Search Results (5,681)

The exposure of microplastics (MPs) to ultraviolet (UV) light in the environment can affect their flotation behavior and removal efficiency. This study investigated the effects of UVC irradiation on the physical and surface characteristics of polypropylene (PP), polyethylene (PE), acrylonitrile butadiene styrene (ABS), polystyrene (PS), polyethylene terephthalate (PET), and polyvinyl chloride (PVC), and evaluated their removal using agglomeration–micro-flotation. MPs were irradiated with UVC for 7 days, and they were characterized using particle size distribution analysis, CIE L*a*b* color analysis, and contact angle measurements. Flotation experiments were conducted using kerosene as a hydrophobic bridging liquid. The results showed that UVC irradiation induced polymer-dependent changes, including fragmentation, apparent shape-related changes, and redistribution behavior, resulting in changes in particle size distribution. Surface discoloration and reduced contact angle were also observed after UV exposure, suggesting photooxidative surface modification and increased surface hydrophilicity. These surface modifications reduced flotation performance at low kerosene dosages, particularly for PET and PVC. However, increasing kerosene dosage improved removal efficiency by enhancing agglomeration and particle–bubble attachment. The results indicated that agglomeration–micro-flotation is a promising approach for removing UV-aged MPs and provided insights into the influence of UV-induced surface modifications on flotation behavior. Full article

Industrial digitalization increasingly requires automated tools capable of extracting structured knowledge from complex engineering documentation, such as Piping and Instrumentation Diagrams (P&IDs). This work proposes an integrated framework that combines object detection and Large Language Models (LLMs) for automated Knowledge Graph (KG) generation. The approach enables the transformation of unstructured P&ID schematics into machine-interpretable representations, supporting data-driven analysis and decision-making. A modular pipeline is developed, including image pre-processing, symbol detection via a YOLO-based model, and identification of semantic relations between schematic elements using LLMs. The proposal also includes the definition of a reference ontology, which is exploited for the construction of the KG, and a diagram dataset designed to test the performance of the object detection model. The KG generation procedure achieves strong results in terms of image reconstruction across a wide set of industrial schematics, while also preserving the semantic integrity and completeness of the original diagrams. The proposed method represents a significant step toward the digitalization of industrial knowledge, bridging traditional engineering documentation and semantic-based technologies. Full article

Access to authentic clinical learning experiences is often limited for undergraduate Health Sciences students. Immersive technologies such as 360-degree video may help bridge this gap, yet evidence regarding their use in home care education and Problem-Based Learning (PBL) remains scarce. To address this gap, we used a Design-Based Research (DBR) approach to develop and implement a 360-degree video-based home care learning experience and evaluated students’ perceptions of the video, VR headsets, and associated educational formats across three curricular tracks. The experiences of 251 undergraduate Health Sciences students across three different tracks (Policy, Mental Health and Digital) at Maastricht University were studied. Each track offered a different educational format using the 360-degree video as part of its Problem-Based Learning (PBL) curriculum. Students responded once to a combination of self-developed and standardized questionnaires, which included subscales from the Technology Acceptance Model 3 (TAM3) and the Video Transportation Scale (VTS). A DBR approach facilitated the iterative development and implementation of a 360-degree video-based home care learning experience embedded within a Problem-Based Learning curriculum. The intervention was successfully integrated across three tracks without compromising key PBL principles. Students generally perceived the 360-degree video and associated educational formats positively, particularly appreciating the opportunities for interaction and contextualized learning. The findings suggest that immersive 360-degree video delivered through VR headsets is a feasible and acceptable educational approach for undergraduate Health Sciences students and may provide meaningful exposure to clinical practice when access to placements is limited. Full article

