Search Results (4,890)

This paper presents the design, implementation, and experimental validation of a Condition Monitoring (CM) circuit for SiC-based Power Electronics Converters (PECs). The paper leverages in situ drain–source resistance (R_ds,on) measurements, interfaced with cloud connectivity for data processing and lifetime assessment, addressing key limitations in current state-of-the-art (SOTA) methods. Traditional approaches rely on expensive data acquisition systems under controlled laboratory conditions, making them unsuitable for real-world applications due to component variability, time delay, and noise sensitivity. Furthermore, these methods lack cloud interfacing for real-time data analysis and fail to provide comprehensive reliability metrics such as Remaining Useful Life (RUL). Additionally, the proposed CM method benefits from noise mitigation during switching transitions by utilizing delay circuits to ensure stable and accurate data capture. Moreover, collected data are transmitted to the cloud for long-term health assessment and damage evaluation. In this paper, experimental validation follows a structured design involving signal acquisition, filtering, cloud transmission, and temperature and thermal degradation tracking. Experimental testing has been conducted at different temperatures and operating conditions, considering coolant temperature variations (40 °C to 80 °C), and an output power of 7 kW. Results have demonstrated a clear correlation between temperature rise and R_ds,on variations, validating the ability of the proposed method to predict device degradation. Finally, by leveraging cloud computing, this work provides a practical solution for real-world Wide Band Gap (WBG)-based PEC reliability and lifetime assessment. Full article

Variations in damper configuration strategies have a direct impact on the seismic damage patterns of long-span deck-type concrete-filled steel tube (CFST) arch bridges. This study developed an analysis and evaluation framework to identify the damage category, state, and progression sequence of structural components. The framework aims to investigate the influence of viscous dampers on the seismic response and damage patterns of long-span deck-type CFST arch bridges under near-fault pulse-like ground motions. The effects of different viscous damper configuration strategies and design parameters on seismic responses of long-span deck-type CFST arch bridges were systematically investigated, and the preferred configuration and parameter set were identified. The influence of preferred viscous damper configurations on seismic damage patterns of long-span deck-type CFST arch bridges was systematically analyzed through the established analysis and evaluation frameworks. The results indicate that a relatively optimal reduction in bridge response can be achieved when viscous dampers are simultaneously installed at both the abutments and the approach piers. Minimum seismic responses were attained at a damping exponent α = 0.2 and damping coefficient C = 6000 kN/(m/s), demonstrating stability in mitigating vibration effects on arch rings and bearings. In the absence of damper implementation, the lower chord arch foot section is most likely to experience in-plane bending failure. The piers, influenced by the coupling effect between the spandrel construction and the main arch ring, are more susceptible to damage as their height decreases. Additionally, the end bearings are more prone to failure compared to the central-span bearings. Implementation of the preferred damper configuration strategy maintains essentially consistent sequences in seismic-induced damage patterns of the bridge, but the peak ground motion intensity causing damage to the main arch and spandrel structure is significantly increased. This strategy enhances the damage-initiation peak ground acceleration (PGA) for critical sections of the main arch, while concurrently reducing transverse and longitudinal bending moments in pier column sections. The proposed integrated analysis and evaluation framework has been validated for its applicability in capturing the seismic damage patterns of long-span deck-type CFST arch bridges. Full article

