Search Results (398)

As core pieces of transport equipment in longwall mining systems, scraper conveyors operate under extremely harsh and dynamic loading conditions. Their operational reliability and service life primarily depend on the performance of critical components within their drive systems, particularly the support bearings. However, complex and often unpredictable load spectra (such as severe impacts, vibrations, and contaminant ingress) pose significant challenges to the dynamic behavior and longevity of these bearings. Traditional static analysis fails to capture their true operating state, as it neglects transient effects, varying contact angles, and internal vibration excitation. This study conducts a comprehensive dynamic analysis of angular contact ball bearing 7205C to elucidate its dynamic response under actual operating conditions of scraper conveyors. Based on Hertzian elastic contact theory and bearing dynamics theory, the comprehensive stiffness of the angular contact ball bearing is derived, and the effects of axial force, rotational speed, and mass eccentricity on bearing performance are analyzed. The findings are expected to provide a theoretical foundation for optimizing bearing selection, predicting service life, and enhancing the overall reliability of mining machinery. Full article

In the context of increasing demands for the reliability and efficiency of electrical complexes and systems, the problem of assessing and monitoring the technical condition (TC) of electrical equipment is becoming particularly relevant. This review is devoted to a comprehensive analysis of the factors affecting the performance of electrical equipment and modern methods for diagnosing its TC. The review article examines in detail the impact of various operational factors, including climatic conditions (temperature fluctuations, humidity, contamination) and electrical equipment operating modes. Special attention is paid to modern methods of technical diagnostics, such as thermographic diagnostics, vibration diagnostics, and chromatographic analysis of dissolved gases, which make it possible to identify defects and predict failures at early stages of their development. A significant part of the review is devoted to modern approaches to predicting the durability indicators of electrical equipment using mathematical modeling and neural networks. The advantages of a condition-based maintenance (CBM) and repair strategy, based on assessing the actual TC of the equipment, are analyzed in detail and compared with the strategy of scheduled preventive maintenance. This review particularly emphasizes the importance of integrating digital technologies, including the internet of things (IoT), digital twins (DT), and intelligent diagnostic monitoring systems, to create effective systems for predicting and managing TC. The review demonstrates that a comprehensive consideration of the actual TC of electrical equipment and its operating conditions can significantly increase the reliability of power systems, optimize maintenance and repair costs, and extend the service life of electrical equipment under various intensities of impacting operational factors. Full article

This study examined the combined effects of chloride ions and stray DC on reinforced concrete (RC) using electromigration and impressed-current methods under varying current densities (0.5, 3.0, 5.0 mA/cm²) and chloride concentrations (50, 1350, 5500 mg/kg). Chloride was identified as the dominant deterioration factor. At 3.0 mA/cm², cracking times in moderate and severe chloride environments decreased by 48.75% and 52.62%, respectively, compared to mild conditions. At 0.5 mA/cm² in severe conditions, the corrosion rate reached 1.317% after 20, 2.75 times that in moderate conditions. Electromigration specimens showed delayed cracking but deeper chloride penetration, while impressed-current specimens exhibited pronounced strip-shaped pitting corrosion. A quadratic polynomial model predicting cracking time based on current density and chloride concentration achieved high accuracy (R² = 0.95, mean relative error = 7.%). Actual corrosion mass loss was lower than theoretical Faraday values, with current efficiency increasing from 0.3–0.8% to 16.5–18.1% as current density and chloride content rose. These findings highlight the synergistic effect of stray current and chloride attack, emphasizing chloride concentration’s greater impact on service life. The model provides a scientific basis for RC durability design in urban rail transit and coastal engineering. Full article

