargo airships may support heavy-lift logistics in regions without runway infrastructure, but payload release produces a rapid buoyancy surplus and changes the vehicle mass properties. This study develops a simulation framework coupling six-degree-of-freedom variable-property flight dynamics, an active seawater ballast system, and constrained ballast-flow allocation. The dynamics are referenced to a fixed body origin and retain the spatial-mass-matrix derivative and declared exchange-momentum wrench. A one-dimensional Method-of-Characteristics (MOC) solution provides a numerical reference for the reduced line-inertance runtime model. Fitting yields $L_{\mathrm{eff}}=55.240$ m and a 15.3% closure-interval normalized root-mean-square error (NRMSE), providing cross-model verification rather than experimental validation. Under the nominal 1201 s mission, the variable-property-aware case satisfies the predeclared criteria with a final-altitude error of 2.985 m and a steady-climb pitch RMS error of ${0.165}^{\circ }$. A fair frozen-inertia ablation also passes and produces slightly lower nominal errors (2.806 m and ${0.157}^{\circ }$); hence, nominal superiority is not claimed. Across the five-point pitch-inertia sweep (0.70 to 1.30 times nominal), the variable-property-aware controller keeps the steady-climb pitch RMS error within ${0.137}^{\circ }$–${0.165}^{\circ }$, whereas the frozen-inertia proportional–integral–derivative (PID) controller spans ${0.153}^{\circ }$–${0.230}^{\circ }$; at 1.30 times nominal inertia, the variable-property-aware controller reduces pitch RMS error by 31.8% and settles 12.9 s earlier. All 21 independently rerun cases that jointly scale the nominal pump and valve time constants from 1.0 to 3.0 satisfy the declared criteria. In the $N=20$, $\pm 5\%$ local parameter-dispersion study, all plotted trajectories remain state-bounded, while the wide altitude spread precludes a uniform tracking or reliability claim. The evidence supports numerical feasibility within the explicitly tested nominal, inertia, and actuator-time-constant cases while requiring configuration-specific trim and controller rematching before extrapolation; it does not constitute a reliability probability or global stability proof. Full article

The transport mechanisms for substrates and nutrients within the heme pathway of the Toxoplasma gondii apicoplast remain poorly understood. However, studies on heme metabolic enzymes have employed disparate genetic manipulation approaches, limiting direct phenotypic comparisons among different enzymes. This research involved screening potential apicoplast proteins in Toxoplasma gondii by cross-referencing and analyzing protein–protein interaction networks. Within the heme enzyme pathway of the apicoplast, eight enzymes were found to be predominantly conserved in the Sarcocystide family. Utilizing the CRISPR-Cas9 system alongside a U1 snRNP-mediated gene-silencing approach, we developed inducible knockdown strains—iKD-PBGD, iKD-UROS, and iKD-UROD—targeting three key metabolic enzymes crucial for the parasite lytic cycle, as demonstrated through replication experiments. To investigate the transport mechanisms for heme-related nutrients or substrates, we knocked down these three enzymes, using TgGRA12 as an initial marker. Continuous fluorescence signals highlighted the parasitophorous vacuole (PV) membrane surrounding tachyzoites during both early and late replication stages, particularly at 48 h post-rapamycin treatment, indicating a transformation of the cyst-like PV resembling that in Toxoplasma gondii. Phenotypically, knockdown of these heme enzymes led to the formation of slowly replicating, cyst-like parasitophorous vacuoles. However, this morphological change did not significantly affect the acute virulence of the parasites in vivo, as determined by mouse survival assays. This study explored the functional roles of the three intermediate metabolic enzymes, offering a novel viewpoint on the gradual demise of Toxoplasma gondii as a potential target for drug development. Full article

