Search Results (24,984)

Emission inventories at fine spatial and temporal scales were developed for light alkanes, volatile organic compounds (VOCs), and nitrogen oxides (NO_x) from upstream and midstream oil and gas operations in the Permian Basin oil and gas production region for 2022–2024. The inventories were spatially aggregated at basin, county, and 12 km by 12 km grid cell levels, and temporally resolved at hourly resolution, with underlying methods capable of generating inventories at other spatial and temporal scales. Spatial and temporal variability in emissions in the Permian were compared at various spatial scales with inventories for the Marcellus oil and gas production region, developed using the same methods. Emission sources that drive spatial and temporal variability differ by regional production characteristics, the level of spatial aggregation, and emitted species. Temporal variability in emissions decreases as the scale of spatial aggregation increases. Among counties with at least 10 active producing wells, maximum-to-annual-average hourly emission rate ratios reached 2.5 for methane, 2.8 for VOCs, and 2.3 for NO_x. At the 12 km by 12 km grid cell level, the corresponding maximum ratios were 33.7, 26.5, and 13.9. These ratios illustrate the magnitude of short-term emission variability and the extent to which peak hourly emissions can exceed annual average estimates, with potential implications for episodic air-quality impact assessment. Compared with the gas-dominated Marcellus Basin, the oil-dominated Permian Basin shows lower temporal variability in hydrocarbon emissions due to fewer episodic gas production related sources (e.g., liquid unloadings) and a greater contribution from near-continuous oil production related sources (e.g., associated gas venting and tank flash). In contrast, NOₓ emissions exhibit higher temporal variability in the Permian due to more frequent preproduction activities associated with new well development. The spatially and temporally resolved emission inventories by source category and chemical species can be further combined with chemical transport modeling and air quality modeling to support assessment of regional air quality events, such as localized and episodic ozone formation. Full article

Metal oxide semiconductor (MOS) gas sensors are widely used for low-cost chemical detection, but their practical performance is still limited by high operating temperature, insufficient selectivity, signal drift, and device-to-device variation. Recent advances in microelectromechanical systems (MEMS), dynamic sensing protocols, and artificial intelligence (AI) provide new opportunities to improve MOS gas sensing from both hardware and data-processing perspectives. MEMS micro-hotplates enable miniaturized devices, low-power heating, rapid thermal control, temperature-modulated operation, and compatibility with integrated readout and interface circuits, while AI methods extract multivariate, nonlinear, and temporal information from cross-sensitive sensor responses. This review summarizes the fundamentals of MOS sensing materials, MEMS micro-hotplate platforms, material–device integration strategies, and AI-assisted data-processing methods ranging from classical statistical analysis to deep learning. Representative strategies are discussed, including single-sensor feature extraction, sensor-array recognition, temperature-modulated sensing, drift compensation, and AI-guided material design. Application studies in food quality assessment, agriculture, medical diagnostics, environmental monitoring, and public safety are further reviewed to show how sensing tasks evolve from odor-fingerprint discrimination to nonlinear feature interpretation, dynamic response analysis, domain adaptation, and deployable intelligent monitoring. Particular attention is given to the role of high-consistency integration of MOS sensing layers on MEMS platforms, since reproducible material loading, morphology, electrode coverage, and thermal coupling are essential for reliable datasets and transferable AI models. Finally, key challenges are discussed, including dataset heterogeneity, long-term drift, edge deployment, and material–device reproducibility. This review highlights that future AI-assisted MOS/MEMS gas sensors require coordinated design of sensing materials, device platforms, fabrication processes, operating protocols, and data-driven models. Full article

