درخت حوزه‌های تخصصی

Background: Background: In Iran, following the introduction of modern technologies, various social groups have responded to them, among whom “Tolab” constitute one category. Due to their religious authority within Iranian society, seminarians exert both direct and indirect influence on people’s social lives. Aims: This study aims to explore how Hawzeh members engage with artificial intelligence (AI) as a manifestation of modern technology, by examining Hawzeh media such as journals and news agencies, and to explain its relationship with modernity and technological application approaches. Methodology: 122 texts from Hawzeh media over a two-year period were selected and analyzed using thematic analysis. Ultimately, three main categories were identified: “Understanding the Nature of Artificial Intelligence among Hawzeh Members”, “The Relationship between Power and Artificial Intelligence among Hawzeh Members”, and “The Operational Attention of the Hawzeh to Artificial Intelligence”. Findings: Hawzeh media perceive AI as having a variable nature, which can be utilized depending on the intention, design, and use by its users. Conclusion: The experience of the People of the Hawzeh with AI demonstrates that the systematic form of religion can also be integrated with technology as a product of modern science, because it is not entirely anti-modern, nor is it modern and subjective in the sense of defining its existence solely as a specific mode of being.

Background: The rapid development of technology and computing has significantly impacted modern societies, leading to increased opportunities for wealth creation and, inversely, transactional offending. Cybercrimes are increasing and have become a major threat to nations, governments, businesses, and individuals, resulting in financial losses, reputational damage, and personal information data breaches. Aims: This study explores how the South African Police Service (SAPS) responds to and combats cybercrimes. Methodology: This research was conducted in Durban, KwaZulu-Natal. The study adopted the qualitative research approach and the phenomenological research design, according to which interpretive and constructivist qualitative research paradigms were deemed appropriate. Data was collected through in-depth interviews using semi-structured interviews, with a sample of 17 participants purposively drawn from the Directorate for Priority Crime Investigation (DPCI) and the Commercial Crimes Unit (CCI) of the South African Police Service (SAPS). The theoretical orientation that guided the study is the Structural Functionalism Theory. Data were analyzed using thematic analysis. Findings: The study revealed that South Africa has been experiencing an increase in cybercrimes. However, has introduced laws and security strategies, such as the Cybercrime and Cybersecurity Bill of 2017, to respond to cybercrimes. However, these are argued to be not adapting fast enough to the constantly changing technological environment. Conclusion: Although limited, there are Police forensic experts in the field who can respond to cybercrimes. The study emphasizes the need for collaboration among all stakeholders, including prosecutors, the judiciary, private security agencies, CSPs, and ISPs, to effectively tackle cybercrimes.

Background: Social media platforms such as Facebook, Instagram, TikTok, and X have redefined the norms of sociality, identity performance, and participation in the public sphere. Aims: This study examines how Persian-language users on X negotiate the visibility of children in online spaces through affective discourse and vernacular governance. Methodology: Analyzing 2,392 posts, we identify seven thematic formations—ranging from family blogging and sharenting to screenshot-based mockery, celebrity child cultures, and rights-based critiques. Using a hybrid methodological approach combining high-recall data retrieval, supervised multi-label topic modeling, and sentiment–intensity analysis, we map how practices like quote-tweeting and screenshotting structure public debates around parental branding, childhood agency, privacy, and consent. Central to this ecosystem is the culturally specific figure of “Arat’s father”, a discursive shorthand for the commodification of childhood under platform economies. Findings: The findings reveal a layered affective landscape where humor, outrage, and pedagogical neutrality coexist, enabling users to police age norms and negotiate ethical boundaries in real time. Conclusion: This study reveals how ordinary users in Iran and Persian-speaking contexts regulate childhood visibility through platform affordances, emotional repertoires, and normative claims. It also proposes a reproducible pipeline for analyzing culturally specific digital publics with methodological transparency and ethical sensitivity.