The war in Ukraine, beginning in February 2022, disrupted continuous medical care for Ukrainian childhood and adolescent cancer patients (UCC), many of whom were relocated to pediatric cancer centers worldwide, including Switzerland. In this Brief Report, we describe their health-related quality of life (HRQoL) after arrival. In a multicenter, cross-sectional survey across five Swiss pediatric oncology centers, we included patients ≤18 years at diagnosis who arrived after 24 February 2022 and were undergoing active treatment. HRQoL was assessed by the PedsQL™ 4.0 Generic Core Scales (self- and parent-reports). Fourteen of 23 eligible families (61%) participated. HRQoL declined with age, particularly in physical functioning, while psychosocial functioning remained relatively stable but lower among adolescents; parent scores closely matched self-reports. Scores were referenced against a published cohort of healthy Ukrainian children and previously reported pediatric cancer populations. Given the small sample and lack of a matched control group, the findings cannot separate the effects of displacement, cancer, and treatment and are hypothesis-generating. They suggest a potential role for age-tailored supportive care in displaced children with cancer and call for larger, controlled studies. Full article

Accurate areal rainfall estimation is essential for hydrological modeling and flood forecasting, yet method selection remains uncertain in small catchments with extremely sparse gauge networks. This event-scale study compared arithmetic mean (AM), Thiessen polygon (TP), inverse distance weighting (IDW), precipitation–elevation linear regression (ELR), multiple linear regression (MLR), and a multi-layer perceptron (MLP) in a 0.719 km² catchment monitored by three gauges. Two complementary evaluations were conducted. Station-wise leave-one-out cross-validation (LOOCV) assessed prediction at an omitted gauge, whereas a radar-referenced comparison assessed catchment-average estimates obtained from the complete gauge network. MLR produced the lowest LOOCV error (RMSE = 0.433 mm; CC = 0.777). In the radar comparison, MLP and MLR produced nearly identical RMSE values of 0.668 and 0.669 mm, respectively, and are therefore interpreted as practically similar rather than meaningfully different. All method rankings are conditional on the selected 60 h event, the three-gauge arrangement, and uncertainty in the radar reference. The findings demonstrate that station-omission performance and full-network areal estimation address different operational questions and should be considered together when selecting an interpolation method for extremely sparse networks. Full article

Focus-measure recommendations for smear microscopy are typically established on one specimen type under one reference definition and one scoring rule, leaving their transferability untested. We benchmark 32 handcrafted focus measures on 26,100 z-stacks spanning five smear-microscopy domains at native acquisition dimensions under one frozen protocol. A fixed ten-voter plurality consensus (REF-B) supplies an identical reference target for every candidate, and two further constructions, single-voter exclusion (REF-A) and a non-derivative four-voter consensus (REF-C), quantify how far the recommendation depends on that target. Ten criteria covering consensus localization, curve shape, perturbation response and kernel cost form a declared cross-domain score; clustered bootstrap resampling, alternative aggregation, weight perturbation and controlled image resampling separate sampling variability from sensitivity to evaluation policy. Gradient responses lead under every analysis. Variance of Gradient ranks first under the primary policy in all 1000 clustered bootstrap replicates and in 67.9% of 1000 sampled weight configurations, while Brenner Gradient attains the lowest consensus deviation, 0.072 planes, at the lowest measured kernel cost. Localization ordering is preserved exactly when the reference is rebuilt from non-derivative voters alone (Spearman ρ = 1.000), although the reference plane itself shifts substantially. The benchmark gives a reproducible, auditable basis for selecting focus measures under stated reference, scoring and image-sampling conditions. Full article

