Search Results (139)

The biological treatment risk associated with wastewater containing the novel pigment intermediate N,N′-(1,4-phenylene)bis(acetoacetamide) has not been previously characterized. This study systematically evaluated the tolerance and performance of a laboratory-scale anaerobic–anoxic–oxic (AAO) system subjected to progressively increasing loadings of high-concentration (COD > 10,000 mg·L⁻¹) wastewater. During a 39-day trial, the influent proportion was incrementally increased from 0.57% to 52.14% without system collapse. Complete microbial adaptation required approximately seven days. The anaerobic unit exhibited the highest sensitivity to shock loads, followed by the oxic unit, while the anoxic unit remained stable. GC-MS analysis confirmed the degradation of complex organic intermediates throughout the treatment stages, and TEST-based predictions indicated that the effluent exhibited lower predicted toxicity than the influent. Notably, cessation of mother liquor addition resulted in system self-recovery, further demonstrating robust shock resistance. This study provides the first experimental evidence of (i) unit-specific shock sensitivity (anaerobic > oxic > anoxic), (ii) a quantified adaptation period of approximately seven days, (iii) an operational threshold of 52.14% mother liquor without causing system collapse, and (iv) self-recovery following load cessation in an AAO system treating wastewater containing N,N′-(1,4-phenylene)bis(acetoacetamide). These findings extend previous AAO toxicity studies on industrial wastewater and present a practical, cost-effective mitigation strategy for full-scale applications. Full article

Overhead transmission conductors are flexible cable structures. Their initial equilibrium configuration is affected by self-weight, tension, boundary constraints, and material deformation, and is required for force analysis, sag calculation, and safety assessment. Existing studies use catenary theory or numerical iterative methods. The direct iterative method is used in conductor form-finding. However, its geometric update ratio is assigned in segments based on empirical thresholds. This may cause unsmooth updates, low efficiency, and numerical instability in sensitive cases. This study investigates a single-span conductor within a nonlinear finite element form-finding framework. The direct iterative method is analyzed in terms of control variables, implementation process, and update-ratio control mode. A continuous error-driven adaptive geometric update strategy is proposed and an adaptive direct iterative method is developed. The two methods are compared under the same finite element model, parameters, loads, constraints, convergence threshold, and maximum iterations. Three factors are selected: element number, nonlinear substep number, and initial strain coefficient. A total of 27 full-factorial cases are calculated. Convergence efficiency, final error, and abnormal case distribution are evaluated. The results show that the proposed method reduces iterations, improves computational efficiency, and enhances numerical stability in sensitive cases without changing the finite element solution framework. Full article

Wind turbine main bearings are critical drivetrain components whose operating status directly affects the stability and safety of the entire unit. However, traditional unsupervised health monitoring methods suffer from difficulty in capturing early weak faults, low anomaly detection sensitivity, and inability to fully exploit axial–radial vibration coupling characteristics. To address these issues, this paper proposes an Axial–Radial Coupling-aware Informer (ARC-Informer) for unsupervised main bearing health monitoring. First, 20 time-frequency domain features are extracted from each of the axial and radial vibration signals and concatenated into a 40-dimensional coupled health feature vector. A cross-attention-based coupling enhancement module with residual fusion explicitly models the dynamic interaction between the two directions. Second, a self-attention channel-gating mechanism adaptively reweights the feature channels, and an Informer backbone captures long-range temporal dependencies for multistep prediction of the coupled features. At last, a health index (HI) is constructed from the prediction residuals, with a 99.7% quantile threshold and a six-step consecutive exceedance criterion for anomaly alarm triggering. Experimental results on real wind turbine data show that the proposed ARC-Informer achieves MSE of 0.180–0.257 across prediction horizons 1–16, with its advantage over TPE-optimized baselines (GRU, LSTM, RNN, TCN) growing from negligible at short horizons to 8.1% MSE reduction at H = 16, validating the effectiveness of the coupling enhancement for long-range forecasting. A cross-turbine case study on 10 healthy segments from five wind turbines confirms zero false alarms, and a simulated fault experiment successfully triggers an early warning, demonstrating practical unsupervised health monitoring capability. Full article

