Search Results (2,043)

Pipelines, as critical infrastructure in energy transmission, municipal facilities, industrial production, and specialized equipment, are essential to national economic security and social stability. This paper systematically reviews the domestic and international research status of pipeline in-line inspection (ILI) technologies, with a focus on four major technological systems: electromagnetic, acoustic, optical, and robotic technologies. The operational principles, application scenarios, advantages, and limitations of each technology are analyzed in detail. Although existing technologies have achieved significant progress in defect detection accuracy and environmental adaptability, they still face challenges including insufficient adaptability to complex environments, the inherent trade-off between detection accuracy and efficiency, and high equipment costs. Future research directions are identified as follows: intelligent algorithm optimization for multi-physics collaborative detection, miniaturized and integrated design of inspection devices, and scenario-specific development for specialized environments. Through technological innovation and multidisciplinary integration, pipeline ILI technologies are expected to progressively realize efficient, precise, and low-cost lifecycle safety monitoring of pipelines. Full article

Green hydrogen is a clean, versatile, and future-oriented fuel that can play a key role in the energy transition, decarbonization of the economy, and climate protection. It offers an alternative to fossil fuels and can be used in various applications, including power generation, industry, and transportation. However, due to its wide flammability range, small molecular size, and high diffusivity, special attention must be paid to ensuring safety during its use, particularly in leakage control. This paper provides a review and analysis of equipment leakage testing methods used for natural gas, with a view to applying these methods to the leakage testing of gas meters intended for hydrogen-containing gas mixtures and pure hydrogen. Tests of simulated leaks were carried out using two common methods: the bubble method and the pressure decay method, for three different gases: nitrogen (most commonly used for leak testing), helium, and hydrogen. The results obtained from the tests and analyses made it possible to verify and select optimum leak-testing methods for gas meters designed for measuring fuels containing hydrogen. Full article

Biomass and other solid wastes create potential environmental and health hazards in our modern society. Conversion of the wastes into energy presents a promising avenue for sustainable energy generation. However, the feasibility of the approach is limited by the challenges in material handling because of the special properties of the materials. Despite their critical importance, the complexities of material handling often evade scrutiny until operational implementation. This paper highlights the challenges inherent in standard solid material-handling processes, preceded by a concise review of common solid waste typologies and their physical properties, particularly those related to biomass and biowastes. It delves into the complexities of material flow, storage, compaction, agglomeration, separation, transport, and hazard management. Specialised characterisation techniques essential for informed process design are also discussed to mitigate operational risks. In conclusion, this paper emphasises the necessity of a tailored framework before the establishment of any further conversion processes. Given the heterogeneous nature of biomaterials, material-handling equipment must demonstrate adaptability to accommodate the substantial variability in material properties in large-scale production. This approach aims to enhance feasibility and efficacy of any energy conversion initiatives by using biomass or other solid wastes, thereby advancing sustainable resource utilisation and environmental stewardship. Full article

Periodic visual inspections are currently conducted to maintain the condition of railway bridges. These inspections rely on direct visual assessments by human inspectors, often requiring specialized equipment such as aerial ladders. However, this method is not only time-consuming and costly but also involves significant safety risks. Therefore, there is a growing need for a more efficient and reliable alternative to traditional visual inspections of railway bridges. In this study, we evaluated and compared the performance of damage detection using U-Net-based deep learning models on images captured by unmanned aerial vehicles (UAVs). The target damage types include cracks, concrete spalling and delamination, water leakage, exposed reinforcement, and paint peeling. To enable multi-class segmentation, the U-Net model was trained using three different loss functions: Cross-Entropy Loss, Focal Loss, and Intersection over Union (IoU) Loss. We compared these methods to determine their ability to distinguish actual structural damage from environmental factors and surface contamination, particularly under real-world site conditions. The results showed that the U-Net model trained with IoU Loss outperformed the others in terms of detection accuracy. When applied to field inspection scenarios, this approach demonstrates strong potential for objective and precise damage detection. Furthermore, the use of UAVs in the inspection process is expected to significantly reduce both time and cost in railway infrastructure maintenance. Future research will focus on extending the detection capabilities to additional damage types such as efflorescence and corrosion, aiming to ultimately replace manual visual inspections of railway bridge surfaces with deep-learning-based methods. Full article

