Search Results (7,002)

Accurate representation of short-term reservoir water-level dynamics is essential for operational analysis and scenario-based assessment under prescribed inflow–outflow conditions. In many practical applications, physically based modelling is limited by incomplete process knowledge, unavailable boundary conditions, or insufficient temporal resolution of input data. This study presents a data-driven framework for hourly conditional simulation of reservoir water level based on a hybrid Conv1D–LSTM architecture. The model learns nonlinear relationships among hydraulic forcing, operational control, and system state from historical observations, and is evaluated in a recursive multi-step simulation (rollout) mode to reflect its intended use and capture error accumulation over time. A systematic analysis of input sequence length and activation function is performed to identify a robust model configuration. On the test set, the selected configuration ($L = 24$, GELU) achieved RMSE = 0.1057 m, MAE = 0.0881 m, and R² = 0.972 in rollout evaluation. The proposed framework is designed for scenario-based simulation rather than one-step deterministic forecasting, enabling rapid operational screening of alternative inflow–outflow regimes. Unlike many previous studies that emphasize one-step predictive accuracy, this work explicitly assesses model stability in recursive multi-step simulation, which is more relevant for reservoir scenario analysis. Full article

In advanced technology nodes, mixed-cell-height circuit designs have become increasingly prevalent, posing significant challenges for legalization. We first formulate the legalization as a class of variational inequality (VI) problems defined over convex sets and then employ an existing self-adaptive inertial projection and contraction algorithm (SIPCA) to solve it. Building upon this framework, we further propose an improved self-adaptive inertial projection and contraction algorithm (SIPCA_IP) by incorporating the subgradient extragradient technique to enhance convergence efficiency and numerical stability. The proposed method preserves the advantages of projection and contraction schemes for handling VIs with nonsymmetric positive semidefinite system matrices while demonstrating faster convergence and improved robustness compared with the baseline SIPCA. Moreover, a rigorous convergence analysis is established to provide theoretical guarantees for the effectiveness of the proposed method. Numerical experiments demonstrate that the proposed method effectively addresses the mixed-cell-height legalization problem and provides a rigorous and extensible framework for solving related quadratic optimization problems. Full article

Nonlinear analysis provides a framework for understanding the complexity and stability of human locomotion by capturing dynamic patterns beyond linear methods. This study examined the effect of data length on seven nonlinear measures: Sample Entropy (SpEn), Approximate Entropy (ApEn), Lyapunov Exponents using Wolf’s (LyE_W) and Rosenstein’s (LyE_R) algorithms, Detrended Fluctuation Analysis (DFA), Correlation Dimension (CD), and the Hurst–Kolmogorov process (HK). A 3500-frame kinematic dataset from a healthy adult performing motor-assisted elliptical training and treadmill walking was segmented from 100 to 3500 frames in 10-frame increments. Data from treadmill and elliptical conditions were analyzed and presented in a combined manner to highlight general stabilization trends across locomotor tasks. Results revealed that increasing data length significantly affected all nonlinear metrics (p ≤ 0.0005). Stabilization occurred at varying minimum lengths: SpEn at ~4.5–8.8 s (540–1060 frames), ApEn at ~5.4–7.7 s (650–920 frames), LyE_W at ~19.1–29.2 s (2290–3500 frames), LyE_R at ~1.3–1.5 s (150–180 frames), DFA at ~29.2 s (3500 frames), CD at ~1.7–15.9 s (200–1910 frames), and HK at ~9.1–9.8 s (1090–1180 frames). Notably, HK achieved stable estimates in approximately one-third of the time required for DFA and substantially less than LyE_W, supporting its suitability for time-constrained or clinical settings. These findings suggest the need to tailor data collection to each nonlinear metric and to report data length explicitly to improve accuracy, reproducibility, and methodological rigor in gait variability research. However, these findings should be interpreted within the limitations of a single-participant, exploratory design. Full article