Train fires in railway bridge–tunnel connection sections (BTCSs) create severe evacuation challenges because tunnel–bridge spatial transitions interact with heat, smoke, visibility loss, and constrained rescue conditions. Existing evacuation management methods remain limited in coupling quantitative risk assessment with adaptive route guidance under evolving fire hazards. To address this issue, this paper proposes a large language model (LLM)-enhanced intelligent decision-support system (IDSS) framework for quantitative risk assessment and dynamic evacuation routing in BTCS fire scenarios. First, a multi-dimensional risk assessment model is established using the analytic hierarchy process and fuzzy comprehensive evaluation to quantify post-stop evacuation risk from the perspectives of evacuation organization, structural damage, and line recovery. Second, a dynamic topology-based routing method is developed to prune fire-threatened nodes and identify safer evacuation paths under evolving hazard conditions. The risk assessment model and routing algorithm are further embedded as callable tools into an LLM-enhanced evacuation IDSS under a perception–reasoning–recommendation architecture, in which an LLM orchestrates tool invocation, situational reasoning, and recommendation generation, thereby enabling autonomous risk interpretation, dynamic route replanning, and cross-regional collaborative decision support. The proposed framework is validated through a representative real-world railway engineering case. The results show that the IDSS-recommended routes achieved higher comprehensive safety scores (80.44 and 79.56) than routes involving fire-affected areas did (77.00 and 77.88). Workflow analysis further indicates that the proposed IDSS reduces the manual route-derivation workload by integrating risk assessment, topology pruning, and route allocation into structured, human-reviewable evacuation recommendations. Expert evaluations further confirm the rationality and compliance of the outputs, with review scores ranging from 1.76 to 1.92 out of 2.00. Overall, the proposed framework offers a feasible decision-support approach for intelligent evacuation management in complex railway fire emergencies. Full article

Rice (Oryza sativa) cultivation is highly relevant for global food security, yet germination may be conditioned by the presence of the rice hull. Using hulled or dehulled rice seeds can affect germination because the hull acts as both a physical barrier and a source of inhibitory compounds. While dehulling may speed germination by improving water and gas exchange and removing allelochemicals, it also increases vulnerability to damage and pathogens. Specific seed treatments can help mitigate these challenges. This study investigated the effects of mechanical dehulling and seed soaking with different elicitors, including hydrogen peroxide (H₂O₂), Hammada scoparia extracts, and ferrous sulfate, on Vialone Nano rice seed emergence and early vigor. Key findings revealed that dehulled seeds presented better emergence compared to hulled seeds, likely due to the removal of physical barriers and improved water uptake. However, hulled seeds were more responsive to the treatments, showing marked improvements in emergence speed and seedling vigor. The results demonstrate that while dehulling provides a natural advantage, seed treatments with elicitors effectively bridges the performance gap for hulled seeds. These strategies may offer sustainable approaches to improve crop establishment and overall productivity in the local rice farming systems. Full article

The LLC resonant converter has gained extensive adoption in recent years, primarily owing to its benefits including high efficiency and high power density. However, the intrinsic electrical traits of the LLC converter fail to accommodate operational requirements involving a broad voltage span for both the input and the output. To tackle the operational scenarios of LLC resonant converters characterized by broad input and output voltage ranges, this study examines the gain properties of LLC subjected to both frequency modulation control and phase shift control techniques, correspondingly, and puts forward a hybrid control approach integrating frequency modulation with phase shift strategy. Through the seamless combination of frequency modulation control and phase shift control within one control loop, the issue of system oscillations occurring during the transition among differing control loops is successfully eliminated. As a result, the voltage gain spectrum of the LLC is substantially widened. A high-power LLC simulation model featuring interleaved and parallel configurations, along with an experimental testing rig, were established. The presented hybrid control strategy, which utilizes frequency modulation and phase shift, was investigated via extensive simulations and empirical testing. The obtained simulation results and experimental data exhibit strong alignment, thereby confirming the accuracy and feasibility of the presented full-bridge LLC hybrid control approach designed for extensive input and output voltage variations. Full article

