Search Results (1,650)

In this study, we describe a non-invasive approach to objectively assess fragrance-induced emotions using multiplex salivary biomarker profiling. Traditional self-reports, physiological monitoring, and neuroimaging remain limited by subjectivity, invasiveness, or poor temporal resolution. Saliva offers an advantageous alternative, reflecting rapid neuroendocrine changes linked to emotional states. We combined four key salivary biomarkers, cortisol, alpha-amylase, dehydroepiandrosterone, and oxytocin, to capture multidimensional emotional responses. Two clinical studies (n = 30, n = 63) and one user study (n = 80) exposed volunteers to six fragrances, with saliva collected before and 5 and 20 min after olfactory stimulation. Subjective emotional ratings were also obtained through questionnaires or an implicit approach. Rigorous analytical validation accounted for circadian variation and sample stability. Biomarker patterns revealed fragrance-induced emotional profiles, highlighting subgroups of participants whose biomarker dynamics correlated with particular emotional states. Increased oxytocin and decreased cortisol levels aligned with happiness and relaxation; in comparison, distinct biomarker combinations were associated with confidence or dynamism. Classification and Regression Trees (CART) analysis results demonstrated high sensitivity for detecting these profiles. Validation in an independent cohort using an implicit association test confirmed concordance between molecular profiles and behavioral measures, underscoring the robustness of this method. Our findings establish salivary biomarker profiling as an objective tool for decoding real-time emotional responses. Beyond advancing affective neuroscience, this approach holds translational potential in personalized fragrance design, sensory marketing, and therapeutic applications for stress-related disorders. Full article

Background/Objectives: Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) can objectively capture the spatiotemporal dynamics of brain activity during affective cognition, and their combination is promising for improving emotion recognition. However, multi-modal cross-subject emotion recognition remains challenging due to heterogeneous signal characteristics that hinder effective fusion and substantial inter-subject variability that degrades generalization to unseen subjects. Methods: To address these issues, this paper proposes DC-AGIN, a dual-contrastive learning attention graph isomorphism network for EEG–fNIRS emotion recognition. DC-AGIN employs an attention-weighted AGIN encoder to adaptively emphasize informative brain-region topology while suppressing redundant connectivity noise. For cross-modal fusion, a cross-modal contrastive learning module projects EEG and fNIRS representations into a shared latent semantic space, promoting semantic alignment and complementarity across modalities. Results: To further enhance cross-subject generalization, a supervised contrastive learning mechanism is introduced to explicitly mitigate subject-specific identity information and encourage subject-invariant affective representations. Experiments on a self-collected dataset are conducted under both subject-dependent five-fold cross-validation and subject-independent leave-one-subject-out (LOSO) protocols. The proposed method achieves 96.98% accuracy in four-class classification in the subject-dependent setting and 62.56% under LOSO. Compared with existing models, DC-AGIN achieves SOTA performance. Conclusions: These results demonstrate that the work on attention aggregation, cross-modal and cross-subject contrastive learning enables more robust EEG-fNIRS emotion recognition, thus supporting the effectiveness of DC-AGIN in generalizable emotion representation learning. Full article

As artificial intelligence (AI) is increasingly used in classrooms, tutoring systems, and learning platforms, it is essential that these tools are not only powerful, but also easy to understand, fair, and supportive of real learning. Many current AI systems can generate fluent responses or accurate predictions, yet they often fail to clearly explain their decisions, reflect students’ cultural contexts, or give learners and educators meaningful control. This gap can reduce trust and limit the educational value of AI-supported learning. This paper introduces the PEARL framework, a human-centered approach for designing and evaluating explainable AI in education. PEARL is built around five core principles: Pedagogical Personalization (adapting support to learners’ levels and curriculum goals), Explainability and Engagement (providing clear, motivating explanations in everyday language), Attribution and Accountability (making AI decisions traceable and justifiable), Representation and Reflection (supporting fairness, diversity, and learner self-reflection), and Localized Learner Agency (giving learners control over how AI explains and supports them). Unlike many existing explainability approaches that focus mainly on technical performance, PEARL emphasizes how students, teachers, and administrators experience and make sense of AI decisions. The framework is demonstrated through simulated examples using an AI-based tutoring system, showing how PEARL can improve feedback clarity, support different stakeholder needs, reduce bias, and promote culturally relevant learning. The paper also introduces the PEARL Composite Score, a practical evaluation tool that helps assess how well educational AI systems align with ethical, pedagogical, and human-centered principles. This study includes a small exploratory mixed-methods user study (N = 17) evaluating example AI tutor interactions; no live classroom deployment was conducted. Together, these contributions offer a practical roadmap for building educational AI systems that are not only effective, but also trustworthy, inclusive, and genuinely supportive of human learning. Full article