To address the issues of uneven accuracy across the entire hole depth and profile distortion caused by multi-source errors in spectral confocal deep-hole measurement, this paper proposes a measurement method involving global calibration using a stepped ring gauge and hierarchical compensation for multi-source errors. By classifying core measurement errors into three categories—geometric deviation, structural error, and dynamic process error—according to their propagation laws, this paper establishes a progressive comprehensive compensation system comprising “geometric calibration–structural correction–dynamic filtering”. Specifically, using a stepped ring gauge as the reference, the system’s intrinsic geometric parameters are identified via the Levenberg–Marquardt (LM) algorithm; structural errors introduced by the deflection of components due to self-weight are quantitatively corrected based on a statics model; periodic harmonic errors are sequentially separated; random noise is effectively suppressed by combining least-squares harmonic fitting with adaptive wavelet threshold filtering. Experimental results demonstrate that this method can limit the maximum absolute deviation in the inner diameter measurement of standard ring gauges to within 0.2 μm, stabilizing the measurement repeatability over the entire depth of deep-hole workpieces with length-to-diameter ratios exceeding 30:1 to within 0.8–1.6 μm, with an expanded uncertainty of $U$ = 3.8 μm ($k$ = 2). This method enables the precise reconstruction of deep-hole inner wall topography, providing a highly versatile technical foundation and implementation scheme for the high-precision non-destructive testing of deep holes with large length-to-diameter ratios. Full article

How older adults psychologically appraise their health while managing multiple chronic conditions is a behavioral-science question as much as a clinical one. This study estimated the weighted prevalence of favorable mental-health self-appraisal, identified its behavioral, social, and functional correlates, and compared the relative salience of diagnosed-condition burden and functional limitation among U.S. adults aged ≥ 50 years with multimorbidity. This retrospective cross-sectional secondary analysis used the 2023 Medical Expenditure Panel Survey (MEPS) Full Year Consolidated Data File (HC-251). Multimorbidity was defined as at least two diagnosed chronic priority conditions. The primary outcome represents favorable mental-health self-appraisal, derived from MNHLTH53 (excellent/very good/good vs. fair/poor). Covariates were organized using Andersen’s Behavioral Model and health-psychology concepts of adaptation, resources, and lived functional burden. Weighted prevalence estimates and survey-weighted logistic regression models were fitted using PERWT23F, VARSTR, and VARPSU. Robustness checks examined a stricter outcome threshold, proxy adjustment/non-proxy restriction, and a physical-health extension model. The analytic sample included 5523 respondents, representing approximately 77.9 million U.S. adults aged ≥ 50 years with multimorbidity. The weighted prevalence of favorable perceived mental-health self-appraisal was 86.6% (95% CI 85.4–87.7). In the fully adjusted core model (complete-case n = 5330), age 65–74 years (aOR 1.52, 95% CI 1.17–1.98) and age ≥ 75 years (aOR 1.79, 95% CI 1.36–2.36) were associated with higher odds of favorable appraisal. Lower odds were observed for Hispanic respondents, non-Hispanic Asian respondents, lower educational attainment, lower income, non-employment, ≥4 diagnosed conditions, and any functional limitation. The strongest inverse association was limitation status (aOR 0.32, 95% CI 0.27–0.39). Sensitivity analyses were directionally consistent. Favorable mental-health self-appraisal remained common in this medically complex older population, but it was socially and functionally patterned. Functional limitation appeared more behaviorally salient than diagnosis count alone. Because the analysis was cross-sectional and based on household-interview reported measures, these results should be interpreted as associations rather than causal effects. Full article

Tomato (Solanum lycopersicum) cultivation is highly vulnerable to fungal, bacterial, and viral leaf diseases that can significantly reduce crop yield and fruit quality when not detected at early stages. Although recent deep learning approaches have achieved remarkable performance in plant disease classification, many state-of-the-art architectures remain computationally expensive and therefore difficult to deploy on resource-constrained edge devices commonly used in smart agriculture environments. To address this challenge, this paper introduces EcoTomHybridNet, an adaptive resource-aware CNN–Transformer framework designed for efficient tomato leaf disease classification under edge-computing constraints. The proposed architecture combines a lightweight convolutional backbone with a dual-branch inference mechanism composed of a fast convolutional branch for computationally efficient prediction and a Transformer-enhanced branch with local self-attention for richer contextual feature extraction. Unlike conventional lightweight hybrid models relying on static inference pipelines, EcoTomHybridNet integrates a lightweight policy-guided routing mechanism that dynamically allocates inputs between the fast convolutional branch and the Transformer-enhanced branch according to input complexity. This adaptive inference strategy dynamically reduces unnecessary Transformer computations for simpler samples while preserving strong predictive performance on more challenging inputs through policy-guided branch allocation. To further improve representation capability without significantly increasing computational complexity, the proposed student network is trained using knowledge distillation from a ViT-Tiny teacher model. Experimental results on the PlantVillage tomato dataset demonstrate that EcoTomHybridNet achieves 99.42% test accuracy and 99.0% validation accuracy under the full hybrid inference configuration. Additional validation strategies, including 5-fold cross-validation and robustness evaluation under Gaussian noise and motion blur perturbations, indicate stable performance across different data splits and moderate image degradations, suggesting improved generalization capability beyond simple dataset memorization. Furthermore, adaptive routing experiments using a lightweight threshold-based policy mechanism achieved 99.20% test accuracy while reducing computational complexity from 0.36 GFLOPs to 0.25 GFLOPs per image, corresponding to approximately 30% computational savings. These results demonstrate the effectiveness of policy-guided adaptive inference for balancing predictive performance and computational efficiency in edge-oriented plant disease classification. Overall, EcoTomHybridNet provides an efficient and adaptive framework for intelligent plant disease monitoring in IoT-enabled smart agriculture systems. Full article

