Search Results (4,625)

Lubricating oil degradation directly impacts friction coefficient, wear rate, and lubrication regime transitions, making precise health quantification essential for predictive tribological maintenance. However, conventional evaluation methods fail to capture subtle tribological changes preceding lubrication failure, often oversimplifying complex multi-parameter relationships critical to friction and wear performance. To address this challenge, this study proposes Seasonal–Trend decomposition using Loess, a Factor Attention Network, a Temporal Convolutional Network, and an Informer with Long Short-Term Memory Variational Autoencoder (SFTI-LVAE) framework for continuous tribological health assessment of diesel engine lubricants. The approach integrates Seasonal–Trend decomposition using Loess (STL) for trend–seasonal separation, a Factor Attention Network (FAN) for multidimensional feature fusion, and a Temporal Convolutional Network (TCN)-enhanced Informer for capturing long-term tribological dependencies. By combining Long Short-Term Memory (LSTM) temporal modeling with Variational Autoencoder (VAE) reconstruction, the method quantifies lubricant health through reconstruction error, establishing a direct correlation between data deviation and tribological performance degradation. Additionally, permutation importance-based feature evaluation and parameter contribution quantification techniques enable deep mechanistic analysis and fault source tracing of lubricant health degradation. Experimental validation using multi-sensor monitoring data demonstrates that SFTI-LVAE achieves a 96.67% fault detection accuracy with zero false alarms, providing early warning 6.47 h before lubrication failure. Unlike traditional anomaly detection methods that only classify conditions as abnormal or normal, the proposed continuous health index reveals gradual tribological degradation processes, capturing subtle viscosity–temperature relationships and wear particle evolution indicating early lubrication regime transitions. The health index correlates strongly with tribological performance indicators, enabling a transition from reactive maintenance to predictive tribological management, providing an innovative solution for equipment health evaluation in the digital tribology era. Full article

Urban tree planting on single-family-home lots represents a critical yet underexplored component of municipal greening strategies. This study examines residents’ perceptions of tree planting in Westpointe, a diverse neighborhood in Salt Lake City, Utah, as part of the city’s Reimagine Nature Public Lands Master Plan development effort. Through a mixed-methods approach combining qualitative interviews (n = 24) and a tree signup initiative extended to 86 residents, with 51 participating, this research explores the complex interplay of demographic, economic, social, and infrastructure factors influencing residents’ willingness to plant trees on single-family-home lots. The findings reveal significant variations based on gender, with women expressing more positive environmental and aesthetic motivations, while men focused on practical concerns including maintenance and property damage. Age emerged as another critical factor, with older adults (65+) expressing concerns about long-term maintenance capabilities, while younger families (25–44) demonstrated future-oriented thinking about shade and property values. Property characteristics, particularly yard size, significantly influenced receptiveness, with owners of larger yards (>5000 sq ft) showing greater willingness compared to those with smaller properties, who cited space constraints. Additional barriers, i.e., maintenance, financial, and knowledge barriers, included irrigation costs, lack of horticultural knowledge, pest concerns, and proximity to underground utilities. Geographic analysis revealed that Spanish-speaking social networks were particularly effective in promoting tree planting. The study contributes to urban forestry literature by providing nuanced insights into single-family homeowners’ tree-planting decisions and offers targeted recommendations for municipal programs. These include gender-specific outreach strategies, age-appropriate support services, sliding-scale subsidy programs based on property size, and comprehensive education initiatives. The findings inform evidence-based approaches to increase urban canopy coverage through private property plantings, ultimately supporting climate resilience and environmental justice goals in diverse urban neighborhoods. Full article