This paper presents an electromechanical coupling model integrating an equivalent magnetic network (EMN) model of a dual three-phase permanent magnet synchronous motor (DTP-PMSM) with the dynamic model of a helical planetary gear transmission system. Using this model, this study analyzes the dynamic characteristics of an electric drive system, specifically motor phase current, electromagnetic torque, and gear meshing force, under self-fault-tolerant control strategies. Simulation and experimental results demonstrate that the self-fault-tolerant control strategy enables rapid fault tolerance during open-phase faults, significantly reducing system fault recovery time. Meanwhile, compared to the open-phase faults conditions, the self-fault-tolerant control effectively suppresses most harmonic components within the system; only the second harmonic amplitude of the electromagnetic torque exhibited an increase. This harmonic disturbance propagates to the gear system through electromechanical coupling, synchronously amplifying the second harmonic amplitude in the gear system’s vibration response. This study demonstrates that self-fault-tolerant control strategies significantly enhance the dynamic response performance of the electric drive system under open-phase faults conditions. Furthermore, this study also investigates the electromechanical coupling mechanism through which harmonics generated by this strategy affect the gear system’s dynamic response, providing theoretical support for co-optimization electromechanical coupling design and fault-tolerant control in high-reliability electric drive transmission systems. Full article

To reveal the influence mechanisms of seasonal climatic factors (wind speed, wind direction, temperature) and leakage direction on hydrogen dispersion and explosion behavior from single-source leaks at typical risk locations (hydrogen storage tanks, compressors, dispensers) in hydrogen refueling stations (HRSs), this work established a full-scale 1:1 three-dimensional numerical model using the FLACS v22.2 software based on the actual layout of an HRS in Xichang, Sichuan Province. Through systematic simulations of 72 leakage scenarios (3 equipment types × 4 seasons × 6 leakage directions), the coupled effects of climatic conditions, equipment layout, and leakage direction on hydrogen dispersion patterns and explosion risks were quantitatively analyzed. The key findings indicate the following: (1) Downward leaks (−Z direction) from storage tanks tend to form large-area ground-hugging hydrogen clouds, representing the highest explosion risk (overpressure peak: 0.25 barg; flame temperature: >2500 K). Leakage from compressors (±X/−Z directions) readily affects adjacent equipment. Dispenser leaks pose relatively lower risks, but specific directions (−Y direction) coupled with wind fields may drive significant hydrogen dispersion toward station buildings. (2) Southeast/south winds during spring/summer promote outward migration of hydrogen clouds, reducing overall station risk but causing localized accumulation near storage tanks. Conversely, north/northwest winds in autumn/winter intensify hydrogen concentrations in compressor and station building areas. (3) An empirical formula integrating climatic parameters, leakage conditions, and spatial coordinates was proposed to predict hydrogen concentration (error < 20%). This model provides theoretical and data support for optimizing sensor placement, dynamically adjusting ventilation strategies, and enhancing safety design in HRSs. Full article

This paper presents a combined theoretical, numerical, and experimental analysis to investigate the lateral plastic crushing behavior and energy absorption of circular and non-circular thin-walled rings between two rigid plates. Theoretical solutions incorporating both linear material hardening and power-law material hardening models are solved via numerical shooting methods. The theoretically predicted force-denting displacement relations agree excellently with both FEA and experimental results. The FEA simulation clearly reveals the coexistence of an upper moving plastic region and a fixed bottom plastic region. A robust automatic extraction method of the fully plastic region at the bottom from FEA is proposed. A modified criterion considering the unloading effect based on the resultant moment of cross-section is proposed to allow accurate theoretical estimation of the fully plastic region length. The detailed study implies an abrupt and almost linear drop of the fully plastic region length after the maximum value by the proposed modified criterion, while the conventional fully plastic criterion leads to significant over-estimation of the length. Evolution patterns of the upper and lower plastic regions in FEA are clearly illustrated. Furthermore, the distribution of plastic energy dissipation is compared in the bottom and upper regions through FEA and theoretical results. Purely analytical solutions are formulated for linear hardening material case by elliptical integrals. A simple algebraic function solution is derived without necessity of solving differential equations for general power-law hardening material case by adopting a constant curvature assumption. Parametric analyses indicate the significant effect of ovality and hardening on plastic region evolution and crushing force. This paper should enhance the understanding of the crushing behavior of circular and non-circular rings applicable to the structural engineering and impact of the absorption domain. Full article