To address the inefficiency of traditional concrete crack detection methods and the heavy reliance of supervised learning on extensive labeled data, in this study, an intelligent assessment method of concrete damage based on pseudo-label semi-supervised learning and fractal geometry theory is proposed to solve two core tasks: one is binary classification of pixel-level cracks, and the other is multi-category assessment of damage state based on crack morphology. Using three-channel RGB images as input, a dual-path collaborative training framework based on U-Net encoder–decoder architecture is constructed, and a binary segmentation mask of the same size is output to achieve the accurate segmentation of cracks at the pixel level. By constructing a dual-path collaborative training framework and employing a dynamic pseudo-label refinement mechanism, the model achieves an F1-score of 0.883 using only 50% labeled data—a mere 1.3% decrease compared to the fully supervised benchmark DeepCrack (F1 = 0.896)—while reducing manual annotation costs by over 60%. Furthermore, a quantitative correlation model between crack fractal characteristics and structural damage severity is established by combining a U-Net segmentation network with the differential box-counting algorithm. The experimental results demonstrate that under a cyclic loading of 147.6–221.4 kN, the fractal dimension monotonically increases from 1.073 (moderate damage) to 1.189 (failure), with 100% accuracy in damage state identification, closely aligning with the degradation trend of macroscopic mechanical properties. In complex crack scenarios, the model attains a recall rate (Re = 0.882), surpassing U-Net by 13.9%, with significantly enhanced edge reconstruction precision. Compared with the mainstream models, this method effectively alleviates the problem of data annotation dependence through a semi-supervised strategy while maintaining high accuracy. It provides an efficient structural health monitoring solution for engineering practice, which is of great value to promote the application of intelligent detection technology in infrastructure operation and maintenance. Full article

The focal “hot spot” neuropathologies in COVID-19 infection are revealing footprints of a hidden underlying collapse of a novel ultrafast ultradian Piezo2 signaling system within the nervous system. Paradoxically, the same initiating pathophysiology may underpin the systemic findings in COVID-19 infection, namely the multiorgan SARS-CoV-2 infection-induced vascular pathologies and brain–body-wide systemic pro-inflammatory signaling, depending on the concentration and exposure to infecting SARS-CoV-2 viruses. This common initiating microdamage is suggested to be the primary damage or the acquired channelopathy of the Piezo2 ion channel, leading to a principal gateway to pathophysiology. This Piezo2 channelopathy-induced neural switch could not only explain the initiation of disrupted cell–cell interactions, metabolic failure, microglial dysfunction, mitochondrial injury, glutamatergic synapse loss, inflammation and neurological states with the central involvement of the hippocampus and the medulla, but also the initiating pathophysiology without SARS-CoV-2 viral intracellular entry into neurons as well. Therefore, the impairment of the proposed Piezo2-induced quantum mechanical free-energy-stimulated ultrafast proton-coupled tunneling seems to be the principal and critical underlying COVID-19 infection-induced primary damage along the brain axes, depending on the loci of SARS-CoV-2 viral infection and intracellular entry. Moreover, this initiating Piezo2 channelopathy may also explain resultant autonomic dysregulation involving the medulla, hippocampus and heart rate regulation, not to mention sleep disturbance with altered rapid eye movement sleep and cognitive deficit in the short term, and even as a consequence of long COVID. The current opinion piece aims to promote future angles of science and research in order to further elucidate the not entirely known initiating pathophysiology of SARS-CoV-2 infection. Full article

With the rise of carbon capture and storage, liquefied carbon dioxide (LCO₂) has emerged as a promising medium for large-scale marine transport. This study evaluates the structural integrity of an IMO Type C cargo tank for a medium-range LCO₂ carrier under four conditions: ultimate limit state, accidental limit state, hydrostatic pressure test, and fatigue limit state, based on IGC Code and classification rules. Seventeen load cases were analyzed using finite element methods with multi-step loading to ensure stability. The highest stress occurred at the pump dome–shell junction due to geometric discontinuities, but all stress and buckling criteria were satisfied. The fatigue damage from wave-induced loads was negligible, with low-cycle fatigue from loading/unloading operations governing the fatigue life, which exceeded 31,000 years. The findings confirm the tank’s structural robustness and its suitability for safe, efficient medium-pressure LCO₂ transport. Full article