This work investigates the intricate dynamics of the Carreau hybrid nanofluid’s inclined magnetohydrodynamic (MHD) flow, exploring both active and passive control modes. The study incorporates critical factors, including Arrhenius activation energy across a stretched sheet, chemical interactions, and nonlinear thermal radiation. The formulation of the boundary conditions and governing equations is inherently influenced by symmetric considerations in the physical geometry and flow assumptions. Such symmetry-inspired modeling facilitates dimensional reduction and numerical tractability. The analysis employs realistic boundary conditions, including convective heat transfer and control of nanoparticle concentration, which are solved numerically using MATLAB’s bvp5c solver. Findings indicate that an increase in activation energy results in a steeper concentration boundary layer under active control, while it flattens in passive scenarios. An increase in the Biot number ($Bi$) and relaxation parameter ($\mathsf{\Gamma }$) enhances heat transfer and thermal response, leading to a rise in temperature distribution in both cases. Additionally, the 3D surface plot illustrates elevation variations from the surface at low inclination angles, narrowing as the angle increases. The Nusselt number demonstrates a contrasting trend, with thermal boundary layer thickness increasing with higher radiation parameters. A graphical illustration of the average values of skin friction, Nusselt number, and Sherwood number for both active and passive scenarios highlights the impact of each case. Under active control, the Brownian motion’s effect diminishes, whereas it intensifies in passive control. Passive techniques, such as zero-flux conditions, offer effective and low-maintenance solutions for systems without external regulation, while active controls, like wall heating and setting a nanoparticle concentration, maximize heat and mass transfer in shear-thinning Carreau fluids. Full article

This study investigates the deterioration of skid resistance and surface macrotexture following preventive maintenance using micro-milling techniques. Field data were collected from 31 asphalt pavement sections located across four climatic zones in Texas. The data encompasses a variety of surface types, milling depths, operational speeds, and drum configurations. A standardized data collection protocol was followed, with measurements taken before milling, immediately after treatment, and at 3, 6, 12, and 18 months post-treatment. Skid number and Mean Profile Depth (MPD) were used to evaluate surface friction and texture characteristics. The dataset was reformatted into a time-series structure with 930 observations, including contextual variables such as climatic zone, treatment parameters, and baseline surface condition. A comparative modeling framework was applied to predict the deterioration trends of both skid resistance and macrotexture over time. Eight regression models, including linear, tree-based, and ensemble methods, were evaluated alongside a time series Transformer model. The results show that the Transformer model achieved the highest prediction accuracy for skid resistance (R² = 0.981), while Random Forest performed best for macrotexture prediction (R² = 0.838). The findings indicate that the degradation of surface characteristics after preventive maintenance is non-linear and influenced by a combination of environmental and operational factors. This study demonstrates the effectiveness of data-driven modeling in supporting transportation agencies with pavement performance forecasting and maintenance planning. Full article

Pathogenic variants of MYH11, which encode smooth muscle myosin heavy chain 11, have been linked to familial thoracic aortic aneurysms and dissections (FTAAD). However, molecular pathways affected by these mutations have not been well understood. To explore downstream consequences of Myh11 disruption, we analyzed transcriptomic and proteomic profiles of aortas from male Myh11 mice with homozygous deletion of lysine 1256 (K1256) and of wild-type controls. Of 6499 proteins quantified, 1763 were differentially expressed (adjusted p < 0.05), including 942 that were downregulated and 821 that were upregulated in mutant aortas. Enrichment analysis of downregulated genes and proteins revealed a consistent reduction in extracellular matrix-related pathways. Among downregulated proteins, we identified tenascin Xb, transforming growth factor β (Tgfb) 2, and Tgfb receptor 1/2, malfunctions of which are linked to connective tissue diseases, such as Ehlers–Danlos and Loeys–Dietz syndromes. Nevertheless, unlike these syndromic diseases, mice with Myh11 pathogenic variants and patients with FTAAD do not exhibit syndromic features, likely reflecting expression of Myh11 restricted to smooth muscle. These results suggest that loss of Myh11 disrupts maintenance of extracellular matrix by SMCs, the loss of which contributes to aortic fragility without affecting other tissues. Full article

This paper presents an assessment of the impact of maintenance of a road lighting luminaire with a high-pressure sodium lamp and an LED luminaire on the lighting parameters on the road and the energy efficiency of the entire road lighting installation. Improper maintenance of road lighting installations, especially of luminaires, can significantly worsen road traffic safety. In addition, after performing maintenance activities, e.g., after replacing a lamp in the luminaire, the energy consumption of the road lighting installation can increase. Both active and reactive energy can increase. Using the examples of a road luminaire with a high-pressure sodium lamp and an LED luminaire, it was shown that such a phenomenon can occur. An assessment of maintenance in terms of energy performance indicators was performed for the luminaire using the indicators described in the lightning standard and those proposed by the authors of this paper. This approach allows for a comprehensive assessment of maintenance on energy performance indicators—energy efficiency. Full article