For a long time, the explanation of the various determinants of oil price fluctuations and their impact on economic activity has been based on the supply and demand mechanism. However, with various volatile changes in the international situation in recent years, such as threats to public health and an increase in regional conflicts, special attention has been paid to the geopolitical context as an additional driver of oil price fluctuations. This study examines the relationship between oil price changes and GDP growth and other macroeconomic variables from the perspective of the vulnerability of oil-importing and oil-exporting countries to unexpected oil price shocks, driven by tense geopolitical events, in three European countries (Norway, Germany, and Poland). We apply the Structural Vector Autoregressive (SVAR) model and orthogonalized impulse response functions, based on quarterly data, in regard to two samples: the first spans 1995Q1–2019Q4 (pre-2020 sample), with relatively gradual changes in oil prices, and the second spans 1995Q1–2024Q2 (whole sample), with sudden fluctuations in oil prices due to geopolitical developments. A key finding of this research is that vulnerability to unpredictable oil price shocks related to geopolitical tensions is higher than in regard to expected gradual changes in oil prices, both in oil-importing and oil-exporting countries. Different causality patterns and stronger responses in regard to GDP growth during the period, including in regard to tense geopolitical events in comparison to the pre-2020 sample, lead to the belief that economies are not more resilient to oil price shocks as has been suggested by some studies, which referred to periods that were not driven by geopolitical events. Our research also suggests that countries implementing policies to reduce oil dependency and promote investment in alternative energy sources are better equipped to mitigate the adverse effects of oil price shocks. Full article

Understanding the utilization patterns of nuclear medicine (NM) services is essential for optimizing resource allocation and service provision. This study aimed to address the regional evidence gap by reporting the demand for NM services by sex and age at a public hospital in Jamaica. This was a non-experimental, retrospective study of NM scans that were completed at the University Hospital of the West Indies from 1 June 2022 to 31 May 2024. While all scans were reported in the descriptive totals, for patients with multiple scans during the study period, only the data from the first visit was used in the inferential statistical analysis. This was performed with the IBM SPSS (version 29.0) software and involved the use of chi-square goodness of fit and multinomial logistic regression. A total of 1135 NM scans for 1098 patients were completed (37 patients had more than one scan); 596 (54.3%) were female and 502 (45.7%) were male, with the ages ranging from 3 days to 94 years old. Among the female patients, there was a greater demand in the ≥60 years age group for cardiac amyloid scans (χ² = 6.40, p < 0.05), while females 18–59 years had a greater demand for thyroid scans (χ² = 7.714, p < 0.05) and bone scans (χ² = 3.904, p < 0.05). On the other hand, significantly more males in the ≥60 age group presented for cardiac amyloid (χ² = 4.167; p < 0.05) and bone scans (χ² = 145.79, p < 0.01). Males were significantly less likely to undergo a thyroid scan than females (p < 0.01, OR = 0.072, 95% CI: 0.021, 0.243) while individuals aged 18–59 years were more likely to undergo this scan than patients aged 60 or older (p = 0.02, OR = 3.565, 95% CI: 1.258, 10.104). Males were more likely to do a cardiac amyloid scan (p < 0.05, OR = 2.237, 95% CI: 1.023, 4.891) but less likely to undergo a cardiac rest/stress test than females (p = 0.02, OR = 0.307, 95% CI: 0.114, 0.828). Prolonged life expectancy and an aging population have the potential to impact NM utilization, thus requiring planning for infrastructure, equipment, work force, and supplies. Cancer-related and cardiovascular indications are a top priority at this facility; hence, age- and sex-specific analysis are useful in establishing models for policy makers with regard to the allocation of economic and human resources for the sustainability of this specialized service. Full article

With the rapid growth of elevator installations, conventional scheduled maintenance struggles to meet the dual demands of ensuring operational safety and cost control. This study proposes an innovative on-demand maintenance method that aligns with the Chinese policy directives on elevator maintenance reform. First, we conduct a historical fault cause analysis to identify the root causes of elevator entrapment incidents. Next, we establish an entrapment prediction model based on our historical data. Then, we design an elevator entrapment risk index report according to the prediction results. Finally, we formulate an on-demand maintenance plan that combines insights from the report with the conclusions of the cause analysis. Field implementation and comparative experiments demonstrate that the proposed on-demand maintenance method outperforms the scheduled one. The result shows significant reductions in accident and maintenance workload, justifying the practical value of this approach for the industry. Full article

To solve the problem of traditional engineering drilling rig propulsion systems being difficult to adapt to complex working conditions due to their bulky structure and poor load adaptability, this study proposes a new type of mechanical hydraulic composite electro-hydraulic proportional propulsion system. The system innovatively adopts a composite design of parallel hydraulic cylinders and movable pulley groups in mechanical structure, aiming to achieve system lightweighting through displacement multiplication effect. In terms of control strategy, a fuzzy adaptive PID controller based on position feedback was designed to improve the dynamic tracking performance and robustness of the system under nonlinear time-varying loads. The study established a multi physics domain mathematical model of the system and conducted joint simulation using AMESim and MATLAB/Simulink to deeply verify the overall performance of the proposed scheme. The simulation results show that the mechanical structure can stably achieve a 2:1 displacement multiplication effect, providing a feasible path for shortening the system size. Compared with traditional PID control, the proposed fuzzy adaptive PID control strategy significantly improves the positioning accuracy of the system. The maximum tracking errors of the master and slave hydraulic cylinders are reduced from 6.3 mm and 10.4 mm to 2.3 mm and 5.6 mm, respectively, and the accuracy is improved by 63.49% and 46.15%, providing theoretical support and technical reference for the design of engineering drilling rig propulsion control systems. Full article

