Search Results (969)

This paper presents a new methodical procedure to monitor in real time the junction temperature of SiC Power MOSFET modules of parallel-connected chips utilized in machine drive systems to develop their reliability modelling and predict their lifetime. The paper implements the on-line measurements of temperature-sensitive electrical parameters (TSEP) approach, particularly the quasi-threshold voltage and the on-state drain to source voltage, to estimate the junction temperature in real time. The proposed procedure firstly applied computational fluid dynamics analysis on the module under study to determine the chip which undergoes the maximum junction temperature during typical operation of the module. Then, a calibration phase, using double-pulse tests on the selected chip, is used to generate look-up tables to relate the TSEPs under study to the junction temperature. Next, the real-time estimation of junction temperature was accomplished during the on-line operation of the three-phase inverter, taking into account the induced distortion/noises due to operation of the parallel-connected chips in the module. After that, a comparison between the two TSEPs under study was provided to demonstrate their advantages/drawbacks. Finally, reliability modelling was developed to predict the lifetime of the studied module based on the estimated junction temperature under a predetermined mission profile. Full article

This paper presents the design, implementation, and experimental validation of a Condition Monitoring (CM) circuit for SiC-based Power Electronics Converters (PECs). The paper leverages in situ drain–source resistance (R_ds,on) measurements, interfaced with cloud connectivity for data processing and lifetime assessment, addressing key limitations in current state-of-the-art (SOTA) methods. Traditional approaches rely on expensive data acquisition systems under controlled laboratory conditions, making them unsuitable for real-world applications due to component variability, time delay, and noise sensitivity. Furthermore, these methods lack cloud interfacing for real-time data analysis and fail to provide comprehensive reliability metrics such as Remaining Useful Life (RUL). Additionally, the proposed CM method benefits from noise mitigation during switching transitions by utilizing delay circuits to ensure stable and accurate data capture. Moreover, collected data are transmitted to the cloud for long-term health assessment and damage evaluation. In this paper, experimental validation follows a structured design involving signal acquisition, filtering, cloud transmission, and temperature and thermal degradation tracking. Experimental testing has been conducted at different temperatures and operating conditions, considering coolant temperature variations (40 °C to 80 °C), and an output power of 7 kW. Results have demonstrated a clear correlation between temperature rise and R_ds,on variations, validating the ability of the proposed method to predict device degradation. Finally, by leveraging cloud computing, this work provides a practical solution for real-world Wide Band Gap (WBG)-based PEC reliability and lifetime assessment. Full article

While IPV is often studied as a predictor of housing insecurity, few U.S. studies explore how different forms of housing instability may contribute to intimate partner violence (IPV) risk. Using a mixed-methods approach and a cross-sectional design, this study examined the association between four housing instability domains and IPV among a sample of tenants that had either experienced eviction or were at high risk for eviction. Tenants in Harris and Travis counties (Texas, USA) completed an online survey (n = 1085; March–July 2024). Housing instability was assessed across four domains: homelessness, lease violations, utility hardship, and poor housing quality. IPV was measured using the Hurt, Insult, Threaten, Scream Screener. Covariate-adjusted logistic regression models suggest indicators within the four housing instability domains were associated with IPV risk. Within the homelessness domain, experiences with lifetime homelessness (AOR = 1.92, 95%CI 1.61–2.28), in the past 12 months living in unconventional spaces (AOR = 2.10, 95%CI 1.92–2.29), and moving in with others (AOR = 1.20, 95%CI 1.06–1.36) were associated with IPV. Within the lease violations domain, missed rent payments (AOR = 1.69, 95%CI 1.68–1.71) and non-payment lease violations (AOR = 2.50, 95%CI 2.29–2.73) in the past 12 months were associated with IPV. Utility shutoffs (AOR = 1.62, 95%CI 1.37–1.91) and unsafe housing (AOR = 1.65, 95%CI 1.31–2.09) in the past 12 months were associated with IPV. Homelessness, housing-related economic hardships and substandard living conditions predict an elevated risk of IPV. Full article