Wearable Inertial Measurement Units (IMUs) are suitable for kinematic analysis in uncontrolled environments. Nonetheless, modeling multi-joint coordination during the execution of functional tasks remains a challenge. In this study is presented a kinematic modeling approach for assessing upper-limb inter-joint kinematic coordination dynamics, designed to: (1) characterize the spatial variance structures and temporal variability during both isolated movements and functional Reach-to-Grasp (RTG) tasks and, (2) determine if these spatial kinematic synergies allow prediction of the stability of temporal coupling. For this purpose, thirteen healthy participants performed five predefined movements and an RTG task while monitored by four wireless IMUs (60 Hz). Joint kinematics were modeled using quaternions; spatial coordination patterns were extracted using Principal Component Analysis (PCA), and their inter-joint temporal stability was quantified via Continuous Relative Phase (CRP) variance. Kinematic analysis shows a high spatial variance of the structure during the evaluation of isolated analytical tasks (<!-- MathType@Translator@5@5@MathML2 (no namespace).tdl@MathML 2.0 (no namespace)@ --> Full article

High-spatiotemporal-resolution evapotranspiration (ET) is critical for precision irrigation management and water resource regulation. Regarding the existing spatiotemporal fusion methods suffering from sparse high-resolution observations and coarse land surface temperature (LST), this study took winter wheat in Luancheng District, Hebei Province, as the research object, and proposed a remote sensing ET retrieval method based on the LST sharpening model. The Data Mining Sharpener (DMS) algorithm combined with Sentinel-2 multispectral data was used to downscale MODIS LST from 1000 m to 10 m, with auxiliary variables (DEM, albedo, NDVI, land cover) integrated into the Cubist regression tree to improve the physical rationality and spatial details of MODIS LST. The 10 m resolution ET was estimated from 10 m sharpened LST and Sentinel-2 multispectral data using the surface energy balance model, and the unmixing–weight ET image fusion model (UWET) was adopted to fuse the 10 m resolution ET with MODIS low-resolution ET to generate a daily 10 m ET dataset covering the entire winter wheat growing season. Validation with eddy covariance flux measurements showed that the correlation coefficient R = 0.921, RMSE = 0.779 mm/day during 2019–2020, and R = 0.900, RMSE = 0.831 mm/day during 2020–2021. The results demonstrate that auxiliary variables significantly enhance the spatial reality of LST, LST sharpening effectively improves the spatial heterogeneity of ET, and Sentinel-2 data compensates for the temporal deficiency of Landsat, thereby greatly promoting the accuracy of spatiotemporal fusion. This method can provide reliable high-spatiotemporal-resolution data support for refined farmland irrigation management and water resources regulation. Full article

Concentrated sourcing is a structural property of the world economy rather than an occasional accident: across 168 national economies and 1118 four-digit product markets reconstructed from harmonized cross-border flow records, 27.7% of macro-level economy–product supply systems draw more than half of their imports from a single origin and 19.3% are critically dependent. Treating each such market as a system rather than as a set of bilateral links changes what can be asked of it, and this paper specifies the economy–product supply system in systems-engineering terms—boundary, elements, internal relations, external environment, state and state transition—and represents it as a time-evolving hyperedge over source countries. Three coupled questions follow, answered jointly by HyperSRM: which dependency state a system will occupy next year, whether it will diversify, reconcentrate, hold, or merely substitute one origin for another, and which origins are most consistent with the observed conditions preceding a material entry. Shared country and product embeddings support two temporal set-encoding branches, a candidate-conditioned branch for origin ranking and a candidate-free branch for state and mode forecasting, a sign-constrained gravity–capability–connectivity prior supplies an observational plausibility score with end use and maritime reachability as its context, and risk weights derived from the state head direct effort toward the most exposed systems. Developed on CEPII BACI, rebuilt independently on Eurostat Comext and audited against U.S. Census data at the level of the labels themselves, the framework returns calibrated state probabilities and a ten-origin shortlist that captures 58.1% of the following year’s risk-weighted material-entry mass and is accompanied by explicit out-of-pool diagnostics. These outputs describe the import-sourcing layer of resilience and are intended for analytical triage rather than a complete assessment of supply resilience. Three system-level regularities carry beyond the model: the arrival of a new origin is a weak proxy for diversification, critical dependency is close to absorbing for specified intermediate inputs but not for final goods, and the 2020–2021 contraction rearranged source sets without widening them—so resilience monitoring built on source counts misreads the direction of change. Full article