Agriculture, as a production sector, is exposed to external shocks. The instability of agricultural markets, changes in prices of inputs, dropping crop prices, or changes in climate patterns put their economic resilience to the test. Agroecological diversification of production is widely cited as a key adaptive strategy to increase farms’ resilience to these shocks. At the same time, empirical evidence linking crop diversity to economic stability across different production systems remains limited. The aim of the study was to assess whether the integration of more complex crop rotations and livestock production increases the economic resilience of organic farms compared to stockless organic farms and conventional farms. The analysis utilized data from the Polish FADN covering the multi-crisis period of 2020–2022, which included the COVID-19 pandemic, Russia’s war against Ukraine, and the sharp rise in fertilizer and energy prices. Farms were grouped by production type. Crop diversity was assessed using the Shannon–Wiener index (H′) and the Pielou evenness index (J′). The economic resilience of tested farms was determined based on their income, income variability during the study period, and the ability to maintain income above the parity threshold. The results indicated the existence of different pathways for building resilience. Organic farms with permanent crops and field crops were characterized by the highest crop diversity on arable land, while organic farms with dairy cows had the highest overall economic resilience, despite relatively low crop diversity on arable land. This phenomenon can be explained by the high proportion of permanent grasslands, which promoted feed self-sufficiency and the internal circulation of nutrients. The results indicate that in organic systems, the integration of crop and livestock production, based on permanent grassland, may be a more effective way to strengthen economic resilience than crop diversification on arable land alone. Full article

Power-system reliability is crucial for sustainable development, but large-scale, long-term monitoring remains challenging. Existing nighttime light (NTL)-based outage detection methods often rely on fixed thresholds or prior information, limiting cross-regional application. To address this, we develop an adaptive thresholding framework using daily NASA Black Marble data. Observations are grouped by view angle to mitigate radiometric instability, and a per-pixel dynamic baseline is constructed from high-radiance statistics, enabling robust anomaly detection without prior outage timing. From the detected anomalies, we formulate a population-weighted NTL power reliability index (NTPRI) to quantify regional electricity service reliability. Validation across six diverse outage events yields an F1 score of 0.807. National-scale analysis shows NTPRI correlates significantly with the World Bank’s System Average Interruption Duration Index (SAIDI). The derived Light Anomaly Rate (LAR) further supports pixel-level frequency analysis. Together, this framework provides a transferable remote-sensing tool for large-scale power-reliability assessment in data-scarce regions, supporting disaster impact evaluation and energy vulnerability analysis. Full article

Acute type A aortic dissection is a life-threatening condition requiring emergency surgery and complex postoperative management. Although survival rates have improved, many patients experience long-term functional impairments, reduced quality of life, and an elevated risk of complications. Despite strong evidence supporting cardiac rehabilitation in other cardiovascular populations, structured programs remain underutilized in patients with surgically resolved acute type A aortic dissection. Exercise-based cardiac rehabilitation appears feasible and can be delivered safely in carefully selected patients when appropriately adapted to individual needs and conducted under close supervision. Postoperative patients are often physically deconditioned, prone to hospital-acquired disability, and may misjudge exercise intensity. Therefore, individualized exercise prescription, guided by exercise testing when available, is important to support safe training thresholds. Early and gradual introduction of physical activity may help prevent complications associated with immobility, support blood pressure control, and contribute to improvements in functional capacity. However, training volume should be purposefully lower than in conventional program settings to reduce hemodynamic stress. Education on safe exercise parameters and self-monitoring plays a central role in enabling long-term adherence and promoting patient autonomy. Cardiac rehabilitation programs should incorporate dietary, nutritional, and psychological support. Although evidence specific to this patient population remains limited, available data suggest the feasibility and potential benefits of cardiac rehabilitation when delivered with appropriate precautions. Our review underscores the need for a tailored, multidisciplinary CR approach aimed at enhancing physical recovery, supporting cardiovascular stability, and improving overall quality of life in patients following surgery. Further research is required to define optimal program protocols. Full article

