Razi University

Alzheimer's disease (AD) is a progressive neurodegenerative condition necessitating early and precise diagnosis to provide prompt clinical management. Given the paramount importance of early diagnosis, recent studies have increasingly focused on computer-aided diagnostic models to enhance precision and reliability. However, most graph-based approaches still rely on fixed structural designs, which restrict their flexibility and limit generalization across heterogeneous patient data. To overcome these limitations, the Meta-Relational Copula-Based Graph Attention Network (MRC-GAT) is proposed as an efficient multimodal model for AD classification tasks. The proposed architecture, copula-based similarity alignment, relational attention, and node fusion are integrated as the core components of metric-based episodic meta-learning, such that the multimodal features, including risk factors (RF), cognitive test scores, and MRI attributes, are first aligned via a copula-based transformation in a common statistical space and then combined by a multi-relational attention mechanism. According to evaluations performed on the TADPOLE and NACC datasets, the MRC-GAT model achieved accuracies of 96.87% and 92.31%, respectively, demonstrating state-of-the-art performance compared to existing diagnostic models. Finally, the proposed model confirms the robustness and applicability of the proposed method by providing interpretability at various stages of disease diagnosis.

Automated ECG classification is increasingly important for wearable and Internet of Medical Things (IoMT) applications, where deep learning models must provide accurate predictions under strict computational and memory constraints. Knowledge distillation can improve model efficiency, but conventional approaches typically rely on separately pre-trained teacher networks. To address these limitations, this study proposes End-to-End Internal Augmentation and Curriculum-based Knowledge Distillation (EIA-CKD), a lightweight end-to-end framework that generates and distills supervisory knowledge within a single network. During training, multiple views are generated internally from each ECG image and processed through a shared backbone along a frozen-consensus path, where gradient tracking is disabled. The resulting logits are aggregated by an attention-based consensus module to form a stable consensus representation that serves as an internal source of supervisory knowledge. A curriculum-based training strategy progressively increases the contribution of the internal consensus, allowing the model to gradually transition from ground-truth supervision to internally generated knowledge during training. The proposed framework relies on a lightweight CNN backbone that combines depthwise separable convolutions(DSC) with convolutional block attention modules (CBAM) to reduce computational overhead while preserving representational power. Experimental evaluation on the PTB-XL and Chapman ECG datasets confirms the effectiveness of the proposed EIA-CKD approach. With only 102 K trainable parameters, the proposed framework achieves AUROC scores of 95.60% and 99.16% and classification accuracies of 84.67% and 94.23% on PTB-XL and Chapman, respectively. These results show that EIA-CKD provides competitive classification performance with a substantially lower computational footprint, supporting its potential for real-time ECG analysis in wearable and resource-constrained IoMT environments.

The delayed evolution of proton transfer (PT) as the sluggish component of proton-coupled electron transfer (PCET) represents a critical limitation in the design of biomimetic artificial leaves. Here, we report a simple metal-free PCET catalyst for the oxygen evolution reaction (OER) operating in both neutral and alkaline media, realized through successive evolutionary stages of a bilayer covalent superstructure. Graphite/graphene oxide hosts a repetitive porphyrin-based covalent organic structure (COS) assembled via bidentate ethylenediamine (EDA) linkages, functioning as a PCET catalyst in both alkaline and neutral environments. The experimentally detected unusual Gerischer impedance signature demonstrates the overwhelming dominance of interfacial proton exchange over natural diffusion, revealing a distinct chemical electrochemical chemical reaction pathway in OER. The H/D isotope and proton inventory studies provide direct and compelling mechanistic insight. The results show a secondary isotope effect, confirming that O–H bond cleavage is not involved in the rate-determining step of the water oxidation reaction. For the first time, this study elucidates how a robust interfacial covalent organic structure (COS), acting as an in situ heterogeneous stimulus, actively reorganizes the hydrogen-bonding network of water. Reflective infrared spectroscopy unequivocally reveals the role of COS in disrupting and agitating interfacial hydrogen-bonded water clusters, liberating free water molecules and thereby promoting adsorption events during OER.

Purpose - The purpose of this paper is to present an experimental study aimed at improving the performance of seismically designed reinforced concrete (RC) beam-column joints (BCJs) with partial failure. Design/methodology/approach - Various retrofitting methods, including carbon fiber reinforced polymer (CFRP) sheets and steel plates (SPs), were tested. Six specimens were subjected to constant vertical and increasing lateral monotonic loads until failure. Five of the specimens failed partially, reaching up to 85% of the ultimate load of the control specimen. The damaged specimens were repaired with epoxy resin and retrofitted using different techniques: SP, CFRP sheets and CFRP sheets with strips. Retrofitted specimens were tested using the same loading protocol. Findings - The results of this study showed that CFRP retrofitting improved peak load, stiffness and energy absorption by up to 25.7%, 46.3% and 47.8%, respectively, compared to the control specimen. SP improved these metrics by up to 58.0%, 44.8% and 71.4%, respectively. SP showed significantly greater improvements in peak monotonic load, stiffness and energy absorption compared to CFRP, indicating superior performance in resisting monotonic loads and absorbing energy. Originality/value - These retrofitting methods significantly enhance joint strength, seismic resistance and energy absorption. Additionally, the experimental results were validated using a highly accurate numerical model.

PurposeThis study examined how educational setting is associated with Iranian English as a Foreign Language (EFL) teachers' emotional intelligence (EI), considering factors that facilitate or hinder its development.Design/methodology/approachEmploying a sequential explanatory mixed-methods design, data from 330 questionnaires were entered into Statistical Package for the Social Sciences 21 and analyzed using independent samples t-tests to compare EI levels between teachers in public schools and those in private institutes. Follow-up interviews were held with participants scoring near the median, and their responses were analyzed thematically in NVivo 12.FindingsResults indicated that teachers in private institutes reported higher scores on all four EI subscales of sociability, self-control, well-being and emotionality. Qualitative data suggested that differences in EI may be associated with policy-, stakeholder- and classroom/curriculum-related factors.Originality/valueThis study frames EI as a context-dependent competency shaped by the work environment rather than an innate characteristic. The study concludes by outlining key practical implications and suggesting promising avenues for future research. These findings suggest that when schools provide supportive leadership and a positive environment, teachers' EI can flourish, benefiting both their professional growth and their students' learning experiences.