Retracted publications continue to influence scholarship long after withdrawal. This study assembles a curated set of 169 Iran-affiliated retractions in computer science, data science, and electrical engineering from 2008 to 2024, links them to citing and cited works through two complementary retrieval pipelines, and constructs an expanded citation network of 1'694 nodes and 1,703 edges. We quantify retraction reasons and timing, community structure, node centrality, self-citation patterns, author and institutional concentration, international co-authorship, and a field-adjusted national benchmark. Misconduct-related causes predominate. The average interval from publication to retraction increased into 2021 and has since begun to shorten. The citation network exhibits strong community structure with three major thematic clusters. Centrality profiling isolates five retracted works that function as hubs, often reinforced by self-citation loops. Contribution is highly concentrated among a small set of authors and institutions, while collaboration extends across multiple regions beyond Iran. A field-adjusted retraction rate places the national record among mid-tier producers. These results identify practical leverage points to reduce downstream spread of invalidated findings: persistent indexing flags on hub retractions, routine screening of citations to retracted work, and focused attention on repeat patterns in self-citation and institutional clusters. The study offers a reproducible dual-pipeline approach, a full centrality profile of an enlarged network, and actor-level diagnostics that support targeted integrity interventions.

Accurate and reliable time is necessary for financial and legal transactions, transportation, distribution systems, and many other applications. Time synchronization protocols such as NTP (the network Time Protocol) have kept clocks of such applications synchronized to each other for many years. Nowadays there are many commercial GPS based NTP time server products at the market but they almost have a high price. In this paper we are going to use a Low Cost GPS engine to build a time server to provide time synchronization with accuracy of a few milliseconds. This time server is relatively very cheap and it can be used in almost all typical applications. We also proposed a software based NTP time server implemented in MATLAB as well.

2Introduction: Due to the increase in the provision of electronic services to citizens in government offices, the number of computer users and the occurrence of musculoskeletal disorders have increased. Therefore, this study aimed to predict and model the complex relationships between the risk factors of musculoskeletal disorders in computer users working in government offices by an artificial neural network. Material and Methods: The current cross-sectional study was conducted in 2020 on 342 employees of various government offices in Saveh city. First, the researcher visited the work environment to identify the problems and measure the environmental factors. Then, ergonomic risk assessment and psychosocial factors were evaluated using the Nordic questionnaire and the ROSA method. The effect of various factors in causing musculoskeletal disorders was investigated using a logistic regression test.Then the resulting data were collected and modeled by one of the neural network algorithms. Finally, artificial neural networks presented an optimal model to predict the risk of musculoskeletal disorders. Results: The results showed that by increasing the level of social interactions, the level of demand, control, and leadership in the job, musculoskeletal disorders in men and women decrease. There was a significant relationship between the prevalence of musculoskeletal disorders and job demand, job control levels, social interaction levels, leadership levels, organizational climate levels, job satisfaction levels, and stress levels, in addition between reports of pain in the neck and shoulder and wrist/hand region. There was a significant relationship with the overall ROSA score. Also, there was a significant relationship between the report of pain or discomfort in the neck area with the phone screen risk score, wrist/hand with the keyboard-mouse risk score, and shoulder, upper back, elbow, and lower back with the chair risk score. The accuracy of the presented model for predicting musculoskeletal disorders was also about 88.5%, which indicates the acceptability of the results. Conclusion: The results showed that several factors play a role in causing musculoskeletal disorders, which include individual, environmental, psychosocial, and workstation factors. Therefore, in the design of an ergonomic workstation, the effects of the mentioned factors should be investigated. Also, predicting the effectiveness of each of the mentioned factors using an artificial neural network showed that this type of modeling can be used to prevent musculoskeletal disorders or other multifactorial disorders.

In recent years, the needs of the Internet are felt in lives of all people. Accordingly, many studies have been done on security in virtual environment. Old technics such as firewalls, authentication and encryption could not provide Internet security completely; So, Intrusion detection system is created as a new solution and a defense wall in cyber environment. Many studies were performed on different algorithms but the results show that using machine learning technics and swarm intelligence are very effective to reduce processing time and increase accuracy as well. In this paper, hybrid SVM and ABC algorithms has been suggested to select features to enhance network intrusion detection and increase the accuracy of results. In this research, data analysis was undertaken using KDDcup99. Such that best features are selected by Support vector machine, then selected features are replaced in the appropriate category based on artificial bee colony algorithm to reduce the search time, increase the amount of learning and improve the authenticity of intrusion detection. The results show that the proposed algorithm can detect intruders accurately on network up to 99.71%.

An immune system is an exciting and efficient computational system with many applications in engineering, especially in intrusion detection systems. It is agent-based, distributed and self-adaptive and works based on the hierarchical layered architecture. In this paper, an agent-based artificial immune system based on computational intelligent techniques such as fuzzy logic and genetic algorithms is proposed for computer networks security. The proposed method uses the existing fuzzy relations between antigens and antibodies. Furthermore, it uses the genetic algorithms for optimizing antibodies. To simulate a typical network and to implement network attacks, the network simulator 2 (ns2) is used. To evaluate the performance of the proposed method, the DARPA standard data set is used in training and test phases. The proposed approach is compared with Forrest's immune system as a benchmark. Results show the proposed algorithm detects non-self entities at significantly higher rate, and detectors created by proposed algorithm are more diverse, more robust, and make fewer errors in attack detection.

Background: Tinnitus known as a central nervous system disorder is correlated with specific oscillatory activities within auditory and non-auditory brain areas. Several studies in the past few years have revealed that in the most tinnitus cases, the response pattern of neurons in auditory system is changed due to auditory deafferentation, which leads to variation and disruption of the brain networks. Objective: In this paper, we introduce an approach to automatically distinguish tinnitus individuals from healthy controls based on whole-brain functional connectivity and network analysis. Material and Methods: The functional connectivity analysis was applied to the resting state electroencephalographic (EEG) data of both groups using Weighted Phase Lag Index (WPLI) for various frequency bands in 2-44 Hz frequency range. In this case-control study, the classification was performed on graph theoretical measures using support vector machine (SVM) as a robust classification method. Results: Experimental results showed promising classification performance with a high accuracy, sensitivity, and specifi city in all frequency bands, specifically in the beta2 frequency band. Conclusion: The current study provides substantial evidence that tinnitus network can be successfully detected by consistent measures of the brain networks based on EEG functional connectivity.