Search Results (51,827)

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

The balance between exploration and exploitation is a fundamental element in the design and performance of bio-inspired optimization algorithms. However, to date, its conceptual evolution and its treatment in the scientific literature have not been systematically characterized from a bibliometric approach. This study performs an exhaustive analysis of the scientific production on the balance between exploration and exploitation using records extracted from the Web of Science (WoS) database. The processing and analysis of the data were carried out through the combined use of Bibliometrix (R package) and VOSviewer, tools that made it possible to quantify productivity, map collaborative networks, and visualize emerging thematic trends. The results show a sustained growth in the volume of publications over the last decade, as well as the consolidation of academic collaboration networks and the emergence of new thematic lines in the field. In particular, metaheuristic algorithms have demonstrated a significant and growing impact, constituting a fundamental pillar in the advancement and methodological diversification of the exploration–exploitation balance. This work provides a quantitative framework and a structured view of the evolution of research, identifies the main actors and trends, and raises opportunities for future lines of research in the field of optimization using metaheuristics, the most prominent instantiation of bio-inspired optimization algorithms. Full article

Lactoferrin (LF) is an 80 kDa iron-binding glycoprotein primarily found in milk, saliva, tears, and nasal secretions. LF is well known for its antibacterial and immunomodulatory effects. However, the extraction of LF from milk is inadequate for large-scale therapeutic applications, presenting a challenge for economic mass production. Recombinant protein expression systems offer a solution to overcome this challenge and efficient production of LF. This review discusses recent progress in the translational research of LF gene transfer and biopharming, focusing on different expression systems such as bacteria, yeast, filamentous fungi, transgenic crops, and animals as well as purification methods. The optimization of expression yields, prospects for genetic engineering, and biotechnology to enhance LF production for biomedical applications are emphasized. This review systematically sourced the literature from 1987 to 2025 from leading scientific databases, including PubMed, Scopus, Web of Science, and Google Scholar. Despite ongoing debates, progress in this field indicates a viable path towards the effective use of LF in therapeutic settings. Full article

Three-dimensional trench electrode silicon detectors play an important role in particle physics research, nuclear radiation detection, and other fields. A novel polysilicon-fill-strengthened etch-through 3D trench electrode detector is proposed to address the shortcomings of traditional 3D trench electrode silicon detectors; for example, the distribution of non-uniform electric fields, asymmetric electric potential, and dead zone. The physical properties of the detector have been extensively and systematically studied. This study simulated the electric field, potential, electron concentration distribution, complete depletion voltage, leakage current, capacitance, transient current induced by incident particles, and weighting field distribution of the detector. It also systematically studied and analyzed the electrical characteristics of the detector. Compared to traditional 3D trench electrode silicon detectors, this new detector adopts a manufacturing process of double-side etching technology and double-side filling technology, which results in a more sensitive detector volume and higher electric field uniformity. In addition, the size of the detector unit is 120 µm × 120 µm × 340 µm; the structure has a small fully depleted voltage, reaching a fully depleted state at around 1.4 V, with a saturation leakage current of approximately $4.8\times {10}^{-10}\mathrm{A}$, and a geometric capacitance of about 99 fF. Full article

Euterpe precatoria Mart. is an increasingly important palm for subsistence and income generation in central and western Amazonia with growing demand for its fruit pulp, which is an alternative source of açaí juice for domestic and international markets. This study synthesizes current knowledge on its systematics, ecology, fruit production in natural populations, fruit quality, uses, population management, and related areas, identifying critical research gaps. A systematic literature survey was conducted across databases including Web of Science, Scopus, Scielo, CAPES, and Embrapa. Of 1568 studies referencing Euterpe, 273 focused on E. precatoria, with 90 addressing priority themes. Genetic diversity studies suggest the E. precatoria may represent a complex of species. Its population abundance varies across habitats: the highest variability occurs in terra firme, followed by baixios and várzeas. Várzeas exhibit greater productivity potential, with more bunches per plant and higher fruit weight than baixios; no production data exist for terra firme. Additionally, E. precatoria has higher anthocyanin content than E. oleracea, the primary commercial açaí species. Management of natural populations and cultivation practices are essential for sustainable production; however, studies in these fields are still limited. The information is crucial to inform strategies aiming to promote the sustainable production of the species. Full article

