Search Results (39,631)

The primary focus of enhancing the efficiency of operations in the Industry 4.0 setting is Predictive and Preventive Maintenance (PPM). The paper introduces a predictive-maintenance system based on the Unified Namespace (UNS), which involves real-time sensor measurements, photogrammetry, and modelling of a digital twin to improve fault prediction and responsiveness to maintenance. This experiment was conducted over six months in a medium-sized discrete electromechanical production plant equipped with motors, Variable Speed Drives (VSDs), robot/cobots, precision grip systems, pipework systems, Magnemotion/linear motor drives, and a CNC machine. The continuous data, such as high-frequency vibration, temperature, current, and pressure, were monitored and analysed with machine-learning models, including support-vector machines, Gradient Boosting, long-short-term memory, and Random Forest, through which temporal degradation can be predicted. UNS architecture integrated all sensor and imaging data into a vendor-neutral data model through OPC UA to help ensure that all experiments could be integrated consistently and be updated in real time to real digital twins. The suggested system correctly identified mechanical and electrical failures and predicted failures before they really took place. Consequently, machine downtime was reduced by 42.25%, and Mean Time to Repair (MTTR) by 36%, compared to the prior six-month baseline period. These improvements were associated with earlier anomaly detection and digital-twin-supported pre-inspection. Overall, the findings indicate that the integration of UNS with multi-modal sensing and digital-twin technologies may enhance predictive maintenance performance in comparable industrial settings. The framework provides a data-driven, scalable solution to organisations that aim to modernise their maintenance processes, attain greater reliability and better equipment utilisation, as well as enhanced Industry 4.0 preparedness. Full article

We report a dual-readout self-resetting CMOS image sensor that achieves a signal-to-noise ratio (SNR) exceeding 70 dB and resolves sub-percent optical contrast variations by effectivly suppressing reset artifacts. The proposed sensor employs a Dual-Readout architecture with two independent scanners operating with a temporal offset; while one readout system is in the self-reset “dead time”, the other remains active, thereby physically ensuring continuous data acquisition. To minimize pixel area while achieving high reconstruction accuracy, a minimum frame-to-frame difference algorithm is utilized for signal restoration without requiring in-pixel counters. A prototype chip fabricated in a 0.35-$\mathsf{\mu }$m process demonstrated SNR characteristics near the shot-noise limit, with a peak SNR exceeding 70 dB. Vascular phantom experiments using a carbon black suspension successfully visualized ±0.25% contrast fluctuations—dynamic signals previously undetectable by conventional sensors. This device provides a powerful platform for high-precision bio-imaging applications, including brain surface blood flow monitoring, where both wide dynamic range and high SNR are essential. Full article

Electricity spot prices jointly encode network physics and strategic bidding outcomes. In a well-functioning market, nodal and temporal price patterns tend to remain approximately invariant under mild perturbations-exhibiting symmetry-preserving regularities in distribution shape, spatial gradients, and temporal variation. Conversely, congestion binding, net-load stress, and abnormal bidding can induce symmetry breaking, manifested as heavy tails, mean shifts, and localized price discontinuities. This study develops a symmetry-guided and explainable diagnostic framework to identify price anomalies and attribute their dominant drivers. First, representative anomaly types (spike and mean shift) are defined using statistically and operationally motivated criteria, together with robustness checks across alternative thresholds. Second, principal component analysis is applied to construct compact, anomaly-specific feature sets, filtering weakly related variables while retaining system stress, congestion proxies, and renewable-induced variability indicators. Third, leveraging the optimization structure of market clearing and the associated KKT conditions, we characterize the price–feature linkage as a piecewise mapping and quantify each feature’s contribution via a sampling-based influence scoring procedure, yielding a ranked causal attribution. Case studies on a regional day-ahead spot market dataset demonstrate that the proposed framework achieves high consistency with expert assessments, with traceability accuracy exceeding 85% overall and particularly strong performance for spike-type anomalies. The method reduces reliance on purely manual diagnosis and black-box learning, and provides symmetry-oriented, actionable evidence for market surveillance and renewable-friendly flexibility and congestion management design. The proposed framework enables transparent identification of dominant structural drivers underlying different types of electricity price anomalies, linking observed price signals to market-clearing mechanisms. The results provide actionable diagnostic insights for market monitoring and regulatory assessment in electricity markets with high renewable penetration. Full article

