Islamic Azad University of Kashan

Trust is recognized as a vital component in ensuring secure and stable interactions within Social Internet of Things (SIoT) networks. This study introduces an advanced model for simulating multidimensional trust relationships in SIoT, which is designed based on Watts–Strogatz (WS) random graphs and successfully reproduces the real topological features of SIoT networks with high fidelity. The proposed model incorporates a variety of relationship types such as Co-Location-Based Relationships (CLOR), Ownership-Based Relationships (OOR), Social Relationships (SOR), and Popularity-Based Relationships (POR), and integrates key attributes including spatial density, interaction frequency, owner reliability, and co-presence time to deliver a flexible and scalable structure. Analysis of the results indicates that the model performs significantly well in replicating topological metrics such as average path length, clustering coefficient, and average degree. For instance, in OOR and SOR graphs, the clustering coefficients reached values of 0.9 and 0.7 respectively, and in the CLOR graph, the average path length was limited to 2.4. Furthermore, in the POR graph, the average degree was consistently maintained at a stable value of 120. A comparison between the proposed model and traditional models such as Erdős–Rényi (ER) and Barabási–Albert (BA) graphs reveals that the use of advanced random graphs alongside the integration of additional trust-related features significantly enhances the accuracy, flexibility, and analytical capability of SIoT network behavior. In addition, the application of gradient descent-based optimization algorithms for fine-tuning model parameters ensures the efficiency and structural balance of the model, thereby positioning it as an effective and scalable solution for the analysis and development of Social Internet of Things networks.

BackgroundDespite the critical role of health literacy in emergency management, no specific questionnaire exists to measure first aid health literacy in the older population. To address this gap, the present study aimed to develop and psychometrically evaluate a first aid health literacy assessment questionnaire for older adults in 2024–2025.MethodsThis methodological study focused on questionnaire development and psychometric evaluation based on the framework proposed by Boateng et al. Initial questionnaire items were generated through an extensive literature review. Content validity and face validity were assessed using quantitative and qualitative methods. A multi-stage random sampling method was employed to recruit 400 eligible older adults who were covered by comprehensive health centers affiliated with Kashan University of Medical Sciences. All participants completed a background information questionnaire, the Persian version of the Abbreviated Mental Test (AMT), and the Health Literacy Instrument for Adults-Short Form (HELIA-SF). Construct validity was evaluated using Principal Component Analysis (PCA) and convergent validity. Internal consistency was assessed using the Kuder-Richardson Formula 21 (KR-21), test-retest reliability was measured via the intra-class correlation coefficient (ICC), and stability was determined using the standard error of measurement (SEM) and the minimal detectable change (MDC). Ceiling and floor effects were also examined. Data analysis was performed using JASP version 0.19.3.ResultsThe content validity ratio (CVR) and content validity index (CVI) for all items exceeded 0.8 and 0.9, respectively, and the modified kappa coefficient was greater than 0.89. Item impacts were all above 4.5. PCA extracted three components, collectively accounting for 58.2% of the variance in the total score. Results revealed a significant positive correlation between the first aid health literacy assessment questionnaire for older adults and the HELIA-SF (p < 0.001). Reliability indices were as follows: KR-21 = 0.704, ICC = 0.955, SEM = 0.707, and MDC = 2.331. No ceiling or floor effects were observed.ConclusionThe first aid health literacy assessment questionnaire for older adults demonstrates good validity and reliability. This questionnaire can effectively measure first aid health literacy levels in the older population and can design targeted educational interventions to improve their health outcomes.Clinical trial numberNot applicable.

