Search Results (6,914)

Background and objectives: Increasing daily steps during weight management programs remains one of the most common recommendations; however, why, when and how many is still unclear. To clarify this, we aim to conduct a systematic review and meta-analysis. Methods: The study was conducted in adherence to the PRISMA guidelines on randomized controlled trials (RCTs), that included weight loss (WL) interventions based on lifestyle modification programs (LSMs), compared to “as usual care” considered as controls, to whom both daily steps and WL% were reported or retrievable at baseline (Time 0), end of WL phase (Time 1, WL1%), and weight maintenance phase (Time 2, WL2%), for both arms. Results: A total of 18 RCTs met the inclusion criteria and were included in the systematic review. Of those, 14 underwent meta-analysis and five main findings were revealed: (i) at baseline (Time 0), no significant difference was observed in mean daily steps between the LSM and controls (7280 vs. 7180, p = 0.336), reflecting a similar lifestyle between arms; (ii) at Time 1, the mean duration was 7.88 months (range = 3–12 months), and the LSM arm showed a significant increase in daily steps with respect to baseline (8454 vs. 7486 steps, p = 0.017) and a significant WL (WL1% = 4.39%, p < 0.001); (iii) at Time 2, the mean duration was 10.27 months (range = 3–24 months), and the LSM arm maintained the level of daily steps achieved by the end of WL phase (8241 vs. 8454 steps, p > 0.05), and also a significant WL% (WL2% = 3.28%, p = 0.001); (iv) the control arm showed no significant changes in daily steps and weight status at all times of assessment; and (v) the meta-regression showed in the LSM arm a positive relationship between daily steps at Time 1 (β = 1.33, p = 0.03) and Time 2 (β = 1.10, p = 0.02), both with WL2%. Conclusions: Our preliminary study results support that during LSM programs, patients should be encouraged to increase their daily steps during the WL phase, targeting approximately 8500 steps/day and maintaining these levels during the maintenance phase, since this strategy appears to be a useful behavioral approach associated with maintaining significant WL in the long term. Full article

The aim of the current study was to examine whether weekly dynamic stress load (DSL) volume could be associated with competition internal and external load outcomes in professional soccer players. Weekly DSL volume was recorded across standardized one-match microcycles. Match outcomes included total distance covered (TDC), high-speed running distance (HSRD), sprint distance (SPRD), high-intensity accelerations (HIACC), high-intensity decelerations (HIDEC), high-metabolic-load distance (HMLD), time spent > 85% of maximum heart rate (HRmax), and Edwards training impulse (Edwards’ TRIMP). Analyses of our results revealed that higher weekly DSL volume was associated with greater time > 85%HRmax in the first half (β = 0.00647; p = 0.002) and second half (β = 0.00764; p = 0.026). In the second half, weekly DSL was negatively associated with HSRD (β = −0.3068; p < 0.001) and SPRD (β = −0.0619; p < 0.001), and positively with HMLD (β = 0.3532; p = 0.002). Across the full match, weekly DSL was negatively associated with TDC (β = −0.5080; p = 0.002), HSRD (β = −0.4159; p < 0.001), SPRD (β = −0.0988; p < 0.001), HIACC (β = −0.0265; p = 0.003), and Edwards’ TRIMP (β = −0.2251; p = 0.001). Weekly DSL volume may represent an important monitoring tool providing useful information for practitioners aiming to manage fatigue and support competition performance maintenance; however, these findings should be interpreted cautiously until confirmed in larger samples. Full article