Chronic epididymitis and orchitis represent significant yet frequently under-recognized contributors to male infertility, particularly among men of reproductive age. These conditions arise from persistent inflammatory or immunological processes affecting the epididymis and testis, leading to impaired spermatogenesis, altered sperm maturation and possible obstruction of the male reproductive tract. Infectious aetiologies, especially those linked to sexually transmitted pathogens and uropathogens, remain predominant; however, non-infectious mechanisms, including autoimmune activation, post-vasectomy changes and idiopathic inflammation, also play critical roles. The persistent inflammatory milieu induces cytokine release, oxidative stress and structural tissue remodelling, ultimately compromising the functional and immune-privileged microenvironment necessary for optimal sperm production and transport. Diagnostic evaluation requires a multimodal approach incorporating clinical examination, microbiological testing, semen analysis and scrotal ultrasonography, with advanced imaging and molecular assays reserved for complex or equivocal cases. Management is individualized and may involve antimicrobial therapy, anti-inflammatory treatment, immunomodulation or microsurgical intervention in cases of ductal obstruction. Assisted reproductive technologies provide additional options when natural conception is not feasible. Despite increased recognition of their impact, chronic epididymitis and orchitis remain insufficiently studied, with gaps in standardized definitions, biomarker validation and long-term outcome data. This review provides a focused synthesis and phenotype-driven clinical framework for chronic epididymitis and orchitis through a fertility-preservation lens, bridging urological and andrological perspectives and integrating evidence on subclinical inflammation, contemporary diagnostic biomarkers and a staged therapeutic pathway. Full article

MYB transcription factors (TFs) play essential roles in diverse biological processes, including anther and pollen development, vegetative growth, seed development and germination, and stress responses. However, functional characterization of MYB TFs in maize (Zea mays) lags far behind that in Arabidopsis thaliana and Oryza sativa. In this study, we performed a genome-wide identification of 196 maize MYB TFs, along with phylogenetic analysis and Gene Ontology (GO) annotation. To bridge the knowledge gap, we established an integrated cross-species comparative workflow that systematically maps functionally characterized MYB TFs from Arabidopsis and rice to their maize orthologs. By coupling this homology-based approach with spatiotemporal expression profiling of developing anthers across multiple inbred lines, we prioritized candidate MYB TFs likely involved in anther and pollen development. This integrated strategy provides a useful reference for translating the rich functional knowledge accumulated in model plants to crops with less-characterized genomes. Our study not only establishes a solid foundation for the functional investigation of maize MYB TFs, but also offers promising targets for the mechanistic dissection and molecular breeding application of male sterility in maize. Full article

Objective Image Quality Assessment (IQA) is a fundamental pillar of computer vision, essential for optimizing tasks ranging from supervised machine learning to real-time video streaming. While IQA aims to quantify image degradation caused by noise and artifacts, a persistent gap remains between technical objective measurements and subjective human perception. Objective IQA has advanced significantly through full-reference (FR) metrics designed to approximate human judgment. Standard measures such as the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and root mean square error (RMSE) provide established benchmarks; however, they frequently fail to capture nuanced human visual preferences, often penalizing perceptually insignificant shifts or favoring overly smoothed images. Conversely, modern deep-learning metrics like LPIPS offer better perceptual alignment but remain computationally prohibitive for real-time, resource-constrained environments. This paper addresses these challenges through a dual-purpose approach. First, it provides a comprehensive survey and tutorial of the IQA landscape, offering self-contained mathematical derivations of classical error sensitivity measures, including MSE, RMSE, MAE, Euclidean distance, RMSLE, and Huber loss, as well as artificial neural network (ANN) approaches. This foundational review ensures a rigorous understanding of the field’s mathematical evolution. We introduce the Adaptive Contrast-Weighted Structural Similarity (ACSSIM) framework. ACSSIM is a lightweight hybrid metric that enhances classical FR-IQA by incorporating local weighting derived from human visual system (HVS) properties. Specifically, it targets Weber’s Law-based contrast and entropy, which are key elements of our hybrid quality assessment logic and key components of non-reference image quality metrics. Extensive numerical experiments on the TID2013 and KADID-10k benchmark show that ACSSIM improves correlation with human subjective judgments compared with the baseline PSNR and SSIM. Our results confirm that ACSSIM maintains low computational overhead, bridging the gap between efficiency and accuracy for practical deployment. We made our code publicly available to facilitate future research in efficient perceptual modeling. Full article