Background and Objectives: Bile duct injury remains a major safety concern during laparoscopic cholecystectomy, and reliable interpretation of hepatocystic anatomy is fundamental to safe dissection. Artificial intelligence (AI)-based computer vision may support anatomical recognition, critical view of safety (CVS) assessment, and intraoperative decision support. This narrative review synthesized the current evidence, clinical applications, readiness for implementation, and future requirements of anatomy-aware AI during laparoscopic cholecystectomy. Materials and Methods: Five bibliographic databases were searched through 15 August 2026, supplemented by citation tracking and targeted searches. Studies were classified according to clinical task, dataset, reference standard, validation design, performance metrics, real-time capability, human factor assessment, and clinical readiness stage. The evidence base comprised 105 verified references: 50 primary AI reports and 55 contextual or methodological sources; 39 direct model development or evaluation reports were characterized in detail. Results: Investigated applications included landmark detection, semantic segmentation, CVS assessment, safe and hazard zone mapping, multimodal analysis incorporating indocyanine green fluorescence, automated documentation, education, and real-time perceptual prompting. Among the 39 direct reports, 24 remained at the offline proof of concept or internal validation stage, eight achieved temporal, external, or multicenter validation, six demonstrated prospective operating-room feasibility, and one reached post-deployment surveillance. The latter evaluated a surgical-process outcome rather than patient morbidity. Generalizability was constrained by dataset overlap, heterogeneous reference standards and metrics, domain shift, and underrepresentation of difficult cholecystectomy. No included study demonstrated reduced bile duct injury or other patient-level benefit. Conclusions: Despite progression to prospective feasibility and one post-deployment process surveillance report, no included study demonstrated a reduction in bile duct injury or another patient-level outcome. Current evidence supports adjunctive applications in documentation, video triage, education, coaching, and quality assurance, while surgeon-facing deployment requires further multicenter, human factor, and comparative-effectiveness evaluation. Full article

Background/Objectives: It is uncertain whether intraoperative blood pressure (BP) summaries improve transportable prediction of prolonged postoperative length of stay (PLOS) after lung resection. We developed and temporally validated baseline and BP-enhanced models and evaluated their unchanged performance in the Korean VitalDB cohort. Methods: This retrospective prediction-model study used 536 local patients (249 PLOS events) for development and 184 later patients (74 events) for temporal validation. External validation included 626 procedure-restricted VitalDB patients; paired model comparisons used 624 patients with all four BP features. PLOS was a prespecified operational outcome defined as postoperative stay >4 days. L2-penalized logistic models were assessed using nested cross-validation, discrimination, calibration, Brier score, and decision-curve analysis. Results: Baseline-model AUROCs were 0.714 (95% confidence interval [CI], 0.667–0.756) internally, 0.719 (0.640–0.788) temporally, and 0.623 (0.579–0.666) externally. Corresponding BP-enhanced AUROCs were 0.721, 0.725, and 0.615. BP features changed temporal AUROC by +0.006 (95% CI, −0.009 to +0.021) and external AUROC by −0.007 (−0.022 to +0.008). Temporal calibration slopes were 1.113 and 1.112, whereas external slopes were 0.570 and 0.534. Sensitivity analyses showed no consistent BP increment. Conclusions: The baseline model retained similar discrimination locally but had limited unchanged external transportability. Four intraoperative BP summaries provided no reproducible incremental predictive value. Neither model is ready for direct cross-center implementation without harmonization, updating, and further validation. Full article