Pedestrian-detection research and pre-crash safety assessment predominantly represent upright pedestrians, although prone, supine, lateral, seated, crouched, kneeling, partially collapsed, and fall-transition states alter target geometry, visibility, sensor signatures, and intervention time. Cross-study comparison is further limited by inconsistent posture labels, data provenance, latency boundaries, uncertainty reporting, and vehicle-response assumptions. We developed NUP-REPORT 1.0 as a provisional reporting and benchmarking framework through a structured narrative synthesis of a 45-source derivation corpus covering epidemiology, sensing benchmarks, uncertainty and assurance methods, reporting-guideline methodology, and public safety protocols. A reconstructed decision ledger documented 46 candidate concepts: 30 were retained as checklist items, 10 were assigned to an extended descriptor set, and six were merged. Each retained item was mapped to supporting evidence and classified as universal core (n = 19), component-contingent core (n = 4), or conditional (n = 7). The framework comprises six domains, a scenario-coverage matrix, five non-overlapping event timestamps, detection-referenced stopping equations, and a 30-item checklist. A purposive feasibility audit of 20 publications, including the adjacent pedestrian-detection literature not designed specifically for non-upright evaluation, illustrated checklist use. Within this sample, target orientation and static-versus-transition state were each reported explicitly in five of 20 publications (25%); none of the 17 applicable papers reported both time-to-first-detection and detection distance, none evaluated confidence calibration, and none of the 20 reported independent-unit uncertainty intervals. Vehicle-response items were non-applicable to papers making no intervention claim. These observations are sample-specific and do not estimate field-wide reporting prevalence. NUP-REPORT is not a consensus standard, certification procedure, or safety score; it is a traceable Version 1.0 proposal for study design, retrospective audit, and stakeholder refinement. Full article

Radiation fields around collimated or shielded sources exhibit strong gradients whose accurate delineation is critical for worker protection and emergency response. Mobile robots can survey such fields, but they sample sparsely and irregularly along their trajectories, and it remains unclear which reconstruction method can be trusted, and where. Using a single dominant collimated source in a two-dimensional indoor setting, this study shows that the answer depends decisively on sampling geometry, and proposes a physics-guided Gaussian process (GP) that performs reliably under trajectory-constrained sampling. A tracked robot combining light detection and ranging (LiDAR)-based simultaneous localization and mapping (SLAM) with a $\gamma$ dose-rate detector surveyed a collimated Cs-137 field in seven independent runs, and all methods were evaluated under both random hold-out (interpolation near visited locations) and spatial block cross-validation (extrapolation into unvisited regions); truth-referenced evaluation against a dense reference field is provided by Poisson-sampled simulations, while experimental accuracy is cross-validated on held-out measurements. Under uniform sampling, a multilayer perceptron (MLP) robustly outperformed GP variants ($R^2=0.95$, stable across 18 seed combinations); under trajectory sampling, its advantage vanished at visited locations and reversed catastrophically in unvisited regions. The proposed physics-guided GP, which uses a fitted collimated-beam template as the GP mean with a Matérn 3/2 residual process, achieved the highest extrapolation $R^2$ (median $0.61$; best baseline $0.31$), reduced the extrapolation error by 32–69% relative to all eight baselines, recovered interpretable source parameters, and provided predictive intervals with approximately calibrated region-level coverage (point-wise error ranking remains weak); a runtime fit-quality gate further renders the correctness of the embedded prior an observable quantity, so the method flags when its own assumptions fail. These results offer quantitative guidance for method selection in robotic radiation mapping under the as low as reasonably achievable (ALARA) principle. Full article

Accurate and scalable driver performance assessment is critical for improving road safety and reducing traffic-related injuries and fatalities, particularly in low- and middle-income countries where the majority of global road deaths occur. This paper presents an exploratory proof-of-concept framework for automated driver evaluation that combines real-world dashcam footage, YOLOv8-based object detection, and multimodal large language models (MLLMs), specifically Gemini 1.5 Flash. Two prompting strategies, narrative and rule-based, were designed to assess driver behavior against standardized licensing criteria derived from the California Department of Motor Vehicles (DMV) driving performance evaluation score sheet. The framework was evaluated across 11 manually curated driving scenarios covering intersections, pedestrian crossings, stop signs, cyclists, and emergency vehicles. Ground-truth labels were established through consensus between two traffic engineering experts cross-referencing official California DMV evaluation criteria. In this preliminary evaluation, the rule-based prompt achieved higher agreement with ground-truth assessments (10/11 scenarios, 90.9%) compared to the narrative prompt (7/11 scenarios, 63.6%), particularly in detecting clear rule violations. The narrative approach demonstrated greater contextual flexibility in ambiguous situations. These results should be interpreted as preliminary, given the small sample size, manually curated dataset, and absence of large-scale statistical validation. Nonetheless, the findings illustrate how combining visual detection with structured language-model prompting may support interpretable, policy-aligned driver evaluation. Key limitations include dependence on video quality, limited scenario diversity, absence of temporal behavioral modeling, and reproducibility constraints tied to proprietary API behavior. Future work should expand validation to larger annotated datasets, incorporate temporal sequence modeling, and explore region-specific regulatory adaptation. Full article