In the context of the global dual-carbon goals and China’s DP strategy, strengthening the coupling between digital productivity (DP) and the high-quality development of the energy industry (HQDEI) is essential for resource-based regions. Doing so can help these regions overcome transition constraints and advance green, low-carbon development. Using panel data for nine prefecture-level cities in Guizhou Province from 2014 to 2023, we construct composite indices for DP and HQDEI with an improved entropy-weight TOPSIS approach. We then characterize their spatiotemporal evolution using a coupling coordination degree (CCD) model and kernel density estimation. Finally, we examine the determinants of coupling coordination through panel regression and threshold models. The results show that: (1) The CCD between DP and HQDEI efficiency continues to increase, with regional differences displaying a periodic convergence–divergence pattern and a spatial structure characterized by core agglomeration and outward diffusion. Gradient disparities in coordinated development are evident between central and peripheral areas. (2) Consumption upgrading and fiscal self-sufficiency significantly promote CC, whereas a traditional resource-dependent growth model significantly suppresses it. Constrained by short-term adaptation and integration costs, digital innovation currently exerts a negative effect, and its enabling potential has not yet been fully realized. (3) Nonlinear tests identify a single digital-infrastructure threshold: the enabling effect of digital innovation turns positive only once infrastructure surpasses a critical level, revealing pronounced interval heterogeneity. This study advances the theoretical understanding of the bidirectional coupling between DP and HQDEI, provides empirical guidance for energy digital transformation and high-quality development in resource-based regions of western China, and offers transferable insights for green, low-carbon transitions in traditional energy regions worldwide. Full article

Autonomous mobile robots operating in outdoor environments must have an understanding of the surrounding terrain geometry to ensure efficient and safe navigation. This article presents a DEM-based intelligent traversability mapping framework to transform open-source geospatial data into slope-aware cost maps for multirobot autonomous navigation within the ROS2 framework. The proposed cv_gdal algorithm automatically processes GeoTIFF elevation data using adaptive slope thresholding based on each robot’s physical capabilities, generating ROS-compatible cell occupancy maps. Six regions of Spain were used to evaluate terrain representation accuracy and navigation performance in kilometer-scale DEMS. This framework enables autonomous perception-to-planning pipelines and supports the deployment of multirobot systems for search and rescue (SAR) tasks. By bridging geospatial analytics with robotic perception and adaptive decision-making, this work contributes to the development of intelligent, self-configuring robotic systems capable of operating safely in complex outdoor environments. Full article

Lightweight day-ahead photovoltaic (PV) forecasting models encounter a significant technical challenge: under resource-constrained deployment conditions, it is difficult to simultaneously address weather-regime heterogeneity, maintain model interpretability, and preserve adaptability as operating conditions evolve. To address this issue, we propose a multimodal short-term photovoltaic (PV) forecasting method that integrates weather-mode partitioning using the Clustering Using Representatives (CURE) algorithm with a self-updating Batch-Lasso model. First, the meteorological-PV dataset is partitioned along two dimensions by combining seasonal grouping with CURE clustering within each season, producing representative weather modes and enhancing the fidelity of weather pattern classification. Second, to extract informative predictors from high-dimensional meteorological inputs while maintaining interpretability, we formulate per-mode Lasso regression and adopt the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) to efficiently solve for the sparse regression coefficients. Third, we introduce a batch-based self-update and correction mechanism with rollback verification, enabling the mode-specific models to be refreshed as new historical data become available while preventing performance degradation. Compared with representative machine learning baselines, the proposed method maintains competitive accuracy with substantially lower computational and storage overhead, enabling high-frequency and energy-efficient inference on resource-constrained terminals, thereby reducing operational burdens and computational energy costs and better meeting the deployment needs of sustainable energy systems under heterogeneous weather conditions. Full article

