Search Results (3,337)

Wearable electroencephalography (EEG) is rapidly evolving toward lightweight, user-friendly systems that enable brain monitoring in naturalistic settings. Traditional multi-channel, gel-based systems provide broad scalp coverage and high signal fidelity but are impractical for unsupervised or long-term use. This review focuses on the emerging generation of smart wearable EEG devices that are easy to wear, require minimal setup, and typically integrate additional physiological sensors such as photoplethysmography (PPG), temperature, or motion sensors. We review wearable EEG systems across four main form factors: head-worn EEG devices, smart EEG patches and tattoos, in-ear and headphone-based EEG, and glasses-integrated EEG. Head-worn systems offer broader signal coverage and support more complex applications such as sleep staging, human–machine interaction, and epilepsy monitoring. Patch-based systems are well suited to comfortable long-term monitoring, particularly in sleep-related applications. Ear-center systems provide high user comfort and stable signal acquisition from non-traditional electrode locations. Glasses-integrated devices represent an emerging option for unobtrusive daytime neurophysiology. Each category is examined in terms of sensor fusion, technical parameters, and embedded algorithms, with particular emphasis on automated signal analysis. We conclude with a discussion on current limitations, regulatory and usability challenges, and future directions toward unobtrusive, AI-powered neurotechnology for home and clinical use. Full article

Background/Objectives: This study aimed to improve the biological profile of oleanolic acid (OA) through structural modification at the C-17 carboxyl group and the C-3 hydroxyl group, with a focus on the design of short-chain alkyl esters and 3-O-furoyl hybrids. Methods: Two series of OA derivatives were synthesized and characterized using spectroscopic methods, including ¹H NMR, ¹³C NMR and MS. In silico structure–activity relationship (SAR) analysis, ADMETox profiling, and molecular docking to the epidermal growth factor receptor (EGFR) tyrosine kinase domain were performed as predictive and hypothesis-generating tools. Anticancer activity was evaluated in vitro using the MTT assay against human cancer cell lines, including HeLa, MCF-7, A-549, SKBR-3, PC-3 and SKOV-3, as well as non-malignant human dermal fibroblasts (HDFs). Antioxidant properties were assessed using cell-free CUPRAC and DPPH assays. Results: The C-17 esterification markedly enhanced cytotoxic potency compared to the parent OA, while the introduction of the 3-O-furoyl moiety further improved antiproliferative activity in several derivatives. Selected compounds showed low-micromolar IC₅₀ values and moderate selectivity toward cancer cells. Molecular docking suggested favorable accommodation of selected derivatives within the EGFR ATP-binding pocket, mainly through hydrophobic and π-related interactions; however, these results do not confirm direct EGFR binding and require experimental validation. The CUPRAC and DPPH assays provided preliminary insight into chemical redox behavior but should not be directly extrapolated to intracellular antioxidant or pro-oxidant activity. Predicted ADMETox profiles indicated moderate permeability and relatively low predicted risk for selected toxicity endpoints, while also highlighting high lipophilicity, poor aqueous solubility and potential metabolic liabilities. Conclusions: Overall, the results identify several OA derivatives as promising anticancer lead compounds for further optimization and mechanistic investigation. Full article

