Search Results (1,582)

Background: Immunological strategies for antibody-guided vaccine design intend to enhance viral neutralization and protection and increase efficacy. Here, we discuss advances in antibody-guided vaccine design and current antibody-guided strategies, including epitope-based, nanoparticle-based, and scaffold-based vaccine approaches. We review the challenges and limitations of vaccines against different pathogens, such as influenza A virus, HIV-1 virus, single-celled malaria parasite, respiratory syncytial virus, and SARS-CoV-2. We summarize the available literature guidance, including emerging techniques in immunological vaccine design, to help understand and improve antibody-based immunity. The search strategy we applied is a comprehensive literature review of major databases, with specific search terms related to antibody-mediated vaccine design, viral neutralization, and immune protection. We discuss the how future directions for next-generation vaccine platforms and personalized vaccines based on immunogenetics will help improve vaccine design for increased specificity and potency of antibodies that neutralize pathogens, offering more precise and effective immune responses and, therefore, protection. Full article

Despite growing scholarly interest in digital transformation, few studies have systematically explored the mechanisms linking digital transformation capability to firm performance. This study examines both the direct and indirect effects of digital transformation capability on firm performance, offering novel insights by incorporating organizational strategic intuition and digital leadership as mediating variables. These mediators align with the emerging emphasis on strategic risk management in the literature. A survey was conducted among 620 high-tech enterprises in the Yangtze River Delta using a structured questionnaire. The data were analyzed using SPSS 23.0 for descriptive and correlational statistics, SmartPLS 4.0 for structural equation modeling (SEM), and PROCESS 4.2 for mediation analysis. The results reveal a significant direct effect of digital transformation capability on firm performance. Mediation analysis further shows that organizational strategic intuition and digital leadership each significantly mediate this relationship, and a chain mediation pathway involving both variables is also confirmed. These findings deepen our understanding of how digital transformation capability drives performance outcomes and offer practical guidance for high-tech firms seeking sustainable competitive advantages in dynamic digital environments. This study advances the theoretical discourse by clarifying the pathways through which digital transformation capability affects firm performance and provides empirical evidence to inform strategic decision-making in high-tech management. Full article

Urban form plays a crucial role in regulating urban thermal environments and air pollution patterns. However, the indirect mechanisms through which urban form influences PM2.5 concentrations via land surface temperature (LST) remain poorly understood. This study investigates these pathways by analyzing representative two- and three-dimensional urban form indices (UFIs) in the central urban area of Taiyuan, China. Multiple log-linear regression and mediation analysis were applied to evaluate the combined effects of urban form on LST and PM2.5. The results indicate that UFIs significantly influence both LST and PM2.5. The frontal area index (FAI) and sky view factor (SVF) emerged as key variables, with LST playing a significant mediating role. The indirect pathways affecting PM2.5 via LST, along with the direct LST-PM2.5 correlation, exhibit pronounced seasonal differences in direction and intensity. Moreover, different urban functional zones exhibit heterogeneous responses to the same form indices, highlighting the spatial variability of these linkages. These findings underscore the importance of incorporating seasonal and spatial differences into urban design. Accordingly, this study proposes targeted urban form optimization strategies to improve air quality and thermal comfort, offering theoretical insights and practical guidance for sustainable urban planning. Full article

Biomimetic lattice structures, inspired by natural architectures such as bone, coral, mollusk shells, and Euplectella aspergillum, have gained increasing attention for their exceptional strength-to-weight ratios, energy absorption, and deformation control. These properties make them ideal for advanced engineering applications in aerospace, biomedical devices, and structural impact protection. This study presents a comprehensive bibliometric analysis of global research on biomimetic lattice structures published between 2020 and 2025, aiming to identify thematic trends, collaboration patterns, and underexplored areas. A curated dataset of 3685 publications was extracted from databases like PubMed, Dimensions, Scopus, IEEE, Google Scholar, and Science Direct and merged together. After the removal of duplication and cleaning, about 2226 full research articles selected for the bibliometric analysis excluding review works, conference papers, book chapters, and notes using Cite space, VOS viewer version 1.6.20, and Bibliometrix R packages (4.5. 64-bit) for mapping co-authorship networks, institutional affiliations, keyword co-occurrence, and citation relationships. A significant increase in the number of publications was found over the past year, reflecting growing interest in this area. The results identify China as the most prolific contributor, with substantial institutional support and active collaboration networks, especially with European research groups. Key research focuses include additive manufacturing, finite element modeling, machine learning-based design optimization, and the performance evaluation of bioinspired geometries. Notably, the integration of artificial intelligence into structural modeling is accelerating a shift toward data-driven design frameworks. However, gaps remain in geometric modeling standardization, fatigue behavior analysis, and the real-world validation of lattice structures under complex loading conditions. This study provides a strategic overview of current research directions and offers guidance for future interdisciplinary exploration. The insights are intended to support researchers and practitioners in advancing next-generation biomimetic materials with superior mechanical performance and application-specific adaptability. Full article

