Search Results (6,132)

The Strait of Messina poses unique challenges for small vessels due to strong currents and complex wave conditions, which critically affect structural integrity and operational safety. This study proposes an integrated methodology that combines seakeeping analysis, a comparison with classification society rules, and fatigue life assessment within a unified and computationally efficient framework. A panel-based approach was used to compute vessel motions and vertical bending moments at different speeds and wave directions. Hydrodynamic loads derived from Response Amplitude Operators (RAOs) were compared with regulatory limits and applied to fatigue analysis. A further innovative aspect is the use of high-resolution bathymetric data from the Strait of Messina, enabling a realistic representation of local currents and sea states and providing a more accurate assessment than studies based on idealized conditions. The results show that forward speed amplifies bending moments, reducing safe wave heights from 2 m at rest to about 0.5 m at 16 knots. Fatigue analysis indicates that aluminum hulls are highly vulnerable to 2–3 m waves, while steel and titanium show no significant damage. The proposed workflow is transferable to other vessel types and supports safer design and operation. The case study of the Strait of Messina, the busiest and most challenging maritime corridor in Italy, confirms the validity and practical importance of the approach. By combining hydrodynamic and structural analyses into a single workflow, this study establishes the foundation for predictive maintenance and real-time structural health monitoring, with significant implications for navigation safety in complex sea environments. Full article

High-frequency vibrations in annular gap type pressure-regulating valves of wind tunnels can induce fatigue, fracture, and operational failures, posing challenges to safe and reliable operation. This study proposes a hybrid leakage fault diagnosis framework that integrates wavelet packet-based signal analysis with advanced deep learning techniques. Time-domain acceleration signals collected from multiple sensors are processed to extract maximum component energy and its variation rate, identified as sensitive and robust indicators for leakage detection. A fluid–solid coupled finite element model of the valve system further validates the reliability of these indicators under different operational scenarios. Based on this foundation, a Vision Transformer (ViT)-based model is trained on a dedicated database encompassing multiple leakage conditions and sensor arrangements. Comparative evaluation demonstrates that the ViT model outperforms conventional deep learning architectures in terms of accuracy, stability, and predictive reliability. The integrated framework enables fast, automated, and robust leakage diagnosis, providing a comprehensive solution to enhance the monitoring, maintenance, and operational safety of wind tunnel valve systems. Full article

Electrified powertrains in the transportation sector have increased significantly in recent years, thanks to the need for decarbonization of the on-the-road transport means. However, management of powertrains still deserves particular attention to assess necessary improvements for reducing electric consumption and increasing the mileage of the vehicles. In this regard, electric motor cooling is essential for maintaining optimal performance and longevity. In fact, as electric motors operate, they generate heat due to electric and magnetic phenomena as well as mechanical friction. If not properly managed, this heat can lead to decreased efficiency, accelerated wear, or even failure of critical components. Effective cooling systems ensure that the motor runs within its ideal temperature range, reducing the occurrence of the mentioned concerns. This improves operational reliability and, at the same time, contributes to energy savings and reduced maintenance costs over the components’ life. In this study, the cooling of the rotor of a 130-kW electric motor via refrigerating fluid circulating inside the shaft has been investigated. Two configurations of fluid passages have been considered: a direct-through flow crossing the shaft along its axis and a hollow shaft with recirculating flow, with three types of rotating helical configurations at different pitches. The benefits when using nanofluids as a cooling medium have also been evaluated to enhance the heat transfer coefficient and decrease temperature values. Compared with the baseline configuration using standard fluids (water), the proposed solution employing nanofluids demonstrates effectiveness in terms of heat transfer coefficients (up to 28% higher than pure water), with limited impact on pressure losses, thus reducing rotor temperature by up to 30 K with respect to the baseline. This study opens the possibility of integrating the cooling of the rotor with whole electric motor cooling for electric and hybrid powertrains. Full article