Visible-Near-Infrared (Vis-NIR) spectroscopy, spanning approximately 400 to 2500 nm, is an innovative technology with growing relevance for diagnostics performed at the point of care (POC). This review explores the potential of Vis-NIR in veterinary medicine, highlighting its advantages over complex techniques like Raman and Fourier transform infrared spectroscopy (FTIR) by being rapid, non-invasive, reagent-free, and compatible with miniaturized, portable devices. The methodology involves directing a broadband light source, often using LEDs, toward the sample (e.g., blood, urine, faeces), collecting spectral information related to molecular vibrations, which is then analyzed using chemometric methods. Successful veterinary applications include hemogram analysis in dogs, cats, and Atlantic salmon, and quantifying blood in ovine faeces for parasite detection. Key limitations include spectral interference from strong absorbers like water and hemoglobin, and the limited penetration depth of light. However, combining Vis-NIR with Self-Learning Artificial Intelligence (SLAI) is shown to isolate and mitigate these multi-scale interferences. Vis-NIR spectroscopy serves as an important complement to centralized laboratory testing, holding significant potential to accelerate clinical decisions, minimize stress on animals during assessment, and improve diagnostic capabilities for both human and animal health, aligning with the One Health concept. Full article

Channel length modulation (CLM) in indium gallium zinc oxide (IGZO) thin film transistors (TFTs) reduces the output resistance (r_o) in the saturation regime. It also degrades current driving accuracy for active matrix organic light emitting diode (AMOLED) backplanes. For top gate, self-aligned devices with nominal channel lengths of 5–15 μm, transmission line method (TLM) analysis yields an effective channel length reduction (ΔL) of about 1.8 μm. This result is consistent with lateral hydrogen redistribution from the self-aligned source/drain (S/D) process. At L = 5 μm, the conventional TFT exhibits r_o = 13.5 ± 2.5 MΩ and an Early voltage (V_A) = 56.1 ± 10.4 V (n = 5). We propose a source connected bottom gate (SCBG) structure that electrostatically stabilizes the pinch-off region and suppresses CLM. The SCBG TFT increases r_o to 475 ± 52 MΩ and V_A to 1159 ± 173 V at L = 5 μm (n = 5), while maintaining normal transfer characteristics. Two-dimensional device simulations reproduce the trend and show that the drain-bias-induced pinch-off shift is reduced, with d(ΔL)/dV_DS decreasing from 0.027 to 0.012 μm/V (about 55%). These results indicate that the SCBG approach is effective for enhancing current saturation in short channel IGZO TFTs for high-resolution AMOLED applications. Full article

People living with and beyond cancer often face ongoing challenges related to nutrition, wellbeing, and long-term health. Many individuals express a need for evidence-based, tailored dietary support, yet practical approaches to sustaining healthy eating behaviours remain limited. Culinary nutrition interventions, which integrate nutrition education with hands-on culinary skills, may help address these needs; however, their effects have not been systematically synthesised. This systematic review evaluates the impact of culinary nutrition interventions, delivered alone or in combination with physical activity or mental health components, on dietary intake, psychosocial and health-related outcomes, anthropometric measures, clinical and metabolic markers, and feasibility among individuals living with or beyond cancer. Following PRISMA guidelines, 18 studies were identified across PubMed, Scopus, EMBASE, CINAHL, and Web of Science (last searched in April 2025) and narratively synthesised. A total of 1173 participants were included, with sample sizes ranging from 4 to 190 participants per intervention. Interventions were well received and rated as highly acceptable, with strong engagement and minimal adverse effects. Across studies, statistically significant improvements were reported in dietary intake (7/13 studies), quality of life (4/5), mental health (5/6), self-efficacy (2/3), symptom management (3/4), self-reported cognitive health (1/1), food-related behaviours (2/2), selected anthropometric measures (4/8), and selected metabolic biomarkers (4/6). The evidence suggests that culinary nutrition interventions hold promise as supportive, behaviour-focused strategies aligned with oncology nutrition guidelines and responsive to patient needs. However, due to heterogeneity across interventions and outcomes, and variability in methodological quality as assessed using the Cochrane risk of bias tool, quantification of effects was not possible, limiting interpretation of the evidence. Further high-quality studies using comparable outcome measures and longer-term follow-up are needed to quantify the magnitude of effects, assess their durability over time, and inform the integration of culinary nutrition programmes into cancer care. This systematic review is registered under the PROSPERO ID CRD42024567041 and was funded by the RCSI Research Summer School Fund. Full article