The external floating roof of a large storage tank directly covers the liquid surface as the liquid level rises and falls, enhancing the tank’s safety and environmental performance. It is fabricated from thin SA516 Gr.70 steel plates, with a carbon equivalent of 0.37% calculated according to AWS standards, using single-sided butt welding. Such plates are susceptible to welding-induced deformations, resulting in irregular warping of the bottom plate. Current research on floating roofs for storage tanks mostly relies on idealized models that assume no deformation, thereby neglecting the actual deformation characteristics of the floating roof structure. To address this, the present study develops an automated modeling approach that reconstructs a three-dimensional floating roof model based on measured deformation data, accurately capturing the initial irregular geometry of the bottom plate. This method employs parametric numerical reconstruction and automatic finite element model generation techniques, enabling efficient creation of the irregular initial deformation caused by welding of the floating roof bottom plate and its automatic integration into the finite element analysis process. It overcomes the inefficiencies, inconsistent accuracy, and challenges associated with traditional manual modeling when conducting large-scale strength analyses under in-service conditions. Based on this research, a strength analysis of the deformed floating roof structure was conducted under in-service conditions, including normal floating, extreme rainfall, and outrigger contact scenarios. An idealized geometric model was also established for comparative analysis. The results indicate that under the normal floating condition, the initial irregular deformation increases the local stress peak of the floating roof bottom plate by 19%, while the maximum positive and negative displacements increase by 22% and 83%, respectively. Under extreme uniform rainfall conditions, it raises the stress peak of the bottom plate by 24%, with maximum positive and negative displacements increasing by 21% and 28%, respectively. Under the extreme non-uniform rainfall condition, it significantly elevates the stress peak of the bottom plate by 227%, and the maximum positive and negative displacements increase by 45% and 47%, respectively. Under the outrigger bottoming condition, it increases the local stress peak of the bottom plate by 25%, with maximum positive and negative displacements remaining similar. The initial irregular deformation not only significantly amplifies the stress and displacement responses of the floating roof bottom plate but also intensifies the deformation response of the top plate through structural stiffness weakening and deformation coupling, thereby reducing the safety margin of the floating roof structure. This study fills the knowledge gap regarding the effect of welding-induced irregular deformation on floating roof performance and provides a validated workflow for automated modeling and mechanical assessment of large-scale welded steel structures. Full article

In the cold extrusion forming of thin-walled, specially shaped holes in aviation motor brush boxes, non-uniform metal flow can easily induce local stress concentrations on the punch, thereby accelerating wear. Reducing the punch wear and equivalent stress is therefore critical for ensuring the forming quality of such thin-walled features and extending the service life of the mold. In this study, a slender punch with a specially shaped cross-section was selected as the research object. The Deform-3D Ver 11.0 software, incorporating the Archard wear model, was employed to investigate the effects of five process parameters—extrusion speed, punch cone angle, punch transition filet, friction coefficient, and punch hardness—on the wear depth and equivalent stress of the punch during the compound extrusion process. A total of 25 orthogonal experimental groups were designed, and the simulation results were analyzed using the Taguchi method combined with range analysis to determine the optimal parameter combination. Subsequently, a multi-objective correlation analysis of the signal-to-noise ratios for wear depth and equivalent stress was conducted using the TOPSIS approach. The analysis revealed that the optimal combination of process parameters was an extrusion speed of 12 mm·s⁻¹, a punch cone angle of 50°, a punch transition filet radius of 1.8 mm, a friction coefficient of 0.12, and a punch hardness of 55 HRC. Compared with the initial process conditions, the integrated application of the Taguchi–TOPSIS method reduced the punch wear depth and equivalent stress by 21.68% and 42.58%, respectively. Verification through actual production confirmed that the wear conditions of the primary worn areas were in good agreement with on-site production observations. Full article