Indoor companion, rehabilitation, logistics, laboratory transport, and service robot scenarios require mobile platforms that can follow a human operator safely and flexibly under lighting changes, occlusion, texture-poor corridors, and dynamic pedestrian environments. Vision-, LiDAR-, and UWB-based following systems can provide high perception capability, but their deployment cost, environmental dependence, and sensing complexity remain limiting factors for low-perception-dependence applications. This paper presents a passive following system for a Mecanum-wheel mobile platform based on gimbal posture perception and orthogonal odometry fusion. A rope-tensioned two-axis gimbal is mounted above a 300 mm × 300 mm × 150 mm omnidirectional chassis, and a six-axis inertial sensor installed at the top of the gimbal detects pitch and roll changes induced by user traction. A piecewise posture-to-velocity mapping model with a dead zone, saturation, low-pass filtering, and acceleration limiting converts the user’s traction intention into planar velocity commands in the vehicle coordinate frame. To reduce pose errors caused by Mecanum-wheel slip and discontinuous roller-ground contact, two orthogonal passive odometry wheels and inertial attitude estimation are fused to provide planar position feedback for closed-loop following. A prototype was implemented using an Infineon TRAVEO CYT4BB77 controller, TI DRV8701E motor drivers, six-axis IMUs, magnetic encoders, and an embedded display interface. Experiments evaluated attitude estimation accuracy, planar localization accuracy, passive following performance, gyroscope compensation, and open-loop/closed-loop following. The compensated attitude module achieved a static yaw drift of 0.45 deg/h and a dynamic attitude RMSE below 0.56 deg. Orthogonal odometry fusion produced an average positioning error of 3.8 mm over a 3000 mm linear displacement, reducing error by approximately 84.6% compared with pure Mecanum-wheel drive odometry. In a 5000 mm forward traction task, closed-loop following reduced the average distance error from 38.6 mm to 11.5 mm compared with open-loop attitude mapping. The results indicate that the proposed gimbal-orthogonal odometry architecture provides a compact, intuitive, and environment-robust solution for passive following on omnidirectional mobile platforms. Full article

Molecular microbiology has revolutionized our understanding of the complex host-associated microbiomes required for normative development and physiology. Horses and other members of the family Equidae are particularly reliant on the early maturation and lifelong maintenance of an unusually rich hindgut microbiome for optimal digestion and overall health and performance. Research on the equine gut microbiome has accelerated in the past several years, necessitating a renewed appraisal of the field. The present work is a comprehensive and critical review of the literature regarding the bacterial gastrointestinal microbiome of horses. First, the developmental trajectory of the foal gut microbiome is discussed, followed by descriptions of the taxonomic membership of the core equine gut microbiome, its primary functions and effects on host physiology, and intrinsic and extrinsic factors that shape the equine microbiome during health, with a focus on diet and supplements. Next, evidence supporting adverse effects on the equine gut microbiome of gastrointestinal conditions including colic and colitis, extraintestinal conditions including obesity and laminitis, and pharmacological interventions including antibiotics and non-steroidal anti-inflammatory drugs is summarized. Lastly, clinical and experimental research investigating the effects of treatments targeting the gut microbiome of horses, including probiotics, prebiotics, and fecal microbiome transfer, is critically examined. Conclusions summarize the connection between natural (i.e., wild) equine behavior and the health of the equine gut microbiome and the impacts of human management. Full article

We propose a hybrid intelligent methodology for forecasting outcomes in complex human-centered systems characterized by uncertainty and reliance on expert knowledge. The framework integrates fuzzy cognitive maps (FCMs), a novel Least-Influence Method for estimating causal arc weights, and genetic algorithms for model tuning. The proposed influence comparison method simplifies expert elicitation by reducing the cognitive load of direct weight estimation, while the genetic algorithm ensures alignment of forecasts with observed or expert-derived data. A forecasting algorithm based on incremental changes in concept levels enhances the sensitivity of the output variable to factor variations. To illustrate the applicability of the framework, we construct a decision-support model for predicting weight-loss success under diverse psychological, behavioral, and environmental conditions. Simulation results demonstrate how factor ranking, scenario modeling, and paired influence analysis provide actionable insights for decision-making. Beyond the weight-loss domain, the approach is generalizable to a wide range of knowledge-based systems requiring robust integration of expert judgment, fuzzy reasoning, and evolutionary optimization. Full article