Background: Intimate partner violence (IPV) represents a major public health problem in Europe, with significant physical, psychological, and social consequences. Nurses are often the first professionals capable of detecting early signs of IPV, yet they lack validated instruments to assess their clinical competency in detection, evaluation, documentation, and intervention. This study aimed to develop and validate the Intimate Partner Violence Nursing Competency Scale (IPVNCS), aligned with the Nursing Intervention Classification (NIC 6403). Methods: A cross-sectional psychometric study was conducted among registered nurses in the Community of Madrid. A 30-item Likert-type self-administered instrument (1–5 scale) was developed based on NANDA, NIC 6403, and NOC frameworks. A total of 202 nurses participated. Reliability was assessed through Cronbach’s alpha. Construct validity was examined using exploratory factor analysis (EFA) with Promax rotation and confirmatory factor analysis (CFA) using AMOS 26. Ethical approval was obtained (CEU San Pablo, code 843/24/104). Results: After item refinement, 26 items remained across four dimensions: (1) Intervention and Referral, (2) Detection and Assessment, (3) Documentation and Recording-keeping, (4) Psychosocial Support. The instrument showed excellent reliability ($\alpha$ = 0.97). KMO was 0.947 and Bartlett’s test was significant (p < 0.001). CFA demonstrated satisfactory fit: ${\chi }^2$/df = 2.066, RMSEA = 0.073, CFI = 0.92, TLI = 0.91, NFI = 0.86. The final model adequately represented the latent structure. After debugging, its psychometric properties were significantly improved. Four redundant items were eliminated, achieving internal consistency ($\alpha$ = 0.97), a KMO value of 0.947 and a significant Bartlett’s test of sphericity. It showed a better fit, according to ${\chi }^2$/df = (2.066); Parsimony = (720.736); RMR (0.0529; RMSEA (0.073); NFI (0.860); TLI (0.910) and CFI (0.920). The final model provides an adequate representation of the latent structure of the data. This study provides initial evidence of construct validity and internal consistency reliability of the IPVNCS. Conclusions: The IPVNCS is a valid and reliable tool to assess nursing competencies for clinical management of IPV. It supports structured evaluation across four core nursing domains, enabling improved educational planning, clinical decision-making, and quality of care for victims. The scale fills a gap in clinical nursing assessment tools and can support protocol development in emergency, primary care, and hospital settings. Full article

As a globally significant food crop, the assessment of wheat quality is essential for ensuring food security and enhancing the processing quality of agricultural products. Conventional methods for assessing wheat kernel quality are often inefficient and markedly subjective, which hampers their ability to accurately distinguish the complex and diverse phenotypic characteristics of wheat kernels. To tackle the aforementioned issues, this study presents an enhanced recognition method for defective wheat kernels, based on the EfficientNet-B1 architecture. Building upon the original EfficientNet-B1 network structure, this approach incorporates the lightweight attention mechanism known as CBAM (Convolutional Block Attention Module) to augment the model’s capacity to discern features in critical regions. Simultaneously, it modifies the classification head structure to facilitate better alignment with the data, thereby enhancing accuracy. The experiment employs a self-constructed dataset comprising five categories of wheat kernels—perfect wheat kernels, insect-damaged wheat kernels, scab-damaged wheat kernels, moldy wheat kernels, and black germ wheat kernels—which are utilized for training and validation purposes. The results indicate that the enhanced model attains a classification accuracy of 99.80% on the test set, reflecting an increase of 2.6% compared to its performance prior to the enhancement. Furthermore, the Precision, Recall, and F1-score all demonstrated significant improvements. The proposed model achieves near-perfect performance on several categories under controlled experimental conditions, with particularly high precision and recall for scab-damaged and insect-damaged kernels. This study demonstrates the efficacy of the enhanced EfficientNet-B1 model in the recognition of defective wheat kernels and offers novel technical insights and methodological references for intelligent wheat quality assessment. Full article