High-voltage diodes, as key devices in power electronic systems, have important significance for system reliability and preventive maintenance in terms of storage life prediction. In this paper, we propose a hybrid modeling framework that integrates the Long Short-Term Memory Network (LSTM) and Transformer structure, and is hyper-parameter optimized by the Improved Artificial Bee Colony Algorithm (IABC), aiming to realize the high-precision modeling and prediction of high-voltage diode storage life. The framework combines the advantages of LSTM in time-dependent modeling with the global feature extraction capability of Transformer’s self-attention mechanism, and improves the feature learning effect under small-sample conditions through a deep fusion strategy. Meanwhile, the parameter type-aware IABC search mechanism is introduced to efficiently optimize the model hyperparameters. The experimental results show that, compared with the unoptimized model, the average mean square error (MSE) of the proposed model is reduced by 33.7% (from 0.00574 to 0.00402) and the coefficient of determination (R²) is improved by 3.6% (from 0.892 to 0.924) in 10-fold cross-validation. The average predicted lifetime of the sample was 39,403.3 h, and the mean relative uncertainty of prediction was 12.57%. This study provides an efficient tool for power electronics reliability engineering and has important applications for smart grid and new energy system health management. Full article

Large-scale battery datasets often contain anomalous data due to sensor noise, communication errors, and operational inconsistencies, which degrade the accuracy of data-driven prognostics. However, many existing studies overlook the impact of such anomalies or apply filtering heuristically without rigorous benchmarking, which can potentially introduce biases into training and evaluation pipelines. This study presents a deep learning framework that integrates autoencoder-based anomaly detection with a residual neural network (ResNet) to achieve state-of-the-art prediction of remaining useful life at the cycle level using only a single-cycle input. The framework systematically filters out anomalous samples using multiple variants of convolutional and sequence-to-sequence autoencoders, thereby enhancing data integrity before optimizing and training the ResNet-based models. Benchmarking against existing deep learning approaches demonstrates a significant performance improvement, with the best model achieving a mean absolute percentage error of 2.85% and a root mean square error of 40.87 cycles, surpassing prior studies. These results indicate that autoencoder-based anomaly filtering significantly enhances prediction accuracy, reinforcing the importance of systematic anomaly detection in battery prognostics. The proposed method provides a scalable and interpretable solution for intelligent battery management in electric vehicles and energy storage systems. Full article

A State of Health (SOH) estimation of lithium-ion batteries is a core function of battery management systems, directly affecting the safe operation, lifetime prediction, and economic efficiency of batteries. However, existing methods still face challenges in balancing feature robustness and model generalization ability; for instance, some studies rely on features whose physical correlation with SOH lacks strict verification, or the models struggle to simultaneously capture the temporal dynamics of health factors and nonlinear mapping relationships. To address this, this paper proposes an SOH estimation method based on incremental capacity (IC) curves and a Temporal Convolutional Network—Relevance Vector Machine (TCN-RVM) model, with core innovations reflected in two aspects. Firstly, five health factors are extracted from IC curves, and the strong correlation between these features and SOH is verified using both Pearson and Spearman coefficients, ensuring the physical rationality and statistical significance of feature selection. Secondly, the TCN-RVM model is constructed to achieve complementary advantages. The dilated causal convolution of TCN is used to extract temporal local features of health factors, addressing the insufficient capture of long-range dependencies in traditional models; meanwhile, the Bayesian inference framework of RVM is integrated to enhance the nonlinear mapping capability and small-sample generalization, avoiding the overfitting tendency of complex models. Experimental validation is conducted using the lithium-ion battery dataset from the University of Maryland. The results show that the mean absolute error of the SOH estimation using the proposed method does not exceed 0.72%, which is significantly superior to comparative models such as CNN-GRU, KELM, and SVM, demonstrating higher accuracy and reliability compared with other models. Full article

IoT devices with sensors and actuators are frequently deployed in environments without access to the power grid. These devices are battery powered and might make use of energy harvesting if battery lifetime is too limited. This article focuses on automatically adapting the duty cycle frequency to the predicted available solar energy so that a continuous operation of IoT applications is guaranteed. The implementation is based on a low-cost solar control board that is integrated with the Serverless IoT Framework (SIF), which provides an event-based programming paradigm for microcontroller-based IoT devices. The paper presents a case study where the IoT device sleep time is pro-actively adapted to a predicted sequence of cloudy days to guarantee continuous operation. Full article