The replacement of synchronous generators (SGs) with inertia-less renewable energy sources (RESs) poses a significant challenge to grid stability due to the reduction of system inertia. To prevent grid instability, energy storage systems (ESSs) with frequency-derivative controls are used to emulate inertia. However, the limited lifetime of ESSs, along with their maintenance requirements, large footprint, and high cost, imposes an additional economic burden on microgrids. This paper proposes an enhanced grid-frequency support approach by coordinating two inertia-emulation mechanisms in parallel: (i) inertia support derived from DC-link capacitor dynamics and (ii) inertia support enabled by operating the PV plant with a power reserve. The proposed method enhances the grid support capacity of the PV energy system and energy sustainability through the efficient utilization of available support resources. Moreover, the DC-link voltage is restored smoothly and naturally to its rated value without the need for a complex control algorithm. The dynamic performance of the proposed system is evaluated under different disturbance conditions and different parameter settings. Simulation results using MATLAB/Simulink R2023a show that, under a 7% load increase, the proposed controller improves the frequency nadir by 0.04 Hz and decreases RoCoF by 10% compared with the baseline controller. Full article

Background: This study investigates the impact of anatomically constrained preprocessing and deep learning architecture selection on benign versus malignant breast lesion classification in contrast-enhanced mammography (CEM), with the goal of improving robustness and clinical reliability across heterogeneous data sources. Methods: In this retrospective multicenter study, CEM images from 300 patients (314 lesions) were combined with 1003 publicly available CEM images, yielding a total of 1120 breast cases. Automatic breast segmentation was performed using the LIBRA framework to generate breast-mask images. Eleven deep learning models, including classical convolutional neural networks, attention-based networks, hybrid convolutional neural networks (CNNs), Transformer architectures, and mammography-specific models, were trained and evaluated using both original DICOM images and breast-mask inputs. Performance was assessed using accuracy, balanced accuracy, sensitivity, specificity, AUROC, and AUPRC on cross-validation and independent test sets. Hyperparameter optimization was conducted for the best-performing architecture. Results: Models trained on breast-mask images consistently outperformed those trained on original DICOM images across all architectures and metrics, with AUROC improvements ranging from +0.06 to +0.21. Among all models, ResNet50 trained on breast-mask images achieved the best performance (AUROC = 0.931; AUPRC = 0.933; balanced accuracy = 0.834), further improved after optimization (balanced accuracy = 0.886; sensitivity = 0.842; specificity = 0.930). Classical CNN architectures demonstrated performance comparable to or exceeding that of more complex hybrid CNN–Transformer models when anatomically focused preprocessing and rigorous optimization were applied. Conclusions: Anatomically constrained preprocessing through breast-mask segmentation substantially enhances deep learning performance and stability in CEM-based breast lesion classification. These findings indicate that input representation quality and training optimization are critical determinants of clinically relevant performance, often outweighing architectural complexity, and may support more reliable AI-assisted decision support in CEM workflows. Full article

Microplastics (MPs) pose a significant threat to soil ecosystems based on their small size and resistance to biodegradation. Soil organic carbon (SOC) in saline–alkaline ecosystems has significantly affected maintain the ecological balance. This paper aims to review the mechanisms underlying the influence of MPs on SOC in saline–alkaline soils combining bibliometric mapping (VOSviewer). The results revealed that: (1) MPs mainly enter the saline–alkaline soil through water irrigation, sewage sludge, and agricultural films. (2) The interaction between the salt ions in saline–alkaline soils and the negatively charged surface of MPs will intensify the dispersion of soil aggregates, resulting in a significant decline in soil structure stability and nutrient imbalance. (3) MPs and the high-salt environment of saline–alkaline soils form a synergistic stress, significantly reducing the activities of key enzymes such as catalase and dehydrogenase in the soil, and it selectively promotes the enrichment of salt-tolerant bacterial communities (such as Halomonas and Bacillus species). (4) Using biodegradable plastic materials, setting up ecological buffer zones and planting halophytic plants (in coastal saline–alkaline areas), adding windbreak and sand-fixing buffer zones (in inland desert-type saline–alkaline areas), promoting precise irrigation and fertilization technologies (in areas with uneven irrigation conditions), and emergency soil amendment treatment (for severely polluted and ecologically fragile saline–alkaline soils) were all effective measures to dealing with the MPs pollution in saline–alkaline soils. This review provides a theoretical basis for the prevention and control of MPs pollution and the sustainable use of saline–alkaline soils. Full article

