Islamic Azad University, Abhar Branch

This paper presents a comprehensive review and comparative evaluation of Fault Current Limiter (FCL) technologies for enhancing the Low-Voltage Ride-Through (LVRT) capability of Wind Turbine Generation Systems (WTGSs). Among various hardware solutions, FCLs have emerged as a particularly efficient and cost-effective approach to limit high fault currents and ensure grid code compliance. While the application of individual FCL types has been explored in the literature, a comparative analysis encompassing their technologies, inherent trade-offs, and performance insights remains lacking. To address this gap, this work examines the application of key FCL categories, including superconducting FCLs (SFCLs), resonance-type FCLs (RFCLs), and solid-state FCLs (SSFCLs), in both fixed-speed and variable-speed WTGS configurations. The paper synthesizes technological principles, assesses practical trade-offs (e.g., cost, response speed, scalability), and discusses critical insights derived from simulation-based comparisons. The consolidated findings aim to guide the selection, design, and future development of FCL solutions to enhance robust LVRT and provide reliable protection for modern wind power systems.

Objective: The purpose of this study was to examine the efficacy of compassion-focused therapy in addressing psychological coherence, self-destructive behavior, and fatigue among drug users. Methods and Materials: The present study was semi-randomized in terms of its purpose, application, and research method, with a pre-test, post-test, and follow-up design with a control group. Among the addicts referred to the Paris addiction treatment center in Tehran in 2021, 30 people which was included in the study were selected and replaced randomly (by lottery) in the experimental group (15 people) and the control group (15 people). The experimental group received compassion-focused therapy during eight sessions, and the control group did not receive therapy. To collect data, the short form of Antonoski's psychological coherence scale (SOC-13), the self-destruction questionnaire of Sanson et al. (SHI), and the fatigue questionnaire of Smets et al. (MFI) were used. Findings: The results of the research showed that between the scores of psychological coherence, self-destruction, and fatigue in the treatment group focused on compassion and the control group in the post-test, there was a significant difference in follow-up (p < 0.05). Therefore, is effective in increasing psychological coherence and reducing self-destruction and fatigue in drug users. Conclusion: As mentioned, the results indicate that compassion -focused therapy is effective for enhancing psychological cohesion, reducing self-destructive tendencies, and addressing fatigue among substance abusers. Additionally, based on the effect size analysis, it was determined that this treatment has a particularly significant impact on psychological cohesion and self-destructive behavior. Therefore, its utilization is strongly recommended for clinical psychologists and psychotherapists aiming to enhance psychological coherence and decrease self-destructive tendencies in individuals with substance use issues.

This paper deals with the free vibration and low-velocity impact response analyses of a cylindrical sandwich panel. The core of the panel is a functionally graded (FG) porous material which is integrated with nanocomposite face sheets. The nanocomposite face sheets are fabricated from a polymeric matrix enriched with nanofillers such as graphene oxide powders (GOPs), graphene nanoplatelets (GNPs), or carbon nanotubes (CNTs). The sandwich panel is modeled based on Murakami’s zig-zag theory, and the mechanical properties of the face sheets are determined utilizing the rule of mixture and the Halpin-Tsai model. Hamilton’s principle is utilized to derive the set of governing equations and boundary conditions, and an analytical solution is presented using the Navier method. The effects of several parameters on the natural frequencies and low-velocity impact response characteristics of the panel are investigated.

Image-to-image (I2I) translation networks have emerged as promising tools for generating synthetic medical images; however, their clinical reliability and ability to preserve diagnostically relevant features remain underexplored. This study evaluates the performance of state-of-the-art 2D/3D I2I networks for converting ultrasound (US) images to synthetic MRI in prostate cancer (PCa) imaging. The novelty lies in combining radiomics, expert clinical evaluation, and classification performance to comprehensively benchmark these models for potential integration into real-world diagnostic workflows. A dataset of 794 PCa patients was analyzed using ten leading I2I networks to synthesize MRI from US input. Radiomics feature (RF) analysis was performed using Spearman correlation to assess whether high-performing networks (SSIM > 0.85) preserved quantitative imaging biomarkers. A qualitative evaluation by seven experienced physicians assessed the anatomical realism, presence of artifacts, and diagnostic interpretability of synthetic images. Additionally, classification tasks using synthetic images were conducted using two machine learning and one deep learning model to assess the practical diagnostic benefit. Among all networks, 2D-Pix2Pix achieved the highest SSIM (0.855 ± 0.032). RF analysis showed that 76 out of 186 features were preserved post-translation, while the remainder were degraded or lost. Qualitative feedback revealed consistent issues with low-level feature preservation and artifact generation, particularly in lesion-rich regions. These evaluations were conducted to assess whether synthetic MRI retained clinically relevant patterns, supported expert interpretation, and improved diagnostic accuracy. Importantly, classification performance using synthetic MRI significantly exceeded that of US-based input, achieving average accuracy and AUC of ~ 0.93 ± 0.05. Although 2D-Pix2Pix showed the best overall performance in similarity and partial RF preservation, improvements are still required in lesion-level fidelity and artifact suppression. The combination of radiomics, qualitative, and classification analyses offered a holistic view of the current strengths and limitations of I2I models, supporting their potential in clinical applications pending further refinement and validation.

Background: In addition to the physical effects caused by coronavirus disease 2019 (COVID-19), the disease is also associated with various psychological problems that have required research and clinical attention. Objectives: The purpose of this study was to compare the effectiveness of in-person and telehealth cognitive-behavioral therapy (CBT) in people who have recovered from COVID-19. Methods: This study was a randomized controlled trial with a pre-post design, a control group, and a three-month follow-up. The statistical population consisted of patients who had recovered from COVID-19 in Tehran between 2022 and 2023. From this population, 45 patients were selected using a convenience sampling method and randomly divided into three groups: An in-person CBT group, a telehealth CBT group (15 patients in each), and a control group (15 patients). The in-person CBT group received 11 sessions, while the telehealth CBT group received 12 sessions. Research instruments included the Depression, Anxiety, and Stress Scale (DASS-21) and the Resilience Scale (RS). The data were analyzed using repeated measures analysis of variance. Results: The results showed that although both treatments led to improvements in psychological distress (P < 0.01) and resilience (P < 0.01), the effectiveness of in-person CBT was greater and statistically significant. The effect sizes for depression, anxiety, stress, and resilience were 0.37, 0.38, 0.44, and 0.64, respectively. Conclusions: The results indicate that both in-person and virtual CBT lead to improvements in psychological distress and resilience in patients with a history of COVID-19, compared to the control group. However, the improvement rate was higher for in-person CBT compared to the virtual group. In-person CBT led to significant improvements in both psychological distress and resilience.

Multiple Sclerosis (MS) is one of the debilitating disorders of the central nervous system. This disease causes lesions in the white matter of the brain tissue. It can also lead to many physical and psychological disorders in movement, vision, and memory. Lesion segmentation in MRI images to determine the number and size of lesions is one of the diagnostic problems for specialists. Using automated diagnostic tools as an aid can help professionals. Traditional image processing and deep learning methods are used to automate lesion segmentation. The U-Net is one of the most widely used deep learning architectures for MS lesion segmentation. The images are used in the Fourier domain in the U-Net network, which does not include all its features. Our proposed method combines the HAR wavelet transform and the Dense net-based U-Net. This makes local features and lesions of different sizes more prominent and leads to higher quality segmentation. The proposed method had a better Dice value than the compared methods in the experiments.