During the actual service process, water-lubricated bearings on ships are often in complex operating environments such as low speed, heavy load and salt-sand water areas. To meet the requirements of high load-bearing capacity, long service life and the ability to discharge sand and dissipate heat during the service of bearings, research has been conducted on water-lubricated bearings made of polyetheretherketone (PEEK) with a semi-groove structure. Mathematical and physical models based on the averaged Reynolds equation have been established. By adopting the method of multi-physics field coupling, the lubrication characteristics of the bearings under the coupling influence of multiple factors in the salt-sand water environment (lubrication interface (the surface roughness of the bearing bush), different working conditions (water supply pressure, rotational speed, eccentricity)) are analyzed. Finally, a water-lubricated bearing test bench is set up to conduct bearing lubrication performance tests under multiple factors. The research shows that compared with liquid water, the salt-sand water environment exhibits better lubrication characteristics. The maximum water film pressure, the deformation amount of the bearing bush and the bearing capacity of the bearings increase with the increase of the rotational speed, water supply pressure and eccentricity, while the friction coefficient decreases. With the increase of the roughness of the bearing bush, these parameters decrease slightly and the friction coefficient increases. The presence of salt-sand particles can weaken the influence of roughness on the lubrication characteristics of the bearings. After considering the thermal effect, the mechanical load and thermal load act on the surface of the bearing bush together, resulting in an increase in the deformation amount of the bearing bush, a 0.11% drop in the water film pressure, and the highest temperature of the water film being concentrated at the outlet of the groove. The local semi-groove structure of PEEK can make the friction coefficient as low as 0.019. The comparison errors between the simulation and the experiment are within 10% (for water film pressure) and 2.6% (for friction coefficient), which verifies the reliability of the model. Full article

Hydrogen production from water electrolysis can not only reduce greenhouse gas emissions, but also has abundant raw materials, which is one of the ideal ways to produce hydrogen from new energy. The hydrogen production power supply is the core component of the new energy electrolytic water hydrogen production device, and its characteristics have a significant impact on the efficiency and purity of hydrogen production and the service life of the electrolytic cell. In essence, the DC/DC converter provides the large current required for hydrogen production. For the converter, its input still needs the support of a DC power supply. Given the maturity and technical characteristics of new energy power generation, integrating energy storage into offshore energy systems enables stable power supply. This configuration not only mitigates energy fluctuations from renewable sources but also further reduces electrolysis costs, providing a feasible pathway for large-scale commercialization of green hydrogen production. First, this paper performs a simulation analysis on the wind–solar hybrid energy storage power generation system to demonstrate that the wind–solar–storage system can provide stable power support. It places particular emphasis on the significance of hydrogen production power supply design—this focus stems primarily from the fact that electrolyzers impose specific requirements on high operating current levels and low current ripple, which exert a direct impact on the electrolyzer’s service life, hydrogen production efficiency, and operational safety. To suppress the current ripple induced by high switching frequency and high output current, traditional approaches typically involve increasing the output inductor. However, this method substantially increases the volume and weight of the device, reduces the rate of current change, and ultimately results in a degradation of the system’s dynamic response performance. To this end, this paper focuses on developing a virtual impedance control technology, aiming to reduce the ripple amplitude while avoiding an increase in the filter inductor. Owing to constraints in current experimental conditions, this research temporarily relies on simulation data. Specifically, a programmable power supply is employed to simulate the voltage output of the wind–solar–storage hybrid system, thereby bringing the simulation as close as possible to the actual operating conditions of the wind–solar–storage hydrogen production system. The experimental results demonstrate that the proposed method can effectively suppress the ripple amplitude, maintain high operating efficiency, and ultimately meet the expected research objectives. That makes it particularly suitable as a high-quality power supply for offshore hydrogen production systems that have strict requirements on volume and weight. Full article

While the capability of generative AI to generate high-quality content is well-recognized, there is still a lack of in-depth research on its actual impact on marketing effectiveness within real-world marketing environments. This study addresses this gap by conducting experiments to examine the effects of AI-generated advertisement images, created using text-to-image diffusion models, on consumer responses and the boundary conditions of these effects. Study 1 (n = 130) found that for coffee ads, attitudes were descriptively higher toward AI-generated images (η_p² = 0.17), whereas for medical-aesthetics and public-service ads, evaluations favored human-made images; none of these differences reached significance. Study 2 (n = 79) revealed that when consumers were informed about the source of the image (AI or human), they showed significantly more positive attitudes toward human-made images than those generated by AI (d = 0.52). Study 3 (n = 209) demonstrated that in commercial advertising contexts where usage motivations were disclosed, consumers’ negative reactions to AI-generated images were moderated by the specific usage motivation (η² = 0.04). When the motivation was privacy protection, evaluations were comparable to human-made images. In contrast, visual appeal produced slightly lower but non-significant ratings, whereas cost efficiency led to significant declines in trust and purchase intention, with attitude showing only marginal decreases and preference no differences. This study aims to understand the innovative potential of generative AI and provides critical insights for businesses, consumers, and policymakers regarding its effective utilization. Full article