Environmental variables and eyrie site characteristics may influence reproductive output, yet their associations with Barbary falcon (Falco peregrinus pelegrinoides) productivity remain poorly understood in the Arabian Peninsula. We quantified fledgling production at 59 monitored eyries in northwestern and southwestern Saudi Arabia during the 2026 breeding season. Poisson generalized linear models evaluated February–May climatic conditions, pair type, eyrie cliff–wind alignment (CosDiff), and elevation and eyrie height where available. Monthly analyses for February–April and COM–Poisson sensitivity models assessed temporal consistency and robustness to departures from Poisson equidispersion. Mean productivity was 1.97 fledglings per eyrie (SD = 1.25; range = 0–4). In the best ranked additive model, a one standard deviation increase in seasonal mean wind speed (0.678 m s⁻¹) was associated with 19.5% lower expected productivity (IRR = 0.805, 95% CI = 0.660–0.982, p = 0.032), and more direct eyrie cliff alignment with incoming prevailing wind was associated with 18.7% lower expected productivity (IRR = 0.813, 95% CI = 0.677–0.976, p = 0.026). Monthly wind estimates were comparable across February–April. These findings may inform preliminary release site evaluation, but multi-year studies with cliff-scale measurements are needed to determine whether these associations persist across breeding seasons. Full article

Synthetic aperture radar (SAR) imaging is vulnerable to deceptive jamming, while conventional whole-aperture subsection shift-frequency modulation sequentially applies multiple frequency shifts in slow time, causing reduced temporal support for each component as the number of designed false targets increases. To overcome this limitation, this work proposes a metasurface-enabled spatial-block subsection shift-frequency modulation method for SAR deceptive jamming. The metasurface aperture is divided into independently controlled spatial blocks, where each block performs an individual subsection shift-frequency sequence within the same synthetic aperture interval. A coherent SAR echo model incorporating spatial-block responses, aperture weighting, complex superposition, and measured static amplitude–phase characteristics is established. Three complete SAR imaging cases are considered, including the unmodulated case and the whole-aperture and spatial-block modulation cases constrained by the measured static response. Component-level control cases are further introduced to separate the effects of participating aperture and temporal support. Although incomplete phase coverage and reflection-magnitude variations introduce additional sidelobes and amplitude imbalance, the dominant false-target positions remain consistent with theoretical predictions. These results verify the effectiveness of spatial–slow-time parallel modulation for enhancing the controllability of metasurface-enabled SAR deceptive jamming. Full article

Soil fertility in mown-grazed grasslands on the Qinghai–Tibet Plateau reflects hydrothermal conditions, terrain, grassland type, and management disturbance. We developed a soil fertility index (SFI) from 1037 topsoil samples collected at 0–30 cm during 2023–2025. The modelling framework combined static ecological background variables, conventional climate indicators, multi-year seasonal hydrothermal statistics, soil hydraulic attributes, and self-supervised temporal embeddings. Spatial-block, year-held-out, and ecological-zone-held-out validation were used with area-of-applicability (AOA) analysis and spatial-block conformal prediction. Multi-year seasonal hydrothermal statistics supplied the strongest predictive information and increased spatial-block R² by 0.2435 relative to the static-background model. A 10-year seasonal window gave the best empirical balance among explained variance, prediction error and rank consistency. The final deployment model achieved R² = 0.5632, RMSE = 0.1105, Spearman = 0.4473 and AUC = 0.696 under spatial-block validation. These values support regional screening and sampling prioritisation, not local deterministic diagnosis or site-level management prescriptions. Grid prediction identified Zone 2 as the main concentration of relative low-SFI risk. Full-sample AOA schemes covered 82.9%, 81.9% and 85.9% of the system-evaluation grid cells, whereas deployment-grid AOA coverage was lower under the stricter deployment setting. This contrast separates feature-space support from operational grid support. Split conformal prediction achieved 90% coverage close to the nominal level (PICP = 0.899, MPIW = 0.264). Natural-background residuals separated relative low-SFI risk from local deviations below expected natural conditions. The framework provides a reproducible screening tool for regional prioritisation and follow-up field verification in alpine grasslands. Full article