Background: Philadelphia-like acute lymphoblastic leukemia (Ph-like ALL) is a high-risk subtype of B-cell ALL characterized by a gene expression profile similar to BCR::ABL1-positive leukemia, but lacking the BCR::ABL1 fusion gene. It is frequently associated with kinase-activating alterations, such as CRLF2 rearrangements, JAK-STAT pathway mutations, and ABL-class fusions. Patients with Ph-like ALL typically experience poor outcomes with conventional chemotherapy, underscoring the need for intensified and targeted therapeutic approaches. Methods: This review summarizes current evidence regarding the role of hematopoietic stem cell transplantation (HSCT) in patients with Ph-like ALL. We analyzed retrospective cohort studies, registry data, and ongoing clinical trials, focusing on transplant indications, molecular risk stratification, measurable residual disease (MRD) status, timing of transplant, and post-transplant strategies. Results: Retrospective data suggest that HSCT in first complete remission (CR1) may improve survival in patients with high-risk molecular lesions or MRD positivity at the end of induction. However, the lack of prospective data specific to Ph-like ALL limits definitive conclusions. Post-transplant relapse remains a challenge, and novel strategies, including the use of tyrosine kinase inhibitors or JAK inhibitors as post-HSCT maintenance therapy, are being explored. Emerging immunotherapies, such as chimeric antigen receptor (CAR) T cells, may reshape the therapeutic landscape and potentially alter the indications for transplantation. Conclusions: HSCT remains a crucial therapeutic option for selected patients with Ph-like ALL, particularly those with poor molecular risk features or persistent MRD. However, further prospective studies are needed to evaluate the indication for HSCT in CR1 and the potential integration of transplantation with targeted and immunotherapeutic strategies. Personalized treatment approaches based on genomic profiling and MRD assessment are essential to improve outcomes in this high-risk subset. Full article

Pain remains one of the most burdensome symptoms in rheumatoid arthritis (RA), often persisting despite inflammatory remission and profoundly impairing quality of life. This review aimed to evaluate the clinical efficacy and mechanistic pathways by which Janus kinase (JAK) inhibitors alleviate RA-related pain. Evidence from randomized clinical trials demonstrates that JAK inhibitors have demonstrated rapid and significant pain relief, often exceeding that of methotrexate or biologic DMARDs. Improvements in patient-reported pain scores seem to typically emerge within 1–2 weeks and are sustained over time. Beyond anti-inflammatory effects, JAK inhibitors modulate central sensitization and nociceptive signaling by attenuating IL-6 and GM-CSF activity, reducing astrocyte and microglial activation, and downregulating nociceptor excitability in dorsal root ganglia and spinal pathways. Preclinical models further suggest that JAK inhibition interrupts neuroimmune feedback loops critical to chronic pain maintenance. Comparative and network meta-analyses consistently position JAK inhibitors among the most effective agents for pain control in RA. However, individual variability in response, partly due to differential JAK-STAT activation and cytokine receptor uncoupling, underscores the need for biomarker-guided treatment approaches. JAK inhibitors represent a mechanistically distinct and clinically impactful class of therapies that target both inflammatory and non-inflammatory pain in RA. Their integration into personalized pain management strategies offers a promising path to address one of RA’s most persistent unmet needs. Full article

Road-surface distress poses a serious threat to traffic safety and imposes a growing burden on urban maintenance budgets. While modern detectors based on convolutional networks and Vision Transformers achieve strong frame-level performance, they often overlook an essential property of road environments—structural symmetry within road networks and damage patterns. We present Graph-MambaRoadDet (GMRD), a symmetry-aware and lightweight framework that integrates dynamic graph reasoning with state–space modeling for accurate, topology-informed, and real-time road damage detection. Specifically, GMRD employs an EfficientViM-T1 backbone and two DefMamba blocks, whose deformable scanning paths capture sub-pixel crack patterns while preserving geometric symmetry. A superpixel-based graph is constructed by projecting image regions onto OpenStreetMap road segments, encoding both spatial structure and symmetric topological layout. We introduce a Graph-Generating State–Space Model (GG-SSM) that synthesizes sparse sample-specific adjacency in $\mathcal{O}\left(M\right)$ time, further refined by a fusion module that combines detector self-attention with prior symmetry constraints. A consistency loss promotes smooth predictions across symmetric or adjacent segments. The full INT8 model contains only 1.8 M parameters and 1.5 GFLOPs, sustaining 45 FPS at 7 W on a Jetson Orin Nano—eight times lighter and 1.7× faster than YOLOv8-s. On RDD2022, TD-RD, and RoadBench-100K, GMRD surpasses strong baselines by up to +6.1 mAP_50:95 and, on the new RoadGraph-RDD benchmark, achieves +5.3 G-mAP and +0.05 consistency gain. Qualitative results demonstrate robustness under shadows, reflections, back-lighting, and occlusion. By explicitly modeling spatial and topological symmetry, GMRD offers a principled solution for city-scale road infrastructure monitoring under real-time and edge-computing constraints. Full article

