Search Results (30,919)

Recent trends in research on rotating machinery diagnosis focus on contactless diagnostic technologies. In this paper, novel higher order spectral technologies, based on spectral moduli, are proposed. The proposed technologies estimate statistical dependencies between moduli of harmonics of bearing defect frequencies. Moduli of harmonics of bearing defect frequencies, which appear due to bearing faults, are statistically dependent. The Third Order Modulus (TOM) is a novel higher order spectral signal processing technology developed for rotating machinery diagnostics. The paper presents mathematical expressions for new technologies as well as a detailed description of the signal processing algorithm of motor current for bearings diagnostics. The TOM technology is comprehensively validated via experimental trials for motor bearing diagnosis via motor current signature analysis. Results of experimental trials clearly show that the TOM technology is highly effective for diagnosis of bearing defects. Estimates of the total probabilities of correct diagnosis provided by the TOM technology are 100%. The TOM technology is experimentally compared with the classic bicoherence (CB) technology using eight bearings: four pristine bearings and four damaged bearings with two damage types. Comparison has shown that the TOM technology is more effective than the CB technology. Full article

The Chebei integral Immersed Tunnel introduced an innovative full-section interval casting process, where post-casting belts impose restraint effects on the full-section casting segments. To mitigate concrete cracking, this study investigates the influence of the bottom steel plate and steel bars in the post-casting belts on the mechanical behavior of full-section casting segments through comparative analysis of field tests and numerical simulations. Requirements for post-casting belt length are proposed. Key findings include: under post-casting belt restraint, the full-section casting segment’s shrinkage strain reached 348 με, with hydration heat-induced cooling and drying shrinkage contributing 60% and 40%, respectively. A temperature-dependent thermal expansion coefficient model was developed to characterize the nonlinear relationship between concrete strain and hydration heat temperature. Restraint effects diminished with increasing post-casting belt length, and the post-casting belt length should be control. At 1.6 m (Chebei design), restraint-induced tensile stress was 1.4 MPa (restraint coefficient β = 0.12), with the bottom steel plate and steel bars contributing about 70% and 30%, respectively. Relationships between post-casting belt length, stress, and restraint coefficient are established for engineering reference. These research findings have been successfully applied in the Chebei Immersed Tunnel, enabling high-quality prefabrication of full-section interval casting immersed tubes. Full article

Ensuring reliability and safety is essential in complex energy systems such as wind turbines, where failures can trigger unexpected downtimes, severe incidents, and significant costs. This study proposes a hybrid BowTie-based reliability framework that integrates Fault Tree Analysis, Reliability Block Diagrams, and BowTie methodology to quantify risk and evaluate the effectiveness of safety barriers. The framework employs key reliability metrics including availability, probability of failure on demand, and probability of failure per hour, and supports scenario-based sensitivity analyses to explore redesign options. A simulation-based case study of a wind turbine generator subsystem is presented, using parameter values drawn from published reliability data. Results highlight that protective relays and automatic trip systems represent critical single points of defence, while improvements such as enhanced oil analysis and redundant dashboards reduce consequence frequency from 2.912 × 10⁻¹⁷ to 8.257 × 10⁻¹⁹ failures/h (a 97.16% reduction, nearly two orders of magnitude). Compared to conventional models, the proposed framework introduces explicit defence in depth modelling, improves computational compactness, and provides a practical decision support tool for asset managers by balancing safety and reliability. At this stage, the study should be regarded as a proof of concept that demonstrates feasibility and sets a foundation for future research and application to larger, more complex infrastructures. Full article