Objective: Several studies have compared the accuracy of digital scans and conventional impressions for post space capture. However, only a few have specifically investigated the precision of intraoral scanners in measuring post spaces of varied lengths. This study aimed to evaluate the accuracy of various intraoral scanning techniques in capturing long and short post spaces. Material and Methods: This study grouped samples into eight categories based on four techniques and two post space depths (7 and 11 mm). After tooth preparation, root canal treatment, and post space preparation, laboratory scans were performed using Duralay. Intraoral scans were obtained directly and indirectly with the Trios fourth generation scanner using the Duralay and PVS techniques. The accuracies, in terms of trueness, and precisions were compared after ten repetitions for each group using the Kruskal–Wallis or Mann–Whitney U tests. Results: The Duralay Intraoral Scan groups demonstrated a high consistency, while the Direct Intraoral Scan groups showed moderate consistency. Variability was higher for the Duralay Lab Scan and PVS Intraoral Scan groups for short post spaces. Conclusions: The capture technique significantly affected the accuracies of the post space measurements. The techniques also demonstrated varying consistency and precision. These findings provide critical insights to guide their selections for clinical and research applications. Clinical Significance: This study is one of the few to compare the accuracy of intraoral scanners for the capture of both short and long post spaces. It addresses a key gap in current dental research and offers practical guidance for clinicians and researchers in selecting appropriate scanning techniques for various clinical scenarios. The findings have the potential to enhance the accuracy and reliability of post space measurements and improve patient outcomes. Full article

The Adaptive Cross Ivy (ACIVY) algorithm is a novel bio-inspired metaheuristic that emulates ivy plant growth behaviors for complex optimization problems. While the original Ivy Optimization Algorithm (IVYA) demonstrates a competitive performance, it suffers from limited inter-individual information exchange, inadequate directional guidance for local optima escape, and abrupt exploration–exploitation transitions. To address these limitations, ACIVY integrates three strategic enhancements: the crisscross strategy, enabling horizontal and vertical crossover operations for improved population diversity; the LightTrack strategy, incorporating positional memory and repulsion mechanisms for effective local optima escape; and the Top-Guided Adaptive Mutation strategy, implementing ranking-based mutation with dynamic selection pools for smooth exploration–exploitation balance. Comprehensive evaluations on the CEC2017 and CEC2022 benchmark suites demonstrate ACIVY’s superior performance against state-of-the-art algorithms across unimodal, multimodal, hybrid, and composite functions. ACIVY achieved outstanding average rankings of 1.25 (CEC2022) and 1.41 (CEC2017 50D), with statistical significance confirmed through Wilcoxon tests. Practical applications in engineering design optimization and UAV path planning further validate ACIVY’s robust performance, consistently delivering optimal solutions across diverse real-world scenarios. The algorithm’s exceptional convergence precision, solution reliability, and computational efficiency establish it as a powerful tool for challenging optimization problems requiring both accuracy and consistency. Full article

Accurately hindcasting waves in semi-enclosed, fetch-limited basins remains challenging for reanalysis models, which tend to underestimate storm peaks near the coast. We developed interpretable ML models for Rijeka Bay (northern Adriatic) using only wind observations from two land-based wind stations to predict buoy H_m0 measurements spanning 2009–2011 (testing) and 2019–2021 (training and validation). The tested tree-based models included Random Forest, XGBoost, and Explainable Boosting Machine. This study introduces a novel approach in the literature by employing weighted schemes and feature engineering to enhance the predictive performance of interpretable, low-complexity machine learning models in hindcasting waves. Representing wind direction as sine–cosine components generally reduced RMSE and BIAS relative to traditional speed–direction inputs, while an exponential sample weight scheme that emphasized storm waves halved extreme H_m0 underprediction without inflating overall RMSE. The best-performing model, a Random Forest model, achieved an RMSE of 0.096 m and a correlation of 0.855 on the unseen test set—30% lower overall RMSE and 50% lower extreme wave RMSE than the MEDSEA and COEXMED hindcasts. Additionally, the underprediction was reduced by 90% compared to these reanalysis models. The method offers a computationally lightweight, transferable supplement to numerical wave guidance for coastal engineering and harbor operations. Full article

