Search Results (416)

Maintaining road infrastructure in underground mines is critical for ensuring efficient transportation, reducing fuel consumption, extending the lifespan of machines, and providing operator safety and comfort. At the same time, the operation of heavy machinery on uneven roads, and the presence of loose rock fragments make it impossible to keep roads in consistently good condition, necessitating continuous condition monitoring and appropriate maintenance planning. This paper proposes a framework based on a single inertial sensor mounted on a mining vehicle for road quality assessment and vehicle speed estimation. The developed methods have a hybrid character, combining the physical interpretability of inertial data with unsupervised AI-based techniques. The integrated analytical system, combining road surface quality assessment with vehicle speed analysis, serves as a decision-supporting tool for pinpointing road segments that are critical for maintenance, safety, transport efficiency, and machine wear. The proposed approach was validated using data collected from haul trucks operating under real-world conditions. The system has the potential to support more efficient and sustainable management of mine road maintenance by reducing unnecessary interventions, resource consumption, and the negative environmental and safety impacts associated with haulage operations. Full article

Background/Objectives: Physical activity behaviours are established early in life and tend to track across developmental stages. However, the role of continuity of sport participation across multiple developmental periods in shaping current physical activity levels remains insufficiently understood. This study aimed to examine the association between continuity of sport participation across developmental stages and current physical activity levels in university students, and to assess whether this association followed a graded pattern and differed by sex. Methods: A cross-sectional study was conducted among 796 fourth-year undergraduate students enrolled in a Primary School Education degree program at the University of Turin. Data were collected using an anonymous online survey. Current physical activity was assessed using the International Physical Activity Questionnaire—Short Form (IPAQ-SF) and categorised as non-active, sufficiently active, or active. Sport participation across six developmental stages was retrospectively assessed and summarised into a three-level continuity variable (discontinuous, intermediate, continuous). Associations were examined using chi-square tests and ordinal logistic regression models adjusted for sex, age, and body mass index (BMI). Predicted probabilities were estimated to aid interpretation. Results: Continuity of sport participation was significantly associated with current physical activity levels (χ²(6) = 67.55, p < 0.001), with a graded pattern evident. In adjusted models, discontinuous participation (OR = 0.24, 95% CI 0.14–0.39) and intermediate participation (OR = 0.62, 95% CI 0.46–0.82) were associated with lower odds of belonging to higher physical activity categories than continuous participation. Predicted probabilities showed a clear dose–response pattern, with progressively higher likelihoods of being active as continuity increased. This pattern was consistent across sexes, although males exhibited higher overall activity levels. Conclusions: Greater continuity of sport participation across developmental stages is associated with higher current physical activity levels. Promoting sustained engagement in sport may support the development of active lifestyles across the lifespan. Full article

Wide-bandgap power devices, particularly silicon carbide (SiC) MOSFETs, have seen widespread adoption in electric vehicle (EV) motor drive systems due to their superior switching characteristics, including high switching speeds and high switching frequencies. However, these advantages exacerbate motor terminal overvoltage, with peaks reaching twice the inverter output voltage, causing insulation breakdown in windings and bearing electro-corrosion, which shorten motor lifespan. Traditional overvoltage prediction methods, such as distributed parameter models or detailed ladder network approaches, require extensive system parameters and involve high computational loads, while simplified models lack generality. To address these issues, this paper proposes a simplified prediction method based on a lumped ladder network model combined with frequency scanning. The approach uses impedance analysis to identify anti-resonance frequencies, enabling direct estimation of overvoltage amplitudes without prior knowledge of cable or motor specifics. Experimental validation on a SiC-based drive system demonstrates prediction errors below 10% and a reduction in computational time compared to conventional methods. Full article

