Search Results (342)

CT scanners are essential tools in modern medical imaging. Sudden failures of their X-ray tubes can lead to equipment downtime, affecting healthcare services and patient diagnosis. However, existing prediction methods based on a single model struggle to adapt to the multi-stage variation characteristics of tube lifespan and have limited modeling capabilities for temporal features. To address these issues, this paper proposes an intelligent prediction architecture for CT tubes’ remaining useful life based on a dual-branch neural network. This architecture consists of two specialized branches: a residual self-attention BiLSTM (RSA-BiLSTM) and a multi-layer dilation temporal convolutional network (D-TCN). The RSA-BiLSTM branch extracts multi-scale features and also enhances the long-term dependency modeling capability for temporal data. The D-TCN branch captures multi-scale temporal features through multi-layer dilated convolutions, effectively handling non-linear changes in the degradation phase. Furthermore, a dynamic phase detector is applied to integrate the prediction results from both branches. In terms of optimization strategy, a dynamically weighted triplet mixed loss function is designed to adjust the weight ratios of different prediction tasks, effectively solving the problems of sample imbalance and uneven prediction accuracy. Experimental results using leave-one-out cross-validation (LOOCV) on six different CT tube datasets show that the proposed method achieved significant advantages over five comparison models, with an average MSE of 2.92, MAE of 0.46, and R² of 0.77. The LOOCV strategy ensures robust evaluation by testing each tube dataset independently while training on the remaining five, providing reliable generalization assessment across different CT equipment. Ablation experiments further confirmed that the collaborative design of multiple components is significant for improving the accuracy of X-ray tubes remaining life prediction. Full article

Rural roads are critical for connecting isolated communities to essential services such as education and health and administrative services, as well as production and market opportunities in low-income countries. More than 70% of movements of people and goods in Sub-Saharan Africa are heavily reliant on rural transport systems, using both motorised but mainly alternative means of transport. However, rural roads often suffer from poor construction due to the use of low-strength, in situ soils and limited financial resources, leading to premature failures and subsequent traffic disruptions with significant economic losses. This study investigates the use of rice husk ash (RHA), a waste byproduct from rice production, as a sustainable supplement to Ordinary Portland Cement (OPC) for soil stabilisation in order to increase durability and sustainability of rural roads, hence limit recurrent maintenance needs and associated transport costs and challenges. To conduct this study, soil samples collected from Mulungushi, Zambia, were treated with combinations of 6–10% OPC and 10–15% RHA by weight. Laboratory tests measured maximum dry density (MDD), optimum moisture content (OMC), and California Bearing Ratio (CBR) values; the main parameters assessed to ensure the quality of road construction soils. Results showed that while the MDD did not change significantly and varied between 1505 kg/m³ and 1519 kg/m³, the OMC increased hugely from 19.6% to as high as 26.2% after treatment with RHA. The CBR value improved significantly, with the 8% OPC + 10% RHA mixture achieving the highest resistance to deformation. These results suggest that RHA can enhance the durability and sustainability of rural roads and hence improve transport systems and subsequently improve socioeconomic factors in rural areas. Full article

Unexpected machine failures in industrial environments lead to high maintenance costs, unplanned downtime, and safety risks. This study proposes a proactive predictive model using a hybrid of eXtreme Gradient Boosting (XGBoost) and Neural Networks (NN) to forecast machine failures. A synthetic dataset capturing recent breakdown history and time since last failure was used to simulate industrial scenarios. To address class imbalance, SMOTE and class weighting were applied, alongside a focal loss function to emphasize difficult-to-classify failures. The XGBoost model was tuned via GridSearchCV, while the NN model utilized ReLU-activated hidden layers with dropout. Evaluation using stratified 5-fold cross-validation showed that the NN achieved an F1-score of 0.7199 and a recall of 0.9545 for the minority class. XGBoost attained a higher PR AUC of 0.7126 and a more balanced precision–recall trade-off. Sample predictions demonstrated strong recall (100%) for failures, but also a high false positive rate, with most prediction probabilities clustered between 0.50–0.55. Additional benchmarking against Logistic Regression, Random Forest, and SVM further confirmed the superiority of the proposed hybrid model. Model interpretability was enhanced using SHAP and LIME, confirming that recent breakdowns and time since last failure were key predictors. While the model effectively detects failures, further improvements in feature engineering and threshold tuning are recommended to reduce false alarms and boost decision confidence. Full article