An assessment and prediction system for the quality of public water networks was developed, using Timișoara, Romania, as a case study. This was implemented on a Google Firebase cloud storage system and comprised twelve ML algorithms applied to test samples for drinkability and used in predictions of upcoming samples. The system compares 17 water quality parameters to the World Health Organization and public reports of Timișoara drinking water standards for 804 samples. The system provides real-time data storage, drinkability prediction for the reservoir water system, and rule-based critical water recommendations for elementary treatment in samples. The most accurate and best-calibrated against random forest, gradient boosting, and Logistic Regression algorithms was the decision tree algorithm of the ML models. The experimental findings also determine the regions of the worst and best water quality and propose respective treatment. In contrast to previous research and structures, the paper demonstrates an approved stable solution for smart water monitoring, correlating practical deployment with sophisticated data-based conclusions. The results contribute to enhancing public health, enhancing water management measures, and upscaling the system for larger-scale applications. Full article

Water systems experience increasing sustainability challenges from climate variability, aging infrastructure, and energy and chemical intensity demands, but AI has typically been assessed against prediction accuracy rather than demonstrated operational success. This PRISMA 2020 systematic review analyzed the role of AI solutions on sustainability in distribution, treatment, and basin management. The database search identified 920 records; after deduplication (n = 185), screening was conducted on n = 735 titles/abstracts and examination of the full text for n = 85, providing a total of n = 41 included peer-reviewed studies for qualitative synthesis and n = 38 for quantitative/bibliometric synthesis with the additional analysis of seven grey-literature sources. Evidence mapping reveals high growth post-2020, and distribution and wastewater operations are dominated by a few companies. The most deployable evidence is found with monitoring, anomaly/leak detection, and short-term forecasting, while optimization and reinforcement-learning control are primarily simulation validated with limited field applications. While accuracy metrics are often reported, transformation into water saved, kWh/m³, chemicals, compliance/reliability/resilience/equity measures are inconsistently and less frequently operationalized. In general, AI is most believable when it is part of analysis-ready workflows, bounded decision support, and measurement-and-verification. Full article

Thermo-physiological comfort of automotive seating is governed by the complex interaction between seat-cover materials, their structural configuration, and the heat and moisture exchange occurring at the seat–body interface during prolonged sitting. While numerous studies have examined individual textile constructions or isolated comfort parameters, integrated evaluations combining objective material testing with dynamic microclimate measurements under realistic loading conditions remain limited. This study thoroughly examined six commercially important vehicle seat-cover materials that represent laminated, warp-knitted, and woven polyester architectures. Standardized laboratory techniques were used to quantify objective comfort qualities, such as air permeability, water vapor permeability, thermal resistance (Rct), and evaporative resistance (Ret) and transient heat flux test (H-test). Simultaneously, a multi-sensor system was used to constantly monitor temperature and relative humidity at the seat–body interface during sitting loading in a controlled subjective microclimate experiment at room temperature. The findings show that lamination technique and textile structure have a major impact on both transient microclimate behavior and steady-state material properties. Increased air and moisture transmission in warp-knitted and more open structures resulted in reduced evaporative resistance and more stable microclimate conditions. Denser laminated structures, on the other hand, exhibited more resistance to heat and evaporation, which led to a greater buildup of moisture when they were seated. Different temporal responses in temperature and humidity were also shown by the multi-sensor microclimate studies, underscoring the significance of assessing comfort beyond static material metrics. This study demonstrates that static thermos-physiological parameters alone are not sufficient to predict real stated comfort behavior. By integrating time-resolved microclimate analysis under realistic seated loading with standardized testing, a more reliable evaluation framework for automotive seat-cover comfort is proposed. Full article