This study explores how artificial intelligence (AI) technologies can be theoretically integrated into literature curriculum development, using the works of Anton Chekhov and Lu Xun as illustrative case texts. The aim is to reduce barriers to language and cultural understanding in literature education and increase the efficiency and accessibility of cross-cultural teaching. We used natural language processing (NLP) techniques to analyze textual features, such as readability index, lexical density, and syntactic complexity, of AI-generated and human-translated “The Chameleon” and “A Madman’s Diary”. Teaching cases from universities in China, Russia, and Kazakhstan are reviewed to assess the emerging practice of AI-supported literature teaching. The proposed theoretical framework draws on hermeneutics, posthumanism, and cognitive load theories. The results of the data-driven analysis suggest that AI-assisted translation tends to simplify sentence structure and improve surface readability. While anecdotal classroom observations highlight the role of AI in initial comprehension, deeper literary interpretation still relies on teacher guidance and critical human engagement. This study introduces a conceptual “AI Literature Teaching Model” that positions AI as a cognitive and cultural mediator and outlines directions for future empirical validation. Full article

Silk fibroin (SF)-based intelligent wearable systems represent a frontier research direction in artificial intelligence and precision medicine. Their core efficacy stems from the inherent advantages of silk fibroin, including excellent mechanical properties, interfacial compatibility, and tunable structure. This article systematically reviews conductive modification strategies for silk fibroin and its research progress in the smart wearable field. It elaborates on the molecular structural basis of silk fibroin for use in smart wearable devices, critically analyzes five conductive functionalization strategies, compares the advantages, disadvantages, and applicable domains of different modification approaches, and summarizes research achievements in areas such as bioelectrical signal sensing, energy conversion and harvesting, and flexible energy storage. Concurrently, an assessment was conducted focusing on the priority performance characteristics of the materials across diverse application scenarios. Specific emphasis was placed on addressing the long-term functional performance (temporal efficacy) and degradation stability of silk fibroin-based conductive materials exhibiting high biocompatibility in implantable settings. Additionally, the compatibility issues arising between externally applied coatings and the native substrate matrix during conductive modification processes were critically examined. The article also identifies challenges that silk fibroin-based smart wearable devices currently face and suggests potential future development directions, providing theoretical guidance and a technical framework for the functional integration and performance optimization of silk fibroin-based smart wearable devices. Full article

Spatial memory is a critical component of spatial cognition, particularly in unfamiliar environments. As navigation systems become integral to daily life, understanding how individuals with varying spatial abilities respond to different navigation modes is increasingly important. This study employed a virtual driving environment to examine how participants with varying spatial abilities (good or poor) performed under three navigation modes, namely visual, audio, and combined audio–visual navigation modes. A total of 78 participants were divided into two groups, good sense of direction (G-SOD) and poor sense of direction (P-SOD), according to their Santa Barbara Sense of Direction (SBSOD) scores. They were randomly assigned to one of the three navigation modes (visual, audio, audio–visual). Participants followed navigation cues and simulated driving behavior to the end point twice during the learning phase, then completed the route retracing task, recognizing scenes task and recognizing the order task. Significant main effects were found for both SOD group and navigation mode, with no interaction. G-SOD participants outperformed P-SOD participants in route retracing task. Audio navigation mode led to better performance in tasks involving complex spatial decisions, such as turn intersections and recognizing the order. The accuracy of recognizing scenes did not significantly differ across SOD groups or navigation modes. These findings suggest that audio navigation mode may reduce visual distraction and support more effective spatial encoding and that individual spatial abilities influence navigation performance independently of guidance type. These findings highlight the importance of aligning navigation modalities with users’ cognitive profiles and support the development of adaptive navigation systems that accommodate individual differences in spatial ability. Full article

Sustainable manufacturing is gaining attention in the scientific literature. However, it remains unclear how to effectively incorporate it within Production Planning and Control (PPC) tasks. All the choices taken in terms of PPC impact sustainability, and sustainability managers and planners or managers involved in tasks, such as scheduling or inventory management, are not conscious of what this means or implies, above all, in terms of the sustainable performance indicators on which their actions can act. While several studies have addressed both PPC and sustainability, there is still limited guidance or structured frameworks specifically aimed at systematically linking PPC tasks with sustainability indicators in a practical and operational industrial context, despite the development of numerous sustainability standards in recent years. For this reason, this research aimed to develop a first detailed framework, specifically based on the Global Reporting Initiative (GRI) standard, that associates the most relevant indicators with the PPC phases, highlighting the type of impact (direct or indirect) of each phase on them. This could help with strategic decisions and promote more informed choices. The overall framework revealed the prevalence of environmental aspects involved in PPC phases (as expected) and a challenge related to the measurability of indicators (above all, the social ones). Furthermore, the Material Requirements Planning (MRP), identified as the most significant phase in terms of its impact on sustainability, was deeply analyzed, providing details related to the decision-making processes of this phase that affect sustainable performance. Full article

