Search Results (5,413)

Sand production constrains artificial-lift reliability, shutdown management, and energy efficiency. This study develops an integrated experimental framework linking particle settling, bulk sand transport, shutdown-related plugging, and electrical demand using 12 anonymized Kumkol wells. The database contains 4380 daily records, 720 laboratory tests, 84 shutdown/restart events, 3600 energy points, 4800 high-frequency restart samples, and 132 maintenance events. In 480 settling column tests, Stokes yielded R² = 0.937 and MAPE = 24.8%; one-parameter calibration improved R² to 0.981 and MAPE to 11.3%, with leave-one-well-out MAPE of 11.4%. In 240 flow loop tests, the full-data non-unstable threshold was 1.052 m/s; nested held-out well accuracy was 87.9%, and the strict stable criterion yielded 83.8%. Shutdown duration was the dominant field predictor: plug odds rose 3.87-fold per 10 h, and nested threshold validation yielded 78.6% accuracy. For ESPs, the ratio-based SEC minimum occurred near 0.773 m/s. A denominator-free active power model controlling for pressure rise, VFD frequency, sand concentration, and well effects achieved R² = 0.851 and retained a positive velocity coefficient in all leave-one-well-out fits. The results define a locally calibrated energy–transport operating window; laboratory transport thresholds are not claimed as direct field setpoints without hydrodynamic scaling and on-well verification. The flow loop threshold block comprises only four independent wells; accordingly, its held-out results are treated as small-cluster evidence, and the numerical velocities remain laboratory reference constraints rather than direct field settings. Full article

The wine sector is increasingly required to improve its environmental performance by reducing the consumption of key resources such as water and energy. However, these two dimensions are often analyzed separately, even though winery operations may generate interdependent demands for both resources. This study explores the water–energy nexus in the wine sector by investigating the relationships among annual water consumption, electrical energy use, outsourcing intensity and production configuration in a sample of 21 Italian wineries. A dedicated dataset was developed from company-level operational data, including winery reference models, outsourcing and packaging indices, production volumes, electricity consumption, water consumption and specific performance indicators. The analysis combined descriptive statistics on winery-average data with Pearson correlation analysis, exploratory pooled multiple regression, Principal Component Analysis and a log-linear mixed-effects model applied to 74 winery-year observations nested within 21 wineries. Pearson correlation analysis showed strong positive associations between electrical energy consumption and water consumption (r = 0.891), between wine outsourcing index (WOI) and electrical energy consumption (r = 0.796), and between WOI and water consumption (r = 0.780). The complete within–between mixed-effects model showed that a 1% higher-than-usual electricity use within a winery was associated with 0.595% higher annual water use (95% CI: 0.418–0.772; p < 0.001) after accounting for within-winery variation in annual production. In the conventional mixed-effects model, the corresponding electricity coefficient was 0.571 (95% CI: 0.406–0.735; p < 0.001), conditional on production and the other covariates. Given the strong collinearity between electricity and production (r = 0.955; VIFs = 11.6 and 13.2), the 0.571 estimate should not be interpreted as an independent electricity effect. These findings indicate that aggregate water and electricity consumption covary strongly across the sampled wineries and that this relationship persists after accounting for repeated winery observations and annual production volume. WOI and production configuration contribute to the structural interpretation of resource demand, although their conditional associations are less stable. The results support the joint assessment of absolute resource demand and specific performance indicators for more context-sensitive winery benchmarking and managerial decision making. Full article

Accurate estimation of aboveground biomass (AGB) is essential for sustainable pasture management, but remains challenging in heterogeneous mountain environments. This study evaluated a remote-sensing framework for estimating AGB across five landscape zones of the Aragats Volcanic Massif, Armenia, using PlanetScope imagery, terrain variables and machine learning. Biomass was measured in 30 plots comprising 90 nested quadrats during five field campaigns from April to July 2025. Predictor selection, algorithm comparison, preprocessing and tuning were performed within a fully nested leave-one-plot-out cross-validation framework. The model achieved an out-of-fold R² of 0.596 (RMSE = 54.0 g m⁻²; MAE = 33.1 g m⁻²) at the 20 × 20 m plot level, decreasing to 0.503 when the same predictions were evaluated against individual quadrats, which isolates the effect of field-observation support. Variance partitioning showed that weak zone-level performance arose from two distinct causes: dominant within-plot variability in the Meadow Steppe, and a strong soil background under sparse early-season cover in the Dry Steppe. Fine-resolution UAV imagery, acquired without reflectance calibration, did not improve prediction in a matched cross-sensor comparison. Applied to 18 pastures covering 1822.5 ha, the model yielded an aggregate campaign-date stock of 1651.45 t (95% uncertainty interval 1207.70–2092.80 t). Full article