Industrial health assessment often faces the challenge of sensor scarcity and a lack of labelled failure datasets, making conventional monitoring difficult to scale. This study addresses these constraints by proposing a state-aware framework that relies exclusively on routinely measured electrical parameters (active power, current, voltage, and power factor). The main challenge lies in distinguishing benign load variations from actual degradation without process-level context. To overcome this, we integrate automated operating-state recognition using XGBoost with per-state regression modelling to estimate the expected active power. A standardized Health Index (HI) is then derived from the residuals to quantify deviations from normal behaviour. Evaluated on a fleet of three-phase injection moulding machines, the framework demonstrates substantial performance improvements: the state-aware approach increased the median coefficient of determination from 0.64 to 0.86 and reduced residual variability by 30% compared to context-agnostic models. These findings show that synergistic system integration provides a stable and interpretable indicator for early degradation detection and fleet-level benchmarking under strict zero-label industrial constraints. Full article

Acute respiratory distress syndrome (ARDS) is a heterogeneous inflammatory lung injury marked by increased alveolar–capillary permeability, reduced respiratory system compliance, and impaired gas exchange. Despite advances in supportive care, ARDS remains associated with high mortality. Lung-protective ventilation with low tidal volumes and prone positioning is the cornerstone of treatment. However, these strategies do not fully account for patient-specific physiological variability. Recent guidelines emphasize a more individualized approach to respiratory support. Key elements include limitation of driving pressure, optimized use of high-flow nasal oxygen, and application of bedside tools such as the SpO₂/FiO₂ ratio and lung ultrasound. These measures improve diagnosis, monitoring, and physiological assessment at the bedside. This narrative review summarizes current evidence supporting contemporary ventilatory and non-invasive strategies in ARDS. It also examines emerging diagnostic and therapeutic approaches that integrate respiratory physiology into clinical decision-making. Finally, we discuss future directions focused on personalized, physiology-guided management to improve outcomes in patients with ARDS. Full article

Agrivoltaic Systems (AV) constitute a viable alternative to mitigate land-use competition by enabling the simultaneous production of agricultural crops and solar photovoltaic energy. However, the heterogeneous shading and microclimatic modifications induced by AV systems can alter solar radiation, crop physiological performance, and, consequently, its biomass. This study evaluated the effects of two static ground-mounted AV systems—semi-transparent (ST) and conventional opaque (CON) panels—on the growth, physiology, soil water variations, and yield of Arugula (Eruca vesicaria) cultivated in southern Italy from August to October 2022; compared with an open-field control (REF). Daily soil temperature and water content were monitored, alongside leaf-level gas exchange measurements at three vegetative stages. Global solar radiation was reduced by 70% under ST and 80% under CON, reducing Photosynthetically Active Radiation (PAR), transpiration, and net photosynthesis, while leaf water use efficiency remained comparable to REF. Sequential harvests showed that although yields were consistently highest in REF, ST 50% and CON 50% exhibited partial recovery in fresh and dry biomass by the third cutting, reflecting the mitigating effect of seasonal temperature declines on shading. Notably, soil water uniformity improved under AV systems, reaching 90% under ST and 94% under CON compared with 85% in REF, due to reduced evaporative losses and enhanced lateral soil water redistribution. Overall, while AV-induced shading limits radiation and yield in short-cycle leafy arugula, microclimate modulation under AV systems can enhance soil water distribution and partially buffer growth under less favorable seasonal conditions. These findings highlight the trade-offs between crop productivity and resource-use efficiency in AV systems and emphasize the importance of tailoring their design to crop type and local climatic conditions, providing valuable guidance for future experimental research and for policymakers aiming to support sustainable agrivoltaic deployment. Full article