Zhuangzi, a seminal figure in ancient Chinese philosophy, offers profound insights into emotional well-being through his Qi-emotion theory. This paper examines Zhuangzi’s approach to emotional well-being by exploring the interplay between Qi (vital energy), atmosphere, and emotions. By drawing comparative perspectives from Neo-Phenomenology’s concept of atmosphere and the Chinese classical concept of Qi-feeling, the study challenges traditional views that emotions are solely internal phenomena. Instead, it proposes that emotions are field-like, arising from dynamic interactions between individuals and their environments. Through an in-depth analysis of Zhuangzi’s philosophy, particularly his methods of self-cultivation such as “fasting the mind” (xin zhai 心齋) and non-action (wu wei 無爲), this paper illustrates how aligning oneself with the Dao (the Way 道) and harmonizing Qi can lead to emotional balance and spiritual freedom. The study integrates Eastern and Western philosophical traditions, highlighting the significance of enlightened mind, embodiment, and atmospheric resonance in achieving emotional well-being. The findings suggest that Zhuangzi’s Qi-emotion theory provides valuable insights for contemporary philosophical practice and therapy by emphasizing the unity of mind, body, and environment. By fostering harmony with the natural world and transcending personal attachments, individuals can attain a state of inner peace and holistic well-being. Full article

Tomato leaf diseases cause substantial yield and quality losses worldwide, yet reliable detection in real fields remains challenging. Two practical bottlenecks dominate current research: (i) limited data, including small samples for rare diseases, class imbalance, and noisy field images, and (ii) multimodal heterogeneity, where RGB images, textual symptom descriptions, spectral cues, and optional molecular assays provide complementary but hard-to-align evidence. This review summarizes recent advances in tomato leaf disease detection under these constraints. We first formalize the problem settings of limited and multimodal data and analyze their impacts on model generalization. We then survey representative solutions for limited data (transfer learning, data augmentation, few-/zero-shot learning, self-supervised learning, and knowledge distillation) and multimodal fusion (feature-, decision-, and hybrid-level strategies, with attention-based alignment). Typical model–dataset pairs are compared, with emphasis on cross-domain robustness and deployment cost. Finally, we outline open challenges—including weak generalization in complex field environments, limited interpretability of multimodal models, and the absence of unified multimodal benchmarks—and discuss future opportunities toward lightweight, edge-ready, and scalable multimodal systems for precision agriculture. Full article

This study examines the systemic integration of entrepreneurship education and the culture of self employment within higher education as a component of sustainable institutional reform. Using a comparative analytical approach, it analyzes international practices across five higher education systems. Finland, the United States, Canada, the United Kingdom, and South Korea were selected to represent diverse yet mature models of entrepreneurship education integration. The findings reveal significant variation in the depth and coherence of integration across national contexts. Rather than identifying a single transferable model, the study shows that effective integration depends on the interaction of key institutional dimensions, including policy alignment, curricular embedding, faculty capacity, infrastructure, external partnerships, and impact evaluation. Finland demonstrates the most coherent configuration, while other systems exhibit partial or fragmented integration shaped by contextual factors. The study concludes that entrepreneurship education is most sustainable when embedded as a system-level institutional strategy rather than implemented through isolated initiatives. It offers an analytical framework, supported by an adapted ADKAR change model, to guide context-sensitive reform. For Arab higher education systems, the primary implication is diagnostic, emphasizing contextual adaptation over direct replication. Full article

Curling presents a challenging continuous-control problem in which shot outcomes depend on long-horizon interactions between complex physical dynamics, strategic intent, and opponent responses. Despite recent progress in applying reinforcement learning (RL) to games and sports, curling lacks a unified environment that jointly supports stable, rule-consistent simulation, structured state abstraction, and scalable agent training. To address this gap, we introduce a comprehensive learning framework for curling AI, consisting of a full-sized simulation environment, a task-aligned Markov decision process (MDP) formulation, and a two-phase training strategy designed for stable long-horizon optimization. First, we propose a novel MDP formulation that incorporates stone configuration, game context, and dynamic scoring factors, enabling an RL agent to reason simultaneously about physical feasibility and strategic desirability. Second, we present a two-phase curriculum learning procedure that significantly improves sample efficiency: Phase 1 trains the agent to master delivery mechanics by rewarding accurate placement around the tee line, while Phase 2 transitions to strategic learning with score-based rewards that encourage offensive and defensive planning. This staged training stabilizes policy learning and reduces the difficulty of direct exploration in the full curling action space. We integrate this MDP and training procedure into a unified Curling RL Framework, built upon a custom simulator designed for stability, reproducibility, and efficient RL training and a self-play mechanism tailored for strategic decision-making. Agent policies are optimized using Soft Actor–Critic (SAC), an entropy-regularized off-policy algorithm designed for continuous control. As a case study, we compare the learned agent’s shot patterns with elite match records from the men’s division of the Le Gruyère AOP European Curling Championships 2023, using 6512 extracted shot images. Experimental results demonstrate that the proposed framework learns diverse, human-like curling shots and outperforms ablated variants across both learning curves and head-to-head evaluations. Beyond curling, our framework provides a principled template for developing RL agents in physics-driven, strategy-intensive sports environments. Full article