Survival-rule induction is commonly guided by log-rank separation, whereas some prognostic questions target conditional future lifetime. We propose a directional finite-horizon mean residual life (MRL) criterion for survival-rule induction under right censoring, combining normalized subgroup support with a survival-weighted restricted-MRL discrepancy. The framework includes favorable and adverse objectives, training-only horizon selection and tuning, overlapping-rule prediction, and finite-candidate plug-in consistency. Across nine simulation scenarios (200 replications each), MRL recovered the true subgroup partition more accurately under delayed benefit, crossing hazards, delayed benefit with 60% censoring, and small-sample crossing; log-rank was stronger under proportional hazards, early-only effects, rare subgroups, and the adverse stress test. In repeated nested analyses, Cox proportional hazards achieved the lowest mean integrated Brier score (IBS) in WHAS100 (0.1797) and malignant melanoma (0.1300); controlled log-rank also yielded lower IBS than MRL (0.1926 vs. 0.2038 and 0.1358 vs. 0.1404, respectively). MRL nevertheless identified different conditional-lifetime structures. These results position MRL-guided rules as an estimand-specific complement to hazard-oriented methods rather than a universally superior predictor. Full article

Hyperspectral image classification (HSI) requires a model to distinguish subtle spectral differences while preserving the spatial structure of land-cover regions. CNN-based methods are effective for local spectral–spatial extraction, but their limited receptive fields can weaken broader context modelling. Transformer-based methods improve long-range dependency modelling, yet fixed patch partitioning may reduce their sensitivity to fine local structures. To address these limitations, this study proposes the Dual-Stream Spatial–Spectral Network with Nested Attention (DSSN), which separates local spectral–spatial feature extraction from multi-scale spatial-context modelling before adaptive fusion. The DSSN combines a cascaded 3D-CNN spectral stream, a nested Transformer spatial stream with pixel-level and patch-level interactions, and a channel-attention-based adaptive fusion module. Experiments on Indian Pines, Pavia University and Salinas show DSSN achieves overall accuracies of 98.11%%, 99.88% and 99.82%, respectively, outperforming other baselines. The ablation experiments confirm that each major component contributes to the final performance. Although the model requires more parameters and longer inference time than several compared baselines, its inference time remains at the millisecond level. These results suggest that decoupled spatial–spectral representation and adaptive multi-scale fusion can improve hyperspectral image classification under the evaluated benchmark settings. Full article

High-density urban renewal requires blue–green infrastructure (BGI) strategies that address surface warming and stormwater runoff under land constraints. This study examined how BGI area and interface configuration regulate water–thermal performance across three purposively selected 100 ha urban fabrics in Beijing. Multi-year Landsat observations and matched 30 m land-cover data quantified radiometric land-surface-temperature (LST) contrasts and distance gradients, while a transparent Python event-runoff model evaluated relative hydrological scenarios using wet-day rainfall-depth quantiles, storage, impervious-area capture, and terrain-informed connectivity. The evidence streams were compared using the scenario cooling–storage index (SCSI), descriptive half-response area (A50), and interface–area substitution ratio (SAR). Stable surface-cooling contrasts occurred in Xicheng and Daxing; Chaoyang supported only exploratory thermal comparison because mapped BGI was sparse. Hydrological outputs represented configuration-dependent simulated runoff-volume responses, not calibrated predictions. In equal-weight 100 ha analyses, bootstrap intervals supported SAR above one across 1–10 ha in Xicheng and 1–3 ha in Daxing, while nested 50 ha windows and thermal weights of 0.2–0.8 shifted transition ranges. Corridor A50 was not identifiable within the tested 1–10 ha range in Daxing, so corridor saturation remains unresolved. Interface compensation was therefore morphology-, scale-, and weighting-dependent. The framework enables conditional comparison of BGI configurations while preserving the distinction between observed thermal and scenario-based hydrological evidence. Full article