Background: Uric acid (UA) is linked to gout, renal dysfunction, and cardiovascular disease. Prior studies often assume linear relationships, potentially oversimplifying physiological complexity. Methods: We analyzed data from 5200 healthy Taiwanese men. Demographic, biochemical, lifestyle, and inflammatory variables were assessed using Pearson correlation, multiple linear regression (MLR), and multivariate adaptive regression splines (MARS), an interpretable machine learning method for detecting nonlinear, threshold-based effects. Results: Pearson correlation showed broad linear associations, whereas MARS identified fewer but more physiologically meaningful predictors. Waist-to-hip ratio (WHR) had a strong threshold effect, influencing UA only below 0.969. Creatinine showed a nonlinear impact, becoming substantial above 0.97 mg/dL, suggesting a renal threshold within the “normal” range. Calcium and high-sensitivity C-reactive protein (hs-CRP) each displayed inflection points (9.5 mg/dL and 3.38 mg/L, respectively), indicating range-specific effects. Notably, betel nut exposure, nonsignificant in linear models, emerged in MARS as a predictor with a complex, non-binary association with UA metabolism. Predictive performance was comparable (RMSE: 1.6694 for MARS vs. 1.6666 for MLR), but MARS offered superior interpretability by highlighting localized nonlinear effects. Conclusions: MARS modeling revealed critical nonlinear, threshold-dependent associations between UA and WHR, creatinine, calcium, hs-CRP, and betel nut exposure, which were not captured by conventional methods. These findings underscore the value of interpretable machine learning in metabolic research and suggest precise thresholds for clinical risk stratification. Full article

At present, the development of power cables shows three notable trends: higher voltage, longer distance and more complex environments. Against this backdrop, the limitations of traditional detection techniques in locating local defects have become increasingly apparent. Frequency Domain Reflectometry (FDR) has garnered sustained research attention both domestically and internationally due to its high sensitivity and accuracy in detecting localized defects. This paper aims to compare the defect localization effectiveness of the Fast Fourier Transform (FFT) method and the Inverse Fast Fourier Transform (IFFT) method within FDR. First, the differences between the two methods are analyzed from a theoretical perspective. Then, field tests are conducted on cables of varying voltage levels and lengths, with comparisons made using parameters such as full width at half maximum (FWHM) and signal-to-noise ratio (SNR). The results indicate that the FFT method is more suitable for low-interference or short cables, while the IFFT method is more suitable for high-noise, high-resolution, or long cables. Full article

This study was focused on addressing the performance degradation in core microstructures of ultra-heavy steel plates (thickness ≥ 50 mm) caused by non-uniform cooling during thermo-mechanical controlled processing. Using microalloyed DH36 steel as the research subject, we systematically investigated the effects of cooling rate on the nucleation and growth of acicular ferrite and its consequent microstructure-property relationships through an integrated approach combining in situ observation via high-temperature laser scanning confocal microscopy with multiscale characterization techniques. Results demonstrate that the cooling rate significantly affects acicular ferrite formation, with the range of 3–7 °C/s being most conducive to acicular ferrite formation. At 5 °C/s, the acicular ferrite volume fraction reached a maximum of 74% with an optimal aspect ratio (5.97). Characterization confirmed that TiOx-Al₂O₃·SiO₂-MnO-MnS complex inclusions act as effective nucleation sites for acicular ferrite, where the MnS outer layer plays a key role in reducing interfacial energy and promoting acicular ferrite radial growth. Furthermore, the interlocking acicular ferrite structure was shown to enhance microhardness by 14% (HV0.1 = 212.5) compared to conventional ferrite through grain refinement strengthening and dislocation strengthening (with a dislocation density of 2 × 10⁸ dislocations/mm²). These results provide crucial theoretical insights and a practical processing window for strengthening-toughening control of heavy plate core microstructures, offering a viable pathway for improving the comprehensive performance of ultra-heavy plates. Full article