Lamellar ichthyosis is a rare congenital disorder of keratinization frequently associated with ocular complications, most commonly cicatricial ectropion and exposure keratopathy. We present a case of severe mixed exposure-related and neurotrophic keratopathy with marked corneal thinning in a 61-year-old man with genetically confirmed lamellar ichthyosis. At presentation, the best-corrected visual acuity (BCVA) in the right eye was limited to hand motion (logMAR 2.3). Slit-lamp examination revealed a large central to inferocentral corneal ulcer measuring approximately 3 × 4 mm with severe stromal thinning in the setting of marked lower eyelid ectropion, incomplete eyelid closure, and chronic ocular surface exposure, while anterior segment optical coherence tomography (AS-OCT) demonstrated a minimal corneal thickness of approximately 165 µm. Microbiological swabs obtained from the conjunctival sac were negative, and no purulent discharge, hypopyon, or anterior chamber inflammatory reaction was present, making active infectious keratitis unlikely. Corneal sensitivity measured with Cochet–Bonnet esthesiometry at presentation, centrally and in all four peripheral quadrants of both eyes, was markedly reduced, more severely in the affected right eye, supporting the presence of a severe neurotrophic component contributing to impaired corneal healing. Intensive conservative therapy including preservative-free lubricants, dexpanthenol gel, autologous serum eye drops, topical insulin, prophylactic antibiotics, and systemic doxycycline was initiated. Serial AS-OCT imaging demonstrated progressive structural recovery, with corneal thickness increasing to 438 µm after one month of treatment and complete corneal epithelialization. The BCVA improved to 0.2 Snellen (0.7 logMAR). This case highlights the diagnostic value of multimodal anterior segment imaging in monitoring severe mixed keratopathy with advanced corneal thinning and demonstrates that intensive conservative therapy may stabilize the ocular surface and prevent corneal perforation in patients with lamellar ichthyosis. Full article

For electric vehicles, accurate real-time estimation of the road friction coefficient is critical for maintaining stability, as the millisecond-level response of electric motors and the integration of regenerative braking demand higher perception fidelity than traditional internal combustion vehicles. This paper proposes a methodological framework for road friction estimation specifically designed for intelligent E-Chassis based on micro-signal features of intelligent tires and deep learning. An intelligent tire system, integrated with tri-axial accelerometers and strain gauges, was installed on the front-left wheel of a test vehicle to capture raw dynamic signals during transitions from cement to snow-covered surfaces across a velocity gradient of 10–50 km/h. The Savitzky–Golay convolutional smoothing algorithm was applied to reconstruct the high-frequency raw signals, enabling the extraction of a five-dimensional feature vector comprising vehicle velocity, peak strain, contact patch width, peak-to-peak acceleration, and signal standard deviation. The study revealed a natural filtering effect originating from the porous elastic properties of snow, resulting in a 60–70% reduction in signal standard deviation compared to cement, accompanied by a cliff-like feature collapse at the moment of snow entry. A BP neural network model with a 5-7-1 architecture achieved an identification accuracy of 96.2% on the test set, facilitating a rapid real-time prediction of the friction coefficient transitioning from 0.75 to 0.23. Unlike traditional methods, the proposed approach does not rely on high slip ratios and can complete identification within the first physical rotation cycle. This provides a robust physical criterion for the torque vectoring and regenerative braking stability of intelligent electric vehicles in extreme environments. Full article

