Search Results (763)

Red bricks serve as an important material for load-bearing or enclosing structures in traditional architecture and are widely used in construction projects both domestically and internationally. Fujian red bricks, due to geographical, trade, and immigration-related factors, have spread to Taiwan and various regions in Southeast Asia, giving rise to distinctive red brick architectural complexes. To further investigate the types of damage, such as cracking and missing bricks, that occur in traditional red brick buildings due to multiple factors, including climate and human activities, this study takes Fujian red brick buildings as its research subject. It employs the YOLOv12 rapid detection method to conduct technical support research on structural assessment, type detection, and damage localization of surface damage in red brick building materials. The experimental model was conducted through the following procedures: on-site photo collection, slice marking, creation of an image training set, establishment of an iterative model training, accuracy analysis, and experimental result verification. Based on this, the causes of damage types and corresponding countermeasures were analyzed. The objective of this study is to attempt to utilize computer vision image recognition technology to provide practical, automated detection and efficient identification methods for damage types in red brick building brick structures, particularly those involving physical and mechanical structural damage that severely threaten the overall structural safety of the building. This research model will reduce the complex manual processes typically involved, thereby improving work efficiency. This enables the development of customized intervention strategies with minimal impact and enhanced timeliness for the maintenance, repair, and preservation of red brick buildings, further advancing the practical application of intelligent protection for architectural heritage. Full article

Three-dimensional ground-penetrating radar can quickly visualize the internal condition of the road; however, it faces challenges such as data splicing difficulties and image noise interference. Scanning antenna and lane size differences, as well as equipment and environmental interference, make the radar image difficult to interpret, which affects disease identification accuracy. For this reason, this paper focuses on road radar image splicing and noise reduction. The primary research includes the following: (1) We make use of backward projection imaging algorithms to visualize the internal information of the road, combined with a high-precision positioning system, splicing of multi-lane data, and the use of bilinear interpolation algorithms to make the three-dimensional radar data uniformly distributed. (2) Aiming at the defects of the low computational efficiency of the traditional adaptive median filter sliding window, a Deep Q-learning algorithm is introduced to construct a reward and punishment mechanism, and the feedback reward function quickly determines the filter window size. The results show that the method is outstanding in improving the peak signal-to-noise ratio, compared with the traditional algorithm, improving the denoising performance by 2–7 times. It effectively suppresses multiple noise types while precisely preserving fine details such as 0.1–0.5 mm microcrack edges, significantly enhancing image clarity. After processing, images were automatically recognized using YOLOv8x. The detection rate for transverse cracks in images improved significantly from being undetectable in mixed noise and original images to exceeding 90% in damage detection. This effectively validates the critical role of denoising in enhancing the automatic interpretation capability of internal road cracks. Full article

Autoimmune diseases are characterized by immune response dysregulation against self-components, leading to chronic inflammation and tissue damage. Vitamin C (VitC), a water-soluble vitamin with established functions in antioxidant defence and collagen synthesis, has also been of interest based on its potential immunomodulatory effects. This review discusses the role of VitC in the course and progression of (A) autoimmune diseases (multiple sclerosis, rheumatoid arthritis, Sjögren’s disease, type 1 diabetes, Hashimoto’s thyroiditis, pernicious anaemia, antiphospholipid syndrome), (B) other immune-mediated diseases (Crohn’s disease, periodontitis), and (C) Alzheimer’s disease, a neurodegenerative disorder with autoimmune features. Results from clinical, observational, and experimental trials show that VitC deficiency is common in many of these diseases and may contribute to increased oxidative stress and immune disequilibrium. Supplementation has been associated with improved antioxidant levels, control of inflammatory mediators, and, in some cases, clinical outcomes like disease activity decrease or symptom load. Although findings vary across conditions and few large, randomized trials are available, the overall evidence indicates that maintaining good VitC status can be useful in maintaining immune homeostasis and reducing inflammation. VitC should be viewed as an adjunct to be employed safely, perhaps and ideally within larger treatment regimens, but not in place of effective therapies. Further research, including large-scale clinical trials, will be required to determine more clearly optimal dosing, timing of treatment, and patient population most likely to benefit. By integration of current knowledge, this review recognizes both promise in VitC for treatment of autoimmune/immune-mediated disease and promise in its potential use within future treatment regimens. Full article