Clean and safe air quality is essential for public health, yet particulate matter (PM) significantly degrades air quality and poses serious health risks. The Gulf Cooperation Council (GCC) countries are particularly vulnerable to frequent and intense dust storms due to their vast desert landscapes. This study presents the first health risk assessment of carcinogenic and non-carcinogenic risks associated with exposure to PM2.5 and PM10 bound heavy metals and polycyclic aromatic hydrocarbons (PAHs) based on air quality data collected during the years of 2016–2018 near Dubai International Airport and Abu Dhabi International Airport. The results reveal no significant carcinogenic risks for lead (Pb), cobalt (Co), nickel (Ni), and chromium (Cr). Additionally, AI-based regression analysis was applied to time-series dust monitoring data to enhance predictive capabilities in environmental monitoring systems. The estimated incremental lifetime cancer risk (ILCR) from PAH exposure exceeded the acceptable threshold (10⁻⁶) in several samples at both locations. The relationship between visibility and key environmental variables—PM1, PM2.5, PM10, total suspended particles (TSPs), wind speed, air pressure, and air temperature—was modeled using three machine learning algorithms: linear regression, support vector machine (SVM) with a radial basis function (RBF) kernel, and artificial neural networks (ANNs). Among these, SVM with an RBF kernel showed the highest accuracy in predicting visibility, effectively integrating meteorological data and particulate matter variables. These findings highlight the potential of machine learning models for environmental monitoring and the need for continued assessments of air quality and its health implications in the region. Full article

The ability to predict capacity retention is critical for ensuring the long-term reliability of supercapacitors in energy storage systems. This study presents a comprehensive machine learning framework that integrates both electrical cycling data and experimentally derived material and structural features to forecast the degradation behavior of commercial supercapacitors. A total of seven supercapacitor samples were tested under various current and voltage conditions, resulting in over 70,000 charge–discharge cycles across three case studies. In addition to electrical measurements, detailed physical and material characterizations were performed, including electrode dimension analysis, Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDS), and Thermogravimetric Analysis (TGA). Three machine learning models, Linear Regression (LR), Random Forest (RF), and Multi-Layer Perceptron (MLP), were trained using both cycler-only and combined cycler + material features. Results show that incorporating material features consistently improved prediction accuracy across all models. The MLP model exhibited the highest performance, achieving an R² of 0.976 on the training set and 0.941 on unseen data. Feature importance analysis confirmed that material descriptors such as porosity, thermal stability, and electrode thickness significantly contributed to model performance. This study demonstrates that combining electrical and material data offers a more holistic and physically informed approach to supercapacitor health prediction. The framework developed here provides a practical foundation for accurate and robust lifetime forecasting of commercial energy storage devices, highlighting the critical role of material-level insights in enhancing model generalization and reliability. Full article

In the context of building a new type of power system, the optimal operation of high-proportion new-energy distribution networks (HNEDNs) is a current hot topic. In this paper, a stochastic distribution robust optimization method for HNEDNs that considers energy-storage refinement modeling is proposed. First, an energy-storage lifetime loss model based on the rainfall-counting method is constructed, and then an optimal operation model of an HNEDN considering energy storage refinement modeling is constructed, aiming to minimize the total operation cost while taking into account the energy cost and the penalty cost of abandoning wind and solar power. Then, a source-load uncertainty model of HNEDN is constructed based on the Wasserstein distance and conditional value at risk (CvaR) theory, and the HNEDN optimization model is reconstructed based on the stochastic distribution robust optimization method; based on this, the multiple linearization technique is introduced to approximate the reconstructed model, which aims to both reduce the difficulty in solving the model and ensure the quality of the solution. Finally, the modified IEEE 33-bus power distribution system is used as an example for case analysis, and the simulation results show that the method presented in this paper, through reducing the loss of life in the battery storage device, can reduce the average daily energy storage depreciation cost compared to an HNEDN optimization method that does not take the energy storage life loss into account; this, in turn, reduces the total operating cost of the system. In addition, the stochastic distribution robust optimization method used in this paper can adaptively adjust the economy and robustness of the HNEDN operation strategy according to the confidence level and the available historical sample data on new energy-output prediction errors to obtain the optimal HNEDN operation strategy when compared with other uncertainty treatment methods. Full article