Reactive oxygen species (ROS) play a dual role as both essential signaling molecules and harmful mediators of damage. Imbalances in the redox state of the liver can overwhelm antioxidant defenses and promote mitochondrial dysfunction, oxidative damage, and inflammation. Complex feedback loops between ROS and immune signaling pathways are a hallmark of pathological liver conditions, such as hepatic ischemia–reperfusion injury (IRI). This is a major cause of liver transplant failure and is of increasing significance due to the increased use of marginally discarded livers for transplantation. This review outlines the major enzymatic and metabolic sources of ROS in hepatic IRI, including mitochondrial reverse electron transport, NADPH oxidases, cytochrome P450 enzymes, and endoplasmic reticulum stress. Hepatocyte injury activates redox feedback loops that initiate immune cascades through DAMP release, toll-like receptor signaling, and cytokine production. Emerging regulatory mechanisms, such as succinate accumulation and cytosolic calcium–CAMKII signaling, further shape oxidative dynamics. Pharmacological therapies and the use of antioxidant and immunomodulatory approaches, including nanoparticles and redox-sensitive therapeutics, are discussed as protective strategies. A deeper understanding of how redox and immune feedback loops interact is an exciting and active area of research that warrants further clinical investigation. Full article

The paper provides an analysis of the causes of accidents in oil and gas pipeline systems. As part of a comprehensive overview of the topic, it also presents the historical development of pipeline systems, from the first commercial oil pipelines in the United States to modern infrastructure projects, with a particular focus on the role of regulatory requirements and measures (prevention, detection, and mitigation) to improve transport efficiency and pipeline safety. The research uses historical accident data from various databases to identify the main causes of accidents and analyse trends. The focus is on factors such as corrosion, third-party interference, and natural disasters that can lead to accidents. A comparison of the various accident databases shows that there are different practises and approaches to operation and reporting. As each database differs in terms of inclusion criteria, the categories are divided into five main groups to allow systematic interpretation of the data and cross-comparison of accident causes. Regional differences in the causes of accidents involving oil and gas pipelines in Europe, the USA, and Canada are visible. However, an integrated analysis shows that the number of accidents is declining in almost all categories. The majority of all recorded accidents are in the “Human factors and Operational disruption” and “Corrosion and Material damage” groups. It is recommended to use the database as required, as each category has its own specifics. Full article

Overfishing represents one of the most alarming threats to marine conservation in the Mediterranean Sea. In particular, deep-sea trawl fishing can severely damage marine habitats that may take decades to recover due to their slow growth rates. Hence, monitoring the health and subsistence of deep-sea ecosystems in fishing hotspots is vital to understand the impacts of deep-sea fishing. This paper presents a methodological study to prepare an expedition in Sardinian (Italy) deep waters. The methodology is composed of three sections: first, it offers a comparative analysis of the proper technological mix to identify fishing hotspots pre-expedition; second, it simulates an in situ expedition to monitor the state of deep-sea ecosystems in proximity of the fishing hotspots identified; and third, it offers recommendations for data analysis and management post-expedition. This study offers a replicable methodology for advancing knowledge on the state of deep-sea ecosystems affected by trawl fishing. Full article

To address the poor thermal performance and low output efficiency of conventional solid-state microchip lasers, this study proposes and implements a bonded Nd:YAG/Cr⁴⁺:YAG laser based on fiber splitter pumping. Experimental results demonstrate that at a 4.02 mJ pump pulse energy and a 100 Hz repetition rate, the system achieves four linearly polarized output beams with an average pulse energy of 0.964 mJ, a repetition rate of 100 Hz, and an optical-to-optical conversion efficiency of 23.98%. The energy distribution ratios for the upper-left, lower-left, upper-right, and lower-right beams are 22.61%, 24.46%, 25.50%, and 27.43%, with pulse widths of 2.184 ns, 2.193 ns, 2.205 ns, and 2.211 ns, respectively. As the optical axis distance increases, the far-field spot pattern transitions from a single circular profile to four fully separated spots, where the lower-right beam exhibits beam quality factors of M_x² = 1.181 and M_y² = 1.289. Simulations at a 293.15 K coolant temperature and a 4.02 mJ pump energy reveal that split pumping reduces the volume-averaged temperature rise in Nd:YAG by 28.81% compared to single-beam pumping (2.57 K vs. 3.61 K), decreases the peak temperature rise by 66.15% (6.97 K vs. 20.59 K), and suppresses peak-to-peak temperature variation by 78.6% (1.34 K vs. 6.26 K). Compared with existing multi-beam generation methods, the fiber splitter approach offers integrated advantages—including compact size, low cost, high energy utilization, superior beam quality, and elevated damage thresholds—and thus shows promising potential for automotive multi-point ignition, multi-beam single-photon counting LiDAR, and laser-induced breakdown spectroscopy (LIBS) online analysis. Full article