The post-fracture mechanical performance of laminated glass (LG) members is well-known to be challenging to assess due to the influence of multiple factors. Even more challenging and scarcely explored is the assessment of the behavior of broken LG elements as a function of the degree of damage that affects it. In this paper, the attention is given to the experimental analysis of 2-ply, small-scale, pre-fractured LG elements composed of annealed (AN) glass and characterized by two different types of interlayers, namely the polymeric Ethylene-Vinyl Acetate (EVA) or the ionoplast SentryGlas^® (SG) bonds. The samples—with total size of 200 mm in length by 50 mm in width—are subjected to n = 10 repeated hard-body impact tests, in a three-point-bending (3PB) setup, to simulate and assess a possible increase in the damage severity. To quantify and compare the behavior of the different interlayers in use, experimental modal analyses are performed both at the beginning of the impact tests (n = 0) and after each hard-body impact repetition (n = 1, …, 10), by means of roving hammer tests based on #14 different control points. The comparison of the experimental outcomes—in particular, the fundamental vibration frequency f₁—gives evidence of a markedly different mechanical response from the EVA and SG interlayers. EVA samples exhibited a major reduction in terms of fundamental frequency, indicating significant propagation of damage following impact repetitions. On the other hand, SG samples appear to be less seriously affected by hard-body impacts. Full article

Ripples in the electromagnetic torque of electric vehicle (EV) motors due to poor stator voltage and control cause jerky movements, equipment failure, discomfort for passengers and drivers, and damage to the associated civil works. This paper presents the implementation of Finite Control Set Model Predictive Control (FCSMPC) for a high-level modified W-type inverter (MWI) driving a three-phase induction motor (IM), along with validation of its performance. The proposed control strategy aims to minimize motor torque ripples and has been tested under various driving torque patterns. The results demonstrate a significant reduction in torque ripples—down to less than 1%—and acceptable levels of total harmonic distortion (THD), as verified through quality analysis of the stator currents. Moreover, a comparative assessment of voltage profiles for the electromagnetic torque and rotor speed curves has been presented for nine cases of simultaneous variations in multiple motor parameters; the results indicate that the MWI-fed motor has the best performance and the lowest sensitivity to the variations. Full article

With global sustainable construction growth, fully recycled coarse aggregate concrete (RCAC)—eco-friendly for cutting construction waste and reducing natural aggregate over-exploitation—has poor durability in seasonally freezing saline-soil regions (e.g., Tumushuke, Xinjiang): freeze-thaw and salt ions (NaCl, Na₂SO₄) cause microcracking, faster performance decline, and shorter service life, limiting its use and requiring better salt freeze resistance. To address this, a field survey of Tumushuke’s saline soil was first conducted to determine local salt type and concentration, based on which a matching 12% NaCl + 4% Na₂SO₄ mixed salt solution was prepared. RCAC specimens modified with fly ash (FA), silica fume (SF), and polypropylene fiber (PPF) were then fabricated, cured under standard conditions (20 ± 2 °C, ≥95% relative humidity), and subjected to rapid freeze-thaw cycling in the salt solution. Multiple macro-performance and microstructural indicators (appearance, mass loss, relative dynamic elastic modulus (RDEM), porosity, microcracks, and corrosion products) were measured post-cycling. Results showed the mixed salt solution significantly exacerbated RCAC’s freeze-thaw damage, with degradation severity linked to cycle count and admixture dosage. The RCAC modified with 20% FA and 0.9% PPF exhibited optimal salt freeze resistance: after 125 cycles, its RDEM retention reached 75.98% (6.60% higher than the control), mass loss was only 0.28% (67.80% lower than the control), and its durability threshold (RDEM > 60%) extended to 200 cycles. Mechanistic analysis revealed two synergistic effects for improved performance: (1) FA optimized pore structure by filling capillaries, reducing space for pore water freezing and salt penetration; (2) PPF enhanced crack resistance by bridging microcracks, suppressing crack initiation/propagation from freeze-thaw expansion and salt crystallization. A “pore optimization–ion blocking–fiber crack resistance” triple synergistic protection model was proposed, which clarifies admixture-modified RCAC’s salt freeze damage mechanism and provides theoretical/technical guidance for its application in extreme seasonally freezing saline-soil environments. Full article