Urban CO₂ marginal abatement costs (MACs) provide important information for designing sustainable low-carbon transition strategies, but their interpretation may be affected by reference technology choices and identification uncertainty. Using 5004 city-year observations from 278 Chinese prefecture-level cities over 2006–2023, this study applies a Global non-radial directional distance function to compare National and four-region Group reference technologies under identical baseline settings. At each fixed frontier projection, we characterize the complete admissible range of supporting shadow prices rather than select a single dual solution and assess sensitivity across seven prespecified modeling dimensions. Under the National benchmark, 87.31% of observations are bounded-set identified. National and Group identified sets overlap in 74.34% of city-years, indicating that strict benchmark ordering is uncommon. Across baseline–alternative comparisons, identification status changes in 9.87% of cases, whereas National–Group direction reversals occur in 0.84%; temporal technology generates the largest identification response (25.92%). Baseline numerical diagnostics show successful primal–dual solutions, unique projections, and no nesting violations. The results support more transparent sustainability-oriented assessments of urban decarbonization by reporting MACs together with identification status, benchmark choice, and specification sensitivity rather than as unconditional scalar values. Full article

Background: Determining the appropriate magnitude of lumbar lordosis (LL) correction in adult spinal deformity (ASD) remains controversial. The fused spinopelvic angle (FSPA), a posture-independent parameter, may provide a geometric measure of fused-construct orientation associated with proximal junctional kyphosis (PJK) risk. Methods: This retrospective single-center study included 258 patients aged ≥65 years with ASD and pure sagittal malalignment associated with lumbar degenerative kyphosis/drop body syndrome who underwent long-segment fixation from T10 to the sacrum with sacropelvic fixation. Patients were divided into non-PJK (n = 135) and PJK (n = 123) groups. Adjusted nested logistic regression models, receiver operating characteristic (ROC) analysis, and DeLong comparison were used to evaluate the association of FSPA with PJK and its incremental value beyond postoperative pelvic incidence–lumbar lordosis (PI-LL). Results: Postoperative FSPA was lower in the PJK group (p < 0.001) and remained independently associated with PJK in the fully adjusted model including postoperative PI-LL (adjusted OR = 0.910 per degree; 95% CI, 0.866–0.957; p < 0.001). FSPA showed greater discriminatory ability than postoperative PI-LL (AUC, 0.694 vs. 0.596; difference, 0.099; 95% CI, 0.035–0.162; DeLong p = 0.002). Adding FSPA to the clinical model containing postoperative PI-LL significantly improved model fit (likelihood-ratio p < 0.001). The cohort-derived FSPA threshold of 2.38° yielded 64.2% sensitivity and 63.7% specificity. Conclusions: Higher postoperative FSPA was independently associated with lower odds of radiographic PJK and provided additional predictive information beyond postoperative PI-LL. The identified threshold of 2.38° may serve as a preliminary reference for postoperative alignment in this selected cohort. Full article

Nano-size-induced shifts in alloy phase-separation critical temperatures ($T_C^{nano}$) are investigated by introducing an atomistic concept of site-specific contributions to the shift (SSCS) associated with different atomic coordination environments in cuboctahedral and truncated-octahedral nanoparticles (NPs). $T_C^{nano}$ previously computed using the Free-energy Concentration Expansion Method (FCEM) for the transformation of three small quasi-Janus Pd-Ir NPs into mixed nanophases are extended here to a substantially broader set of 22 NP sizes, ranging from 147 to 49,049 atoms. This dataset provides the basis for the present modeling. The main objective is to elucidate the deviations of the critical-temperature shifts in small NPs from the finite-size-scaling (FSS) inverse-size power law previously proposed on the basis of non-atomistic thermodynamic modeling. Within the SSCS approach, these deviations are described in terms of contributions from face, edge, and vertex sites. The contributions are proportional to the fractions of the corresponding surface-site types and can be approximated by terms proportional to $n^{-1}$, $n^{-2}$, and $n^{-3}$, respectively, where $n$ is the number of nested atomic shells. The SSCS approach can also be applied to other phase transitions in nanoparticles of various shapes and sizes. Full article

Understanding the ecological drivers shaping animal foraging and interactions remains a central question in ecology. Among the most studied systems in this field are the ant–plant interactions, although disentangling the complexity of factors acting in different contexts remains a relevant question. This study investigated how habitat structure, liquid food rewards, and interspecific competition interact to modulate the foraging patterns of the abundant ant Camponotus termitarius on the rosette-shaped plant Eryngium chamissonis in the Brazilian Pampa, where ant–plant interactions are still poorly studied. We monitored 115 plants across three sampling events, measuring ant foraging, trophobiont abundance, vegetation density, plant size, and local nest distributions, and analyzed the relationships using Piecewise Structural Equation Modeling (pSEM). The pSEM revealed that surrounding vegetation density negatively affected C. termitarius nest density, nest extensions, and hemipteran trophobionts. Conversely, denser vegetation and larger plants favored the aggressive competitor Camponotus rufipes. While trophobiont presence and proximal nesting infrastructure directly facilitated C. termitarius activity, hostplant inflorescences promoted the construction of nest extensions on plants. We conclude that C. termitarius foraging is regulated by a multidimensional network where microhabitat complexity mediates spatial niche partitioning and competitive dynamics between sympatric ants. Full article