The risks associated with hazardous materials (HazMat) transportation exhibit typical characteristics of chain-like distribution, spatiotemporal regularity, and individual heterogeneity. A personalized trip-chain feature spectra recognition framework for HazMat vehicles is proposed to enhance the capability to assess and analyze individual risks using vehicle positioning data. The proposed framework addresses the challenges of deriving personalized risk feature maps arising from missing real-time trajectory data, complex sub-trip-chain segmentation, and the extraction of personalized risk feature representations. An improved conditional Wasserstein Generative Adversarial Network (WGAN) model is initially developed to impute trajectories with missing positional data, and it can robustly reconstruct trajectories with large-scale missing segments by integrating a multi-head self-attention mechanism and a gradient penalty. A two-layer clustering algorithm, K-Means-multiplE-THreshOlds-adaptive-DBSCAN (KMETHOD), which combines an adaptive mechanism with threshold rules, is subsequently designed to identify the dwell time and related spatial attributes of dwell points along vehicle trips. A BERT-based model is incorporated to filter Points of Interest (POIs) around dwell points, which enables the extraction of their detailed location semantics and trip characteristics and thus supports trip chain identification and segmentation. A threshold-activated multilayer trajectory feature-map method (TAFEM) is constructed to generate feature maps for each trip chain. The Liquefied Natural Gas (LNG) transportation trajectory data from Guangdong Province is selected to evaluate the effectiveness of the proposed methods. The experimental results demonstrate that the proposed framework can effectively identify trip chains and generate their corresponding feature maps. The trajectory imputation model achieved the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Dynamic Time Warping (DTW) of 2.34–3.33, 6.05–7.74, and 0.74–1.21, respectively, across different missing-rate scenarios, outperforming other benchmark models. The identification accuracy of dwell-point duration and location reaches 98.35%. The BERT-based method achieves a maximum accuracy of 92.83% in origin–destination (OD) point recognition, effectively capturing comprehensive trip-chain information. TAFEM accurately characterizes the spatiotemporal distribution and potential causal factors of personalized HazMat transportation safety risks, providing a reliable foundation for risk identification and safety management strategies. Full article

In photovoltaic remote sensing image segmentation tasks, fully supervised methods can achieve high accuracy. However, the high cost of pixel-level annotation significantly limits their scalability in large-scale scenarios. To overcome this annotation bottleneck, this paper proposes a zero-shot cross-modal segmentation framework based on the visual-language pre-trained foundation model (CLIP). This approach harnesses CLIP’s cross-modal knowledge transfer capabilities to achieve precise extraction of photovoltaic targets without requiring any downstream training. This paper first introduces the Layer-wise Augmented Residual Attention (LARA) mechanism to enhance fine-grained detail representation in the feature space. Subsequently, a Cross-modal Semantic Attribution Module (CMSA) is designed to generate precise activation maps by leveraging image-text alignment gradient information. Finally, the Confidence-Aware Refinement Strategy (CARS) replaces the conventional training-based denoising process, directly producing high-quality binary segmentation masks through adaptive thresholding. Comparative experiments were conducted to evaluate the proposed method against various baselines using several public datasets with varying resolutions in Jiangsu Province including Unmanned Aerial Vehicles imagery, Beijing-2, Gaofen-2, and a self-created Sentinel-2 imagery covering multiple countries. Notably, the proposed method achieved an IoU of 70.3% on the Gaofen-2 PV03 dataset with a spatial resolution of approximately 0.3 m and 50.8% on the self-created Sentinel-2 PV_Sentinel-2 dataset with a spatial resolution of 10 m. Experimental results demonstrate that our proposed approach maintains excellent cross-domain generalisation capabilities while reducing annotation costs, thereby providing an efficient and viable technical pathway for the automated monitoring of large-scale photovoltaic facilities. Full article

Indoor 3D point cloud semantic segmentation is a fundamental task for fine-grained scene understanding and intelligent perception. Due to the prohibitive cost of dense point-wise annotations, weakly supervised learning has emerged as a promising alternative for indoor point cloud segmentation. However, existing weakly supervised methods commonly rely on fixed confidence thresholds for pseudo-label selection, which exhibit limited generalization caused by threshold sensitivity, underutilization of informative low-confidence regions, and progressive noise accumulation during self-training. To address these issues, we propose TGR-T, a weakly supervised framework for indoor 3D point cloud semantic segmentation that incorporates truncated-Gaussian-weighted reliability with adaptive dynamic thresholding. Specifically, a reliability-adaptive dynamic thresholding strategy is introduced to guide pseudo-label selection based on the evolving confidence statistics of unlabeled mini-batches, with exponential moving average smoothing employed to produce stable global estimates and robust separation of reliable and ambiguous regions. To further exploit uncertain regions, a learnable truncated Gaussian weighting function is designed to explicitly model prediction uncertainty within the ambiguous set, providing soft supervision by assigning adaptive weights to low-confidence predictions during optimization. Extensive experimental results demonstrate that the proposed framework significantly enhances the exploitation of unlabeled data under extremely limited supervision: extensive experiments conducted on standard indoor 3D scene benchmarks demonstrate that TGR-T achieves competitive or superior segmentation performance under extremely sparse supervision and can even outperform several fully supervised baselines trained with dense annotations while using only 1% labeled points, thereby substantially narrowing the performance gap between weakly supervised and fully supervised 3D semantic segmentation methods. Full article