Accurate prediction of the RUL of electric drive bearings over the entire service life cycle for new energy vehicles optimizes maintenance strategies and reduces costs, addressing clear application needs. Full life data of electric drive bearings exhibit long time spans and abrupt degradation, complicating the modeling of time dependent relationships and degradation states; therefore, a piecewise linear degradation model is appropriate. An RUL prediction method is proposed based on degradation assessment and spatiotemporal feature fusion, which extracts strongly time correlated features from bearing vibration data, evaluates sensitive indicators, constructs weighted fused degradation features, and identifies abrupt degradation points. On this basis, a piecewise linear degradation model is constructed that uses a path graph structure to represent temporal dependencies and a temporal observation window to embed temporal features. By incorporating GAT-LSTM, RUL prediction for bearings is performed. The method is validated on the XJTU-SY dataset and on a loaded ball bearing test rig for electric vehicle drive motors, yielding comprehensive vibration measurements for life prediction. The results show that the method captures deep degradation information across the full bearing life cycle and delivers accurate, robust predictions, providing guidance for the health assessment of electric drive bearings in new energy vehicles. Full article

Background: Nurses play a central role in the management of inflammatory joint diseases (IJD), of which the success depends on patient adherence to treatment, self-monitoring, timely detection of adverse drug reactions (ADRs), and adopting a healthy lifestyle. This study sought to examine the opinions of patients with IJD regarding the educational and supportive contributions of nurses. Methods: The research is based on a cross-sectional survey of patients with IJD treated with biologic disease-modifying antirheumatic drugs (bDMARDs) in two rheumatology clinics in Plovdiv, Bulgaria, from the beginning of August 2024 to the end of January 2025. The group included patients of three diagnoses: (1) rheumatoid arthritis (RA), (2) psoriatic arthritis (PsA), and (3) axial spondyloarthritis (axSpA). Results: Regardless of the diagnosis, and after adjusting for covariates, patients rated the roles of nurses in disease treatment and management, the acquisition of self-injection skills for bDMARDs, the implementation of a healthy lifestyle, and the maintenance of psychological well-being at the higher end of the 0 to 4 scale. However, the axSpA patients were less affirmative in their responses compared to the RA and PsA patients. In the RA and PsA groups, the working patients were associated with the lowest ratings, followed by retirees with disability. Conclusions: Our findings indicate that nurse-led education in patient self-management skills is greatly appreciated by patients with IJD. Further developments in specialized training programs tailored to the specific needs of different diagnoses and in consideration of patients’ social status will lead to increased patient satisfaction and a better overall quality of life. Full article

This study investigates how short- and long-term aging affect the microwave self-healing of steel slag asphalt mixtures (SSAMs). Binder-level healing was tested using a dynamic shear rheometer (DSR), and mixture-level crack behavior was analyzed using beam bending tests (BBTs) and digital image correlation (DIC). Aging clearly reduced self-healing, with long-term aging causing the largest decline. Among the mixtures, OGFC-13 was most sensitive, while SMA-13 was least affected. Aging increased stiffness, reduced crack resistance, and shortened crack initiation time, leading to lower healing efficiency under microwave treatment. After heating, cracks propagated faster, indicating increased brittleness. These results quantify the impact of aging on performance and highlight the limitations of microwave repair, providing guidance for maintenance strategies and mixture design to improve long-term pavement performance. Full article

Underground mining faces unique challenges in equipment maintenance due to extreme operating conditions and intensive use, which limit the effectiveness of traditional methods. This study proposes a predictive maintenance (PdM) framework based on artificial intelligence (AI) to optimize efficiency and reduce costs, focusing on early fault detection. The methodology integrates IoT sensors to monitor key parameters (temperature, pressure, oil analysis, and wear) in real time, combined with machine learning models to identify predictive patterns. The results demonstrate an 8% reduction in maintenance costs and a 10% increase in equipment availability, validating the system’s ability to anticipate failures and minimize unplanned downtime. It is concluded that this approach not only enhances productivity but also raises safety standards, offering a scalable model for critical industrial environments. The findings are supported by empirical data collected from actual operations, with no theoretical extrapolations. Full article