The concept of “Healthocide,” first defined by Abi-Rached and colleagues, describes the deliberate and systematic destruction of health systems as a weapon of war. Nowhere is this phenomenon more extensively documented than in Gaza, where the collapse of healthcare infrastructure since October 2023 has been rapid, wide-ranging, and intentionally sustained. The consequence is not only immediate excess mortality, but also profound, long-term loss of population health measured in life-years, a metric that captures both premature death and reductions in expected lifespan. To address the aftermath of such destruction, we propose the framework of “Healthogenesis,” defined as a Palestinian-led, equity-driven, and rights-anchored approach to health system reconstruction in which international actors serve as enablers rather than agenda-setters. The aim of Healthogenesis is not merely to restore pre-war capacity, but to build a resilient, sovereign, and future-proof health ecosystem. Using available demographic and mortality data, we estimate that more than three million life-years have already been lost in Gaza since October 2023. Projection models suggest that an additional 1.1 to 2.2 million life-years could be lost over the coming decade unless an organized programme of reconstruction begins immediately. Quantifying harm in life-years reframes the discourse from moral outrage to measurable obligation. If Healthocide names the crime, then Healthogenesis outlines the cure: a coherent, data-anchored, ethically grounded roadmap for rebuilding a devastated health system and protecting the health futures of an entire population. Full article

Breast prostheses are widely used for both aesthetic and medical purposes. Unless clinical or subjective factors impose early removal, these implants can remain in place for extended periods of time, often exceeding 20 years. Understanding the expected changes in their performance over time, in addition to medical issues, is crucial for decisions regarding potential removal or replacement. This study investigates the long-term aging effects on silicone breast implants by evaluating changes in the mechanical properties of the elastomeric shell and the viscoelastic behavior of the inner gel. Accelerated aging tests were conducted at different temperatures, and the data were analyzed using established predictive models to estimate mechanical performance over extended periods. These results provide valuable insights into the expected durability and lifespan of breast implants, supporting improved predictions of long-term safety. Full article

Unbiased stereology represents the most accurate approach for estimating the total number of neurons of specific brain regions; however, its reliability critically depends on the use of rigorously defined and anatomically appropriate sampling parameters. The brain nucleus Locus Coeruleus (LC) plays a key role in several brain functions. LC impairment has been associated with a range of disorders affecting individuals across the lifespan, from infancy to adulthood. In animal models of these conditions, precise estimation of LC neuronal number is essential. The LC analysis poses specific methodological challenges due to its small size, indistinct anatomical boundaries, and age-dependent changes in neuronal density. In this study, we present a detailed and reproducible stereological workflow for the quantification of LC neurons in the mouse brain across the lifespan. Using C57BL/6J mice at postnatal, adult, and aged stages, we optimized all key components of the Optical Fractionator method, LC neurons were identified by immunoperoxidase staining for tyrosine hydroxylase (TH) and quantified using systematic-random sampling implemented in Stereo Investigator^® software. We show that age-specific adjustment of stereological parameters is necessary to obtain reliable estimates, particularly at early postnatal stages characterized by high neuronal packing density. With the optimized protocols described here, TH+ LC neuron counts consistently met accepted precision criteria, as assessed by the Gundersen coefficient of error. Full article

Background/Objectives: While adequate mineral intake is essential for brain health and cognitive function across the lifespan, the potential impact of excessive consumption remains underexplored. This study aimed to examine the association between dietary intake of selected minerals, with particular focus on iron and zinc, and cognitive performance in Spanish adults with obesity, particularly in executive-related domains such as reasoning, cognitive flexibility, and working memory. Methods: A cross-sectional study was conducted in 230 Spanish adults (18–65 years) from the Tech4Diet-Person project. Sociodemographic, dietary, and cognitive data were collected between 2021 and 2024. Cognitive function was assessed using the validated computerized CogniFit battery, and mineral intake was estimated through a food frequency questionnaire (93 items). Individuals with neurological, metabolic, or psychiatric disorders, as well as pregnant or lactating women, were excluded. Results: Participants had a mean age of 45.91 (±9.92) years. Nominal differences in mineral intake were observed across specific executive cognitive domains. Higher dietary iron intake was associated with lower performance in reasoning and cognitive flexibility, while higher zinc intake was associated with lower working memory performance. In adjusted logistic regression models, higher iron intake was independently associated with increased odds of low reasoning performance (OR = 1.25; p = 0.006), and higher zinc intake was associated with increased odds of low working memory performance (OR = 1.36; p = 0.024), after controlling for age, educational level, BMI, and total energy intake. Conclusions: Higher self-reported intake of iron and zinc showed nominal associations with lower performance in specific executive domains. These findings should be considered exploratory and require confirmation in longitudinal and biomarker-based studies. Full article