Background: Nomophobia, the fear of being without a mobile phone, is increasingly prevalent among nursing students and may impact their clinical performance. Understanding this relationship is essential for developing strategies to enhance focus and professional competence in clinical settings. This study aimed to address this research gap by investigating the relationship between nomophobia and clinical performance among nursing students. Objectives: This study aimed to address this research gap by investigating the relationship between nomophobia and clinical performance among nursing students. Methods: A cross-sectional study was conducted from September 2024 to January 2025 to examine the relationship between nomophobia and clinical performance among nursing students at Islamic Azad University of Kashan and Kashan University of Medical Sciences. Using a convenience sampling method, 200 undergraduate nursing students aged 19 - 27 years were recruited. Data were collected through self-administered pencil-and-paper questionnaires, including the Nomophobia Questionnaire (NMP-Q) to assess nomophobia levels and the Clinical Performance Questionnaire to evaluate self-efficacy in clinical performance. Statistical analyses were conducted using SPSS version 26, employing a general linear model (GLM) to assess the impact of nomophobia on clinical performance while controlling for demographic variables. Results: Among the participants, 62.5% were female, and 70.5% were aged 19 - 21 years. Moderate nomophobia was the most prevalent (56%), followed by mild (29%) and severe nomophobia (4%). The GLM analysis revealed that nursing students with mild nomophobia had significantly higher clinical performance scores (B = 34.24, P = 0.002) compared to those with severe nomophobia. Additionally, students who checked their smartphones 10 - 29 times per day had significantly higher clinical performance scores (B = 22.19, P = 0.01) than those who checked their phones more than 100 times daily. Gender (B = -10.09, P = 0.01) and smartphone ownership duration (B = -14.20, P = 0.02) also demonstrated significant associations with clinical performance. Conclusions: This study found that higher levels of nomophobia were associated with lower clinical performance in nursing students. Moderate smartphone use appeared to correlate with better clinical outcomes, while excessive checking was linked to reduced performance. Future interventions should focus on managing smartphone use to enhance nursing students' clinical efficiency.

This study examines the hierarchical structure of risks influencing the financial distress of banks using the Interpretive Structural Modeling (ISM) method. Financial distress, regarded as the stage preceding bankruptcy, arises from the complex interaction of internal and external factors affecting banks and has far-reaching implications for financial stability and economic growth. In this regard, key risk components—including liquidity risk, credit risk, market risk, and operational risk—were first identified, and their interrelationships were analyzed using ISM. The research findings revealed that the ratio of net profit to total bank assets and the ratio of net working capital to total bank assets, with impact coefficients of 99% and 94%, respectively, play the most significant roles in reducing the financial distress risk of listed banks. Furthermore, market risk, the market-to-book value ratio, and the total debt-to-total assets ratio, each with an impact coefficient of 91%, showed a similar effect in mitigating this risk. Other variables, such as operational risk, interest rate risk, net profit to equity, and total asset size, also demonstrated approximately 80% influence and ranked in subsequent positions. The power–dependence analysis showed that liquidity risk, with 100% driving power and 17% dependence, is positioned in the independent zone, whereas credit risk and operational risk, characterized by high driving power and medium dependence, are situated in the linkage zone. Variables such as net profit, working capital, and market risk, with 100% dependence but low driving power, are placed in the dependent zone.

In recent years, corporate social performance has attracted significant attention from academics and business professionals. Corporate social performance has been recognized as a new strategic choice for enhancing corporate image, gaining competitive advantage, and ultimately increasing firm value. The present study aimed to examine the moderating role of corporate governance in the relationship between social performance and financial efficiency of companies. This study is classified as applied research in terms of purpose and as a descriptive study in terms of methodology. Among descriptive studies, it falls within the category of correlation research. The statistical population of this study consisted of all companies listed on the Tehran Stock Exchange from 2019 to 2022, with a sample size of 130 firms selected using the systematic elimination method. The research hypotheses were tested using multiple linear regression with a panel data model. The results indicated that social performance has a positive and significant impact on financial efficiency, and the internal and organizational mechanisms of corporate governance strengthen the positive relationship between social performance and financial efficiency. Based on these findings, it is recommended that legislators, policymakers, and standard-setters in accounting consider incorporating sustainability reporting formats into new reporting standards in Iran. Additionally, regulatory bodies, policymakers, and the Securities and Exchange Commission should adopt appropriate measures and legal requirements to mandate corporate disclosure of sustainability performance information.