The advancement of artificial intelligence (AI), deep learning, and radiomics has introduced novel methodologies for the detection, classification, prognosis, and treatment evaluation of pancreatic ductal adenocarcinoma (PDAC). As the integration of AI into medical imaging continues to evolve, its potential to enhance early detection, refine diagnostic precision, and optimize treatment strategies becomes increasingly evident. However, despite significant progress, various challenges remain, particularly in terms of clinical applicability, generalizability, interpretability, and integration into routine practice. Understanding the current state of research is crucial for identifying gaps in the literature and exploring opportunities for future advancements. This literature review aims to provide a comprehensive overview of the existing studies on AI applications in PDAC, with a focus on disease detection, classification, survival prediction, treatment response assessment, and radiogenomics. By analyzing the methodologies, findings, and limitations of these studies, we aim to highlight the strengths of AI-driven approaches while addressing critical gaps that hinder their clinical translation. Furthermore, this review aims to discuss future directions in the field, emphasizing the need for multi-institutional collaborations, explainable AI models, and the integration of multi-modal data to advance the role of AI in personalized medicine for PDAC. Full article

Background: Tranexamic acid is an antifibrinolytic medication often used to prevent hemorrhage. The dosage and route of administration can vary depending on specialty and indication, although one of the most common routes includes intravenous application. Other possible administration modalities include intramuscular and topical applications or irrigation. Although not the most common method, more research is emerging on the topical application of the drug to prevent bleeding. Methods: Specific search terms regarding the topical administration of tranexamic acid were input into PubMed and were reviewed via Covidence. Selected studies were stratified based on specialty (ears, nose, and throat; cardiology; plastic surgery; and orthopedics), and hematologic outcomes regarding tranexamic acid use were reviewed. Results: An evaluation of the studies demonstrated the feasibility of tranexamic acid in the topical form; however, it can depend on the specialty-specific indications. Each field utilizes unique procedures or surgeries, which can play a role in the effectiveness of the medication. Conclusions: While the current literature demonstrates the feasibility of tranexamic acid, further research is needed to understand its viability in other fields, such as obstetrics. Full article

Botrytis cinerea is a highly versatile pathogenic fungus, causing significant damage across a wide range of plant species. A central focus of this review is the recent advances made through proteomics, an advanced molecular tool, in understanding the mechanisms of B. cinerea infection. Recent advances in mass spectrometry-based proteomics—including LC-MS/MS, iTRAQ, MALDI-TOF, and surface shaving—have enabled the in-depth characterization of B. cinerea subproteomes such as the secretome, surfactome, phosphoproteome, and extracellular vesicles, revealing condition-specific pathogenic mechanisms. Notably, in under a decade, the proportion of predicted proteins experimentally identified has increased from 10% to 52%, reflecting the rapid progress in proteomic capabilities. We explore how proteomic studies have significantly enhanced our knowledge of the fungus secretome and the role of extracellular vesicles (EVs), which play key roles in pathogenesis, by identifying secreted proteins—such as pH-responsive elements—that may serve as biomarkers and therapeutic targets. These technologies have also uncovered fine regulatory mechanisms across multiple levels of the fungal proteome, including post-translational modifications (PTMs), the phosphomembranome, and the surfactome, providing a more integrated view of its infection strategy. Moreover, proteomic approaches have contributed to a better understanding of host–pathogen interactions, including aspects of the plant’s defensive responses. Furthermore, this review discusses how proteomic data have helped to identify metabolic pathways affected by novel, more environmentally friendly antifungal compounds. A further update on the advances achieved in the field of proteomics discovery for the organism under consideration is provided in this paper, along with a perspective on emerging tools and future developments expected to accelerate research and improve targeted intervention strategies. Full article