Hydropower is a cornerstone of global renewable energy; however, reservoir sedimentation directly undermines its benefits and operational lifespan. A critical, often overlooked, aspect of sedimentation is the compaction of fine-grained deposits, which introduces systematic discrepancies between standard siltation calculation methods. This study addresses this gap by developing a machine learning-based model to quantify sediment compaction and correct siltation estimates using the Xiluodu Hydropower Station on the Jinsha River, China, as a case study from 2014 to 2020. Based on hydrological, sediment, and fixed-section monitoring data, we applied five machine learning algorithms (Linear Regression, Neural Network, Random Forest, Gradient Boosting, and Support Vector Regression) to establish a relationship between the compaction thickness and the following key predictors: Year, Cumulative Sediment Thickness, Annual Sediment Thickness, and Distance to the Dam. The results demonstrate that the Neural Network (NN) model significantly outperforms traditional models, effectively capturing complex, nonlinear compaction dynamics with strong predictive accuracy (test R² = 0.766, RMSE = 0.047 m) and no significant overfitting. SHAP analysis revealed the dominant influences of consolidation time (years) and overburden stress (Cumulative Sediment Thickness), linking the model’s predictions to fundamental geotechnical principles. Applying the NN model to correct for the cross-sectional volume method markedly improved its consistency with the independent sediment transport method, reducing the average relative difference from −33.7% to −6.5% (2016–2020). This study provides the first quantitative, continuous (198 km, 221 sections) assessment of reservoir-scale sediment compaction, confirming its widespread existence and demonstrating its critical role in the long-standing methodological discrepancies. Our study transformed compaction from an acknowledged phenomenon into a quantifiable correction, offering a novel, data-driven framework to enhance the accuracy of reservoir sedimentation assessments globally. Full article

Unbalanced supply voltage (USV) represents a critical power quality challenge in industrial environments, significantly degrading the performance, efficiency, and operational lifespan of three-phase induction motors. Accurate real-time estimation of sequence impedances ($Z_a,Z_b,Z_c$) and detection of the Negative Voltage Factor (NVF) are essential for effective condition monitoring and preventive maintenance strategies. While existing machine learning methods have demonstrated promising accuracy, they often rely on manual feature engineering, lack hierarchical representation learning, and treat impedance estimation and fault detection as isolated tasks. This paper proposes a unified Deep Multi-Task Learning framework that leverages Residual Multilayer Perceptron (ResMLP) architectures for feature-based learning and Temporal Convolutional Networks (TCNs) for end-to-end raw signal learning. Our contributions include: (1) introduction of a Multi-Head ResMLP architecture that jointly optimizes phase impedance and fault detection, achieving superior NVF accuracy (MAE = 0.0007) and a fault detection F1-score of 0.8831; (2) investigation of raw-voltage TCN models for voltage-only diagnostics, with analysis of the trade-offs between end-to-end learning and feature-based approaches; (3) extensive ablation studies demonstrating the impact of network depth, data augmentation, and training protocols on model generalization; and (4) deployment of PyTorch (v2.0.1)-based models suitable for embedded systems with real-time inference capabilities (2.3 ms per prediction). Experimental validation on a 1.1 kW three-phase motor dataset under diverse load conditions (0–10 Nm) and USV magnitudes (5–15 V) confirms the robustness and practical applicability of the proposed approach for industrial fault diagnosis and condition monitoring systems. Full article