Aurhor: Ehsan DehghanTitle: The Discursive-Digital Link: Antagonism and Polarisation in Digital Spaces Year of Published: August 2025352ppIn EnglishISBN: 9781032487274Publisher: New York: Routledge

Background: Cryptocurrencies have a variety of unique qualities, from cutting-edge technology to highly secure architecture. Additionally, the ability to invest in cryptocurrency, as an asset or a function of its prosperity has made crypto-currencies attractive to venture capitalists, computer scientists, and statisticians. Aims: In this study, we concentrated on a collection of documents web-scrapped from the market section of CNBC, where each document is associated with a response variable. Methodology: These documents contain preprocessed words/terms of day-to-day reportage on cryptocurrency (Bitcoin). The corresponding response variables are the daily opening and closing price of Bitcoin prices. The Supervised Latent Dirichlet Allocation(sLDA), a statistical model of labeled documents, was used to analyze the textual data alongside their corresponding response variables, since our study aims to predict the response variable for unlabeled new documents. Results: Hidden Topics with their unique terms from the preprocessed articles were exposed through a Natural language processor. Mean absolute error (MAE), Mean absolute percentage error (MAPE), and Root mean square error (RMSE) graphs were constructed for the sLDA models with ‘k = 3,10,20,30,50,75,100 and 200 Topics’ values where the model with the best evaluation metric, was selected for prediction purpose. Conclusion: It was discovered that the sLDA model with k = 20. A posterior covariance matrix which shows the proportion of terms from the documents, making up a Topic. Coefficient values were generated in other to graphically visualize how important the discovered topics are and how they affect the market trend. Finally, the prediction of new labels (numeric-decoded closing prices) for the unlabeled documents was done and comparisons were made; the predicted labels follow a similar pattern to that of the time series closing price trend.

Background: Understanding how humans perceive and estimate distances in built environments is critical not only for advancing perceptual psychology but also for informing the design of computational models in computer vision, robotics, and architectural design. Aims: This study investigates the mechanisms and limitations of human distance estimation within a controlled architectural environment. Methodology: While some previous experiments focused on estimating distances in virtual settings, the current study examines real-world estimation accuracy across a series of predefined points within an unobstructed corridor. Participants were asked to visually estimate the distance between their position and seven distinct target locations, ranging from near to far without the aid of physical reference cues. The core objective was not simply to measure accuracy, but to identify the perceptual threshold beyond which estimation errors significantly increase. A one-way ANOVA model was employed to assess the influence of variables such as actual distance and participant age on perceptual accuracy. Results: Results revealed a consistent estimation performance up to approximately 2 m, beyond which the margin of error grew increasingly pronounced. Notably, a critical threshold was identified at 7.476 m, where estimation errors sharply escalated. The maximum observed discrepancy occurred at a distance of 10.186 m, suggesting a cognitive boundary in spatial awareness. Conclusion: These findings contribute to the understanding of visual-spatial perception mechanisms and offer theoretical insights relevant to applications in robotics, image processing, virtual reality, and navigation system design.

Background: Network security and penetration pose a significant challenge in the extensive IoT research of recent years. System security and user privacy demand security solutions that are carefully planned and diligently maintained. Aims: This paper introduces a novel three-stage hybrid IDS, IoT-APOSCA, leveraging machine learning and meta-heuristics for attack detection; stages include pre-processing, feature selection, and attack detection. The pre-processing steps are: cleaning, visualization, feature engineering, and vectorization. Methodology: Networks use Intrusion Detection Systems (IDSs) to monitor and detect malicious activities as a key security feature. The Arctic Puffin Optimization (APO) and Sine-Cosine Algorithm (SCA) are used in the feature selection stage, while a changed Random Neural Network (RNN) is employed in the attack detection stage. Results: The proposed technique is assessed using the DS2OS dataset, and the outcomes show that the approach, integrating multiple learning models, led to an accuracy enhancement to 99.66%. Also, the values Recall and False Alarm Rate obtained are equal to 0.9926 and 0.003, respectively. Conclusion: Intrusion detection system efficacy is directly tied to the quality of its classification method. Enhanced neural network performance is achievable through adjustments to parameters, such as network weights.