Tsunami fragility modeling plays a central role in probabilistic coastal risk assessment; however, representing structural vulnerability under near-field tsunami conditions remains challenging due to complex hydrodynamic loading, strong spatial variability, and the presence of pre-existing earthquake damage. This paper advances near-field tsunami fragility modeling through three specific contributions, each bridging simulation-based methods and empirical damage survey observations. First, it demonstrates how a successive earthquake–tsunami simulation framework can generate conditional fragility surfaces that explicitly account for pre-existing seismic damage without relying on statistically intractable probabilistic decompositions. Second, it develops and validates a distance-dependent intensity-shifting approach—derived from analysis of the 2011 Great East Japan tsunami survey dataset—that adapts baseline fragility curves to near-field and near-coast conditions in a physically interpretable and practically deployable manner. Third, it establishes an explicit cross-validation pathway between simulation-derived fragility surfaces and empirical damage observations through machine-learning-assisted feature importance analysis, a connection largely absent from prior literature. Together, these contributions provide a physically consistent and data-informed foundation for near-field tsunami fragility modeling that is directly applicable—as a methodological framework—to loss and resilience estimation platforms such as IN-CORE and HAZUS and to risk-informed coastal infrastructure design in subduction-zone regions, subject to typology-specific calibration; the simulation results are demonstrated for a US Reinforced Concrete (RC) moment-frame archetype and the empirical results for Japanese wood-frame construction, so direct quantitative application to other structural typologies requires recalibration of the respective model components. Full article

Viral vaccine development requires both mechanistic understanding of protective immunity and translational study designs that connect preclinical data with human outcomes. Animal models remain important for early assessment of safety, immunogenicity and protective efficacy, but their predictive value depends on the question being asked, the pathophysiology of infection, the immune mechanisms expected to mediate protection, and the biomarkers chosen to bridge animal and human data. This review focuses on viral vaccines and examines innate and adaptive mechanisms of vaccine-induced protection, including B cell and antibody responses, Fc-mediated functions, Fc glycosylation, T cell memory and CD8+ cytotoxic responses. We discuss common reasons for clinical failure and show how preclinical endpoints can be classified as human-counterpart, surrogate or comparative/mechanistic readouts. Influenza and COVID-19 examples illustrate how different models can be combined across discovery, challenge, transmission and late-stage bridging studies. Emerging tools such as systems serology, omics, AI/ML and new approach methods can improve candidate prioritization, but their value depends on assay standardization, biological validation and cautious interpretation. A mechanism-driven model cascade, paired with human-relevant immunological readouts, can improve preclinical interpretation and reduce the risk of advancing candidates that are unlikely to succeed in clinical trials. Full article

Mineralogical data acquired from drillcores provide important constraints for resource estimation, geometallurgical modelling, mineral exploration, and geological interpretation. While hyperspectral imaging is rapidly gaining traction for these applications, it lacks the ability to accurately quantify mineral abundances without extensive calibration data. Here, we build on previous work to demonstrate and benchmark workflows that combine scanning electron microscope (SEM) mineral maps with large-extent multimodal hyperspectral imaging data. The goal is to relate hyperspectral features and mineral abundances using supervised machine learning models, and then apply these models to infer mineralogy across entire drillcores. We adapt the learning process to the non-uniform (unbalanced) composition of most rocks, and achieve reasonable accuracy for most rock-forming minerals. However, we also find that prediction accuracy depends strongly on the representativity of training data—so models often fail to produce accurate maps of rare and accessory minerals. Robust, adaptive and ideally semi-automated sampling approaches might address this shortcoming by identifying locations which ensure optimal coverage of hyperspectral variance. We also emphasise that upscaling from SEM to drillcore scale inevitably involves extrapolation, meaning predictions should always be validated. However, once validated, upscaled mineralogy predictions could provide crucial quantitative data that bridge the scale gap between petrographic observations, metallurgical tests, and geometallurgical models. Full article