Despite advances in diagnostics, the objective and repeatable assessment of patients with neurological deficits (e.g., stroke) remains a major challenge. Modern methods based on artificial intelligence (AI) are of interest to researchers and clinicians in this area. This study presents a comparative analysis of different AI approaches used to analyze gait of stroke patients using a retrospective dataset of 120 individuals. The main objective is to evaluate the effectiveness, accuracy, and clinical relevance of machine learning (ML) and deep learning (DL) models in identifying gait abnormalities and predicting rehabilitation outcomes. Multiple AI techniques—including support vector machines (SVM), random forests (RF), k-nearest neighbors (k-NN), and convolutional neural networks (CNN)—were trained and tested on time-series gait data with spatiotemporal parameters. Performance metrics such as accuracy, precision, recall, and area under the curve (AUC) were used to compare model results. Initial results indicate that DL models, particularly CNNs, outperform traditional ML methods in capturing complex gait patterns and providing reliable classification. However, simpler models showed advantages in interpretability and computational efficiency. This study highlights the potential and shortcomings of AI-based gait analysis tools in supporting clinical decision-making and planning personalized stroke rehabilitation. Full article

Against the background of global warming, the carbon emission of cities accounts for more than 70%, and its carbon sink increase and emission reduction have become the research focus. The water bodies and green spaces in the urban blue–green space have a synergistic carbon sequestration effect, but current research pays less attention to the small and medium scales. Therefore, taking the waterfront green space on both sides of Qinhuai New River in Nanjing as the research object, this paper explores the impact of the synergy between water and greenery on the carbon sequestration efficiency of green space. The study first estimates the carbon sequestration efficiency of green spaces by integrating measured Leaf Area Index (LAI) data with the mean carbon sequestration rate per unit leaf area for typical tree and shrub species. It then constructs a set of water–green spatial relationship indicators and applies a random forest regression model to identify the key factors influencing carbon sequestration efficiency. Finally, multiple scenario models are developed to simulate the effects of green spaces on CO₂ reduction, thereby validating the roles of the identified influencing factors. The study found that waterfront green spaces tended to exhibit slightly higher carbon sequestration efficiency compared with non-waterfront green spaces. The proportion of 10 m forest land area and the proportion of 10–20 m forest land area had a higher impact on the carbon sequestration capacity of waterfront green space; that is, the closer the distance between the green space and the water, the better the carbon sequestration capacity. In order to improve the carbon sequestration efficiency of the waterfront area, the green space should be arranged along the water bank as much as possible, the depth of the green space should be increased, the proportion of the forest land area should be increased, the arbor and shrub should be planted evenly, and ribbon planting should be avoided. The study confirmed the synergistic effect of water and greenery in carbon sequestration benefits, providing data support and theoretical reference for the optimization and renewal of urban waterfront green space, and contributing to the realization of urban waterfront green space planning, design, and renewal with the goal of a high carbon sink. Full article

Micro-hydropower technologies are increasingly attracting attention due to their potential to contribute to sustainable energy generation. With the growing global demand for electricity, it is essential to research and innovate in the development of devices capable of harnessing hydroelectric potential through such technologies. In this context, the Archimedes screw generator (ASG) stands out as a device that potentially offers significant advantages for micro-hydropower generation. This study aimed, through a simplified yet effective method, to analyze and determine the simultaneous effects of the number of blades, inclination angle, and flow rate on the torque, mechanical power, and efficiency of an ASG. Computational Fluid Dynamics (CFD) was employed to obtain the torque and perform the hydrodynamic analysis of the devices, in order to compare the results of the optimal geometric and operational characteristics with previous studies. This proposal also helps guide future work in the preliminary design and evaluation of ASGs, considering the geometric and flow conditions that take full advantage of the available water resources. Under the specific conditions analyzed, the most efficient generator featured three blades, a 20° inclination, and an inlet flow rate of 24.5 L/s, achieving a mechanical power output of 117 W with an efficiency of 71%. Full article