Vascular dementia (VaD) represents the second-most common dementia type after Alzheimer’s disease since it results from complications of cerebrovascular disease. Mixed pathologies combining vascular and neurodegenerative processes are the rule rather than exception in elderly dementia patients. The condition known as VaD includes various types of vascular damage that affect both large and small blood vessels in the brain which results in cerebral hypoperfusion, blood–brain barrier disruption, glymphatic dysfunction, and molecular cascades causing neuronal damage. The mechanisms of VaD include endothelial dysfunction, oxidative stress, chronic neuroinflammation, impaired glymphatic clearance, white matter demyelination, and synaptic failure. The disease susceptibility of individuals depends on genetic factors which include NOTCH3 mutations and vascular risk polymorphisms. The diagnostic field uses neuroimaging tools and fluid biomarkers such as neurofilament light chain, inflammatory markers, and Aβ/tau ratios for mixed pathology. The current practice of vascular risk management combines with new therapeutic approaches that use phosphodiesterase inhibitors for cerebral perfusion and NLRP3 inflammasome inhibitors for neuroinflammation, senolytics for cellular senescence, and remyelination agents for white matter repair. However, the majority of new treatment methods remain investigational with limited Phase III data. Future medical treatment development will depend on precision medicine approaches which use biomarker-guided treatment selection and combination strategies targeting multiple pathological mechanisms. Full article

Co- and post-seismic earthquake-induced hydrogeological anomalies (EQHAs) in Greece are mainly associated with moderate to strong earthquakes (M_w = 6.0–7.0), particularly when seismic intensities reach IX or above. The highest frequencies are observed in the Peloponnese and Ionian Islands, followed by Central Greece and the North Aegean, characterized by dense faulting and frequent strong earthquakes. EQHAs are classified into six main types, with hydraulic variations being the most common. About 77% of earthquakes produced only one or two types of EQHA, suggesting localized hydrogeological effects, while only a few induced multiple types. Strong events (M_w = 6.0–7.0), often historic, generated the broadest variety, highlighting the influence of local geological, hydrological, and tectonic conditions on magnitude alone. Springs and wells, representing 81% of the cases, dominate the affected systems, while lakes and rivers respond less often but significantly. Most EQHAs occur in Greece’s second seismic hazard zone (74%) due to its larger geographic area. EQHAs primarily develop in karstic and porous formations but also appear in impermeable rocks due to fracturing or karst. Larger earthquakes trigger anomalies at greater distances (>100 km). Though rarely fatal, EQHAs can damage water infrastructure, contaminate supplies, and cause shortages, underscoring the need for systematic monitoring and post-earthquake water resource management. Full article

Early-stage dynamic responses of naval structures under underwater explosion shock loads exhibit high-frequency, intense amplitude fluctuations and short durations, serving as critical factors for the development of plastic deformation and other damage characteristics. These structural dynamics demonstrate prominent nonlinear and non-stationary features. This study focuses on the nonlinear evolutionary patterns of early-stage plastic shock responses in underwater explosion-impacted ship structures. Utilizing phase space reconstruction, unimodal mapping, and symbolic dynamics theory, we analyze the nonlinear and non-stationary characteristics along with their evolutionary patterns in experimental data. First, scaled model experiments under varying shock factors were conducted based on a stiffened cylindrical shell prototype, investigating the spatiotemporal evolution of nonlinear and non-stationary dynamic responses under different shock loads while characterizing their uncertainty features. Second, model tests were performed on deck-type cabin structures and plate frameworks derived from a naval vessel’s deck prototype, further analyzing the evolutionary patterns of early-stage plastic dynamic responses and verifying the method’s effectiveness and universality. Research findings indicate that (1) early-stage plastic shock responses of ships under underwater explosions exhibit multiple dynamical behaviors including chaotic motion, periodic motion, and quasi-periodic motion, and (2) during the initial plastic phase, orbital parameters approximate 0.8, providing guidance for test condition setup and initial parameter selection in underwater explosion experiments on naval structures. Full article