Background: Adults with disabilities living in low-resource communities experience persistent inequities in access to healthcare, mental health services, and community participation. However, qualitative data capturing lived experiences in the Deep South remain limited. This study aimed to identify priority needs among adults with mobility disabilities residing in economically distressed communities near Birmingham, Alabama, to inform future telehealth programming. Methods: Fifteen adults (mean age = 60 ± 10 years), predominantly African American and female, completed semi-structured phone interviews exploring basic needs, neighborhood accessibility, health priorities, and perceived supports. Interviews were audio-recorded, transcribed verbatim, and analyzed using Braun and Clarke’s six-phase thematic analysis. Results: Five themes emerged: (1) seeking stability amid severe mental health strain and inadequate supports; (2) constrained food environments shaped by cost, location, and safety; (3) feeling forgotten: systemic neglect and restricted participation in community life; (4) physical health deprioritized by competing needs and structural barriers; and (5) remote support as a viable but unrealized option. Participants described how safety concerns, transportation barriers, and rising food costs constrained daily functioning, while unmet mental health needs compounded isolation. Despite widespread cardiometabolic disease, immediate needs related to mental health, food, and housing consistently superseded physical health. Mental health support was identified as the most feasible area for remote delivery, though poor awareness of available resources limited engagement with any service model. Conclusions: Findings demonstrate that disability-related disparities in low-resource communities are driven largely by structural and environmental factors rather than individual choice. Telehealth and mobile-based services may provide a feasible access strategy for mental health and supportive care in under-resourced settings, particularly when integrated with broader community supports. Addressing foundational needs is essential for advancing health equity among people with disabilities in the Southeast. Full article

Coking coal flotation is a typical nonlinear, multi-variable, and multi-objective process in which concentrate quality and combustible matter recovery must be balanced under fluctuating feed and operating conditions. To improve both predictive reliability and decision support, this study proposes an integrated data-driven framework that combines particle swarm optimization-back propagation (PSO-BP) prediction, SHapley Additive exPlanations (SHAP) based interpretation, Non-dominated Sorting Genetic Algorithm II (NSGA-II) optimization, and entropy-weighted Technique for Order Preference by Similarity to Ideal Solution (Entropy-TOPSIS) decision-making. After three-sigma outlier screening, 2000 valid distributed control system (DCS) samples were retained for model development and temporal holdout evaluation, and an additional 200 later-period industrial samples were used for independent validation. The data were partitioned chronologically, with months 1–4, month 5, and month 6 used for training, validation, and temporal holdout testing, respectively, while the months 7–8 dataset was reserved for later-period validation. The results show that PSO-BP consistently outperformed conventional BP under both temporal holdout and later-period validation. SHAP analysis identified raw coal ash and collector dosage as the dominant factors for product-quality prediction, while collector dosage and frother dosage contributed most strongly to tailing heat of combustion. NSGA-II further revealed the trade-off among clean coal ash, clean coal sulfur, and tailing heat of combustion, and Entropy-TOPSIS converted the Pareto-optimal candidate set into a practically balanced operating recommendation. Sensitivity and robustness analyses indicated acceptable stability of both the optimization process and the final decision result. Overall, the proposed framework provides an interpretable prediction–optimization–decision workflow for coking coal flotation and offers a practical basis for future DCS-assisted intelligent regulation. Full article

Long-term stability of dam infrastructure is crucial for flood protection, water resource management, and drinking water supply. In many regions, the increasing impact of climate change and structural aging necessitates advanced monitoring approaches for embankment and gravity dams. PSI has emerged as a valuable technique for detecting surface deformation rates with millimeter precision. This study presents a comprehensive monitoring concept that combines satellite-based PSI analyses with the first operational use of ECRs at dam sites in North Rhine-Westphalia (NRW), Germany. Over a period of more than two years, ECRs were observed under real-world conditions using Sentinel-1 data. Compared to traditional passive reflectors, ECRs offer improved signal stability and a compact design, making them particularly suitable for confined or sensitive dam environments. The analysis of displacement time series confirms the suitability of ECRs for long-term deformation monitoring in complex dam settings. Intercomparison of two PSI time series demonstrated high internal consistency (correlation > 0.9, RMSE < 1 mm), while validation against in situ measurements confirmed millimeter-level agreement with RMSE values between 2 and 5 mm and correlations up to 0.7. In addition, a dedicated web-based platform was developed to provide processed ECR-based PSI results to dam operators, offering interactive visualizations, time-series access, and standardized downloads. This integration of advanced interferometric synthetic aperture radar (InSAR) methods, innovative hardware, and user-oriented service delivery marks a significant step toward operational dam monitoring using satellite remote sensing. Full article