Atmospheric corrosion of carbon and low-alloy steels causes direct economic losses that are estimated at around 3.4% of the global GDP, and its accurate multi-year prediction is essential for protective coating selection, service-life estimation, and infrastructure maintenance scheduling. In this study, machine learning (ML) algorithms, including gradient boosting regressor (GBR), eXtreme gradient boosting (XGBoost), random forest (RF), support vector regression (SVR), and ridge regression, were trained on a 600-sample physics-grounded dataset to predict the cumulative atmospheric corrosion loss (µm) of low-alloy steels over 1–10 years of exposure. The dataset was constructed using the exact ISO 9223:2012 dose–response function (DRF) for a first-year corrosion rate and the ISO 9224:2012 power-law multi-year kinetic model (C(t) = C₁·t0.5), spanning ISO 9223 corrosivity categories C2–CX across 11 environmental and material input features. All models were evaluated on the original (untransformed) corrosion scale under an 80/20 train/test split and five-fold cross-validation. Gradient boosting achieved the best overall performance with test set R² = 0.968, CV-R² = 0.969, RMSE = 10.58 µm, MAE = 5.99 µm, and MAPE = 12.6%. XGBoost was a close second (R² = 0.958, CV-R² = 0.960). RF achieved an R² of 0.944. SHAP (SHapley Additive exPlanations) analysis identified SO₂ deposition rate, exposure time, relative humidity, Cl⁻ deposition rate, and temperature as the five most influential predictors. The dominance of the SO₂ deposition rate (mean |SHAP| = 26.37 µm) and the high second-place ranking of exposure time (13.67 µm) are fully consistent with the ISO 9223:2012 dose–response function and ISO 9224:2012 power-law kinetics, respectively, while among the material features, Cu and Cr contents showed the strongest negative SHAP contributions, confirming their corrosion-inhibiting roles in weathering steels. These results establish a physics-consistent, interpretable ML benchmark exceeding R² = 0.90 for multi-year cumulative corrosion loss prediction and provide a quantitative tool for alloy screening, coating selection in aggressive atmospheric environments, and service-life planning. Full article

Body temperature regulation in ectotherms is influenced by numerous environmental, morphological, and physiological factors, some of which operate in population-specific ways. Understanding how these factors shape thermal biology is important for species conservation. The nose-horned viper, an ecologically significant yet understudied mesopredator of southeastern Europe and Asia Minor, occupies diverse ecosystems facing ongoing degradation. Over five years, we investigated how 12 environmental, behavioral, morphological, and physiological variables influenced field body temperature across three climatically distinct populations of nose-horned vipers. Using an information-theoretic approach with model averaging, we identified important predictors and assessed population-specific effects. Air temperature at 5 cm above the snake’s position, humidity, and wind were highly important predictors across all populations, whereas physiological states (shedding and digestion) exerted weaker effects. Microhabitat type and time of day emerged as highly important population-specific predictors, while body size showed weaker, population-dependent effects. Neither sex, cloud cover, nor behavioral state contributed meaningfully to model fit. Mean body temperatures were similar across populations and sexes. By integrating environmental, behavioral, physiological, and morphological variables, this study comprehensively identifies predictors of body temperature in nose-horned vipers. Site-tailored maintenance of structurally diverse habitats is essential for preserving thermoregulatory opportunities and ensuring long-term persistence of nose-horned vipers. Full article

Road construction, inspection and maintenance are activities that often require workers near heavy equipment, traffic, and dangerous materials. This proximity to potential hazards along with the characteristics of highway and street work zones—transient and in restricted areas—increases the possibility of accidents and near-misses. Recent developments in remote-controlled technology can provide workers and inspectors with the ability to conduct activities from a safer distance. This paper aims to scan and evaluate several promising remote-controlled technologies that could be used to improve safety in highway and streets work zones. The technology scanning highlighted over twenty technologies in several levels of development that met this goal. Each technology was briefly evaluated not only based on safety features but also on productivity, data processing, and requirements for implementation. Finally, recommendations for implementation of selected technologies were provided. This consolidated review provides a unique and timely resource for researchers and practitioners. Full article

Nuclear transport is a vital system that mediates movement of essential biomolecules between the nucleus and cytoplasm. It is tightly regulated by the Importin (IMP) superfamily to maintain separation of cellular compartments. Cellular stress in various forms, particularly oxidative, can suspend nuclear transport and lead to cell death. Prolonged oxidative stress manifests in myriad conditions, including cancer, viral infection and metabolic disease. An IMP protein, Importin-13 (IMP13), retains function under stress, while all other IMP family members tested to date do not. Phylogenetic and structural analysis revealed Transportin-3 (TNPO3) as the closest homologue of IMP13, suggesting it may also retain its function under stress. Subcellular localisation studies indicated that TNPO3 maintained its typical subcellular localisation, even in the presence of stress, unlike most IMP family members. Also, fluorescence recovery after photobleaching (FRAP) demonstrated that TNPO3 shuttling is unaffected under stress. Co-immunoprecipitation studies examining cargo binding revealed the capacity of TNPO3 to bind its cargo in the presence of stress. This demonstrated for the first time that TNPO3 retains functionality under stress conditions, in contrast to other IMPs, but similar to IMP13. Furthermore, both IMP13 and TNPO3 appear to protect against the potentially critical mislocalisation of Ran, a key molecule involved in the maintenance of the nuclear transport system. Full article