Generative artificial intelligence (GenAI) is increasingly shaping assessment practices in higher education. However, its emergence has raised important questions about instructors’ readiness, ethical awareness, and professional support needs. This study examined EFL instructors’ assessment literacy for GenAI in Saudi higher education, with a focus on their assessment literacy level, perceptions of ethical, practical, and pedagogical implications, and training needs. A cross-sectional survey design was employed, and data were collected from 216 EFL instructors across Saudi universities. The instrument was validated through factor analyses, resulting in six constructs that include familiarity and use, assessment literacy, assessment application, ethical fairness, self-efficacy, and professional development. The findings indicate that instructors reported a high level of GenAI-related assessment literacy, especially in areas related to conceptual understanding and evaluative judgment. Ethical concerns were strongly expressed, including issues concerning academic integrity, privacy, and the need for human oversight. Although participants reported confidence in aligning GenAI with pedagogical goals, their practical use was less consistent in tasks requiring contextual judgment. Significant differences were observed across gender, rank, and teaching experience, while age showed a selective pattern. Overall, the study documents the importance of aligning individual competence with institutional support for responsible GenAI use in language assessment. Full article

Background/Objectives: Diagnostic metabolites measured in newborn screening, inherited metabolic disease, lysosomal storage disease, oncometabolite testing and routine clinical biochemistry are direct read-outs of human metabolic state. Their mechanistic interpretation requires linking measured metabolites to enzymes, pathways, regulatory context, disease knowledge and, increasingly, AI-assisted quantitative systems pharmacology (AI-QSP) workflows. We developed EHMN2026^®T as a license-aware AI-QSP integration framework that connects the EHMN2026^® metabolic backbone with licensed geneXplain knowledge resources while keeping ownership, licensing and redistribution constraints explicit. Methods: EHMN2026^®T integrates the SBML-encoded EHMN2026^® metabolic backbone with licensed TRANSFAC^® 2025.2, TRANSPATH^® 2025.2 and HumanPSD™ 2025.2 resources. TRANSFAC^® position weight matrices were used for promoter-level analysis of EHMN metabolic genes. The resulting transcription factor (TF)–gene connections were mapped to EHMN genes, TRANSPATH^® signalling/molecular-state entries and HumanPSD™ disease/drug context. The framework is positioned as a controlled component of the IQANOVA AI-QSP environment, but only aggregate statistics, non-proprietary EHMN-derived summaries and manuscript-level examples are reported publicly unless separate permission is obtained from the relevant rightsholders. Results: Promoter analysis of 1681 EHMN2026^® metabolic genes using 1147 mapped TRANSFAC^® matrices identified 291,387 ENSG-level TF–gene regulatory-potential connections involving 398 TFs and 1,107,264 predicted binding sites. The diagnostic panel contained 80 covered genes (63.5%), including complete coverage of oncometabolite enzymes and high coverage of organic acidaemia, steroidogenesis and fatty-acid oxidation categories. Mapping to TRANSPATH^® expanded the EHMN genes into 144,529 molecular-state representations and 14,879 gene–pathway or gene–chain pairs. HumanPSD™ was used as a licensed translational context layer; EHMN-specific HumanPSD™ outputs are treated as license-controlled derived outputs and are therefore not redistributed as open detailed tables in this manuscript. Conclusions: EHMN2026^®T provides a license-aware AI-QSP integration framework for tracing a diagnostic metabolite from a measured clinical value to candidate enzyme nodes, regulatory potential, signalling/molecular-state context and disease or therapeutic interpretation. PWM-derived TF–gene links are presented as regulatory hypotheses, not proof of active regulation. Public release should be limited to aggregate statistics and non-proprietary EHMN-derived components; detailed TRANSFAC^®, TRANSPATH^® and HumanPSD™-derived edges, mappings, annotations and SBML outputs remain subject to geneXplain ownership and licensing terms. Full article