The world is facing a state of “global boiling,” causing damage to various sectors. Developing pedestrian systems is a key to mitigating it, especially in tropical and humid cities where the climate discourages walking and increases the need for shaded walkways. Recent research shows a lack of data and in-depth studies on the built environment promoting walkability in such climates, creating a research gap this study aims to fill. Using Singapore as a case study, four locations—Marina Bay, Orchard Road, Boat Quay, and Chinatown—were surveyed and analyzed through visual decoding and questionnaires. Results show that natural light is the most frequently observed and important element in pedestrian pathway design in tropical and humid areas. Trees and sidewalks are also important in creating a walk-friendly environment. Green spaces significantly influence the desire to walk, though no clear positive outcomes were found. Additionally, “Other Emotions” negatively affect the decision to walk, suggesting these should be avoided in future pedestrian pathway designs to encourage walking. Full article

Transmission towers constitute critical power infrastructure, yet structural damage may accumulate over their long-term service, underscoring the paramount importance of research on damage identification. This paper presents a cross-correlation function amplitude vector (CorV) method for damage localization based on time-domain response analysis. The approach involves calculating the CorV of structural members before and after damage using dynamic response data, employing the CorV assurance criterion (CVAC) to quantify changes in CorV, and introducing first-order differencing for damage localization. Taking an actual transmission tower in Jiangmen as the engineering backdrop, a finite element model is established. Damage conditions are simulated by reducing the stiffness of specific members, and parameter analyses are conducted to validate the proposed method. Furthermore, experimental validation in a lab is performed to provide additional confirmation. The results indicate that the CVAC value of the damaged structure is significantly lower than that in the healthy state. By analyzing the relative changes in the components of CorV, the damage location can be accurately determined. Notably, this method only requires acquiring the time-domain response signals of the transmission tower under random excitation to detect both the existence and location of damage. Consequently, it is well suited for structural health monitoring of transmission towers under environmental excitation. Full article

Forest fire detection is an essential application in environmental surveillance since wildfires cause devastating damage to ecosystems, human life, and property every year. The effective and accurate detection of fire is necessary to allow for timely response and efficient management of disasters. Traditional techniques for fire detection often experience false alarms and delayed responses in various environmental situations. Therefore, developing robust, intelligent, and real-time detection systems has emerged as a central challenge in remote sensing and computer vision research communities. Despite recent achievements in deep learning, current forest fire detection models still face issues with generalizability, lightweight deployment, and accuracy trade-offs. In order to overcome these limitations, we introduce a novel technique (FireNet-KD) that makes use of knowledge distillation, a method that maps the learning of hard models (teachers) to a light and efficient model (student). We specifically utilize two opposing teacher networks: a Vision Transformer (ViT), which is popular for its global attention and contextual learning ability, and a Convolutional Neural Network (CNN), which is esteemed for its spatial locality and inductive biases. These teacher models instruct the learning of a Swin Transformer-based student model that provides hierarchical feature extraction and computational efficiency through shifted window self-attention, and is thus particularly well suited for scalable forest fire detection. By combining the strengths of ViT and CNN with distillation into the Swin Transformer, the FireNet-KD model outperforms state-of-the-art methods with significant improvements. Experimental results show that the FireNet-KD model obtains a precision of 95.16%, recall of 99.61%, F1-score of 97.34%, and mAP@50 of 97.31%, outperforming the existing models. These results prove the effectiveness of FireNet-KD in improving both detection accuracy and model efficiency for forest fire detection. Full article