In parallel with technical training on knowledge and skills of task-specific medical or surgical procedures, wide arrays of soft skills training would contribute to obstetric safety in the contemporary healthcare setting. This article, as a service evaluation, explored the effect of a specialty-based Crew Resource Management (CRM) training series that transforms the concept of human factors into sustainable measures in fostering clinical safety culture of the Department of Obstetrics and Gynaecology (O&G) in the Queen Elizabeth Hospital. Within the last decade, a tri-phasic programme has been implemented by an inter-professional workgroup which consists of a consultant anaesthesiologist, medical specialists and departmental operations manager from O&G, a nurse simulation specialist, hospital administrators, and a research psychologist. (1) Phase I identified different patterns of attitudinal changes (in assertiveness, communication, leadership, and situational awareness, also known as “ACLS”) between doctors and nurses and between generic and specialty-based sessions for curriculum planning. (2) Phase II evaluated how these specific behaviours changed over 3 months following CRM training tailored for frontline professionals in O&G. (3) Phase III examined the coping style in conflict management and the level of sustainability in self-efficacy over 3 months following specialty-based CRM training. The findings showed the positive impacts of O&G CRM training on healthcare professionals’ increased attitude and behaviour in “ACLS” by 22.7% at a p < 0.05 level, character strengths in conflict management, and non-inferior or sustained level of self-efficacy under tough conditions in the clinical setting up to 3 months after training. As a way forward, incorporating a scenario-based O&G CRM programme into existing skills-based training is expected to change service framework with an innovative approach. In addition, exploring actual clinical outcomes representing a higher level of organisational impacts can be a strategic direction for further studies on the effect of this practical and educational approach on obstetric safety culture. Full article

The paper examines a thin-walled steel road bridge that has been in service for over fifty years. Due to damage observed during detailed inspections, a comprehensive investigation was necessary to evaluate the structural safety of the superstructure. For this purpose, the new national guidelines for assessing the safety of existing road bridges were used for the first time. These guidelines are based on the new Eurocode prEN 1990-2, which provides the foundation for assessing existing structures. To enable reliable and rational decisions regarding repair or strengthening, a finite element analysis was performed considering the condition survey, NDT, and material testing. The analysis showed that the resistance of some superstructure elements was exceeded by over 600%, and about 180 elements are inadequately safe to carry the actual minimum traffic loads according to the safety standards mandated by the new national guidelines. A comparison between the analysis results and the condition survey identified the same elements where local plastic deformations were observed. Based on the experimental and numerical results within this new assessment framework, the final decision was made to close the bridge for service and replace the existing steel structure. Full article

This study investigates the transverse loads acting on high-speed train bogie frames under actual service conditions. To enable direct identification, the locating arms were instrumented as bending sensors and calibrated under realistic lateral-stop constraints, ensuring robustness of the measurement channels. Field tests were conducted on a CR400BF high-speed EMU over a 226 km route at six speed levels (260–390 km/h), with gyroscope and GPS signals employed to recognize typical operating conditions, including straights, curves, and switches (straight movement and diverging movements). The results show that the proposed recognition method achieves high accuracy, enabling rapid and effective identification and localization of typical operating conditions. Under switch conditions, the bogie frame transverse loads are characterized by low-frequency, large-amplitude fluctuations, with overall RMS levels being higher in diverging switches and straight-through depot switches. Curve parameters and speed levels exert significant influence on the amplitude of the transverse-load trend component. On curves with identical parameters, the trend-component amplitude exhibits a quadratic nonlinear relationship with train speed, decreasing first and then increasing in the opposite direction as speed rises. In mainline curves and straight sections, the RMS values of transverse loads on Axles 1 and 2 scale proportionally with speed level, with the leading axle in the direction of travel consistently producing higher transverse loads than the trailing axle. When load samples are balanced across both running directions, the transverse load spectra of Axles 1 and 2 at the same speed level show negligible differences, while the spectrum shape index increases proportionally with speed level. Full article