With the rapid development of transportation infrastructure, bridges increasingly face prominent issues of dynamic response and fatigue damage induced by vehicle–bridge interaction (VBI). To effectively suppress the coupled vibrations and enhance both vehicle ride comfort and bridge service life, this paper proposes an active inerter-spring-damper (ISD) suspension system based on Particle Swarm Optimization (PSO) algorithm and Linear Quadratic Regulator (LQR) control. By establishing a VBI model considering general boundary conditions and employing the modal superposition method to solve the system response, an LQR controller is designed for multi-objective optimization targeting the vehicle body acceleration, suspension dynamic travel, and tire dynamic load. To further improve control performance, the PSO algorithm is utilized to globally optimize the LQR weighting matrices. Numerical simulation results demonstrate that, compared to passive suspension and unoptimized LQR active suspension, the PSO-LQR control strategy significantly reduces vertical body acceleration and tire dynamic load, while also improving the convergence and stability of the suspension dynamic travel. This research provides a new insight into the control method for VBI systems, possessing both theoretical and practical engineering application value. Full article

Background/Objectives: Schizophrenia is associated with cognitive deficits that may compromise everyday functioning, including driving. This review systematically examined recent original research (2015–2025) on driving performance in individuals with schizophrenia with a focus on neuropsychological factors, applying a narrative synthesis given the heterogeneity of designs and outcomes, while no quantitative meta-analysis was feasible. Methods: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a structured search of PubMed and Scopus was conducted on 4 May 2025. The inclusion criteria were original studies involving individuals diagnosed with schizophrenia, published between 2015 and 2025. Studies on animals, other psychiatric or neurological conditions, and healthy populations were also excluded. Critical appraisal was performed using the Joanna Briggs Institute (JBI) tools. Extracted data included sample demographics, cognitive deficits, neuropsychological assessments, brain imaging, and the main findings. A narrative synthesis was then performed. Results: Six high-quality studies met the inclusion criteria. Findings were grouped into three categories: (1) driving behavior: fitness to drive varied widely across individuals, (2) cognitive deficits and brain activity: poorer driving-related performance was consistently associated with specific impairments in cognition and brain structure, and (3) medication effects: individuals taking certain atypical antipsychotics demonstrated better driving performance compared to those on other types of medication, while extrapyramidal symptoms negatively influenced driving fitness. Conclusions: Driving in schizophrenia is shaped by cognitive, clinical, and pharmacological factors. These findings highlight the clinical relevance of individualized evaluations, integration into personalized care and targeted rehabilitation to promote driving autonomy and community inclusion. This area remains under-researched, as only six studies met the inclusion criteria, which restricts the robustness and generalizability of the conclusions. Funding: This review received no funding from any external sources. Registration: The review protocol was submitted to PROSPERO (International Prospective Register of Systematic Reviews) under registration number CRD420251060580. Full article

Introduction: The 9th edition of the TNM classification for lung cancer implemented significant revisions, notably the subdivision of the N2 and M1c categories, to enhance anatomical precision and prognostic accuracy. Nonetheless, the actual effects of these modifications on stage distribution, histology-specific patterns, and clinical interpretation remain to be fully evaluated. Objectives: To compare lung cancer staging distributions between the 8th and 9th TNM editions, analyze patterns of stage migration, and evaluate histology-specific reclassification trends. Although TNM 9 applies the same descriptors across all histological subtypes, the magnitude of stage migration varies. In our cohort and in international datasets, adenocarcinoma demonstrated a higher likelihood of reclassification into advanced stages compared to other subtypes. Methods: A retrospective analysis was performed on a cohort of lung cancer patients staged according to the 8th and 9th editions of the TNM classification. Stage distribution alterations were analyzed by chi-squared tests, whereas McNemar’s test examined the directional shifts in upstaging and downstaging. Further investigations evaluated the correlation between histological subtype and stage reclassification. Results: A statistically significant redistribution of stages was noted (χ² = 1013.03, df = 64, p < 0.0001), with a notable prevalence of upstaging (p = 0.0019). The most significant proportional increase was observed in stage IIIA, mostly attributable to the N2 subdivision (N2a vs. N2b). Adenocarcinoma was the predominant histological subtype at all stages and showed a greater tendency for reclassification into advanced stages, specifically IIIA and IIIB. Squamous cell carcinoma was predominantly observed in stages IIB and IIIA, whereas small cell and large cell carcinomas were concentrated in advanced stages. These histology-specific patterns correspond with international findings, including research confirming the prognostic relevance of N2 subdivision. Conclusions: The 9th edition of the TNM classification results in significant stage migration, particularly in adenocarcinoma cases, indicating the improved sensitivity of the updated criteria in identifying advanced nodal disease. These modifications significantly impact prognostic evaluation and global comparability of clinical cohorts, supporting the implementation of TNM 9 as a more anatomically and biologically relevant staging system. Full article