Background: Ulcerative colitis (UC) and sarcoidosis are chronic inflammatory diseases that share immunological pathways but rarely coexist. The increasing use of biologic agents in inflammatory bowel disease (IBD) has raised concerns regarding paradoxical inflammatory manifestations, including sarcoidosis-like reactions. Case presentation: We report the case of a 63-year-old man with long-standing UC treated with vedolizumab who developed systemic sarcoidosis characterized by bilateral hilar lymphadenopathy, mediastinal and abdominal lymph node enlargement, pulmonary involvement, and erythema nodosum. Extensive diagnostic work-up, including imaging and histopathology, confirmed non-necrotizing granulomatous disease consistent with sarcoidosis, while alternative infectious, malignant, and drug-induced causes were excluded. Vedolizumab was temporarily discontinued, leading to UC relapse, and subsequently reintroduced with rapid clinical remission of UC. Discussion: Sarcoidosis remained clinically and radiologically stable despite vedolizumab re-initiation, suggesting a coincidental association rather than a direct causal relationship. This case highlights the diagnostic challenges and therapeutic dilemmas in patients with immune-mediated diseases receiving biologic therapy. Conclusion: The coexistence of UC and sarcoidosis during vedolizumab therapy is rare. Although causality cannot be established, our findings suggest that vedolizumab may be safely continued in selected patients under close multidisciplinary monitoring. Full article

Background and objectives: Antiseizure medications are essential for epilepsy management but often cause adverse effects that impact treatment adherence and quality of life. This study investigates the incidence rate and determinants of high-burden adverse effects of antiseizure medications. Materials and Methods: This study was a cross-sectional study including data extraction by a medical record review and administration of a standardized scale. It was conducted at an epilepsy outpatient clinic in Saudi Arabia and included adult patients on antiseizure medications. The validated Arabic version of the Liverpool Adverse Events Profile (LAEP) was used. The total LAEP scores ranged from 19 to 76. In this study, LAEP scores ≥ 45 were classified as high-burden adverse effects. Results: Of 153 included patients, 84 (54.9%) had high-burden adverse effects. The overall mean (SD) LAEP score was 45.63 (21.04). The most frequently rated adverse effects were difficulty in concentrating, with a mean score of 2.71 out of 4, followed closely by disturbed sleep (2.69), sleepiness (2.63), and memory problems (2.56). Of examined variables, generalized seizure and polytherapy were significantly associated with increased adverse effects. Likewise, uncontrolled seizure and presence of depression comorbidity were also associated with increased risk of adverse effects, but not statistically significant. Conclusion: The study reported a high rate of adverse effects of antiseizure medications and identified patients at high risk of adverse effects. Early recognition of these patients is important to provide appropriate care, including counselling, regular monitoring, and management of psychiatric comorbidities. Central nervous system symptoms were the most frequently reported adverse effects. Initiation of antiseizure medications with low doses and gradual titration may improve tolerability. Future research should focus on prediction adverse effects using pharmacogenomic AI-based decision-making tools. Full article

Accurate predictions of CNC machine tool energy consumption are crucial for sustainable manufacturing but remain challenging due to complex nonlinear dynamics. This paper proposes a mechanism–data hybrid framework combining physical modeling with an Attention–LSTM network. Unlike existing parallel hybrid models, this approach embeds the mechanism model’s output as a strong prior into the neural network, explicitly guiding the learning of nonlinear residuals. First, a hierarchical decoupled mechanism model is constructed to establish the physical baseline of energy consumption. Second, an Attention–LSTM network is designed to compensate for dynamic errors caused by tool wear and thermal variations. Finally, experimental validation on a three-axis CNC milling machine demonstrates that the proposed method significantly outperforms meaningful baselines, achieving a Root Mean Square Error (RMSE) of 0.0610 and an R² of 0.9936. The framework provides a robust, physically interpretable solution for energy monitoring in intelligent manufacturing systems. Full article