As the core component of electrified railway power supply systems, the fatigue performance and reliability of catenary support structures are directly related to the operational safety of high-speed railways. To address the problem of structural fatigue damage caused by increasing train speed and high-frequency operation, this study develops a refined finite element model including a support structure, suspension system and support column, and the dynamic response characteristics and fatigue life evolution law under train operation conditions are systematically analyzed. The results show that under the conditions of 250 km/h speed and 100 times daily traffic, the fatigue lives of the limit locator and positioning support are 43.56 years and 34.48 years, respectively, whereas the transverse cantilever connection and inclined cantilever have infinite life characteristics. When the train speed increases to 400 km/h, the annual fatigue damage of the positioning bearing increases from 0.029 to 0.065, and the service life is shortened by 55.7% to 15.27 years, which proves that high-speed working conditions significantly aggravate the deterioration of fatigue in the structure. The reliability analysis based on Monte Carlo simulation reveals that when the speed is 400 km/h and the daily traffic is 130 times, the structural reliability shows an exponential declining trend with increasing service life. If the daily traffic frequency exceeds 130, the 15-year reliability decreases to 92.5%, the 20-year reliability suddenly decreases to 82.4%, and there is a significant inflection point of failure in the 15–20 years of service. Considering the coupling effect of environmental factors (wind load, temperature and freezing), the actual failure risk may be higher than the theoretical value. On the basis of these findings, engineering suggestions are proposed: for high-speed lines with a daily traffic frequency of more than 130 times, shortening the overhaul cycle of the catenary support structure to 7–10 years and strengthening the periodic inspection and maintenance of positioning support and limit locators are recommended. The research results provide a theoretical basis for the safety assessment and maintenance decision making of high-speed railway catenary systems. Full article

As 6G networks become increasingly complex and heterogeneous, effective classification of network slicing is essential for optimizing resources and managing quality of service. While recent advances demonstrate high accuracy under controlled laboratory conditions, a critical gap exists between algorithm performance evaluation under idealized conditions and their actual effectiveness in realistic deployment scenarios. This study presents a comprehensive comparative analysis of two distinct preprocessing methodologies for 6G network slicing classification: Pure Raw Data Analysis (PRDA) and Literature-Validated Realistic Transformations (LVRTs). We evaluate the impact of these strategies on algorithm performance, resilience characteristics, and practical deployment feasibility to bridge the laboratory–reality gap in 6G network optimization. Our experimental methodology involved testing eleven machine learning algorithms—including traditional ML, ensemble methods, and deep learning approaches—on a dataset comprising 10,000 network slicing samples (expanded to 21,033 through realistic transformations) across five network slice types. The LVRT methodology incorporates realistic operational impairments including market-driven class imbalance (9:1 ratio), multi-layer interference patterns, and systematic missing data reflecting authentic 6G deployment challenges. The experimental results revealed significant differences in algorithm behavior between the two preprocessing approaches. Under PRDA conditions, deep learning models achieved perfect accuracy (100% for CNN and FNN), while traditional algorithms ranged from 60.9% to 89.0%. However, LVRT results exposed dramatic performance variations, with accuracies spanning from 58.0% to 81.2%. Most significantly, we discovered that algorithms achieving excellent laboratory performance experience substantial degradation under realistic conditions, with CNNs showing an 18.8% accuracy loss (dropping from 100% to 81.2%), FNNs experiencing an 18.9% loss (declining from 100% to 81.1%), and Naive Bayes models suffering a 34.8% loss (falling from 89% to 58%). Conversely, SVM (RBF) and Logistic Regression demonstrated counter-intuitive resilience, improving by 14.1 and 10.3 percentage points, respectively, under operational stress, demonstrating superior adaptability to realistic network conditions. This study establishes a resilience-based classification framework enabling informed algorithm selection for diverse 6G deployment scenarios. Additionally, we introduce a comprehensive explainable artificial intelligence (XAI) framework using SHAP analysis to provide interpretable insights into algorithm decision-making processes. The XAI analysis reveals that Packet Loss Budget emerges as the dominant feature across all algorithms, while Slice Jitter and Slice Latency constitute secondary importance features. Cross-scenario interpretability consistency analysis demonstrates that CNN, LSTM, and Naive Bayes achieve perfect or near-perfect consistency scores (0.998–1.000), while SVM and Logistic Regression maintain high consistency (0.988–0.997), making them suitable for regulatory compliance scenarios. In contrast, XGBoost shows low consistency (0.106) despite high accuracy, requiring intensive monitoring for deployment. This research contributes essential insights for bridging the critical gap between algorithm development and deployment success in next-generation wireless networks, providing evidence-based guidelines for algorithm selection based on accuracy, resilience, and interpretability requirements. Our findings establish quantitative resilience boundaries: algorithms achieving >99% laboratory accuracy exhibit 58–81% performance under realistic conditions, with CNN and FNN maintaining the highest absolute accuracy (81.2% and 81.1%, respectively) despite experiencing significant degradation from laboratory conditions. Full article