Background: Autism spectrum disorder (ASD) is often diagnosed well after developmental concerns first emerge, and evidence on factors associated with the duration of the recognition-to-diagnosis pathway remains limited in the Middle East. This study aimed to identify factors associated with the overall recognition-to-diagnosis interval and potential areas for earlier recognition, referral, and access to appropriate assessment. Methods: This multisite cross-sectional survey of 384 caregivers of children with confirmed ASD was conducted across Jordanian governorates. Diagnostic timing was known for 338 participants. Five sequential nested ordinal logistic regression models were fitted on a common complete-case sample (N = 299), successively adding background characteristics, recognition, caregiver response, help-seeking route, and access/professional response variables. Robustness was assessed via grouping-specific binary models, multiple imputation, and bootstrap resampling. Results: Caregivers reported first concerns at a median age of 2.0 years. Among those with known diagnostic timing, 60.7% were in the “6 Months to 1 Year” delay category or longer, 46.2% were in the “1–2 Years” category or longer, and 24.0% were in the “More than 2 Years” category. The background characteristics model showed limited explanatory capacity (Nagelkerke R² = 0.021), and adding recognition variables did not improve model fit (p = 0.562). Fit improved significantly with caregiver response (p = 0.001), help-seeking route (p = 0.008), and access/professional response (p < 0.001). The final model reached a Nagelkerke R² = 0.178, indicating modest overall explanatory capacity. Longer diagnostic delay was independently associated with caregivers who reported that early signs had initially not been acted upon because they were interpreted as part of normal development (AOR = 2.21). It was also associated with first contact via a speech/learning center (AOR = 2.27) or other service (AOR = 2.72) rather than a pediatrician. Caregiver-reported previous professional reassurance that the child did not have ASD was also associated with longer delay (AOR = 2.18). Professional reassurance was the most consistent correlate across sensitivity analyses. Definite appointment difficulty showed a significant Yes-versus-No contrast (AOR = 1.81), although the appointment difficulty variable was not statistically significant in the global test. Sociodemographic factors showed no independent association. Among 11 exploratory barriers, only prior misdiagnosis survived multiplicity correction (AOR = 2.21). Conclusions: In this Jordanian cohort, the length of the recognition-to-diagnosis interval was associated with factors operating after developmental concerns were first recognized, rather than with the timing or breadth of recognition itself. Caregiver response, entry route into care, and professional response emerged as potentially important pathway markers. However, the modest explanatory capacity of the final model indicates that substantial variability in diagnostic delay remains unaccounted for by the measured variables. These findings support provider- and system-level measures, including clearer referral pathways, explicit follow-up when reassurance is provided, improved appointment access, and expanded diagnostic capacity, complemented by caregiver-facing information and support. Full article

Groundwater is the main source of drinking, irrigation, and ecological water in the Baiyangdian Lake Basin (BLB), but growing human activity has raised concerns about its quality. This study assesses major ion geochemistry and multi-receptor environmental risks across the BLB using 1113 groundwater samples (983 shallow, 130 deep). Risks to drinking water supply, agricultural irrigation, and wetland health are evaluated within a single framework, and Positive Matrix Factorization (PMF) is used for quantitative source apportionment. Shallow groundwater shows markedly higher dissolved solids and nitrate than deep groundwater, with 20.7% of shallow samples exceeding the WHO nitrate guideline. Multi-receptor assessment finds that 41.2% of shallow wells pose risk to at least one endpoint and 12.5% to two or more simultaneously. PMF identifies three sources—geogenic weathering, agricultural nitrate, and wastewater discharge—with nitrate as the largest single contributor to water quality impairment. The findings point to an urgent need for targeted agricultural non-point source control and provide a basis for nested, multi-scale groundwater management in the BLB and similar intensively exploited alluvial aquifers. Full article