Background: Lynch syndrome (LS) is an autosomal dominant disease caused by germline pathogenic variants in one of the DNA mismatch repair (MMR) genes (MLH1, MSH2, MSH6, and PMS2) or the EPCAM gene. LS patients harboring genetic variants in one of the MMR genes display a heterogeneous phenotype in terms of cancer penetrance (lifetime cancer risk) and expressivity (malignancies in gastrointestinal or other specific organs). Methods: DNA samples from the index cases of Family 1 and Family 2 were analyzed using a next-generation sequencing (NGS) multigene panel comprising 25 genes involved in major hereditary cancer predisposition syndromes. This NGS analysis revealed a variant of uncertain significance (VUS) in the PMS2 gene (NM_000535.7: c.184G>A; p.Gly62Arg) of both index cases, which was validated by Sanger sequencing. The structural and functional impact of this VUS was evaluated in silico using twelve different prediction tools and by immunohistochemical analysis of MMR proteins. Results: Based on the personal and family history of the two families, tumor pathology, and protein in silico analysis, the novel PMS2 gene variant described in this study may be associated with hereditary LS. Considering the low penetrance of PMS2 gene variants in LS-associated tumors and the intrafamilial variability of the associated clinical phenotypes, the multidisciplinary approach proposed in this study could significantly support the evaluation of suspected LS cases carrying PMS2 variants. Full article

Introduction: Chronic alcohol use is a complex condition influenced by psychological, behavioral, and socio-demographic factors. This study aimed to develop a comprehensive psychosocial profile of individuals with alcohol use disorder (AUD) by examining associations between psychometric variables and relapse risk including repeated psychiatric hospitalizations. Methodology: A cross-sectional observational analytical study was conducted on a sample of 104 patients admitted for alcohol withdrawal management at the “Prof. Dr. Al. Obregia” Psychiatric Clinical Hospital in Bucharest between March 2023 and September 2024. Participants completed a set of validated psychometric tools: the Drinker Inventory of Consequences—Lifetime Version (DrInC), Readiness to Change Questionnaire—Treatment Version (RTCQ), Drinking Expectancy Questionnaire (DEQ), and Drinking Refusal Self-Efficacy Questionnaire (DRSEQ). Additional data were collected on the socio-demographic (education level, socio-professional category), genetic (family history of alcohol use), and behavioral factors (length of abstinence, tobacco use, co-occurring substance use disorders). Results: Higher alcohol-related consequence scores (DrInC) were significantly associated with lower education (p < 0.001, η² = 0.483), disadvantaged socio-professional status (p < 0.001, η² = 0.514), and family history of alcohol use (p < 0.001, η² = 0.226). Self-efficacy (DRSEQ) was significantly lower among individuals with co-occurring substance use (p < 0.001) and nicotine dependence (p < 0.001). Logistic regression showed that the DrInC scores significantly predicted readmission within three months (OR = 1.09, p = 0.001). Conclusions: Psychometric tools are effective in identifying individuals at high risk. Personalized, evidence-based interventions tailored to both psychological and socio-professional profiles, combined with structured post-discharge support, are essential for improving long-term recovery and reducing the readmission rates. Full article

Toxoplasma gondii is an Apicomplexan parasite that possesses a well-developed system of scavengers of reactive oxygen species (ROS). Among its components, T. gondii mitochondrial superoxide dismutase (TgSOD2) is essential, as predicted by the CRISPR phenotype index and evidenced by the non-viability of its constitutive knockouts. As an obligate intracellular parasite, TgSOD2 is upregulated during extracellular stages. Herein, we generated a viable TgSOD2 knockdown mutant using an inducible auxin–degron system to explore the biological role of TgSOD2 in T. gondii. Depletion of TgSOD2 led to impaired parasite growth and replication, reduced mitochondrial membrane potential (MMP), abnormalities in the distribution of ATP synthase within its mitochondrial electron transport chain (mETC), and increased susceptibility to mETC inhibitors. Through a proximal biotinylation approach, we identified the interactions of TgSOD2 with complexes IV and V of its mETC, suggesting that these sites are sensitive to ROS. Our study provides the first insights into the role of TgSOD2 in maintaining its mitochondrial redox homeostasis and subsequent parasite replication fitness. Significance: Toxoplasma gondii infects nearly a third of the world population and can cause fetal miscarriages or life-threatening complications in vulnerable patients. Current therapies do not eradicate the parasite from the human hosts, rendering them at risk of recurrence during their lifetimes. T. gondii has a single mitochondrion, which is well-known for its susceptibility to oxidative damage that leads to T. gondii’s death. Therefore, targeting T. gondii mitochondrion remains an attractive therapeutic strategy for drug development. T. gondii’s mitochondrial superoxide dismutase is an antioxidant protein in the parasite mitochondrion and is essential for its survival. Understanding its biological role could reveal mitochondrial vulnerabilities in T. gondii and provide new leads for the development of effective treatments for T. gondii infections. Full article