As power grids continue to expand in scale, the occurrence of cascading failures within them can lead to significant economic losses. Therefore, assessing the criticality of grid nodes is crucial for ensuring the secure and stable operation of power systems and for mitigating losses when cascading failures occur. The classical Local Link Similarity (LLS) algorithm in complex networks evaluates the importance of network nodes from a neighborhood topology perspective, but it suffers from issues such as the excessive weighting of node degrees and the neglect of electrical parameters. Based on the classical algorithm, this paper first develops the Improved Local Link Similarity (ILLS) algorithm by substituting alternative similarity metrics and comparatively evaluating their performance. Building upon the ILLS, we then propose the Electrical LLS (ELLS) algorithm by integrating node power flow and electrical coupling connectivity as multiplicative factors, with optimal combinations determined via simulation experiments. Compared to classical approaches, ELLS demonstrates superior adaptability to power grid contexts and delivers enhanced accuracy in power system node importance assessments. These algorithms are applied to rank the node importance in the IEEE 300-bus system. Their performance is evaluated using the loss-of-load-size metric, comparing ELLS, ILLS, and the classical algorithm. The results demonstrate that under the loss-of-load-size metric, the ELLS algorithm achieves approximately 25% higher accuracy compared to both the ILLS and the classical algorithm, validating its effectiveness. Full article

Modern energy storage systems for both power and transportation are highly related to lithium-ion batteries (LIBs). However, their safety depends on a potentially hazardous failure mode known as thermal runaway (TR). Predicting and classifying TR causes can widely enhance the safety of power and transportation systems. This paper presents an advanced machine learning method for forecasting and classifying the causes of TR. A generative model for synthetic data generation was used to handle class imbalance in the dataset. Hyperparameter optimization was conducted using Optuna for four classifiers: Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), tabular network (TabNet), and Extreme Gradient Boosting (XGBoost). A three-fold cross-validation approach was used to guarantee a robust evaluation. An open-source database of LIB failure events is used for model training and testing. The XGBoost model outperforms the other models across all TR categories by achieving 100% accuracy and a high recall (1.00). Model results were interpreted using SHapley Additive exPlanations analysis to investigate the most significant factors in TR predictors. The findings show that important TR indicators include energy adjusted for heat and weight loss, heater power, average cell temperature upon activation, and heater duration. These findings guide the design of safer battery systems and preventive monitoring systems for real applications. They can help experts develop more efficient battery management systems, thereby improving the performance and longevity of battery-operated devices. By enhancing the predictive knowledge of temperature-driven failure mechanisms in LIBs, the study directly advances thermal analysis and energy storage safety domains. Full article

Gearbox failures are a major cause of unplanned downtime and increased maintenance costs, making accurate diagnosis crucial in ensuring wind turbine reliability and cost-efficiency. However, most existing diagnostic methods fail to fully extract the spatiotemporal features in SCADA data and neglect the impact of class imbalance, thereby limiting diagnostic accuracy. To address these challenges, this paper proposes a fault diagnosis model for wind turbine gearboxes based on CNN-SA-GRU and Focal Loss. Specifically, a CNN-SA-GRU network is constructed to extract both spatial and temporal features, in which CNN is employed to extract local spatial features from SCADA data, Shuffle Attention is integrated to efficiently fuse channel and spatial information and enhance spatial representation, and GRU is utilized to capture long-term spatiotemporal dependencies. To mitigate the adverse effects of class imbalance, the conventional cross-entropy loss is replaced with Focal Loss, which assigns higher weights to hard-to-classify fault samples. Finally, the model is validated using real wind farm data. The results show that, compared with the cross-entropy loss, using Focal Loss improves the accuracy and F1 score by an average of 0.24% and 1.03%, respectively. Furthermore, the proposed model outperforms other baseline models with average gains of 0.703% in accuracy and 4.65% in F1 score. Full article