This study diagnoses the level and profile of university lecturers’ digital competence at a Chilean higher education institution, drawing on the DigCompEdu and OpenEdu frameworks. A non-experimental correlational design was used, based on a self-perception questionnaire adapted from the DigCompEdu Check-In tool and administered to 569 lecturers through the Qualtrics platform. The instrument underwent external expert validation and demonstrated excellent internal consistency (Cronbach’s α = 0.96). Results indicate that 44% of lecturers position themselves at the “Integrator” level, 22% at the “Explorer” level, and 19% at the “Expert” level, with three clearly differentiated competence profiles. These findings informed the development of a structured training programme centred on three components: the pedagogical use of digital technologies, the incorporation of open educational practices aligned with OpenEdu, and the strengthening of students’ digital competence. The programme includes modular workshops, mentoring led by high-competence lecturers, and the creation of open educational resources. Overall, the study provides empirical evidence to guide institutional policies and to foster a reflective, ethical, and pedagogically grounded integration of digital technologies in university teaching. Full article

In this work, we report the first systematic examination of how the In composition in the intrinsic In_xGa_1-xAs layer and the p-type doping concentration in the p-type GaAsSb layer affect the device performance of side-gate p-GaAs_0.5Sb_0.5/i-In_xGa_1-xAs/n-In_0.53Ga_0.47As TFETs, using the technology computer-aided design (TCAD) simulations with a non-local band-to-band model. By tuning these two parameters, a moderate staggered alignment is achieved, enabling self-off operation at zero gate bias while maintaining high on-current. This tunability is an intrinsic and significant advantage of the p-GaAsSb/i-In_xGa_1-xAs heterojunction that has not been previously explored. It is found that the best device performance does not occur in the TFET with an In composition of 0.53 in the intrinsic layer, which is lattice-matched to the InP substrate, but rather occurs in the device with a higher In composition of around 0.59 in the InGaAs layer, which has been verified by experimental data to some extent. Optimal parameter combinations yield a minimum subthreshold swing of 13.51 mV/dec and an ON-state current of 35.39 μA/μm at V_DS = V_GS = 0.5 V due to the enhanced tunneling capability. Full article

Background: Upper limb loss has a profound impact on individuals’ daily activities, self-image, and social interactions. Despite continuous technological advances in upper-limb prosthetics, high rates of device abandonment persist, highlighting the need to better understand users’ functional and psychosocial needs. Methods: To gain a deeper understanding of the perspectives of upper limb amputees and the synthesis of their needs across ergonomic, functional, and psychological dimensions, this study was conducted. A systematic review was conducted following PRISMA guidelines to synthesize user-reported evidence on upper-limb prosthesis use. Articles indexed in the Web of Science database between 2016 and December 2023 were screened using predefined search terms related to upper-limb amputation, prostheses, social impact, and user needs. Studies were included if they reported direct perspectives of upper-limb prosthesis users regarding usability, functionality, and lived experience. Results: Out of 239 papers identified, 31 were included and analyzed. The findings reveal that functional performance, comfort, weight, intuitive control, and reliability are strongly interconnected with psychosocial factors such as confidence, embodiment, social participation, and acceptance. Technological advances have not consistently translated into improved alignment between prosthetic solutions and user needs, which is reflected in continued dissatisfaction and abandonment. Conclusions: This review provides a structured synthesis of user-reported needs across functional, ergonomic, and psychosocial dimensions, translating these insights into design-relevant guidelines. Emphasizing a user-centered and interdisciplinary perspective, the findings aim to support the development of upper-limb prosthetic devices that are more usable, acceptable, and aligned with users’ expectations, ultimately bridging the gap between user expectations and technological capabilities and promoting long-term adoption and quality of life. Full article