The longitudinal study investigated the impact of age at first mating (AFM) on milk yield (MY) across the productive lifespan of Alpine goats born between 2005 and 2018. Data from 740 animals across three herds and 3200 lactations were analyzed. The AFM of the studied population ranged from 7 to 23 months. The impact of AFM on MY was estimated using a linear mixed model, accounting for the fixed effects of parity, litter size, season, herd, and suckling and milking durations, with the individual goat included as a random effect to control for repeated measures. The impact of AFM on lifetime production was estimated by regressing total milk yield (TMY) and number of lactations (TNL) on AFM, while accounting for herd effect. The study revealed a notable shift in productivity patterns across the animal’s life. Every additional month of AFM significantly increased milk yield in the first lactation (13.28 kg; p < 0.001), but this influence vanished in subsequent parities (p > 0.05). These higher initial yields were insufficient to compensate for the losses caused by a shortened productive lifespan. Specifically, each month of mating delay resulted in a loss of ~0.08 TNL and 34 kg TMY, totaling ~1 lactation and ~400 kg of milk for a 12-month delay. Results suggest that earlier mating may improve lifetime productivity under intensive production systems. Full article

In traditional wind turbine (WT) operation and maintenance, fault diagnosis and repair have long been relied on, yet the demand for continuous operation under faults persists. To address this, this study proposes a triggered fault-tolerant control framework for wind turbines with zonotope-based interval estimation. The method enhances safety from point to range estimation of FDI, reduces network traffic load via a WT load region-based adaptive event-triggered mechanism, and enables fast, robust fault diagnosis/isolation using interval residuals. A damage equivalent load (DEL)-sensitive cost term balances structural fatigue suppression while ensuring power tracking and safety constraints. Theoretically, Linear Matrix Inequality (LMI) conditions based on common quadratic Lyapunov ensure closed-loop stability and bounded observation errors, with proven interval residual fault sensitivity and triggering reliability. Numerically, on the standard NREL 5-MW WT model under multi-conditions (turbulence, faulty communication), it achieves an average power tracking accuracy of 95.56%, 28.68% fatigue suppression, and 67.40% bandwidth saving. Overall, it synergistically optimizes robust estimation, economical communication, and fatigue-aware control, providing a theoretically rigorous and experimentally validated technical framework for engineering-scale WT reliability improvement and lifespan extension. Full article

Lanternfishes (Myctophidae) dominate mesopelagic ecosystems and play a central role in pelagic food webs through their high biomass and diel vertical migration, yet detailed information on their age structure and growth dynamics remains limited in the Northwest Pacific Ocean. This study reconstructs age, growth patterns, and life-history strategies of D. gigas and D. perspicillatus using sagittal otolith microstructure analysis. Specimens were collected during oceanographic surveys conducted in 2023 and 2024, and individual ages were estimated by counting daily otolith growth increments. Somatic growth trajectories were evaluated using multiple nonlinear growth models, including the von Bertalanffy, Gompertz, and Logistic functions, and growth dynamics were further assessed through derivative-based growth speed analyses. The results reveal pronounced interspecific differences in growth strategy and longevity. D. perspicillatus exhibited rapid early somatic growth, a compressed age structure, and an early approach to asymptotic length, indicating a short-lived life-history strategy characterized by early growth deceleration and high population turnover. In contrast, D. gigas showed faster early growth, prolonged somatic development, greater inter-individual variability, and substantially larger maximum body size, reflecting delayed maturation and extended lifespan. Otolith microstructural zonation clearly corresponded to larval, juvenile, and adult growth phases in both species. The predominance of younger age classes in the catch and interannual differences in size structure were primarily attributed to ontogenetic habitat shifts, cohort composition, and sampling availability rather than intrinsic changes in growth dynamics. Full article