This case report describes a novel intronic mutation, CYP21A2:c.738+75C>T (rs1463196531), identified in a 4-year-old male with congenital adrenal insufficiency, and expands the known mutation spectrum associated with this condition. The patient, born full-term to unrelated parents, presented with adrenal failure within the first month of life, characterized by acute adrenal crisis symptoms such as vomiting, dehydration, weight loss, hypotension, and electrolyte imbalances. Hormonal evaluations confirmed primary adrenocortical insufficiency, necessitating ongoing hydrocortisone and fludrocortisone therapy. Using family trio-based amplicon sequencing of the CYP21A2 gene, we identified compound heterozygosity consisting of a full gene deletion and a novel pathogenic intronic mutation. Additionally, analysis of WGS data was performed to rule out pathogenic variants in genes that might lead to a similar phenotype, thereby eliminating the possibility of other genes contributing to the proband’s disease. This case demonstrates the potential of using amplicon sequencing in molecular genetic diagnostic testing to detect rare intronic variants in the CYP21A2 gene in cases of early-onset adrenal failure. It also contributes to a better understanding of the genetic basis of congenital adrenal hyperplasia (CAH), which remains a significant autosomal recessive disorder affecting cortisol and aldosterone production, with an incidence of 1 in 10,000 to 1 in 15,000 globally. Full article

Nitrile rubber (NBR) is prone to adhesion and hysteresis deformation when in contact with hard materials, leading to wear failure. To mitigate this issue, the deposition of diamond-like carbon (DLC) films onto the rubber surface is a commonly employed method. By utilizing pulsed arc ion plating technology and adjusting the arc voltage of the pulsed arc ion source, tetrahedral amorphous carbon (ta-C) films with varying sp³ content were prepared on the surface of NBR. The effects of arc voltage on the structural composition and friction performance of NBR/ta-C materials were examined. A scanning electron microscopy analysis revealed that the ta-C film applied to the surface of NBR was uniform and dense, exhibiting typical network crack characteristics. The results of Raman spectroscopy and X-ray photoelectron spectroscopy indicated that as the arc voltage increased, the sp³ content in the film initially rose before declining, reaching a maximum of 72.28% at 300 V. Mechanical tests demonstrated that the bonding strength and friction performance of the film are primarily influenced by the percentage of sp³ content. Notably, the ta-C film with lower sp³ content demonstrates enhanced wear resistance. At 200 V, the sp³ content of the film is 58.16%, resulting in optimal friction performance characterized by a stable friction coefficient of 0.38 and minimal wear weight loss. This performance is attributed to the protective qualities of the ta-C film and the formation of a graphitized transfer film. These results provide valuable insights for the design and development of wear-resistant rubber materials. Full article

The effectiveness of an innovative retrofit scheme using near-surface-mounted (NSM) X-shaped CFRP ropes for the strengthening of substandard RC beam–column joints was investigated experimentally. Three large-scale beam–column joint subassemblages were constructed with poor reinforcement details. One specimen was subjected to cyclic lateral loading, exhibited shear failure of the joint region and was used as the control specimen. The other specimens were retrofitted and subsequently subjected to the same history of incremental lateral displacement amplitudes with the control subassemblage. The retrofitting was characterized by low labor demands and included wrapping of NSM CFPR-ropes in the two diagonal directions on both lateral sides of the joint as shear reinforcement. Single or double wrapping of the joint was performed, while weights were suspended to prevent the loose placement of the ropes in the grooves. A significant improvement in the seismic performance of the retrofitted specimens was observed with respect to the control specimen, regarding strength and ductility. The proposed innovative scheme effectively prevented shear failure of the joint by shifting the damage in the beam, and the retrofitted specimens showed a more dissipating hysteresis behavior without significant loss of lateral strength and axial load-bearing capacity. The cumulative energy dissipation capacity of the strengthened specimens increased by 105.38% and 122.23% with respect to the control specimen. Full article