Direct-to-cell (D2C) non-terrestrial networks (NTNs) based on 5G technology are emerging as a key complement to terrestrial cellular networks, extending connectivity to underserved and remote areas while enabling integration with existing mobile ecosystems. As these systems begin operating in frequency bands already used by terrestrial International Mobile Telecommunications (IMT) networks, coexistence becomes critical. This paper presents a victim-centric methodology for evaluating D2C interference into terrestrial 5G networks in the 694/698 MHz-2.7 GHz regulatory study range. Seven downlink carrier cases and four uplink carrier cases between 734 and 2620 MHz are evaluated. For a prescribed external interference-to-noise ratio, the external contribution is referenced to receiver thermal noise, while terrestrial intra-network interference remains part of the baseline and interfered signal-to-interference-plus-noise ratio (SINR). The resulting throughput loss is translated into candidate power-flux-density (PFD) and equivalent-power-flux-density (EPFD) protection levels. A reference non-geostationary-satellite-orbit (NGSO) system is used only to motivate the assumed receiver-exposure fractions; the numerical network results are therefore conditional on those exposure assumptions. The same external interference level produces approximately three times greater network throughput loss at base stations than at user equipment, so direction-specific protection levels are required. For downlink protection, the tested 3 dB noise-rise case gives candidate PFD levels from −109.23 to −98.17 dB(W/(m²·MHz)); for opposite-direction cross-border uplink protection, I/N = −6 dB gives candidate EPFD levels from −138.23 to −130.18 dB(W/(m²·MHz)) for non-AAS base stations. The approximately 11 dB offset for AAS cases results from the maximum-gain normalization used in EPFD and should not be interpreted as evidence of greater satellite exposure. These values are tested candidate levels rather than estimates of an exact 5% crossing point. Full article

Corrosion-severity grading of grounding-grid connectors from optical images supports proactive power-infrastructure maintenance. Existing approaches rely on single-time-point, manually thresholded hue–saturation–value (HSV) metrics and static multi-criteria decision-making (MCDM) frameworks that cannot capture corrosion dynamics. In this paper we present a pipeline that (1) defines a four-class corrosion grade from an HSV area fraction (S_corr) measured on RGBA optical images, and validates those labels against a baseline-referenced CIEDE2000 metric zero-referenced to each connector’s as-received appearance; (2) generates 240 color-prior-constrained procedural synthetic images from 53 real images across six connector types; (3) fine-tunes a ResNet-18 to estimate corrosion coverage continuously, deriving the reported severity class from that estimate rather than predicting it directly; and (4) fits power-law kinetics C(t) = k·tⁿ to the S_corr time series, propagates bootstrap uncertainty into a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) framework, and reports kinetics-aware rankings as rank probabilities. The label validation quantifies two limitations of single-threshold HSV grading: a material-color offset that scores an unexposed copper connector at S_corr = 0.442, and insensitivity to achromatic corrosion products covering roughly 80% of the aluminum and galvanized-steel surface. Ablation experiments replicated over five random seeds show that neither contribution claimed from a single run survives replication: synthetic augmentation changes macro-F1 by +0.050 (p = 0.46) under the adopted checkpoint-selection rule and by −0.059 (p = 0.43) under the rule used in the original experiments, and the monotonicity-consistency loss by −0.011 (p = 0.87) and +0.001 (p = 0.99) respectively; the previously reported single-run values of 0.208 and 0.494 are draws from opposite tails of the same seed distributions (0.403 ± 0.140 and 0.344 ± 0.073). The one formulation that improves significantly is the continuous one adopted here, which raises Spearman agreement with the independent metric from 0.316 ± 0.150 to 0.698 ± 0.108 (p = 0.005). Measured against controls, a classifier that never sees the image reaches macro-F1 = 0.425 and, after Holm–Bonferroni correction, no deep configuration is distinguishable from it; none exceeds a one-dimensional linear rule on S_corr (0.664); and under leave-one-material-out cross-validation the network does not improve on S_corr used directly as a predictor (ρ = +0.627 against +0.744, paired p = 0.14). Time-resolved energy-dispersive X-ray spectroscopy (EDS) provides a partial chemical consistency check, with welding at ρ = 0.82 (raw p = 0.023), but no material survives Holm correction across the six tested. A U-Net segmentation head supervised only by synthesis-derived masks attains Dice = 0.85 in-domain and collapses to a 0.033 output range on real images, 5% of the HSV metric’s range; the photometric-stability advantage previously claimed for it is an artifact of that collapse and is withdrawn. Kinetics-aware MCDM with propagated uncertainty resolves 9 of 15 pairwise orderings, placing stainless steel above welding at 30 chamber days with probability 1.000 and reversing the static ranking. The pipeline, code and fixed data split are fully reproducible (random seed 42). Full article