Accurate and reliable modeling of pavement deterioration is critical for effective infrastructure management. This study proposes a probabilistic machine learning framework using Bayesian-optimized Natural Gradient Boosting (BO-NGBoost) to predict the International Roughness Index (IRI) of asphalt pavements in cold climates. A dataset only for cold regions was constructed from the Long-Term Pavement Performance (LTPP) database, integrating multiple variables related to climate, structure, materials, traffic, and constructions. The BO-NGBoost model was evaluated against conventional deterministic models, including artificial neural networks, random forest, and XGBoost. Results show that BO-NGBoost achieved the highest predictive accuracy (R² = 0.897, RMSE = 0.184, MAE = 0.107) while also providing uncertainty quantification for risk-based maintenance planning. BO-NGBoost effectively captures long-term deterioration trends and reflects increasing uncertainty with pavement age. SHAP analysis reveals that initial IRI, pavement age, layer thicknesses, and precipitation are key factors, with freeze–thaw cycles and moisture infiltration driving faster degradation in cold climates. This research contributes a scalable and interpretable framework that advances pavement deterioration modeling from deterministic to probabilistic paradigms and provides practical value for more uncertainty-aware infrastructure decision-making. Full article

Objective: To evaluate the short-term clinical impact of daily use of calibrated interdental brushes (IDBs) on gingival bleeding among dental and dental hygiene students within academic curricula. Methods: A prospective cohort of 117 students from three Italian universities was followed over three months. All participants received personalized training and calibrated interdental brushes matched to their interdental spaces. The primary outcome was the percentage of interdental sites exhibiting bleeding on interdental brushing (BOIB), assessed at baseline (T0), one month (T1), and three months (T2). Adherence was self-reported. Statistical analyses included Wilcoxon tests, multivariate regression, and adjusted ANCOVA models. Results: Median bleeding scores decreased from 50.0% [IQR: 26.9–69.2] at baseline to 15.4% [IQR: 3.8–30.8] at one month and further to 11.5% [IQR: 0.0–26.9] at three months (p < 0.001). Regular interdental brush users showed a 15 to 16 percentage point greater reduction in bleeding compared to occasional users (p < 0.001). Dental hygiene students had significantly lower baseline bleeding scores than dental students, but both groups experienced comparable benefits from the intervention. Adjusted analyses confirmed a sustained beneficial effect of regular interdental brushing. Initial weak and transient correlations between behavioral factors and bleeding likely reflect multifactorial influences and variable adherence. Conclusions: Daily use of calibrated interdental brushes produces a rapid, significant, and sustained reduction in gingival bleeding among dental students. Systematic integration of this protocol within dental education programs is feasible and effective, promoting early adoption and maintenance of essential preventive oral health behaviors. Full article

With the widespread implementation of bridge structural health monitoring (SHM) systems, monitored bridge networks have gradually formed. Understanding vehicle loads and considering spatio-temporal correlations within bridge networks is critical for structural condition assessment and maintenance decision making. This study aims to predict traffic flows by investigating traffic flow correlations within a bridge network using multi-bridge data, thereby supporting bridge network-level SHM. A transformer-based traffic flow prediction model considering spatio-temporal correlations of bridge networks (ST-TransNet) is proposed. It integrates external factors (processed via fully connected networks) and multi-period traffic flows of input bridges (captured by self-attention encoders) to generate traffic flow predictions through a self-attention decoder. Validated using weigh-in-motion data from an 8-bridge network, the proposed ST-TransNet reduces prediction root mean square error (RMSE) to 12.76 vehicles/10 min, outperforming a series of baselines—SVR, CNN, BiLSTM, CNN&BiLSTM, ST-ResNet, transformer, and STGCN—with significant relative reductions of 40.5%, 36.9%, 36.6%, 37.3%, 35.6%, 31.1%, and 22.8%, respectively. Ablation studies confirm the contribution of each component of the external factors and multi-period traffic flows, particularly the recent traffic flow data. The proposed ST-TransNet effectively captures underlying the spatio-temporal correlations of traffic flow within bridge networks, offering valuable insights for enhancing bridge assessment and maintenance. Full article