Cardiac cachexia (CC) is an advanced stage of heart failure (HF) characterized by structural and functional abnormalities in skeletal muscle, leading to muscle loss. Aerobic training provides benefits; however, the underlying molecular mechanisms remain poorly understood. This study aimed to investigate the therapeutic effects of aerobic training on transcriptomic alterations associated with disease progression in cachectic skeletal muscle. HF was induced in male Wistar rats by a single monocrotaline injection (60 mg/Kg). Aerobic training consisted of 30 min treadmill running at ~55% of maximal capacity, 5×/week for 4 weeks. Assessments included body mass, right ventricle mass, skeletal muscle fiber size and exercise tolerance. RNA-seq analysis was performed on the medial gastrocnemius muscle. Sedentary cachectic rats exhibited 114 differentially expressed genes (DEGs) while exercised cachectic rats had only 18 DEGs. Enrichment pathways analyses and weighted gene co-expression network analysis (WGCNA) identified potential key genes involved in disrupted lipid metabolism in sedentary cachectic rats, which were not observed in the exercised cachectic rats. Validation of DEGs related to lipid metabolism confirmed that Dgat2 gene expression was modulated by aerobic training in CC rats. These findings suggest that aerobic training mitigates transcriptional alterations related to lipid metabolism in rats with CC, highlighting its therapeutic potential. Full article

Interest in personal growth is expanding in both the popular press and the scientific literature. These expansions incorporate varied theoretical approaches and multiple areas of life. In the current work, we propose a novel perspective that focuses on managing failure to reach self-improvement goals and improving wellbeing. Specifically, we introduce an iterative mindset, which is the belief that making adaptations combined with deliberate practice and neutralizing of failure is critical for lasting transformations. We seek to contribute to the personal growth and mindset literature in two key ways. First, we developed and validated a new measure, called an Iterative Mindset Inventory (IMI), examining factor structure, reliability, and validity. Second, we investigated the links between iterative mindsets, self-improvement, and wellbeing, extending existing work on the power of beliefs to shape self-development. In both studies (Study 1, N = 871; Study 2, N = 345), we incorporated online samples that resembled the adult population of the United States. In Study 1, we found evidence for the proposed theoretical three-factor structure of an iterative mindset, which we label iterate, practice, and assess. In Study 2, using a longitudinal approach across three weeks, we confirmed the three-factor structure and found high test–retest reliability. Iterative mindsets were also positively linked to weight-loss success across both studies and to self-efficacy and wellbeing in Study 2. Full article

With the deterioration of the global climate, the losses caused by distribution network failures during natural disasters such as typhoons have become increasingly serious. In the whole process of disaster resistance, it is very important to effectively identify the weak links in distribution networks during typhoon disasters. In this paper, the weak links in distribution networks during typhoons are identified dynamically from four indexes: real-time failure rate, load loss caused by line disconnection, line degree, and line betweenness. First, the Batts typhoon model is established to simulate the whole process of the typhoon and obtain the real-time failure rate of the distribution network. Secondly, the distribution network is powered by distributed generators when there are line disconnections, and a mixed integer linear programming model is established to solve the problem. Then, the line degrees and the line betweenness are calculated to obtain the structure indexes of the line, both of which are dynamically related to the power flow and the loads of the distribution network. Finally, the four indexes are comprehensively analyzed, and the dynamic identification of the weak links in the distribution network are realized by the analytic hierarchy process (AHP)—entropy weight (EW)—technique for order preference by similarity to an ideal solution (TOPSIS) method. The results of the case study show that the proposed method can effectively identify the weak links in a distribution network during a typhoon and provide a reference to resist extreme disasters. Full article