The Qinghai–Tibet Plateau (QTP), a globally significant tourist destination and critical ecological barrier, faces an intrinsic conflict between development and conservation. The scientific identification of suitable tourism zones is therefore crucial for formulating sustainable development policies. Conventional suitability assessments, however, which typically rely on subjective, expert-based weighting and static, supply-side data, often fail to capture the complex, non-linear dynamics of actual tourist–environment interactions. To overcome these limitations, an innovative analytical framework is presented, integrating massive tourist trajectory big data (66.7 million GPS points) as an objective, demand-driven suitability proxy, a Geo-detector model to identify key drivers and their interactions, and a Random Forest algorithm for spatial prediction. The framework achieves high predictive accuracy (AUC = 0.827). The results reveal significant spatial heterogeneity: over 85% of the QTP is unsuitable for tourism, while suitable zones are intensely concentrated in southeastern river valleys, forming distinct agglomerations around core cities and along primary transport arteries. Analysis demonstrates that supporting conditions—particularly transport accessibility and service facility density—are the dominant drivers, their influence substantially surpassing that of natural resource endowment. Furthermore, the formation of high-suitability zones is not attributable to any single factor but rather to the synergistic coupling of multiple conditions. This research establishes a replicable, data-driven paradigm for tourism planning in environmentally sensitive regions, offering a robust scientific basis to guide the sustainable development of the QTP. Full article

Engine oil condition critically affects vehicle performance, fuel efficiency, and engine durability. While conventional oil change strategies are based on fixed intervals or mileage thresholds, they often neglect real operating conditions and the actual state of lubricant degradation. This study investigates nine used engine oil samples collected from passenger vehicles operating in diverse environments, including city traffic, highway routes, hybrid systems, and diesel engines. The oils were assessed using kinematic viscosity measurements and Fourier transform infrared (FTIR) spectroscopy to monitor key degradation indicators—oxidation, nitration, sulfonation, fuel dilution, soot contamination, and additive depletion. Each case is fully documented with detailed operational histories, facilitating a nuanced, real-world understanding of oil aging. The results demonstrate that degradation levels vary considerably, even under similar mileage ranges, highlighting the influence of urban usage patterns and engine design. In several cases, premature or delayed oil changes were observed, confirming that standard service intervals may be suboptimal. FTIR proved effective in detecting subtle chemical transformations, particularly in samples affected by biofuel components or prolonged thermal stress. These findings emphasize the value of integrating laboratory diagnostics into oil change decision-making and support more tailored maintenance strategies. Such an approach can reduce unnecessary oil replacement, limit waste generation, and extend engine lifespan, contributing to both environmental and economic sustainability. This study supports the implementation of condition-based oil change strategies to minimize lubricant waste and promote maintenance practices aligned with sustainability principles. Full article