Digital Twins (DTs) have become essential tools for the design, diagnostics, and prognostics of mechanical systems. In gearbox applications, DTs are often built using physics-based simulations guided by ISO standards. However, standards-based approaches may suffer from complexity, licensing limitations, and computational costs. The concept of symmetry is inherent in gear mechanisms, both in geometry and in operational conditions, yet practical applications often face asymmetric load distributions, misalignments, and asymmetric and symmetric nonlinear behaviors. In this study, we propose a hybrid method that integrates data-driven modeling with standard-based simulation to develop efficient and accurate digital twins for gear transmission systems. A digital twin of a spur gear transmission is generated using KISSsoft^®, employing ISO standards to compute safety factors across varied geometries and load conditions. An automated MATLAB-KISSsoft^® (COM-interface) enables large-scale data generation by systematically varying key input parameters such as torque, pinion speed, and center distance. This dataset is then used to train a neural network (NN) capable of predicting safety factors, with hyperparameter optimization improving the model’s predictive accuracy. Among the tested NN architectures, the model with a single hidden layer yielded the best performance, achieving maximum prediction errors below 0.01 for root and flank safety factors. More complex failure modes such as scuffing and micropitting exhibited higher maximum errors of 0.0833 and 0.0596, respectively, indicating areas for potential model refinement. Comparative analysis shows strong agreement between the NN outputs and KISSsoft^® results, especially for root and flank safety factors. Performance is further validated through sensitivity analyses across seven cases, confirming the NN’s reliability as a surrogate model. This approach reduces simulation time while preserving accuracy, demonstrating the potential of neural networks to support real-time condition monitoring and predictive maintenance in gearbox systems. Full article

Rail corrugation and roughness represent typical irregularities on railway and tramway tracks, which cause increased dynamic forces, high-frequency vibrations, reduced riding comfort, shorter track lifespan, higher maintenance costs, and increased noise levels. Roughness and corrugation can be measured by evaluating the unevenness of the rail longitudinal running surface, which can be conducted using handheld devices or trolleys (directly on the track). Alternatively, vehicle or track-based indirect methods offer practical solutions for determining the condition of the rail running surface. This paper presents a methodology for rail corrugation and roughness evaluation, using bogie frame vibration data from an instrumented in-service tramway vehicle operating on Zagreb’s tramway network. Furthermore, it investigates the effects of various factors on the evaluation method, including wheel roughness, lateral positioning, signal processing methods, horizontal geometry, wheel–rail contact force, and tramway vehicle vibroacoustic characteristics. It was concluded that a simplified methodology that did not include transfer functions or wheel roughness measurements yielded relatively good results for evaluating rail corrugation and roughness across several wavelength bands. To improve the presented methodology, future research should assess the vehicle’s vibroacoustic characteristics with experimental hammer impact tests, measure the influence of wheel roughness on wheel–rail contact and bogie vibrations, and refine the measurement campaign by increasing test runs, limiting speed variation, and conducting controlled tests. Full article

Although chemotherapy is the preferred treatment for gastric cancer, the therapeutic drugs currently available have limited efficacy and severe side effects. Cancer stem cells within tumor masses have the distinctive properties of self-renewal, maintenance, and resistance to chemotherapy. Hence, agents capable of targeting stemness in gastric tumors with minimal side effects are urgently required. Enzymes that generate reactive oxygen species contribute to the high oxidation levels observed in tumors. Additionally, nuclear factor erythroid 2-related factor 2 (Nrf2), an antioxidant transcription factor, regulates cancer stemness. Increasing evidence highlights the potential of nutritional supplementation to treat cancer stemness. ω-3 polyunsaturated fatty acids support human health and offer benefits for cancer treatment. Linolenic acid (LA), an ω-3 polyunsaturated fatty acid, inhibits the expression of proteins associated with stemness and promotes apoptosis in gastric cancer cells. Our findings indicated that LA treatment substantially inhibited key characteristics of gastric cancer stemness and induced oxidative stress and caspase-3-mediated apoptosis by downregulating Nrf2-mediated expression. These results suggest that LA is a promising nutritional supplement for targeting cancer stemness in the treatment of gastric cancer. Full article