The increase in the elderly population globally reflects a change in the population’s mindset regarding preventive health measures and necessitates a rethinking of healthcare strategies. The integration of machine learning (ML)-type algorithms in geriatrics represents a direction for optimizing prevention, diagnosis, prediction, monitoring, and treatment. This paper presents a systematic review of the scientific literature published between 1 January 2020 and 31 May 2025. The paper is based on the applicability of ML techniques in the field of geriatrics. The study is conducted using the Web of Science database for a detailed discussion. The most studied algorithms in research articles are Random Forest, Extreme Gradient Boosting, and support vector machines. They are preferred due to their performance in processing incomplete clinical data. The performance metrics reported in the analyzed papers include the accuracy, sensitivity, F1-score, and Area under the Receiver Operating Characteristic Curve. Nine search categories are investigated through four databases: WOS, PubMed, Scopus, and IEEE. A comparative analysis shows that the field of geriatrics, through an ML approach in the context of elderly nutrition, is insufficiently explored, as evidenced by the 61 articles analyzed from the four databases. The analysis highlights gaps regarding the explainability of the models used, the transparency of cross-sectional datasets, and the validity of the data in real clinical contexts. The paper highlights the potential of ML models in transforming geriatrics within the context of personalized predictive care and outlines a series of future research directions, recommending the development of standardized databases, the integration of algorithmic explanations, the promotion of interdisciplinary collaborations, and the implementation of ethical norms of artificial intelligence in geriatric medical practice. Full article

This study evaluates the rheological behavior and mechanical performance of Ultra-High-Performance Fiber-Reinforced Concrete (UHPFRC) mixes with varying superplasticizer dosages, aiming to optimize their use in pavement rehabilitation overlays on sloped surfaces. A reference self-compacting UHPFRC mix was modified by reducing the superplasticizer-to-binder ratio in incremental steps, and the resulting mixes were assessed through rheometry, mini-Slump, and Abrams cone tests. Key rheological parameters—static and dynamic yield stress, plastic viscosity, and thixotropy—were determined using the modified Bingham model. The results showed that reducing superplasticizer content increased yield stress and viscosity, enhancing thixotropic behavior while maintaining ultra-high compressive (≥130 MPa) and flexural strength (≥20 MPa) at 28 days. A predictive model was validated to estimate the critical yield stress needed for overlays on slopes. Among the evaluated formulations, the SP-2 mix met the stability and performance criteria and was successfully tested in a prototype overlay, demonstrating its viability for field application. This research confirms the potential of rheology-tailored UHPFRC as a high-performance solution for durable and stable pavement overlays in demanding geometric conditions. Full article

Public transport companies are aware that the success of their operations largely depends on the proper sizing and optimization of their processes. Among the key activities are the maintenance and repair of the vehicle fleet. This paper presents the application of mathematical optimization methods from the field of operations research to improve the efficiency of service workshops for bus maintenance and repair. Based on an analysis of collected data using queueing theory, the authors assessed the current system performance and found that the queueing system still has spare capacity and could be downsized, which aligns with the company’s management goals. Specifically, the company plans to reduce the number of bus repair service stations (servers in a queueing system). The main question is whether the system will continue to function effectively after this reduction. Three specific downsizing solutions were proposed and evaluated using queueing theory methods: extending the daily operating hours of the workshops, reducing the number of arriving buses, and increasing the productivity of a service station (server). The results show that, under high system load, only those solutions that increase the productivity of individual service stations (servers) in the queueing system provide optimal outcomes. Other solutions merely result in longer queues and associated losses due to buses waiting for service, preventing them from performing their intended function and causing financial loss to the company. Full article

In the medical field, magnesium (Mg) alloys have been widely used due to their excellent antibacterial properties and biodegradability. However, in the marine environment, the antibacterial effect may be greatly attenuated, and consequently, microorganisms in the ocean are likely to adhere to the surface of Mg alloys, resulting in biocorrosion damage, which is really troublesome in the maritime industry and can even be disastrous to the navy. Currently, there is a lack of research on the biocorrosion of Mg alloys that may find important applications in marine engineering. In this paper, the biocorrosion mechanism of the Mg alloy Mg-3Nd-2Gd-Zn-Zr caused by Chlorella vulgaris (C. vulgaris), a typical marine microalga, was studied. The results showed that the biomineralization process in the artificial seawater containing a low concentration of C. vulgaris cells was accelerated compared with that in the abiotic artificial seawater, leading to the deposition of CaCO₃ on the surface to inhibit the localized corrosion of the Mg alloy, whereas a high concentration of C. vulgaris cells produced a high content of organic acids at some sites through photosynthesis to significantly accelerate the surface film rupture at some sites and severe localized corrosion there, but meanwhile, it resulted in the formation of a more protective biomineralized film in the other areas to greatly alleviate the corrosion. The contradictory biocorrosion behaviors on the Mg-3Nd-2Gd-Zn-Zr alloy induced by C. vulgaris were finally explained by a mechanism proposed in the paper. Full article