Photovoltaic (PV) inverter fault prediction is critical for maintaining system reliability and minimizing energy loss. While recent studies have improved predictive accuracy using data-driven approaches, most evaluations remain focused on offline settings and do not address how probabilistic predictions are translated into operational decisions. This study investigates multi-horizon fault prediction for PV inverters under real-world constraints, with a particular focus on decision-level behavior. A modular prediction framework is implemented by combining transformer-based TimeXer embeddings with probabilistic classification using XGBoost. The model operates on sliding-window sensor data and produces fault probabilities across multiple future horizons. To support operational use, these probabilities are aggregated into a single risk score, and threshold-based alarm policies are evaluated through a systematic threshold sweep. The results show that predictive performance varies across horizons, with usable lead-time information concentrated in near-term predictions. Under severe class imbalance, imbalance-aware training significantly improves detection performance in precision–recall space, but performance remains sensitive to temporal variation. Most importantly, the threshold-sweep analysis reveals a structural trade-off between detection performance and alarm burden, where achieving moderate early-warning capability requires substantially increased alarm rates. These findings indicate that improving predictive accuracy alone is insufficient for practical deployment. Instead, decision-level behavior must be explicitly considered when designing predictive maintenance systems under operational constraints. Full article

Monitoring oil pipelines is crucial for effective infrastructure management and maintenance, as it helps prevent threats such as vandalism and leaks that can lead to catastrophic events. Pipeline leaks pose significant environmental and economic risks; however, existing detection methods are often expensive, slow, or unreliable, limiting their effectiveness for real-time applications. This study proposes a lightweight thermal-imaging-based intelligent leak detection system that integrates Convolutional Neural Networks (CNN), Autoencoder (AE), and Knowledge Distillation (KD), suitable for deployment on edge devices. The proposed system addresses challenges associated with existing pipeline detection techniques, including large model sizes, high transmission latency, and excessive energy consumption. Thermal images of pipelines are captured and compressed using an autoencoder before being processed by a CNN model optimized through knowledge distillation. The model was trained and tested on a locally collected thermal image dataset and designed for deployment on edge devices such as Raspberry Pi to simulate edge computing scenarios. Experimental results demonstrate that the proposed CNN + KD + AE model achieved 98% accuracy, 98% precision, 98% recall, and an F1-score of 98%, outperforming baseline models such as MobileNetV2 (91%), InceptionV3 (84%), EfficientNet-Lite (81%), and ResNet (74%). Furthermore, the number of trainable parameters was significantly reduced to 1.18 million, with a compact model size of 4.51 MB. These findings confirm the system’s suitability for real-time leak detection in remote and resource-constrained environments, contributing to the development of cost-effective, scalable, and energy-efficient solutions for intelligent pipeline monitoring. Full article

Water distribution networks are part of critical infrastructure, and ensuring a rapid return to service after failures is vital for public health and economic resilience. While numerous studies have quantified failure rates and examined factors that influence the duration of repairs, the seasonal variability of repair time itself has received little attention. This study analyses 264 monthly observations (January 2004–December 2025) from a large urban water supply system in south-eastern Poland. We evaluate the seasonality of failure counts, average repair time per event, and the total labour hours needed to restore service. Methods include descriptive statistics, seasonal indices, non-parametric tests, kernel density estimation, parametric distribution fitting, empirical exceedance curves of monthly mean repair duration, and time-series decomposition. The results show a pronounced seasonal pattern in the number of failures and total labour hours, with peaks in winter and minima in spring, whereas the monthly mean repair duration remained stable at approximately 8 h and showed no significant seasonal variation. Among the positive-support candidate distributions, the log-normal model provided a slightly better fit than the Weibull model. Empirical exceedance analysis and non-parametric tests confirmed no significant differences in monthly mean repair duration between seasons or calendar months. Decomposition reveals a small downward trend in total repair hours after 2010. These findings provide new insights for maintenance planning and indicate that winter workload peaks are driven primarily by higher failure counts rather than by longer mean repair duration. Full article