The landscape of cancer immunotherapy must shift from personalized neoantigen vaccines toward universal platforms that leverage innate immune activation. This review examines a novel mRNA vaccine strategy that encodes non-tumor-specific antigens, carefully selected pathogen-derived or synthetic sequences designed to transform immunologically “cold” tumors into inflamed therapy-responsive microenvironments. Unlike conventional approaches requiring patient-specific tumor sequencing and 8–12-week manufacturing timelines, this platform utilizes pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs) to trigger broad innate immune activation through multiple pattern recognition receptors (PRRs). The key therapeutic mechanism is epitope spreading, where vaccine-induced inflammation reveals previously hidden tumor antigens, enabling the immune system to mount responses against cancer-specific targets without prior knowledge of these antigens. Delivered via optimized lipid nanoparticles (LNPs) or alternative polymer-based systems, these vaccines induce epitope spreading, enhance checkpoint inhibitor responsiveness, and establish durable antitumor memory. This approach offers several potential advantages, including immediate treatment availability, a cost reduction of up to 100-fold compared to personalized vaccines, scalability for global deployment, and efficacy across diverse tumor types. However, risks such as cytokine release syndrome (CRS), potential for off-target autoimmunity, and challenges with pre-existing immunity must be addressed. By eliminating barriers of time, cost, and infrastructure, this universal platform could help democratize access to advanced cancer treatment, potentially benefiting the 70% of cancer patients in low- and middle-income countries (LMICs) who currently lack immunotherapy options. Full article

HIV-associated neurocognitive impairment (HIV-NCI), a comorbidity of human immunodeficiency virus (HIV) infection, affects up to 50% of people with HIV (PWH). HIV-infected monocytes that transmigrate across the blood–brain barrier and mature into macrophages establish a central nervous system (CNS) viral reservoir that activates and infects parenchymal cells, contributing to neuronal damage that characterizes HIV-NCI. Methamphetamine (meth) use is prevalent in PWH and further impairs cognitive functioning. To examine whether meth-mediated dysregulation of macrophage functions may contribute to increased HIV-NCI, we characterized differential gene expression in primary human HIV-infected macrophages treated daily with meth for five days by RNA-sequencing. We identified increases in multiple gene isoforms of metallothionein 1 (MT1), a heavy metal binding protein involved in protective mechanisms against metal toxicity and oxidative stress. Nuclear localization of MT1 protein was previously shown to either positively or negatively affect nuclear factor κB (NF-κB) activity in a cell type specific manner, with nuclear MT1 contributing to LPS-induced TNF-α and IL-6 in macrophages. We found that daily meth treatment for one to five days increased nuclear localization of MT1 in macrophages acutely infected with HIV which was associated with increased LPS-induced CXCL8 and CCL8, and a trend towards increased basal and/or LPS-induced expression of other cytokines/chemokines, including TNF-α and IL-6, that was donor specific. Reactive oxygen species (ROS) levels were not changed with meth treatment although there was a donor specific trend towards increased ROS with multiple days of meth treatment. These data indicate that repeated exposure of macrophages to meth in the context of HIV increases nuclear MT1 localization, which is associated with increased inflammatory mediator production, and therefore may be a mechanism that contributes to meth-mediated exacerbation of HIV-NCI. Full article

As an outstanding UNESCO World Heritage Site, the Jiangnan gardens feature both exquisite and fragile components. Reduction-fired cyan square bricks, serving as crucial paving materials, are long-term exposed to natural and anthropogenic factors, making them prone to various types of surface damage and urgently requiring efficient, non-destructive detection methods to support scientific conservation. Traditional manual inspection methods suffer from low efficiency, strong subjectivity, and potential disturbance to the fragile heritage structures. This study focuses on developing an intelligent detection method based on advanced computer vision, employing the YOLOv12 object detection model to achieve non-contact, automated identification of typical tile surface damage types in the Jiangnan gardens (such as cracking, stains, water stains, and wear). A total of 691 images of reduction-fired cyan square bricks collected on-site were used as training samples. The main conclusions of this study are as follows: (1) By constructing a dataset containing multiple samples and multiple scenes of reduction-fired cyan square brick images in Jiangnan gardens, the YOLOv12 model was trained and optimized, enabling it to accurately identify subtle damage features under complex texture backgrounds. (2) Overall indicators: Through the comparison of the confusion matrices of the four key training nodes, model C (the 159th epoch, highest mAP50–95) has the most balanced overall performance in multiple categories, with an accuracy of 0.73 for cracking, 0.77 for wear, 0.60 for water stain, and 0.65 for stains, which can meet basic detection requirements. (3) Difficulty of discrimination: Compared with stains and water stains, cracking and wear are easier to distinguish. Experimental results indicate that the detection method is feasible and effective in identifying the surface damage types of reduction-fired cyan square bricks in Jiangnan gardens. This research provides a practical and efficient “surface technology” solution for the preventive protection of cultural heritage, contributing to the sustainable preservation and management of world heritage. Full article