Ecological security pattern (ESP) planning typically emphasizes ecological structure and connectivity, but may provide limited information on long-term functional degradation occurring within ecologically important areas. To address this gap, this study develops a baseline-referenced framework for diagnosing ecological resilience degradation (ERD) by integrating ecosystem service value (ESV) dynamics, minimum cumulative resistance (MCR) modeling, and explainable machine learning (XGBoost–SHAP). Using the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) as a case study, ERD was operationally defined as long-term functional degradation within ecologically important components of the 2000 baseline network, with net ESV decline from 2000 to 2020 used as the functional-degradation signal. Two complementary ERD patterns were distinguished: source erosion and corridor interruption. The results showed that ERD exhibited a pronounced core–periphery pattern, with higher degradation intensity concentrated along the Guangzhou–Foshan–Dongguan–Shenzhen urban development corridor. Integrated ERD covered 5452.69 km², of which source erosion accounted for 4238.32 km² (77.73%) and corridor interruption for 1214.37 km² (22.27%). Source erosion represented the dominant ERD pattern in terms of spatial extent. The XGBoost model showed moderate predictive performance under spatial block cross-validation (mean validation R² = 0.638 ± 0.043), and SHAP analysis indicated that vegetation condition, proximity to water bodies, nighttime light, and elevation were among the most influential factors associated with spatial variation in ERD intensity. NDVI contributions shifted from positive to negative around 0.65, while the distance-to-water response changed most rapidly within 200–300 m. These values are interpreted as empirical transition ranges in the model response. Overall, the proposed framework links long-term ecosystem functional degradation with baseline ecological network position and provides a spatially explicit basis for identifying functionally degraded ecological areas and supporting differentiated spatial prioritization and ecological management. Full article

This study examined the cross-sectional associations among psychological distress, student engagement, academic burnout, and dropout risk, with particular attention to the indirect role of engagement. Questionnaire data were obtained from 3099 students across 31 departments at a single institution, the University of Patras, Greece. Competing measurement models were evaluated before structural estimation. Psychological distress was represented by a HADS bifactor model, engagement and burnout by higher-order structures, and dropout risk by an Academic-reference bifactor-(S−1) model retaining Personal, Institutional, Social, and Economic residual specific factors. The final model was estimated using robust maximum likelihood with full-information maximum likelihood and department-clustered robust inference, showing acceptable approximate fit. General psychological distress was negatively associated with engagement (β = −0.386), while engagement was negatively associated with burnout (β = −0.577) and Academic-referenced dropout risk (β = −0.664). Distress also retained positive direct associations with burnout (β = 0.375) and dropout risk (β = 0.244). Significant indirect associations through engagement were observed for burnout (β = 0.223) and dropout risk (β = 0.256). The findings indicate that engagement is strongly associated with both outcomes, while burnout and dropout risk retain distinct multidimensional measurement structures. Given the cross-sectional design, the indirect effects should be interpreted as statistical associations rather than evidence of causal mediation. Full article