The maturity of Korla fragrant pears directly influences their harvesting, packaging, transportation, and storage. Investigating the mechanical properties of fragrant pears at various maturity stages can help minimize damage during postharvest handling. This study employs micro-CT technology combined with reverse model scanning to develop a numerical model for force damage across different maturity stages, supported by experimental validation. The results demonstrate that both rupture force and rupture strain progressively decrease as the maturity of Korla fragrant pears increases, exhibiting a sudden transition. Simultaneously, the fruit’s microstructure shifts from distinct cellular organization to an irregular, collapsed state. The proposed numerical model, which accounts for this abrupt change, provides a better fit than models based on a single physical parameter, with the R² value improving from 0.7922 to 0.9665. Furthermore, this model accurately quantifies the mechanical properties of fragrant pears at all stages of maturity. These findings offer technical support for reducing postharvest losses and serve as a reference for developing damage prediction models for other fruits and vegetables. Full article

The influence of the copper (Cu) content on the corrosion resistance of 3.5%Ni low-carbon weathering steel was investigated using periodic dry–wet cycle accelerated corrosion tests. The mechanical properties of the steels were assessed via tensile and low-temperature impact tests, while corrosion resistance was evaluated based on weight loss measurements. Surface oxide layers were characterized using three-dimensional laser confocal microscopy, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and electrochemical methods. Electron probe microanalysis (EPMA) was employed to examine the cross-sectional morphology of the oxide layer after 72 h of accelerated corrosion tests. The results indicate that the solution state of Cu increased the strength of 3.5%Ni steels but significantly damaged the low-temperature toughness. As the Cu content increased from 0.75% to 1.25%, the corrosion rate decreased from 4.65 to 3.74 g/m² h. However, when there was a further increase in the Cu content to 2.15%, there was little decrease in the corrosion rate. With the increase in the Cu content from 0.75% to 2.15%, the surface roughness of 3.5%Ni weathering steel after corrosion decreased from 5.543 to 5.019 μm, and the corrosion behavior was more uniform. Additionally, the α/γ protective factor of the oxide layer of the surface layer increased from 2.58 to 2.84 with an increase in the Cu content from 0.75% to 1.25%, resulting in the oxide layer of the surface layer being more protective. For 1.25%Cu steel, the corrosion current density of rusted samples is lower (ranging from 1.2609 × 10⁻⁴ A/cm² to 3.7376 × 10⁻⁴ A/cm²), and the corrosion potential is higher (ranging from −0.85544 V to −0.40243 V). Therefore, the rusted samples are more corrosion resistant. The Cu in the oxide layer of the surface layer forms CuO and CuFeO₂, which are helpful for increasing corrosion resistance, which inhibits the penetration of Cl⁻. Full article

Oxidative stress (OS) occurs when there is an imbalance between oxidants and antioxidants, leading to disruptions in cellular signaling and causing damage to molecules. Glutathione S-transferase (GST) enzymes are crucial for maintaining redox balance by facilitating glutathione conjugation. Increased oxidative damage has been noted during the COVID-19 pandemic, and its persistence may be linked to the onset of long COVID. This systematic review aimed to assess the relationship between GST gene polymorphisms and the prognosis of COVID-19, including long COVID. Adhering to the PRISMA guidelines, a thorough search was carried out in MEDLINE, CENTRAL, PubMed, and EMBASE for studies published from September 2020 to February 2025. Out of an initial selection of 462 articles, ten studies (four concerning COVID-19 severity and six related to long COVID) satisfied the inclusion criteria. The findings regarding GST polymorphisms were not consistent, especially concerning the GSTM1 and GSTT1 isoforms. Nevertheless, evidence indicates a sustained state of oxidative stress in patients with long COVID. The majority of research has focused on cytosolic GSTs, while the functions of microsomal and mitochondrial GST families remain largely unexplored. These findings suggest that further research into the various GST subfamilies and their genetic variants is necessary to enhance our understanding of their impact on COVID-19 severity and the pathophysiology of long COVID. Full article