Accurate estimation of brain age from structural magnetic resonance imaging (MRI) serves as a vital biomarker for quantifying individual neurobiological aging and identifying risks for neurological disorders. However, developing robust models that generalize across the entire lifespan (from infancy to aging) remains challenging due to heterogeneous maturation/degeneration patterns, limited cross-center generalizability, and insufficient temporal reliability evaluation. To address these limitations, we curated a large-scale, multi-center T1-weighted MRI dataset across 27 public cohorts. Of these, 22,271 scans from 17 cohorts (aged 0–96 years) formed the primary foundation for model development, complemented by 10 additional cohorts utilized for independent multi-center evaluation and robustness testing. We propose ResNet-CrossViT, a novel hybrid architecture that synergistically combines a 3D Contextual Transformer-ResNet (CoTResNet3D) backbone for enriched local feature extraction and a CrossVision Transformer (CrossViT) module for cross-scale global dependency modeling. The model was rigorously evaluated on an internal test set, an unseen external dataset for cross-center validation, a longitudinal dataset for assessing temporal consistency, and a test–retest dataset for measuring reproducibility. On the internal test set, ResNet-CrossViT achieved a mean absolute error (MAE) of 2.72 years and a maximal MAE (mMAE) of 5.10 years, demonstrating marked performance improvements, particularly within the challenging adolescent cohort. The model maintained strong generalization on the unseen dataset (MAE = 4.19 years) and exhibited superior longitudinal consistency (Mean Absolute Difference Error, MAdE = 3.68) and excellent test–retest reliability (Intraclass Correlation Coefficient, ICC = 0.994). By integrating a large-scale, heterogeneous lifespan dataset with a hybrid architecture that effectively captures both local structural details and global long-range interactions, our study provides a precise, generalizable, and reliable framework for brain age estimation. Full article

Current national-scale building stock models mainly focus on structural materials, overlooking the significant resource potential of Mechanical, Electrical, and Plumbing (MEP) systems. These systems are resource-intensive and contain standardized components with high-value materials such as copper and steel, yet their potential remains largely untapped due to fragmented data. This study introduces the novel systematic framework to estimate MEP components at high granularity and national scale. It integrates harmonized public data, machine-learning imputation (>90% accuracy under sparse conditions), and parametric rules reflecting building type, energy system, and construction decade. A Swiss case study yields scalable material stock estimates and lifespan-based turnover projections, showing strong consistency with existing GHG benchmarks. The framework highlights contrasting patterns across regions and building types, indicating where policy and industry can upscale reuse and recovery. Its modular design enables transferability and integration with circular economy planning and material-efficiency targets. Full article

Improper internal states in proton exchange membrane fuel cells (PEMFCs), such as insufficient reactant concentration, lower membrane water content, and excessive liquid water, will lead to significant reductions in durability and reliability, which is a bottleneck restricting the large-scale commercial application of the PEMFC system. Closed-loop management with internal state feedback is regarded as a promising strategy for prolonging its lifespan and enhancing its reliability. The key issue for the closed-loop management strategy is how to estimate the internal operating state of the PEMFC stack accurately and quickly. Consequently, an estimation method of stack internal operating states based on the medium frequency impedance phase angle measurement, which has the characteristics of short acquisition time, small measurement error, and high resolution, is proposed in this paper. The sensitivity, monotonicity, correlation analysis in the steady state, and response characteristics analysis in the dynamic state show that the proposed method is effective, competent, and qualified for internal state estimation. Then, the estimated internal state is applied to the system’s closed-loop management as feedback. The experiment results show that the PEMFC can be maintained at the expected state and that improper states will be avoided. The proposed estimation technology will significantly facilitate the system’s closed-loop management, thereby enhancing the reliability and durability of PEMFCs. Full article