Effective management of rye cover crops in cash-crop systems relies heavily on accurate biomass estimation. Low-altitude Unmanned Aerial Vehicle (UAV) imagery offers a promising high-resolution alternative, yet unlocking its full potential requires moving beyond basic estimation models to more integrative and explanatory models. This study obtains the measured height (MH), SPAD (Soil and Plant Analyzer Development) values, and measured dry biomass (MDB) and applies UAV remote sensing and machine learning to acquire the crop canopy height, vegetation indices (VIs), and vegetation fraction (VF) across growth stages. Among single-parameter biomass estimation models, the estimated height yields the best at the overall growth stage (R² = 0.935), whereas selected VIs perform the best at the non-seedling stage (R² = 0.851). For multi-parameters modeling, models combining height, VF, and VIs significantly outperform the single-parameter models, achieving better estimation results throughout each growth stage (Best R² = 0.951). Structural equation modeling clarifies the direct and indirect contributions of these parameters to biomass accumulation, revealing their synergistic effects. This study demonstrates the potential of UAV-based multi-parameter biomass estimation model to support more informed decisions in cover crop management and to advance broader precise agriculture practices. Additionally, the analytical framework developed here offers a transferable approach for high-resolution biomass monitoring in other crop systems. Full article

Many species within the family Moronidae possess biological traits that facilitate their success as invasive species in freshwater ecosystems. In Iowa, USA, non-native Yellow Bass (Morone mississippiensis) have expanded their range into at least 19 glacial natural lakes, yet their trophic interactions in these complex systems remain poorly understood. From 2018 to 2020, we evaluated the open-water diet composition of 1300 Yellow Bass across six Iowa natural lakes to quantify diet composition, feeding intensity, and ontogenetic dietary shifts. While zooplankton numerically dominated diets across most systems (>80% by number) biomass was driven primarily by benthic invertebrates and fish. Feeding intensity was not uniform, characterized by a distinct suppression of foraging during late spring followed by intense feeding in early summer. Overall, we found that Yellow Bass foraging is highly plastic but heavily constrained by spatial (lake identity, season, and year) and biological (ontogeny, age, and sex) filters. Spatial heterogeneity was the primary driver of diet composition ${(R}^2=0.407)$, with individual lakes explaining the largest portion of variance ${(R}^2=0.126)$. The interaction between lake size and population history ${(R}^2=0.054)$ was also significant, highlighting that the ecological impact of Yellow Bass is context-dependent, differing among established populations in small lakes versus recent invasions in large lakes. We identified distinct ontogenetic breakpoints at 114 mm and 252 mm; fish < 114 mm were obligate zooplanktivores, while significant piscivory was restricted to large adults (>252 mm). These results suggest that the successful colonization of Yellow Bass is supported by high dietary plasticity, which may lead to intensive resource competition with native juveniles. Our findings provide a critical baseline for fisheries managers to assess the ecological risks associated with Yellow Bass expansion and emphasize the importance of monitoring trophic shifts to preserve the integrity of native fish communities in the Midwest. Full article

The mining industry still faces major environmental and socioeconomic problems as a result of tailings dam failures, which highlights the urgent need for improved monitoring and early-warning systems. This research offers practical recommendations for improved monitoring and safer design practices, in addition to investigating the use of digital image processing (DIP) as a non-invasive technique for tracking slope deformation in tailings dam models subjected to incremental pore water pressure increases. To replicate real-world conditions as closely as possible, a scaled laboratory embankment was built using coarse and fine tailings. During controlled pore-pressure loading, more than 500 high-resolution photos were taken, recording the entire deformation sequence from initial displacement to slope failure. The images were processed using Mathematica to generate pixel-by-pixel displacement fields and vector plots, providing a detailed visualization of deformation mechanisms. The findings demonstrated that DIP accurately detects and measures surface displacement, revealing the mechanisms, direction, and intensity of deformation. This study illustrates the extensive potential of DIP for real-time monitoring by directly connecting slope instability triggered by incremental pore water pressure with visual indications of slope deformation. While the results confirm the strong potential of DIP for deformation monitoring with a minimum detectable displacement of approximately 1.0 mm under controlled laboratory conditions, its field application may be affected by scale effects, variable lighting, and environmental occlusion. The mining industry benefits greatly from the insights gained through in-depth image analysis, which promotes safer tailings dam design and management. Overall, DIP can provide a reliable, scalable foundation for real-time deformation monitoring in operational tailings dams, where continuous image-based measurements can help identify early signs of instability and support proactive risk management. Full article