Corporate integrity is considered as part of the company's development strategies, which in the long run can lead to the increased firms' financial transparency to stakeholders. The purpose of our study is to present a corporate integrity model and, then to investigate its effect on firms' information asymmetry. In this study, to measure the corporate integrity, we use Meta-synthesis and Delphi analysis in the qualitative part. Then, in the quantitative section, the corporate integrity questionnaires were sent to the managers of the sample firms. Subsequently, a total of 138 questionnaires were completed and sent back, which were used as the final samples for analysis. In addition, information asymmetry is measured using the three different proxies, namely bid-ask spread, turnover, and Amihud illiquidity measure. Our findings show a significant and negative effect of corporate integrity on information asymmetry. This results suggest that corporate integrity, by promoting behavioral values based on truthfulness and commitment, the structures will enhance the corporate governance mechanisms and, thereby, firstly motivate the managers to reduce the agency gap and, secondly, implement a more effective level of the supervisions on the firm's performances in front of the stakeholders by accelerating the circulation of information and giving timely and reliable feedback to the stakeholders. This is the first study that presents a corporate integrity model through qualitative analysis and then, investigates the effect of corporate integrity on firms' information asymmetry. Therefore, our study can contribute to the extant literature of this context.

Abstract This paper presents the generation and transmission expansion planning (GTEP) in electricity and gas networks by considering their resilience against floods and earthquakes. These networks supply electricity, heat, and gas consumption energies as a multi‐carrier microgrid. The scheme is expressed in the form of bi‐level optimization, the upper level of which is the minimization of generation and transmission planning cost (total investment cost and expected operating cost) in the mentioned networks constrained to the investment budget and the planning model of the mentioned elements. Lower‐level formulation minimizes the total expected annual operating cost of these networks and the expected outage cost of electricity, heat, and gas consumers in the event of floods and earthquakes. This formulation is bound by the power flow equations of electricity and gas networks, the operation and resilience constraints of the networks, and the limitation on generation capability. In this problem, the expected energy not‐supplied and the outage cost during natural disasters are considered resilience indicators. Next, a single‐level model for the proposed design is extracted from the Karush–Kuhn–Tucker (KKT) method. The basic requirement of this method is the convexity of the lower‐level constraints. For this purpose, first, a linear approximation model is obtained for the lower‐level constraints of the problem. Furthermore, stochastic optimization is adopted to model the uncertainty of load, renewable power, and network equipment availability during floods and earthquakes. Finally, the extracted numerical results confirm the capability of the proposed scheme in improving the operation and resilience of the mentioned networks using optimal generation and transmission planning.

A brain tumor is a dangerous cancer that develops when cells divide uncontrollably and abnormally. Recent advancements in deep learning have aided the medical imaging industry in diagnosing various disorders medically. Convolutional neural networks are the most often used machine learning algorithm for visual recognition and learning. Additionally, we demonstrate by using CNN to classify brain MRI images into two categories: cancer and non-cancer. Using the transfer learning method, we evaluated our convolutional model’s performance to previously trained ResNet-v2-152, Inception-v3, and Inception-Resnet-v2 models. As a result of the experiment, a moderate dataset was used. However, the test result indicates that the suggested model’s accuracy was adequate, reaching 99 percent, compared to 98 percent for ResNet-v2-152, 98 percent for Inception-v3, and 97 percent for Inception-Resnet-v2. The suggested model requires far less computational resources and is more efficient.

Breast cancer is among the most common diseases and one of the most common causes of death in the female population worldwide. Early identification of breast cancer improves survival. Therefore, radiologists will be able to make more accurate diagnoses if a computerized system is developed to detect breast cancer. Computer-aided design techniques have the potential to help medical professionals to determine the specific location of breast tumors and better manage this disease more rapidly and accurately. MIAS datasets were used in this study. The aim of this study is to evaluate a noise reduction for mammographic pictures and to identify salt and pepper, Gaussian, and Poisson so that precise mass detection operations can be estimated. As a result, it provides a method for noise reduction known as quantum wavelet transform (QWT) filtering and an image morphology operator for precise mass segmentation in mammographic images by utilizing an Atrous pyramid convolutional neural network as the deep learning model for classification of mammographic images. The hybrid methodology dubbed QWT-APCNN is compared to earlier methods in terms of peak signal-to-noise ratio (PSNR) and mean square error (MSE) in noise reduction and detection accuracy for mass area recognition. Compared to state-of-the-art approaches, the proposed method performed better at noise reduction and segmentation according to different evaluation criteria such as an accuracy rate of 98.57%, 92% sensitivity, 88% specificity, 90% DSS, and ROC and AUC rate of 88.77.