The treatment paradigm for HER2-positive metastatic breast cancer has evolved from continuous chemotherapy-based regimens to a model of finite chemotherapy induction followed by sustained antibody-driven disease control. The CLEOPATRA trial established dual HER2 blockade with trastuzumab and pertuzumab plus a taxane as the biological and clinical anchor of this approach, demonstrating that chemotherapy is administered for a defined induction period, after which antibody maintains disease suppression. An increasing body of clinical evidence indicates that antibody-based regimens can be combined with targeted agents, including CDK4/6 inhibitors or HER2 tyrosine kinase inhibitors, to achieve durable disease control without the need for continuous chemotherapy. In the PATINA trial, the addition of palbociclib to trastuzumab, pertuzumab, and endocrine therapy was associated with a clinically meaningful improvement in progression-free survival in hormone receptor-positive, HER2-positive metastatic breast cancer. At the same time, quality of life was maintained despite higher rates of hematologic toxicity. More recently, HER2-CLIMB-05 demonstrated that the addition of tucatinib to dual HER2 antibody therapy significantly prolonged progression-free survival, supporting a model of sustained, multi-agent HER2 pathway suppression. The monarcHER trial provided biological proof of concept that antibody plus CDK4/6 inhibition can achieve disease control without chemotherapy in hormone receptor-positive, HER2-positive disease. Collectively, these advances support a translational framework in which antibody therapy serves as a central component of treatment strategies, with targeted partners selected according to tumor biology and prior therapy. This review summarizes the biological basis, clinical evidence, and future perspectives of antibody-driven maintenance in HER2-positive metastatic breast cancer. Full article

This paper presents an adaptive noise reduction method based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) to address the non-stationary characteristics and noise interference present in friction vibration signals from mechanical equipment. and friction testing machine simulation experiments. The performance of CEEMD and Ensemble Empirical Mode Decomposition (EEMD) was compared through MATLAB R2023b simulations and experiments conducted on a friction testing machine. CEEMD achieved a computational efficiency 85.6% higher than that of EEMD and effectively reduced mode aliasing. Among them, the adaptive correlation coefficient screening method performed well in signal reconstruction, and the high correlation (correlation coefficient > 0.8) between the denoised signal and the laboratory noise signal was verified using the multi-scale permutation entropy (MPE) theory, which is of great significance for early diagnosis of mechanical faults, prediction of equipment life and timely maintenance decisions. Full article

High-voltage bushings, the fault-prone key electrical components of transformers, are critical for real-time and high-accuracy fault monitoring and management. Intelligent fault detection via infrared images is plagued by low classification accuracy due to massive interference from similar tubular objects and small target characteristics. This study proposes a lightweight deep learning model, MTrip–YOLO, an improved YOLO11n integrated with Mamba-2 (Structured State Space Duality, SSD) and Triplet Attention, to achieve efficient fault monitoring in complex backgrounds. The training and validation dataset comprises open-source images, on-site data from a substation, and field-collected infrared images, categorized into four types: normal bushings, poor contact, oil shortage, and high dielectric loss faults. Mamba-2 captures the long-range global context of infrared features with its linear-complexity long-range modeling capability to enhance feature extraction, while Triplet Attention suppresses complex background radiation noise through cross-dimensional interaction without dimensionality reduction, enabling the model to focus on small targets and accurately classify bushings from morphologically similar strip-shaped objects. Experimental results show that MTrip–YOLO achieves a top mAP50 of 91.6% and a minimal parameter count of 1.9 M, outperforming Faster R-CNN, RT-DETR, and YOLO26n across all evaluated metrics and being potentially suitable for edge deployment on UAV-mounted or handheld infrared platforms, pending hardware validation on embedded computing devices. Ablation experiments verify the independent contributions of Mamba-2 (0.8027% mAP50 improvement) and Triplet Attention (0.89327% mAP50 improvement), with a synergistic effect from their combination. MTrip–YOLO provides a potential edge-deployable solution for high-voltage bushing fault monitoring, offering important application value for the intelligent operation and maintenance of substations. Full article

