Razi University

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.

Consecutive drought is a major limitation for cultivation in arid and semiarid areas. Therefore, irrigation and fertilizing methods should be changed to increase grain yield and improve the efficiency of water and fertilizer use. A field experiment was carried out as a factorial in a semiarid area in 2020 and 2021. The factors included four levels of irrigation (wick (W) and Furrow 50 (S50), 70 (S70), and 100 (S100)) and two levels of nitrogen fertilizer (50 (F50) and 100 (F100)

This study evaluated the protective effects of coenzyme Q₁₀ (CoQ₁₀) in the cryopreservation medium against oxidative stress in canine sperm. Semen was collected from five adult dogs, and only ejaculates with motility > 70

With the rapid expansion of intelligent healthcare systems, the demand for accurate yet lightweight deep learning models capable of real-time electrocardiogram (ECG) analysis has grown substantially. While traditional deep models achieved high diagnostic accuracy, their high computational resource requirement hinder integration into clinical decision support and wearable health-monitoring systems. To overcome this challenge, this study introduces a Hierarchical Multi-Teacher Knowledge Distillation (HMT-KD) framework, where three deep neural networks with different structure and model size collaboratively try to enhance and train a lightweight student model. At first, transfer learning technique along with fine tuning is employed for two high-capacity teacher networks, ResNet-18 with SENet and MobileNetV2, to act as advanced feature extractors. Then, a Teacher Assistant (TA) network, as an effective intermediary model, is trained using the Knowledge Distillation (KD) method based on the responses by both teachers. Finaly the lightweight student, which has similar architecture to the TA is trained under the joint supervision of the two teachers and the TA through two parallel learning pathways: 1) response-based KD from both primary teachers and the TA, and 2) feature-based KD from the intermediate layers of the TA. Experimental results on PTB-XL and Chapman datasets show that the student model reduces parameter count by over 100 times compared to the ResNet-18 +SENet teacher while maintaining high diagnostic performance 85.45% accuracy and 96.53% AUROC on PTB-XL and 94.42% accuracy and 98.86% AUROC on Chapman. These findings demonstrate the proposed framework's effectiveness in creating lightweight, accurate, and real-time ECG-based diagnostic models.