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.

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%.

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.