Sport injury recognition is rapidly evolving with the integration of new emerging technologies. This systematic review aims to identify and evaluate technologies capable of detecting injuries during sports participation. A comprehensive search of PUBMED, Sport Discus, Web of Science, and ScienceDirect was conducted following the PRISMA 2020 guidelines. The review was registered on PROSPERO (CRD42024608964). Inclusion criteria focused on prospective studies involving athletes of all ages, evaluating tools which are utilised to identify injuries in sports settings. The review included research between 2014 and 2024; retrospective, conceptual, and fatigue-focused studies were excluded. Risk of bias was assessed using the Critical Appraisal Skills Program (CASP) tool. Of 4283 records screened, 70 full-text articles were assessed, with 21 studies meeting the final inclusion criteria. The technologies were grouped into advanced imaging (Magnetic Resonance Imaging (MRI), Diffusion Tensor Imaging (DFI), and Quantitative Susceptibility Mapping (QSM), with biomarkers (i.e., Neurofilament Light (NfL), Tau protein, Glial Fibrillary Acidic Protein (GFAP), Salivary MicroRNAs, and Immunoglobulin A (IgA), and sideline assessments (i.e., the King–Devick test, KD-Eye Tracking, modified Balance Error Scoring System (mBESS), DETECT, ImPACT structured video analysis, and Instrumented Mouth Guards (iMGs)), which demonstrated feasibility for immediate sideline identification of injury. Future research should improve methodological rigour through larger, diverse samples and controlled designs, with real-world testing environments. Following this guidance, the application of emerging technologies may assist medical staff, coaches, and national governing bodies in identifying injuries in a sports setting, providing real-time assessment. Full article

Digital transformation has profoundly impacted engineering education, demanding new pedagogical approaches that ensure effective and sustainable learning. Educational psychology plays a fundamental role in strategically integrating educational technologies, fostering more inclusive, interactive, and efficient learning environments. This article explores the intersection of technological innovation, engineering education, and educational psychology, analyzing how digital tools such as Artificial Intelligence, virtual reality, gamification, and remote laboratories can optimize the teaching–learning process. It also examines the psychopedagogical impact of these technologies, addressing challenges like cognitive load, student motivation, digital accessibility, and emotional well-being. Finally, the article presents guidelines for sustainable implementation aligned with the Sustainable Development Goals (SDGs), promoting efficient, equitable, and student-centered education. As a theoretical and exploratory study, it also points to directions for future empirical investigations and practical applications. The insights provided offer strategic guidance for academic managers and educational policymakers seeking to implement sustainable, inclusive, and pedagogically effective digital innovation in engineering education. Full article

The HepACAGA (Hepatic Arteriography and C-arm CT-Guided Ablation) technique, which integrates C-arm CT guidance with transcatheter C-arm CT hepatic arteriography (C-arm CTHA), significantly improves liver tumor ablation outcomes by enhancing tumor visualization, navigation, and the intraprocedural assessment of ablation margins. The two key advantages of using C-arm CT over conventional CT for image guidance are firstly that the entire procedure can be performed in the angiography suite, eliminating the need for patient transfer between the angiography suite (catheterization) and CT-room (ablation), and secondly, that integrated C-arm needle guidance software can greatly reduce the difficulty of needle placement. Beyond these advantages, the HepACAGA technique offers additional benefits across four domains: (1) the direct conversion of ablation to intra-arterial liver-directed therapies (e.g., radioembolization or chemoembolization) upon the intraprocedural detection of disease progression; (2) the direct combination of ablation with intra-arterial treatments or portal vein embolization in one session; (3) the enhanced ablation effect through heat sink effect reduction with adjunct bland embolization or balloon occlusion; and (4) the immediate hemorrhage control through direct embolization. This pictorial essay demonstrates the advantages of combining C-arm CT guidance with real-time C-arm CTHA in the percutaneous thermal ablation of liver tumors, with clinical cases illustrating each of the aforementioned four key domains. Full article

Justice-oriented mathematics aims to support students’ understanding of the relationship between mathematical knowledge and political knowledge to examine how they conspire to shape reality. The notion of the formatting power of mathematics is helpful here in that it calls for an excavation of mathematics that makes explicit the actual use of mathematics hidden in social structures and routines. In this paper, the author examines how a mathematical unit on home mortgages was carried out to support 12th grade students’ understanding of the mathematics of mortgages, revealing the formatting power that mortgage lenders hold in reordering the reality of marginalized communities. Drawing on a qualitative analysis of student journals, student work, post-class student interviews, and teacher/researcher journals, the findings revealed two pedagogical features that contributed to students’ reading and writing the world with mathematics: engaging mathematics from multiple directions and attending to the formatting power of the mathematical and political knowledge dialectic. These findings offer pedagogical guidance for practitioners and teacher educators in curriculum design and implementation of critical mathematics. Full article