Background: Brain health is a growing public health priority due to the high global burden of neurological and mental disorders. Promoting brain health across the lifespan supports individual and societal well-being, creativity, and productivity. Objective: To address the need for specialized education in this field, the University of Bern developed a Certificate of Advanced Studies (CAS) in Brain Health. This article outlines the program’s rationale, structure, and goals. Program Description: The one-year, 15 ECTS-credit program is primarily online and consists of four modules: (1) Introduction to Brain Health, (2) Brain Disorders, (3) Risk Factors, Protective Factors and Interventions, and (4) Brain Health Implementation. It offers a multidisciplinary, interprofessional, life-course approach, integrating theory with practice through case studies and interactive sessions. Designed for healthcare and allied professionals, the CAS equips participants with skills to promote brain health in clinical, research, and public health contexts. Given the shortage of trained professionals in Europe and globally, the program seeks to build a new generation of brain health advocates. It aims to inspire action and initiatives that support the prevention, early detection, and management of brain disorders. Conclusions: The CAS in Brain Health is an innovative educational response to a pressing global need. By fostering interdisciplinary expertise and practical skills, it enhances professional development and supports improved brain health outcomes at individual and population levels. Full article

As large language models (LLMs) increasingly enter specialized domains, inference without external resources often leads to knowledge gaps, opaque reasoning, and hallucinations. To address these challenges in power equipment defect grading, we propose a zero-shot question-answering framework that requires no task-specific examples. Our system performs two-stage retrieval—first using a Sentence-BERT model fine-tuned on power equipment maintenance texts for coarse filtering, then combining TF-IDF and semantic re-ranking for fine-grained selection of the most relevant knowledge snippets. We embed both the user query and the retrieved evidence into a Chain-of-Thought (CoT) prompt, guiding the pre-trained LLM through multi-step reasoning with self-validation and without any model fine-tuning. Experimental results show that on a held-out test set of 218 inspection records, our method achieves a grading accuracy of 54.2%, which is 6.0 percentage points higher than the fine-tuned BERT baseline at 48.2%; an Explanation Coherence Score (ECS) of 4.2 compared to 3.1 for the baseline; a mean retrieval latency of 28.3 ms; and an average LLM inference time of 5.46 s. Ablation and sensitivity analyses demonstrate that a fine-stage retrieval pool size of k = 30 offers the optimal trade-off between accuracy and latency; human expert evaluation by six senior engineers yields average Usefulness and Trustworthiness scores of 4.1 and 4.3, respectively. Case studies across representative defect scenarios further highlight the system’s robust zero-shot performance. Full article

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

Biocatalysis is one of the oldest fields that has been used in industrial applications, with one of the earliest purposeful examples being the mass production of acetic acid from an immobilized Acinetobacter strain in the year 1815. Efficiency, specificity, reduced reaction times, lower overall costs, and environmental friendliness are some advantages biocatalysis has over conventional chemical synthesis, which has made biocatalysis increasingly used in industry. We highlight three necessary fields that are fundamental to advancing industrial biocatalysis, including biocatalyst engineering, solvent engineering, and mechanistic engineering. However, the fundamental mechanism of enzyme function is often overlooked or given less attention, which can limit the engineering process. In this review, we describe how mechanistic enzymology benefits industrial biocatalysis by elucidating key fundamental principles, including the k_cat and k_cat/K_m parameters. Mechanistic enzymology presents a unique field that provides in-depth insights into the molecular mechanisms of enzyme activity and includes areas such as reaction kinetics, catalytic mechanisms, structural analysis, substrate specificity, and protein dynamics. In line with the objective of protein engineering to optimize enzyme activity, we summarize a range of strategies reported in the literature aimed at improving the product release rate, the chemical step of catalysis, and the overall catalytic efficiency of enzymes. Further into this review, we delineate kinetic solvent viscosity effects (KSVEs) as a very efficient, cost-effective, and easy-to-perform method to probe different aspects of enzyme reaction mechanisms, including diffusion-dependent kinetic steps and rate-limiting steps. KSVEs are cost-effective because simple kinetic enzyme assays, such as the Michaelis–Menten kinetic approach, can be combined with them without the need for specialized and costly equipment. Other techniques in protein engineering and genetic engineering are also covered in this review. Additionally, we provide information on solvent systems in enzymatic reactions, details on immobilized biocatalysts, and common misconceptions that misguide enzyme design and optimization processes. Full article