Background/Objectives: Trabecular bone exhibits brittle behaviour governed by microscale deformation and damage, yet quantifying crack progression is difficult because classical fracture-mechanics approaches do not apply to architecturally discontinuous porous tissue. This pilot study evaluates whether synchrotron X-ray computed tomography (XCT) combined with digital volume correlation (DVC) can provide a practical, geometry-normalised approach for quantifying crack-opening behaviour in human trabecular bone. Methods: Semicylindrical specimens from femoral heads of hip-fracture donors (n = 5) and non-fracture controls (n = 5) underwent stepwise three-point bending during XCT imaging. Full-field displacement maps were used to measure crack mouth opening displacement (CMOD), crack length (a), and their ratio CMOD/a, used here as a geometry-normalised comparative descriptor of brittle response rather than an intrinsic material property. Automated phase-congruency crack detection (PCCD) was compared with manual measurement. Results: XCT–DVC resolved three-dimensional displacement discontinuities during crack initiation and propagation in all specimens. Hip-fracture donors exhibited significantly lower critical crack-opening ratios (CMOD/a)* than Controls (median 0.31 vs. 0.47; p = 0.008) and reached instability at lower applied loads. Total crack extension (Δa*) was similar between groups. Automated crack tracking using phase-congruency-based segmentation showed excellent agreement with manual measurements (r² = 0.98), supporting reliable extraction of crack geometry from DVC displacement fields. Conclusions: In this small pilot sample, XCT–DVC provided a feasible, geometry-normalised approach for comparing crack-opening behaviour where classical fracture-mechanics parameters cannot be applied. The close agreement between automated and manual crack measurements supports the reproducibility of the displacement-based measurement pipeline. The lower critical CMOD/a in hip-fracture specimens may indicate a more brittle comparative response. However, given the small sample, differing sex distribution, and lower bone volume fraction in the hip-fracture group, these findings are preliminary and require confirmation in larger cohorts. Establishing whether the observed difference reflects intrinsic tissue brittleness, architectural factors, or both is an important objective for future work in microstructure-matched cohorts. Full article

The ultimate goal of integrating artificial intelligence into education is to ensure the long-term stability, quality, and sustainability of the educational process, turning it into a tool that consistently improves teaching and learning. Yet its sustainable and responsible integration depends largely on a positive mindset and the pedagogical willingness of future teachers. This study examines the attitudes and readiness of pre-service teachers, specializing in preschool, primary, and subject-specific science education, toward AI integration, with a specific focus on sustainable science education and Green Lab concepts. A mixed-methods study was conducted on a sample of 251 students from the University of Split. Data were analyzed using exploratory factor analysis, standard and Welch ANOVA with Tukey’s HSD and Games–Howell post hoc tests, and multiple linear regression in IBM SPSS 20, and qualitative content analysis. The findings reveal perceived usefulness as a primary driver of AI acceptance across all groups. Science students demonstrated the highest levels of ethical and critical sensitivity but provided the lowest ratings for AI’s practical application in sustainable science education, expressing cautious attitudes and distinct concerns about system reliability. However, no significant difference was found between students with and without a science background in regard to AI’s potential to facilitate sustainable scientific concepts. Furthermore, behavioral analysis demonstrated that even initial, occasional exposure to AI tools significantly boosted students’ perceptions of its utility and sustainable application compared to non-users, whereas increasing the frequency of use resulted in no additional gains. The transition toward sustainable science education requires moving beyond technical literacy toward a comprehensive framework that integrates pedagogical usefulness with ethical responsibility and sustainable scientific application. Future studies should explore potential models that combine the methodological creativity of pre-service educators and teachers with the analytical rigor of science students. Ultimately, this research underscores that an educational policy must integrate digital advancements while strictly maintaining ethical standards and the essential role of human supervision. Full article