Visible and near-infrared (Vis–NIR) spectroscopy enables the rapid prediction of soil properties but faces three limitations with conventional machine learning: information loss and overfitting from high-dimensional spectral features; inadequate modeling of nonlinear soil–spectra relationships; and failure to integrate multi-scale spatial features. To address these challenges, we propose ReSE-AP Net, a multi-scale attention residual network with spatial pyramid pooling. Built on convolutional residual blocks, the model incorporates a squeeze-and-excitation channel attention mechanism to recalibrate feature weights and an atrous spatial pyramid pooling (ASPP) module to extract multi-resolution spectral features. This architecture synergistically represents weak absorption peaks (400–1000 nm) and broad spectral bands (1000–2500 nm), overcoming single-scale modeling limitations. Validation on the LUCAS2009 dataset demonstrated that ReSE-AP Net outperformed conventional machine learning by improving the R² by 2.8–36.5% and reducing the RMSE by 14.2–69.2%. Compared with existing deep learning methods, it increased the R² by 0.4–25.5% for clay, silt, sand, organic carbon, calcium carbonate, and phosphorus predictions, and decreased the RMSE by 0.7–39.0%. Our contributions include statistical analysis of LUCAS2009 spectra, identification of conventional method limitations, development of the ReSE-AP Net model, ablation studies, and comprehensive comparisons with alternative approaches. Full article

Under laboratory conditions for recording a small amount of data, the characteristics of the phenomena distribution become a limitation of machine learning and advanced deep learning concepts for the diagnosis and localization of mechanical wear faults. In this paper, we adopt the combination of the diffusion model and TTT (test-time training), based on the sample distribution of feature data under the laboratory conditions, and we use the pre-trained decoder to decode the data into a continuous potential representation of natural language for sampling, to achieve data regeneration. Subsequently, the TTT algorithm becomes a model with weights in the hidden state itself. The gradient step on the self-supervised loss is selected as the update rule, which is trained synchronously during the testing time, adhering to the concept of migration learning, to construct a high-dimensional mapping relationship between the feature parameters and the failure modes of the mechanical wear. The final validation results show that the diagnosis accuracy reaches more than 95% for six types of typical aero-engine mechanical wear faults. Full article

Background: Aging and the female sex are considered risk factors for the development of heart failure with preserved ejection fraction (HFpEF). Unlike other risk factors, such as hypertension, obesity, or diabetes, they do not represent therapeutic targets. Methods: In a recently developed two-hit murine HFpEF model (angiotensin II + high-fat diet; MHS), we studied the relative contributions of the biological sex, aging, and gonadal hormones to cardiac remodeling and function. We aimed to reproduce a frequent HFpEF phenotype in mice characterized by aging, hypertension, the female sex, menopause, and metabolic alterations. Using the MHS mouse model, we studied cardiac remodeling and function in C57Bl6/J mice of both sexes, young (12 weeks) and old (20 months), that were gonadectomized (Gx) or not. Results: We observed that in mice, aging was associated with body weight gain, cardiac hypertrophy (CH), left ventricle (LV) concentric remodeling, and left atrial (LA) enlargement. Diastolic parameters such as E and A wave velocities were modulated by aging but only in females. Submitting young and old mice to MHS for 28 days induced the expected HFpEF phenotype consisting of CH, LV wall thickening, LA enlargement, and diastolic dysfunction with a preserved EF except for old males, in which it was significantly reduced. Young mice were Gx at five weeks, and old mice at six months (over a year before MHS). Gx increased myocardial fibrosis in MHS females and helped preserve the EF in males. Conclusions: Our results suggest that MHS has sex-specific effects on old mice, and the loss of gonadal hormones significantly impacts the observed heart failure phenotype. Full article