This study examines how the concept of academic success is constructed and represented both in international scientific literature and in the perceptions of higher education students, using the OPSA 2.0 project at the Polytechnic University of Leiria as a case study. Adopting an exploratory multimethod approach, the research combines bibliometric analysis of publications indexed in Scopus (2020–2025) with qualitative content analysis of students’ responses collected through participatory workshops. The bibliometric results reveal that academic success is increasingly conceptualised as a multidimensional construct, structured around institutional, pedagogical, psychological, and identity-related dimensions. However, the analysis of students’ perceptions shows a predominance of instrumental and performance-oriented representations, particularly associated with grades, course completion, and employability. At the same time, emerging references to well-being, resilience, and personal fulfilment suggest a gradual shift towards more holistic understandings of success. By articulating global research trends with local student narratives, the study highlights the coexistence of traditional and emergent conceptualisations of academic success in higher education. The findings underline the relevance of institutional strategies, such as OPSA 2.0 Project, that promote a comprehensive and preventive approach to student success. Methodologically, the study demonstrates the potential of combining bibliometric mapping with qualitative analysis to bridge macro-level scientific developments and micro-level lived experiences. Full article

Lactic acid bacteria (LAB), particularly members of the genus Lactobacillus, have emerged as promising biological agents with antimicrobial and anti-biofilm properties. While numerous individual studies have reported their inhibitory effects against pathogenic microorganisms, a systematic understanding that integrates their functional components, molecular mechanisms, and material-based applications remains lacking. In this review, we provide a comprehensive and component-oriented overview of LAB-mediated antimicrobial strategies. We first summarize secreted factors, including organic acids, bacteriocins, hydrogen peroxide, and extracellular vesicles, which collectively contribute to direct pathogen inhibition and environmental modulation. We then discuss cell-associated components such as surface-layer proteins and exopolysaccharides, highlighting their roles in adhesion interference and competitive exclusion. In addition, we examine whole-cell effects, including niche competition, quorum sensing disruption, and host immune modulation. Importantly, we place particular emphasis on the anti-biofilm activity of lactobacilli, detailing mechanisms involved in the prevention of the pathogen initial adhesion, disruption of extracellular polymeric substance matrices, and destabilization of mature biofilms. Finally, we explore emerging strategies that integrate lactobacilli with biomaterials, particularly hydrogel-based systems, to achieve controlled delivery, enhanced stability, and sustained antimicrobial activity. These biohybrid approaches represent a promising direction for the development of next-generation antimicrobial materials. These findings support the concept of LAB-based living antimicrobial materials as a next-generation strategy to combat biofilm-associated infections. Overall, this review aims to bridge the gap between molecular functions and translational applications of lactobacilli, providing new insights into its potential as a versatile platform for antimicrobial and anti-biofilm interventions. Full article

While existing curricula often focus on theoretical aspects of sustainability, they frequently fail to equip students with practical design skills required by the green industry. To address this disconnect, this study seeks to answer: How can a structured pedagogical framework effectively enhance students’ ability to translate abstract sustainability principles into concrete technical solutions? This study introduces a comprehensive CDIO-based framework reform for Embedded Intelligent Systems education, weaving sustainability throughout every phase. We put forward a “Sustainable CDIO Capability Model” that charts a progressive pathway—starting from basic resource awareness and advancing through to sophisticated sustainable system innovation. Our four-dimensional teaching strategy brings this model to life: first, project-based learning driven by real sustainability challenges; second, a hybrid ecosystem blending online resources, hands-on practice, and immersion in green industry contexts; third, hierarchical team-based pedagogy backed by personalized support mechanisms; and fourth, a multi-dimensional assessment system that weights energy efficiency, resource stewardship, and social value creation alongside conventional metrics. We implemented this approach with Intelligent Science and Technology majors at Wuhan Institute of Technology. The results show the model effectively bridges the persistent gap between dry technical content and the practical demands of green industry. Students made substantial gains not merely in core engineering capabilities—system architecture, hardware-software co-development—but crucially in sustainable design awareness and their capacity to untangle complex sustainability challenges. This work offers a readily transferable framework for embedding Education for Sustainable Development (ESD) into engineering curricula worldwide. It provides practitioners with a concrete, tested model for cultivating the next generation of engineers who naturally think and act with sustainability in mind. Full article