Tremella fuciformis extract (TFE) is used in dermocosmetic formulations due to its moisturising, antioxidant, and skin-supportive properties. The present study compared the effects of commercial TFE and hyaluronic acid (HA) on selected functions of human dermal fibroblasts (HDF). The cells were treated with TFE at concentrations of either 200 µg/mL or 500 µg/mL, or with HA at a concentration of 500 µg/mL. The following parameters were the focus of the study: cell viability, DNA and collagen biosynthesis, prolidase activity, scratch-wound closure, and immunofluorescence of selected signalling- and extracellular matrix-related markers. The findings of this study demonstrate that neither TFE nor HA had any effect on HDF viability. TFE led to a significant increase in DNA biosynthesis at both concentrations, while HA had no significant effect. The synthesis of collagen was found to be considerably elevated by both HA and TFE500, with no such effect observed in the presence of TFE200. Prolidase activity was observed to be highest in the HA group and also elevated in the TFE500 group; however, these results should be regarded as descriptive due to the nature of the pooled-sample data. Immunofluorescence analysis revealed increased phosphorylated protein kinase B (p-AKT) fluorescence in images treated with TFE, while phosphorylated mechanistic target of rapamycin (p-mTOR) remained close to the control level. Higher levels of β1-integrin, Insulin-Like Growth Factor-I Receptor (IGF-1R), prolidase, and phosphorylated Extracellular Signal-Regulated Kinases (p-ERK1/2) fluorescence were also observed in selected groups. The mean scratch-wound closure was found to be highest for TFE500. Overall, TFE was found to be associated with DNA biosynthesis, whereas HA and TFE500 were found to enhance collagen biosynthesis. Further studies are required to confirm biological reproducibility and the mechanism. Full article

This paper examines how Small and Medium-sized Enterprises (SMEs) in the construction industry approach Building Information Modeling (BIM) adoption in a context marked by slow uptake and market fragmentation, with reflections on the Portuguese context. BIM is internationally recognized as a technology that can improve project coordination, productivity, and information management, but its level and patterns of use vary widely. Based on a structured exploratory literature review and an interpretive scoring framework, this paper identifies challenges and enabling factors associated with BIM adoption in SMEs. The findings suggest that BIM adoption should not be understood as a simple technological change; rather, it can be interpreted as a complex socio-technical process shaped by interdependence among technical, financial, organizational, and human dimensions. This study conceptualizes fragmentation in the construction industry as a reinforcing condition that may amplify barriers and help explain why many implementation approaches have limited effectiveness. Given this context, SME progression may require incremental, context-adapted approaches based on collaboration, information management, and capacity-building initiatives. This article contributes an exploratory conceptual framework for understanding BIM adoption in economically constrained and difficult-to-transform environments. It provides a basis for future empirical studies and SME-oriented strategies, including potential applications within the Portuguese construction sector. Full article

Objective: This systematic review aimed to evaluate the effects of biomimetic surface engineering strategies applied to dental implants on osseointegration and peri-implant bone regeneration compared with conventional implant surfaces. Materials and Methods: A comprehensive literature search was conducted in the Web of Science, PubMed, and Scopus databases in accordance with the PRISMA guidelines, covering the period from January 2021 to January 2026. A total of 12 studies, including in vivo animal experiments and in vitro investigations, that met the inclusion criteria were analyzed. Risk of bias assessment was performed using the SYR-CLE tool and the ARRIVE guidelines. Results: Biomimetic strategies, including laser texturing, sulfonation, bioactive coatings, and growth factor/peptide functionalization (e.g., BMP-2, FGF-2, and PRF), significantly increased bone–implant contact (BIC), new bone volume (BV/TV), and biomechanical stability (pullout strength and reverse torque) compared to conventional surfaces. These surfaces enhance fixation under conditions of low bone density, such as osteoporosis, and improve infection resistance through antibacterial activity. In addition, these modifications enhance cellular adhesion, osteogenic differentiation, angiogenesis, and immune modulation. Conclusions: Current experimental evidence suggests that biomimetic implant surface engineering transforms dental implants from passive biomaterials into multifunctional bioactive interfaces capable of simultaneously regulating osteogenesis, immune response, angiogenesis, and antibacterial activity. Although promising outcomes have been demonstrated in preclinical studies, standardized long-term human clinical studies are still required to validate translational potential and long-term clinical efficacy. Full article