Existing building management systems face critical limitations in real-time data integration, primarily relying on static models that lack dynamic updates from IoT sensors. To address this gap, this study proposes a novel system integrating MQTT over WebSocket with Three.js visualization, enabling real-time sensor-data binding to Building Information Models (BIM). The architecture leverages MQTT’s lightweight publish-subscribe protocol for efficient communication and employs a TCP-based retransmission mechanism to ensure 99.5% data reliability in unstable networks. A dynamic topic-matching algorithm is introduced to automate sensor-BIM associations, reducing manual configuration time by 60%. The system’s frontend, powered by Three.js, achieves browser-based 3D visualization with sub-second updates (280–550 ms latency), while the backend utilizes SpringBoot for scalable service orchestration. Experimental evaluations across diverse environments—including high-rise offices, industrial plants, and residential complexes—demonstrate the system’s robustness: Real-time monitoring: Fire alarms triggered within 2.1 s (22% faster than legacy systems). Network resilience: 98.2% availability under 30% packet loss. User efficiency: 4.6/5 satisfaction score from facility managers. This work advances intelligent building management by bridging IoT data with interactive 3D models, offering a scalable solution for emergency response, energy optimization, and predictive maintenance in smart cities. Full article

This paper presents a data-driven flexible hybrid architecture which explores the use of a Specific Energy Consumption (SEC) index for predicting the Remaining Useful Lifetime (RUL) of spare mechanical parts of an industrial electric machine. The architecture carries out a hybrid process between a physics-based and data-driven deterioration model, and a similarity model based on a recursive database continuously enriched with real data on current used electrical power and the flow of raw material. The architecture enriches the production database with both synthetic and real data through continuous improvement based on the extraction of features from new incoming real data. This recursive process of database construction is carried out to improve the robustness, accuracy, and precision of estimations. The integration of the architecture aims to enhance predictive maintenance. As an example to illustrate the architecture, the case of an industrial shredder machine is analyzed from real data. The proposed architecture successfully predicts the RUL of sugarcane shredder spare parts from the recursive database and a defined threshold condition. The RUL prognosis converges toward a representative trajectory of the database after a given early time with respect to the total useful life. Full article

Induction motors (IMs) are crucial in many industrial applications, offering a cost-effective and reliable source of power transmission and generation. However, their continuous operation imposes considerable stress on electrical and mechanical parts, leading to progressive wear that can cause unexpected system shutdowns. Bearings, which enable shaft motion and reduce friction under varying loads, are the most failure-prone components, with bearing ball defects representing most severe mechanical failures. Early and accurate fault diagnosis is therefore essential to prevent damage and ensure operational continuity. Recent advances in the Internet of Things (IoT) and machine learning (ML) have enabled timely and effective predictive maintenance strategies. Among various diagnostic parameters, vibration analysis has proven particularly effective for detecting bearing faults. This study proposes a hybrid diagnostic framework for induction motor bearings, combining vibration signal analysis with Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs) in an IoT-enabled Industry 4.0 architecture. Statistical and frequency-domain features were extracted, reduced using Principal Component Analysis (PCA), and classified with SVMs and ANNs, achieving over 95% accuracy. The novelty of this work lies in the hybrid integration of interpretable and non-linear ML models within an IoT-based edge–cloud framework. Its main contribution is a scalable and accurate real-time predictive maintenance solution, ensuring high diagnostic reliability and seamless integration in Industry 4.0 environments. 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

The integration of artificial intelligence (AI) into bioclimatic building design is reshaping the architecture, engineering, and construction (AEC) industry by addressing critical challenges in sustainability and efficiency. By aligning structures with local climates, bioclimatic design addresses global challenges such as energy consumption, urbanization, and climate change. Complementing these principles, AI technologies—including machine learning, digital twins, and generative algorithms—are revolutionizing the sector by optimizing processes across the entire building lifecycle, from design and construction to operation and maintenance. Amid the diverse array of AI-driven innovations, this research highlights digital twin (DT) technologies as a key to AI-driven transformation, enabling real-time monitoring, simulation, and optimization for sustainable design. Applications like façade optimization, energy flow analysis, and predictive maintenance showcase their role in adaptive architecture, while frameworks like Construction 4.0 and 5.0 promote human-centric, data-driven sustainability. By bridging AI with bioclimatic design, the findings contribute to a vision of a built environment that seamlessly aligns environmental sustainability with technological advancement and societal well-being, setting new standards for adaptive and resilient architecture. Despite the immense potential, AI and DTs face challenges like high computational demands, regulatory barriers, interoperability and skill gaps. Overcoming these challenges will be crucial for maximizing the impact on sustainable building, requiring ongoing research to ensure scalability, ethics, and accessibility. Full article