Improving outdoor thermal comfort is a critical objective in urban design, particularly in densely built urban environments. Elevated semi-open spaces—outdoor areas located beneath raised building structures—have been recognized for enhancing pedestrian comfort by improving airflow and shading. However, previous studies primarily focused on warm or temperate climates, leaving a significant research gap regarding their thermal performance in cold climate zones characterized by extreme seasonal variations. Specifically, few studies have investigated how these spaces perform under conditions typical of northern Chinese cities like Xi’an, which is explicitly classified within the Cold Climate Zone according to China’s national standard GB 50176-2016 and experiences both severe summer heat and cold winter conditions. To address this gap, we conducted field measurements and numerical simulations using the ENVI-met model (v5.0) to systematically evaluate the microclimatic performance of elevated ground-floor spaces in Xi’an. Key microclimatic parameters—including air temperature, mean radiant temperature, relative humidity, and wind velocity—were assessed during representative summer and winter conditions. Our findings indicate that the height of the elevated structure significantly affects outdoor thermal comfort, identifying an optimal elevated height range of 3.6–4.3 m to effectively balance summer cooling and winter sheltering needs. These results provide valuable design guidance for architects and planners aiming to enhance outdoor thermal environments in cold climate regions facing distinct seasonal extremes. Full article

Aqueous redox flow batteries (ARFBs) have attracted significant attention in the field of electrochemical energy storage due to their high intrinsic safety, low cost, and flexible system configuration. However, the advancement of this technology is still hindered by several critical challenges, including capacity decay, structural optimization, and the design and application of key materials as well as their performance within battery systems. Addressing these issues requires systematic theoretical foundations and scientific guidance. Numerical modeling has emerged as a powerful tool for investigating the complex physical and electrochemical processes within flow batteries across multiple spatial and temporal scales. It also enables predictive performance analysis and cost-effective optimization at both the component and system levels, thus accelerating research and development. This review provides a comprehensive overview of recent progress in the modeling of ARFBs. Taking the all-vanadium redox flow battery as a representative example, we summarize the key multiphysics phenomena involved and introduce corresponding multi-scale modeling strategies. Furthermore, specific modeling considerations are discussed for phase-change ARFBs, such as zinc-based ones involving solid–liquid phase transition, and hydrogen–bromine systems characterized by gas–liquid two-phase flow, highlighting their distinctive features compared to vanadium systems. Finally, this paper explores the major challenges and potential opportunities in the modeling of representative ARFB systems, aiming to provide theoretical guidance and technical support for the continued development and practical application of ARFB technology. Full article

This study develops and validates an artificial intelligence (AI)-assisted internship learning platform for automotive electronics based on the Llama 3 large language model, aiming to enhance pedagogical effectiveness within vocational training contexts. Addressing critical issues such as the persistent theory–practice gap and limited innovation capability prevalent in existing curricula, we leverage the natural language processing (NLP) capabilities of Llama 3 through fine-tuning based on transfer learning to establish a specialized knowledge base encompassing fundamental circuit principles and fault diagnosis protocols. The implementation employs the Hugging Face Transformers library with optimized hyperparameters, including a learning rate of 5 × 10⁻⁵ across five training epochs. Post-training evaluations revealed an accuracy of 89.7% on validation tasks (representing a 12.4% improvement over the baseline model), a semantic comprehension precision of 92.3% in technical question-and-answer assessments, a mathematical computation accuracy of 78.4% (highlighting this as a current limitation), and a latency of 6.3 s under peak operational workloads (indicating a system bottleneck). Although direct trials involving students were deliberately avoided, the platform’s technical feasibility was validated through multidimensional benchmarking against established models (BERT-base and GPT-2), confirming superior domain adaptability (F1 = 0.87) and enhanced error tolerance (σ² = 1.2). Notable limitations emerged in numerical reasoning tasks (Cohen’s d = 1.15 compared to human experts) and in real-time responsiveness deterioration when exceeding 50 concurrent users. The study concludes that Llama 3 demonstrates considerable promise for automotive electronics skills development. Proposed future enhancements include integrating symbolic AI modules to improve computational reliability, implementing Kubernetes-based load balancing to ensure latency below 2 s at scale, and conducting longitudinal pedagogical validation studies with trainees. This research provides a robust technical foundation for AI-driven vocational education, especially suited to mechatronics fields that require close integration between theoretical knowledge and practical troubleshooting skills. Full article