Tailings management presents a critical challenge for the mining industry, particularly in mountainous regions with high seismicity and steep slopes. This article presents the development and design criteria for dry stacking of filtered tailings as a sustainable and safe alternative to conventional slurry tailings storage facilities (TSFs). The study focuses on the extreme conditions of a mountainous location characterized by complex topography with 10% slopes, space constraints, and significant seismic activity defined by a peak ground acceleration (PGA) of 0.3 g. The design methodology, which incorporates layered compaction of the filtered tailings to achieve a geotechnically stable structure, is detailed for a filtered TSF consisting of 7 terraces, each 10 m high, reaching a total height of 70 m. This approach minimizes the risk of liquefaction and prepares the filtered tailings surface for progressive closure, with unit operating costs (OPEX) of 2.5 USD/t. The results of the physical stability analysis confirm the viability of this solution: pseudo-static stability analysis yielded a safety factor of 1.22, demonstrating a significant reduction in water consumption and potential environmental impact. It is concluded that the dry disposal of filtered tailings is a technically robust option for tailings management in extreme mountainous environments, offering greater long-term safety guarantees and facilitating landscape integration, thus setting a precedent for mining projects in similar geographies. Full article

With the acceleration of urbanization, deep and large foundation pit projects have become increasingly common, posing challenges for retaining structural performance. This study investigates the mechanism of the recently proposed vertical–inclined pile wall (VIPW) through physical model tests. Six sets of large-scale model tests of foundation pit excavation under 1 g gravity conditions were carried out. Among these tests, one employed the soldier pile wall (SPW) as the support system, while the remaining five adopted the VIPW. By monitoring and analyzing the distribution and variation in the vertical pile deformation, surface settlement, pile bending moment, and inclined pile top axial force during the excavation process, the action mechanism of the VIPW was revealed, and it was verified that VIPWs exhibit better support performance than SPWs. Furthermore, four key parameters, including the embedded depth, the inclination angle, the support position of the inclined piles, and the embedded depth of the vertical piles, were varied to study their influence on the deformation and force characteristics of the VIPW, providing a theoretical basis for structural optimization design. Moreover, by comparing the instability and failure characteristics of the foundation pit, it was proved that the VIPW can effectively ensure the stability of the foundation pit. Full article

The automated classification of Hebrew, a morphologically rich language (MRL), presents unique challenges, particularly when high-quality labeled data are scarce. This study investigates the interplay between handcrafted feature engineering and transformer-based representations in a low-resource news classification setting (n = 306). We evaluate a multi-task classification across four distinct dimensions: domain, sentiment, gender, and source. Our methodology employs an extensive feature space of 2149 stylistic and content-based attributes, optimized through a systematic Hill-Climbing selection process. We contrast five classical machine learning architectures with five BERT-based models, integrating five oversampling strategies to mitigate class imbalance. The results reveal that in scenarios of extreme data scarcity, the performance gap between deep learning and optimized classical ML becomes marginal, with stylistic features providing critical stability and interpretability. This study contributes a curated Hebrew news dataset and establishes a robust benchmark, demonstrating that linguistically aware feature engineering remains a vital component for MRL processing when large-scale fine-tuning is impractical. Full article

Technical and vocational education and training (TVET) colleges in South Africa continue to experience challenges related to staff commitment, organisational performance, and institutional effectiveness. These challenges highlight the need to better understand leadership approaches that sustain academic engagement and stability. This study examines the relationship between transformational leadership and organisational commitment among academic staff at a selected TVET college in Gauteng, South Africa. Grounded in the transformational leadership theory of Bass and Avolio, the study adopted a quantitative, cross-sectional survey design. Data were collected from 203 academic staff across six campuses using a structured self-administered questionnaire. Descriptive statistics and multiple regression analysis were performed using SPSS. The findings revealed low levels of organisational commitment among academic staff. While transformational leadership, as a composite construct, did not significantly predict organisational commitment, specific components—namely intellectual stimulation, inspirational motivation, and individualised consideration—showed significant positive relationships with organisational commitment. Theoretically, the study refines the application of transformational leadership theory within the TVET context by demonstrating that its components may operate differentially rather than as a unified construct in predicting organisational commitment. These findings challenge assumptions regarding the holistic predictive power of transformational leadership and extend leadership scholarship within under-researched TVET settings in developing-country contexts. Practically, the results provide evidence-based guidance for TVET management to design targeted leadership development interventions that emphasise specific transformational leadership behaviours to enhance academic staff commitment. Full article