Fine-grained energy management in smart-campus buildings requires accurate load forecasting together with reliable and interpretable anomaly diagnosis. This study presents an integrated forecasting–diagnosis framework for building energy systems. Hourly energy demand is modeled using a Transformer-based sequence-to-sequence architecture, in which a domain-aware attention mechanism is introduced to separately represent historical consumption dynamics, environmental influences, and temporal regularities commonly observed in building energy use. Anomaly diagnosis is conducted through a dual-scale strategy that supports both the timely detection of abrupt abnormal events and the identification of gradual performance degradation. Short-term anomalies are detected from forecasting residuals using adaptive thresholds, while long-term anomalies are identified by comparing current residual patterns with same-season historical baselines and validating multi-window trends over a 48 h horizon. The two detection streams are jointly used to distinguish point, pattern, and composite anomalies. To support practical operation and maintenance, SHAP-based explanations are provided to interpret both energy predictions and detected anomalies. Case studies on two educational buildings from the Building Data Genome Project 2 demonstrate that the proposed framework achieves the best overall forecasting performance against both conventional baselines and stronger recent Transformer-based models, with mean absolute percentage errors of approximately 3%. The results indicate that the proposed framework provides a practical solution for data-driven energy monitoring and decision support in smart buildings. Full article

Maintenance is crucial for the durability of the existing building stock and should be perceived as an opportunity to improve the built environment. The implementation of thermal retrofitting measures to the building’s envelope enhances global energy performance, which is economically and environmentally beneficial. Building-related energy consumption during the operation phase is key to tackling carbon neutrality and climate change. Introducing thermal retrofitting within the context of maintenance planning can be cost-optimizing, as it reveals the technical–economic synergy between building pathology and energy efficiency. Maintenance activities and energy demand throughout the building’s service life influence life-cycle costs (LCCs). Decision-making based on LCC awareness is an advantage for owners. This study discusses the impact of implementing an optimal retrofitting solution (ORS), according to different maintenance strategies, on the LCC of an existing single-family home. The ORS comprises the following measures: adding an external thermal insulation composite system (ETICS) to external walls, extruded polystyrene (XPS) panels to the roof, and replacing the existing windows with others with improved thermal performance. The three maintenance strategies involve different complexity levels, concerning the type, number and timing of activities. Moving beyond isolated assessments, this study develops an integrated framework that bridges based on two existing background methodologies, involving optimal thermal retrofitting and condition-based maintenance planning, which, combined with new research, enable the assessment of maintenance, energy and global LCC for a time horizon of 100 years. The evaluation of energy-related LCC is based on simulations. The results indicate that these costs represent the majority of the global LCC. The ORS has a considerable positive impact on energy and global LCC. Adopting a maintenance strategy characterized by fewer planned activities and an earlier schedule of replacement interventions, which determines the implementation of the retrofitting measures, is better in terms of LCC savings. Full article

High-quality process data are essential for modern manufacturing processes to enable advanced control techniques, fault detection, and predictive maintenance. However, real-world industrial datasets often contain missing values due to sensor failures, power outages, and equipment maintenance. This paper proposes a novel implicit–explicit diffusion model that jointly utilizes both hidden and visible properties for industrial data imputation. The model employs a dual-branch architecture: one branch uses multi-scale dilated causal convolutions to capture hierarchical periodic patterns inherent in industrial time series, while the other branch leverages structured state space (S4) models to learn long-term dependencies. A gated fusion mechanism adaptively combines these complementary representations. Extensive experiments on Debutanizer and Sulfur Recovery Unit (SRU) datasets demonstrate that the proposed method achieves the best root mean squared error (RMSE) across all tested missing rates (20–80%) on both datasets, and exhibits particularly strong advantages in high-missingness scenarios (60–80%), where the proposed multi-scale and long-range modeling capabilities prove most beneficial. Full article