Background: HIV-related stigma and discrimination remain barriers to equitable HIV prevention, treatment, and well-being in Thailand despite their longstanding inclusion in the national HIV response. This study examined contemporary experiences of stigma and discrimination among people living with HIV. Methods: The people living with HIV Stigma Index 2.0 was a community-led mixed-methods study conducted from August 2022 to January 2023. The quantitative survey enrolled 2508 people living with HIV from 24 provinces covering all 13 health regions, including general adults living with HIV, four key population groups, migrant workers, and young people. A nested qualitative study included 174 in-depth interviews across six population groups. Results: Sixteen percent of participants reported HIV-related discrimination in healthcare services in the previous 12 months, 4.7% reported community stigma in the past 12 months, 39.0% reported internalized stigma, and 2.7% experienced rights abuse in the past 12 months. Younger people living with HIV reported significantly higher levels of healthcare discrimination, community stigma, internalized stigma, and rights abuse than older participants and the general people living with HIV reference group. Compared with general people living with HIV, sex workers and people who use drugs reported significantly higher levels of community stigma; men who have sex with men, sex workers, people who use drugs, and migrant workers reported significantly higher internalized stigma; and transgender people and sex workers reported significantly higher levels of rights abuse. Qualitative findings highlighted anticipated and enacted stigma, confidentiality concerns, internalized stigma, intersecting identities, rights violations, and resilience. Conclusions: HIV-related stigma remains evident in healthcare, community, and personal domains. Younger people living with HIV and key populations were particularly vulnerable to HIV-related stigma, discrimination, and rights abuse. Responses should combine updated public communication, stigma-free and person-centered healthcare, protection of privacy and rights, support for internalized stigma and mental well-being, and sustained leadership of people living with HIV. Full article

Reliable prediction and interpretation of photosynthetic rate are important for precision environmental management in greenhouse tomato production, but model stability is often limited by variable redundancy, measurement-session effects, and environmental heterogeneity. This study developed an integrated gas-exchange-data-based framework for key-factor selection, photosynthetic-rate reconstruction, cross-session validation, environmental correction, and physiology-informed limitation screening. Core predictors were first identified from high-dimensional gas-exchange variables using K-means clustering and random-forest importance analysis. A CatBoost–ExtraTrees–RBF-SVR stacked ensemble was then constructed to reconstruct the leaf photosynthetic rate from selected gas-exchange variables, and its cross-session generalization was evaluated using nested cross-validation and leave-one-file-out (LOFO) extrapolation. Environmental correction was further applied to improve cross-session comparability, and rule-based limitation screening was used to classify potential environmental constraints associated with reduced photosynthesis. The stacked model achieved an RMSE of 0.806 and an $R^2$ of 0.9918 under nested cross-validation, and an RMSE of 0.814 and an $R^2$ of 0.9917 under LOFO extrapolation. After environmental correction, the cross-session variability of the environmentally corrected photosynthetic indicator was reduced by 96.0%, reflecting improved cross-session comparability. Under deployable sensor inputs, performance decreased markedly (A1: RMSE = 4.578, $R^2$ = 0.735; A2: RMSE = 4.610, $R^2$ = 0.731), compared with the gas-exchange baseline (RMSE = 0.833, $R^2$ = 0.991). Thus, routine greenhouse sensor models should be regarded as approximate rather than high-accuracy substitutes for the gas-exchange-based model. Full article

Predicting driver-state evolution in extra-long tunnel corridors remains challenging because of prolonged spatial confinement and repeated lighting transitions. This study uses a statistical human–vehicle composite, the comprehensive driving index (CDI), as a reproducible quantitative target for predictive auditing. In this study, multimodal information denotes synchronized ocular, physiological, vehicle-motion, and environmental sensor signals; the objective is to quantify their incremental predictive value rather than introduce a new fusion architecture. Fully nested leave-one-driver-out cross-validation with a prespecified 120 s unsupervised initialization estimated all preprocessing, scaling, PCA, model-selection, and calibration parameters from training data only. The five components explained 60.36% of target variance. In the original-range 30 s task (4835 evaluation windows), history-only ridge regression achieved an RMSE of 0.08294 and an R² of 0.166, while directly tuned AR achieved an RMSE of 0.08261 and an R² of 0.168. On the common 4259-window sample, expanded ridge and AR achieved RMSEs of 0.08123 (R² 0.187) and 0.08153 (R² 0.182). Adding coarse scene information produced an ΔRMSE = +0.00002 (95% CI −0.00026 to 0.00027), whereas external environmental summaries produced an ΔRMSE = −0.00019 (95% CI −0.00037 to −0.00003). HistGradientBoosting did not improve performance. The primary contribution is a leakage-controlled predictive-audit framework for screening candidate information sources before deployment decisions. Full article