Maritime logistics and railway logistics are crucial in cross-border logistics, and their integration forms a sea-rail cross-border intermodal logistics network. Against the backdrop of frequent unexpected events in today’s world, the normal operation of the sea-rail cross-border intermodal logistics network is under considerable threat. Therefore, researching the vulnerability of the intermodal network is extremely urgent. To this end, this paper first constructs a topological model of the sea-rail cross-border intermodal logistics network, designed to reflect the crucial process of “inter-layer transshipment” via transshipment nodes. Subsequently, a cascading failure model is developed to evaluate network vulnerability, featuring a load redistribution process that distinguishes between transshipment and non-transshipment nodes. The paper yields three primary findings. First, it identifies the optimal values for the capacity factor, overload factor, and inter-layer load transfer rate that most effectively mitigate the network’s vulnerability. Second, compared to a single sub-network (such as a maritime logistics network or a railway logistics network), the sea-rail cross-border intermodal network exhibits lower vulnerability when facing attacks. Third, it highlights the critical role of transshipment nodes, confirming that their failure will make the entire sea-rail cross-border intermodal logistics network more vulnerable. Full article

To address the greenhouse effect and environmental pollution stemming from fossil fuels, the development of new energy sources is widely regarded as a critical pathway toward achieving carbon neutrality. Microalgae, as a feedstock for third-generation biofuels, have emerged as a research hotspot for producing biojet fuel due to their high photosynthetic efficiency, non-competition with food crops, and potential for carbon reduction. This paper provides a systematic review of technological advancements in the catalytic hydrogenation of microalgal oil for biojet fuel production. It specifically focuses on the reaction mechanisms and catalyst design involved in the hydrogenation–deoxygenation and cracking/isomerization processes within the Oil-to-Jet (OTJ) pathway. Furthermore, the paper compares the performance differences among various catalyst support materials and between precious and non-precious metal catalysts. Finally, it outlines the current landscape of policy support and progress in industrialization projects globally. Full article

Following restrictions on antibiotic growth promoters in poultry production, there is growing interest in natural feed additives that support health and productivity. Among these, black cumin (Nigella sativa) has emerged as a promising candidate due to its antioxidant, antimicrobial, and immunomodulatory properties. Most studies report that black cumin, in the form of whole seeds, seed meal, or seed oil, improves body weight gain and feed conversion ratio, enhances antioxidant and immune status, and provides additional benefits on lipid profiles, liver enzymes, and cecal microbial balance. This review provides a focused synthesis of recent studies (2014–2025) on black cumin supplementation in broiler chickens, considering its various forms (whole seeds, seed meal, seed oil, and nano-formulations) and production contexts (healthy, heat-stressed, and disease-challenged birds). Specifically, this review compares responses across different forms and doses, evaluates effects on growth performance, immune function, gut health, antioxidant status, liver metabolism, and meat and carcass quality, and highlights inconsistencies among studies. Additionally, it identifies key research gaps to guide future investigations, including optimal dosing, long-term safety, and practical applications in commercial production. Overall, black cumin shows potential as a natural alternative to antibiotics, but further standardized, large-scale studies are needed to confirm its efficacy and feasibility in sustainable poultry farming. Full article