This PRISMA 2020 systematic literature review analyzes efficient transformer-based video object detection (VOD). Across five databases through August 2025, 1343 records were identified, with 16 studies meeting the final inclusion criteria after full-text screening. Backward citation tracking added five additional eligible studies, yielding a small final evidence base of 21 studies (22 reports). This review’s major contributions are (i) a focused narrative synthesis at the intersection of efficient model design, temporal modeling, and video object detection transformers, due to methodological heterogeneity in the corpus; (ii) ROB-CVA, a proposed risk-of-bias framework for computer vision; and (iii) an evidence-based analysis of temporal modeling strategies and evaluation practices. Furthermore, we examine evaluation inconsistencies, including dataset fragmentation and risk-of-bias. Fifty-two percent of studies were rated as low risk-of-bias using the review-specific, non-validated ROB-CVA framework, driven mainly by non-public code and datasets; this distribution is sensitive to our risk-of-bias aggregation rule. The certainty of evidence is low to moderate. The review outlines open challenges and future directions. This review is registered on OSF (doi: 10.17605/OSF.IO/Q9PJ4), supported by project AEOLUS (PHD IN INDUSTRY/1123/0145), and funded by the Republic of Cyprus through the Research and Innovation Foundation. Full article

The clinical integration of artificial intelligence has outpaced the development of robust evaluative frameworks, raising critical safety concerns. This perspective establishes a clear taxonomy distinguishing probabilistic language models from deterministic classifiers and applies a multi-dimensional combinatorial model to calculate the requirements for complete diagnostic coverage. Our analysis demonstrates that comprehensive diagnostic coverage requires between 50,000 and 150,000 distinct, task-specific classifiers under subspecialty-level clinical granularity; conservative aggregated estimates (4500–18,750 binary classifiers) do not reflect the multiplicative expansion introduced by subtype differentiation, severity staging, temporal variants, demographic stratification, and equipment variation, whereas currently cleared devices cover less than one percent of this clinical space. More fundamentally, although population-trained models can generate conditional patient-specific risk estimates when predictors are informative and calibration is adequate, these statistical parameters optimized on population-scale data cannot provide the categorical certainty required for individual diagnostic decisions, which is a gap that clinical judgment must bridge. Because clinical AI tools are inherently statistical and perform reliably only on common, highly represented presentations while failing on rare, atypical cases rare in their training data, attempting to automate routine tasks leaves human clinicians with only the most challenging diagnostics. Furthermore, selective automation of these low-complexity cases introduces severe occupational hazards, including cognitive surrender, diagnostic complacency, and rapid expertise atrophy. Rather than pursuing the computationally and logistically unfeasible goal of complete diagnostic classification, developers should prioritize predictive, prognostic trajectory modeling. This paradigm shift aligns the probabilistic nature of machine learning with clinical utility, reinforcing clinical judgment as the irreplaceable diagnostic integrator. Full article

Global Navigation Satellite Systems (GNSS) positioning accuracy is affected by multiple error sources, including atmospheric delays, multipath propagation, and receiver noise, which can significantly reduce positioning reliability in low-cost receivers. This study investigates the use of a feedforward neural network to compensate for positioning errors in a static GNSS receiver scenario. A synthetic dataset was generated in MATLAB/Simulink by simulating positioning perturbations around a known reference location. Consecutive coordinate differences were used as input features, and a compact feedforward neural network with 45 hidden neurons was trained using the Levenberg–Marquardt algorithm to estimate positioning error components. The proposed approach was evaluated through residual error distribution, regression, temporal dispersion, and spatial scatter analyses. The results indicate that, for the primary 10 m error scenario, neural network-based compensation reduced temporal dispersion by approximately 46% and produced a more compact spatial distribution of corrected positions around the reference location. The residual errors remained concentrated near zero, indicating improved positioning consistency under the investigated simulation conditions. Sensitivity analysis across nominal error radii of $R_{95}$ = 1, 5, 10, 15, and 20 m showed consistent reductions in both RMSE and standard deviation for radii of 10 m and above, whereas no consistent improvement was observed at lower error levels. In a preliminary comparison with random forests, XGBoost, Long Short-Term Memory (LSTM), and Gated Recurrent Unit models using the same training, validation, and test samples, the Feedforward Neural Network (FNN) achieved competitive test MSE while requiring substantially less training time and runtime memory than the LSTM. These findings support the proof-of-concept feasibility of lightweight FNN-based correction for simulated static GNSS positioning. Future work will focus on validation using real GNSS measurements and extension to dynamic positioning applications. Full article