Backgrounds: To examine the association between stages of exercise behavior change, as defined by the transtheoretical model (TTM), and obesity progression among Chinese children and adolescents, with attention to gender and urban–rural differences. Methods: A total of 5006 Chinese children and adolescents aged 9–18 years were assessed in 2019 and followed up in 2020. Participants were categorized into five TTM stages: precontemplation, contemplation, preparation, action, and maintenance. Logistic regression models evaluated the associations between the TTM stages and obesity outcomes, including incident obesity and transitions from normal or overweight to obesity. Analyses were stratified by gender and urban–rural residence, and interaction effects were tested. Results: Compared to the maintenance stage, precontemplation (OR = 2.08, 95% CI: 1.45–2.99) and contemplation (OR = 1.48, 95% CI: 1.05–2.08) stages had higher obesity risk, with similar trends in follow-up incident obesity (precontemplation: OR = 1.63, 95% CI: 1.17–2.28; contemplation: OR = 1.47, 95% CI: 1.10–1.98). These associations were more pronounced among boys and rural residents. Significant interactions were observed between TTM stages, sex (p = 0.029), and residence (p = 0.005) in obesity transition. Conclusions: Exercise behavior stages are associated with obesity progression, particularly among boys and rural children. These findings underscore the importance of stage-specific interventions tailored to individual readiness for behavior change and contextual factors. Full article

Corrosion is the predominant failure mechanism in marine steel, and accurate corrosion prediction is essential for effective maintenance and protection strategies. However, the limited availability of corrosion datasets poses significant challenges to the accuracy and generalization of prediction models. This study introduces a novel integrated model designed for predicting marine corrosion under small sample sizes. The model utilizes dynamic marine environmental factors and material properties as inputs, with the corrosion rate as the output. Initially, a genetic algorithm (GA)-optimized machine learning framework is employed to derive the optimal GA-XGBoost model. To further enhance model performance, a virtual sample generation method combining Gaussian Mixture Model and Regression Generative Adversarial Network (GMM-RegGAN) is proposed. By incorporating these generated virtual samples into the base model, the prediction accuracy is further improved. The proposed framework is validated using corrosion datasets from six types of marine steel. Results demonstrate that GA optimization substantially improves both the performance and stability of the model. Virtual sample generation further enhances predictive performance, with reductions of 14.94% in RMSE, 15.55% in MAE, and 14.04% in MAPE. The results indicate that the proposed method offers a robust and effective framework for corrosion prediction in scenarios with limited sample data. Full article

Housing vacancy rate is a key indicator for evaluating urban sustainable development. Due to rapid urbanization, population outflow and insufficient industrial support, the housing vacancy problem is particularly prominent in China’s underdeveloped regions. However, the lack of official data and the limitations of traditional survey methods restrict in-depth research. This study proposes a vacancy rate estimation method based on Baidu Street View residential exterior images and deep learning technology. Taking Nanning, Guangxi as a case study, an automatic discrimination model for residential vacancy status is constructed by identifying visual clues such as window occlusion, balcony debris accumulation, and facade maintenance status. The study first uses Baidu Street View API to collect images of residential communities in Nanning. After manual annotation and field verification, a labeled dataset is constructed. A pre-trained deep learning model (ResNet50) is applied to estimate the vacancy rate of the community after fine-tuning with labeled street view images of Nanning’s residential communities. GIS spatial analysis is combined to reveal the spatial distribution pattern and influencing factors of the vacancy rate. The results show that street view images can effectively capture vacancy characteristics that are difficult to identify with traditional remote sensing and indirect indicators, providing a refined data source and method innovation for housing vacancy research in underdeveloped regions. The study further found that the residential vacancy rate in Nanning showed significant spatial differentiation, and the vacancy driving mechanism in the old urban area and the emerging area was significantly different. This study expands the application boundaries of computer vision in urban research and fills the research gap on vacancy issues in underdeveloped areas. Its results can provide a scientific basis for the government to optimize housing planning, developers to make rational investments, and residents to make housing purchase decisions, thus helping to improve urban sustainable development and governance capabilities. Full article