The risk of developing symptomatic knee osteoarthritis (KOA) during a lifetime, i.e., pain, aching, or stiffness in a joint associated with radiographic KOA, was estimated in 2008 to be around 40% in men and 47% in women. The clinical and scientific communities lack an efficient diagnostic method to effectively monitor, evaluate, and predict the evolution of KOA before and during the therapeutic protocol. In this review, we summarize the main methods that are used or seem promising for the diagnosis of osteoarthritis, with a specific focus on non- or low-invasive methods. As standard diagnostic tools, arthroscopy, magnetic resonance imaging (MRI), and X-ray radiography provide spatial and direct visualization of the joint. However, discrepancies between findings and patient feelings often occur, indicating a lack of correlation between current imaging methods and clinical symptoms. Alternative strategies are in development, including the analysis of biochemical markers or acoustic emission recordings. These methods have undergone deep development and propose, with non- or minimally invasive procedures, to obtain data on tissue condition. However, they present some drawbacks, such as possible interference or the lack of direct visualization of the tissue. Other original methods show strong potential in the field of KOA monitoring, such as electrical bioimpedance or near-infrared spectrometry. These methods could permit us to obtain cheap, portable, and non-invasive data on joint tissue health, while they still need strong implementation to be validated. Also, the use of Artificial Intelligence (AI) in the diagnosis seems essential to effectively develop and validate predictive models for KOA evolution, provided that a large and robust database is available. This would offer a powerful tool for researchers and clinicians to improve therapeutic strategies while permitting an anticipated adaptation of the clinical protocols, moving toward reliable and personalized medicine. Full article

Background: Physical activity (PA) has been shown to prevent Alzheimer’s disease (AD) by reducing amyloid accumulation, lowering inflammatory factors, and increasing hippocampal grey matter. While high lifetime PA engagement is associated with a reduced risk of AD, the relationship between specific types of PA and early-stage AD remains unclear. As AD primarily affects cognitive function before physical capabilities, PA engagement—an important factor in PA—needs further investigation. Objectives: This study explores the potential association between current participation in open-skill sports (OSSs) versus closed-skill sports (CSSs) and early-stage AD. Methods: The sample (N = 128) included a cognitively healthy (HC, n = 78) group and an Alzheimer’s disease (AD) group, combining amnestic mild cognitive impairment due to AD patients (n = 22) and early-stage Alzheimer’s dementia patients (n = 28), reflecting the continuum of progression from aMCI to dAD (n = 50). PA was assessed with the Physical Activity Scale for the Elderly questionnaire, specifically focusing on PA within the last seven days. The statistical analyses included Mann–Whitney U tests and backwards stepwise logistic regression models. Results: Key predictors of group classification (AD vs. NC) included sex, high frequency of PA, and high duration of PA, each for the last seven days. Participation in OSS was significantly associated with medium PA frequency, high PA duration, both within the last seven days, and age, but not with diagnostic status. No statistically significant differences in PA levels (OSSs or CSSs) executed within the last seven days were observed between the AD and HC groups. Conclusions: Participation in OSSs or CSSs within the last seven days was only a marginally significant predictor of AD vs. HC status, and a diagnosis of AD was not predictive of OSS participation within the last seven days. Given the protective role of PA in AD, future research should aim to identify specific PA types that effectively support cognitive health in older adults with early cognitive decline. Full article