Background/Objectives: First NIBUT, Average NIBUT, and tear meniscus height (TMH) are routinely used to characterize tear film stability and tear volume at individual visits, whereas their longitudinal behavior across the hospital-attending population is less well characterized. Aggregating routine examinations over time may provide a continuous hospital-level view of ocular surface status and enable the short-term forecasting of expected trajectories. We therefore evaluated a benchmark-first framework for monthly aggregated adult ocular surface indicators. Methods: This retrospective time-series study used de-identified adult eye-level Keratograph 5M records from July 2018 to November 2023. After cleaning, 31,492 records from 13,749 patients and 15,334 examination occasions were aggregated across 65 calendar months (64 observed months; March 2020 had no eligible records). Seven benchmark models and two exploratory deep learning comparators were evaluated using eight rolling-origin 3-month test windows. Results: Linear trend had the lowest mean origin-level macro-normalized RMSE (0.875; 95% bootstrap CI, 0.497–1.421), followed by simple exponential smoothing (0.907) and SARIMA(1,0,0)(1,0,0,12) (0.940). The paired difference between linear trend and simple exponential smoothing was small and did not show clear superiority (mean difference, −0.032; 95% bootstrap CI, −0.217 to 0.135; p = 0.789). The model with the lowest pooled error differed by target, while patient-month and sample-size-weighted sensitivity analyses gave a similar overall benchmark pattern. The exploratory LSTM and Transformer did not show a consistent advantage over the leading simple models. Conclusions: In this short hospital-level monthly series, simple forecasting models remained competitive, while no single model showed consistent superiority across forecast origins and sensitivity analyses. By extending ocular surface assessment from isolated examinations to longitudinal hospital-level trajectories, this framework provides a methodological basis for monitoring temporal changes in tear film stability and tear volume and for future quality monitoring, clinical, and epidemiological applications. Full article

Activity–travel pattern reconstruction infers latent paths, destinations, activities, and timing from incomplete mobility observations and supports travel-demand analysis and activity-based simulation. A structured search and citation tracking identified 157 core studies. Existing studies, however, remain fragmented across data sources, local reconstruction tasks, modeling techniques, and evaluation settings. This survey develops an integrated framework linking observation mechanisms, mathematical models, real-data applications, and performance evaluation. It first formulates reconstruction as inference over a latent activity–travel chain conditioned on partial observations and contextual information. Major mobility data sources are then compared according to their Eulerian or Lagrangian observation mechanisms and their spatial, temporal, and semantic information. Reconstruction methods are organized into model-driven, data-driven, and hybrid approaches, with emphasis on their mathematical structures, real-data applications, and ability to represent network, temporal, behavioral, and uncertainty constraints. Evaluation methods are reviewed at the element, chain, and population levels, while distinguishing missing-only performance from full-output performance. This review identifies four priorities for future research: joint reconstruction of complete chains, principled multi-source data fusion, calibrated uncertainty representation, and transferable benchmarks with realistic missingness and independent testing. This framework clarifies the current state of the field and supports the development of more reliable and behaviorally meaningful reconstruction methods. Full article