DNA repair is a multifaceted biological process that involves multiple pathways to counter the types of damage the genome encounters throughout life. In the past decade zebrafish became a popular model organism to study various aspects of vertebrate DNA repair, and the characterization of several mutant lines deficient in key players of the repair pathways has significantly contributed to our understanding of the roles the corresponding proteins play in the maintenance of genomic integrity. Interestingly, the base-excision repair (BER) pathway remained one of the less characterized DNA repair processes in fish. Here we provide a detailed characterization of zebrafish deficient in one of the key components of BER, the uracil-DNA glycosylase Unga. We show that while these fish are viable, they display an altered response to genotoxic stress and unga mutant males show an interesting form of subfertility. Full article

The growing concerns over nuclear power plant safety in the wake of extreme impact events have highlighted the need for containment structures with superior resistance to large commercial aircraft strikes. Conventional reinforced concrete containment has shown limitations in withstanding high-mass and high-velocity impacts, posing potential risks to structural integrity and operational safety. Addressing this challenge, this study focuses on the dynamic impact resistance and vibration behavior of steel–ultra-high-performance concrete (S-UHPC) composite containment, aiming to enhance nuclear facility resilience under beyond-design-basis aircraft impact scenarios. Validated finite element models in LS-DYNA were developed to simulate impacts from four representative large commercial aircraft types, considering variations in wall and steel plate thicknesses, UHPC grades, and soil–structure interaction conditions. Unlike existing studies that often focus on isolated parameters, this work conducts a systematic parametric analysis integrating multiple aircraft types, structural configurations, and foundation conditions, providing comprehensive insights into both global deformation and high-frequency vibration behavior. Comparative analyses with conventional reinforced concrete containment were performed, and floor response spectra were evaluated to quantify high-frequency vibration characteristics under different site conditions. The results show that S-UHPC containment reduces peak displacement by up to ~24% compared to reinforced concrete of the same thickness while effectively localizing core damage without through-thickness failure. In addition, aircraft impacts predominantly excite 90–125 Hz vibrations, with soft soil conditions amplifying acceleration responses by more than four times, underscoring the necessity of site-specific dynamic analysis in nuclear containment and equipment design. Full article

Efficient and explainable vehicle damage inspection is essential due to the increasing complexity and volume of vehicular incidents. Traditional manual inspection approaches are not time-effective, prone to human error, and lead to inefficiencies in insurance claims and repair workflows. Existing deep learning methods, such as CNNs, often struggle with generalization, require large annotated datasets, and lack interpretability. This study presents a robust and interpretable deep learning framework for vehicle damage classification, integrating Vision Transformers (ViTs) and ensemble detection strategies. The proposed architecture employs a RetinaNet backbone with a ViT-enhanced detection head, implemented in PyTorch using the Detectron2 object detection technique. It is pretrained on COCO weights and fine-tuned through focal loss and aggressive augmentation techniques to improve generalization under real-world damage variability. The proposed system applies the Weighted Box Fusion (WBF) ensemble strategy to refine detection outputs from multiple models, offering improved spatial precision. To ensure interpretability and transparency, we adopt numerous explainability techniques—Grad-CAM, Grad-CAM++, and SHAP—offering semantic and visual insights into model decisions. A custom vehicle damage dataset with 4500 images has been built, consisting of approximately 60% curated images collected through targeted web scraping and crawling covering various damage types (such as bumper dents, panel scratches, and frontal impacts), along with 40% COCO dataset images to support model generalization. Comparative evaluations show that Hybrid ViT-RetinaNet achieves superior performance with an F1-score of 84.6%, mAP of 87.2%, and 22 FPS inference speed. In an ablation analysis, WBF, augmentation, transfer learning, and focal loss significantly improve performance, with focal loss increasing F1 by 6.3% for underrepresented classes and COCO pretraining boosting mAP by 8.7%. Additional architectural comparisons demonstrate that our full hybrid configuration not only maintains competitive accuracy but also achieves up to 150 FPS, making it well suited for real-time use cases. Robustness tests under challenging conditions, including real-world visual disturbances (smoke, fire, motion blur, varying lighting, and occlusions) and artificial noise (Gaussian; salt-and-pepper), confirm the model’s generalization ability. This work contributes a scalable, explainable, and high-performance solution for real-world vehicle damage diagnostics. Full article