Background/Objectives: Artificial intelligence is increasingly applied to dental imaging, yet favorable internal performance does not necessarily indicate clinical transferability. This systematic review evaluated whether imaging-based dental artificial intelligence models have progressed beyond internal algorithmic development toward external validation, generalizability, reproducibility, privacy-preserving learning, and clinical implementation readiness. Methods: Searches were conducted in PubMed/MEDLINE, Scopus, and Embase up to May 2026. Eligible studies were primary empirical investigations based on human dental or oral imaging data that assessed at least one translational-validation dimension beyond internal development, including external testing, multicenter or multi-device validation, cross-dataset reproducibility, or privacy-preserving learning. Evidence was synthesized using a structured narrative synthesis reported according to the Synthesis Without Meta-analysis framework. Results: Fifteen studies published between 2023 and 2026 were included. They addressed caries detection, periodontal bone loss, gingival inflammation, root morphology, palatal radicular grooves, radiographic quality control, tooth-width estimation, and dental-structure segmentation. Translational-readiness domains included external validation, generalizability, reproducibility, privacy-preserving learning, transparency, and workflow relevance. Validation varied across cohorts, repositories, centers, devices, cross-dataset benchmarks, and federated-learning settings. Reproducibility, annotation harmonization, uncertainty reporting, explainability, workflow evaluation, and code or model availability were inconsistent. Quantitative pooling was not performed because tasks, modalities, units of analysis, reference standards, validation designs, and metrics were highly heterogeneous. Conclusions: Within this selected subset of externally tested studies, translational progress is emerging but remains uneven. Implementation readiness requires stronger reproducibility, clinically meaningful validation, workflow evaluation, and attention to regulatory, organizational, and human-factor barriers. Full article

Dynamic insulin secretion and glucose homeostasis are dependent on the appropriate maturation and function of pancreatic β-cells. The islet-enriched musculoaponeurotic fibrosarcoma oncogene family A (MAFA) transcription factor acts as a master regulator of β-cell identity and function, coordinating gene expression networks required for glucose-stimulated insulin secretion. Dysregulation of MAFA contributes to β-cell dysfunction, as reduced expression is detected early in the pathogenesis of Type 1 Diabetes and Type 2 Diabetes, while long-lived variants can drive monogenic forms of diabetes. In this review, we summarize the current understanding of MAFA on β-cell maturation and function in both the mouse and human. This includes structural features of the MAFA protein, regulation of MAFA transcription, post-translational modifications, and emerging areas of research for therapeutic potential. Full article

Background: Strengthening, coordinating, and sustaining rail freight services is one of the most pressing yet neglected challenges for transport planners in developing economies. Focusing on the institutional context of Thai freight transport, we assess, contextualise, and rank the collaboration factors shaping rail freight competitiveness. Methods: MICMAC analysis was selected because it does not require a pre-defined hierarchical structure and it is computationally straightforward for small variable sets. A qualitative study comprising semi-structured interviews was conducted with participants from government transport agencies, the State Railway of Thailand (SRT), private logistics operators, and freight customers. Results: The analysis extracted six key factors: government policy, finance, infrastructure readiness, technology and innovation, information sharing, and human resource capacity. Government policy alone occupies the driving quadrant, exerting the strongest systemic influence. Finance and infrastructure readiness emerge as linkage factors, simultaneously shaping and being shaped by the wider network, whereas technology, information sharing, and human resources sit in the dependent quadrant and respond to change. Conclusions: This classification allows us to derive seven collaboration strategies matched with appropriate policy levers; the three highest-ranked are policy collaboration, economic instruments, and multimodal integration. Full article