مطالب

Background: The conceptualization of Moroccan cyberspace is intrinsically linked to the country's international and regional dynamics, in particular those of its African and Mediterranean neighborhood.Methodology: we have mobilized quantitative techniques and potential comparative analysis to address our crucial research questions around the strengths and weaknesses within Morocco's national cyber strategy by analyzing and comparing them with other international experiences, and finally recommending best practices to maintain the regional leadership position.Discussion: Although Morocco has acquired considerable expertise in comparison with this environment, regarding proven mechanisms of cyber stability, and the institutionalization and organization of national cyber security. However, one of the main obstacles lies in the centralization of these practices by the State, as well as in the sectorial and selective approach chosen by Moroccan policymakers to secure the national cyber space. This strategy, contrary to the country's principles of openness, was developed on the basis of the arguments, based on the priorities and strategic challenges specific to each sector.Aims: We open up our local experience to comparison with other known global experiences in the field of national cybersecurity, to decipher the strengths and weaknesses of national digital strategies and to fill the gaps causing the weakening of our replicating processes.Conclusion: The question is whether a simple reform of the central state's public policies on national cybersecurity is effective or, on the contrary, the situation requires a drastic and broader intervention, materialized by major investments and structural reforms in entire development strategies.

The concept of management ability is one of the most important factors in determining and determining the audit fee. Management ability is a dimension of human capital in companies that is classified as intangible assets. Financial distress is also an important factor that may affect audit fees. The findings of this study contribute to the emerging literature on management ability and audit costs by showing whether auditing services pricing is a result of higher management ability in the ongoing financial crisis. In light of the above, this study seeks to examine the impact of managerial skills and auditing fees on the condition of financial distress. This research has been reviewed during a 10-year study from 2009 to 2018. The results of this study show that there is a negative and significant relationship between managerial ability and audit fees despite financial distress. There is also a significant inverse relationship between financial distress and management ability.

Despite the growth of financial technologies (FinTechs) and their impact on transforming financial services, the implementation of fintech for gaining a competitive advantage in banks appears essential. The aim of this article is to model and determine the relationships and relative importance of the dimensions and components of implementing fintech in the financial-banking system. To this end, the research was conducted with a qualitative and quantitative approach in 2023. The research community included 12 experts, comprising managers and senior specialists from state banks, who were selected purposefully and theoretically. The data collection tools were semi-structured interviews for the qualitative section and a pairwise comparison questionnaire for the quantitative section. The results of the qualitative section, analysed through content analysis, indicated that the model includes dimensions such as "infrastructure and technical", "organizational policies", "products and services", "market", "marketing and sales", "economic", "human resources" and "ecosystem." The quantitative results, analysed using fuzzy DEMATEL, showed that "products and services" is the most influential and critical dimension in the model, suggesting that the success of implementing the model depends on improving this dimension. "Ecosystem" is the most impactful dimension with the strongest relationship in implementing the model, leading to the model's improvement. The findings of the network analysis process also indicated that the components of "product and service diversity", "security and privacy protection", "product and service adaptability" and "creating a platform and sharing it" are the most important. Based on the results, it is recommended that banks invest in those financial technologies (fintech) that can quickly respond to the desires and needs of their customers for successful fintech implementation.

While there are varying perspectives on the bases of reflective practice within the teaching context, there is a broad consensus that reflection is fundamental to building a teacher's professional identity and improving their development. This study sought to investigate the perceptions of EFL teachers of reflective skills of Iranian effective teachers. To this end, a grounded theory design was used. Participants consisted of 30 conveniently selected female intermediate EFL learners from a language institute in Mashhad. For data collection, semi-structured interview and written reflective journal were used. Data analysis was done through manual thematic analysis via open, axial and selective coding. The results showed the following perceptions of EFL teachers of reflective skills of Iranian EFL effective teachers: 1. Behaving in a socially acceptable manner; 2. Being able to motivate students; 3. Managing classroom; 4. Attending to students' diversity of needs; 5. Planning appropriate teaching and evaluation methods and strategies; etc. Moreover, according to the results, the least important reflective skills of Iranian EFL effective teachers were attending to students' diversity of needs, and being able to motivate students. In addition, the most important reflective skills of Iranian EFL effective teachers were evaluating one's own teaching, making appropriate modifications in one's own teaching, thinking skillfully, planning appropriate teaching and evaluation methods and strategies, and managing classroom. The results have some implications for policymakers, teacher educators, authorities in charge of recruiting teachers.

This quasi-experimental research investigated the comparative effects of gamified versus non-gamified flipped classrooms on the grammar proficiency of Iranian English as a Foreign Language (EFL) learners, with a focus on the acquisition of past tenses. Sixty intermediate-level EFL learners (aged 12–15) were selected non-randomly from a pool of 90 students based on their scores on the Cambridge Preliminary English Test. The participants were randomly assigned into two experimental groups: one experienced a gamified flipped classroom and the other a non-gamified flipped classroom. Both groups received instruction on English past tenses through pre-class videos and in-class activities over 12 sessions. The gamified group used game-based platforms, while the non-gamified group followed traditional methods. Grammar pre-test and post-test were administered to assess learning outcomes. Analysis using one-way ANCOVA revealed that gamified flipped classroom group significantly outperformed the non-gamified group in acquiring English past tenses. The findings suggest that integrating gamification into flipped classroom models can significantly enhance acquisition of past tenses among EFL students. These results hold implications for EFL educators, curriculum designers, and policymakers seeking to adopt engaging and effective instructional strategies.

This study aimed to compare the effects of Automated Writing Evaluation Feedback (AWEF) and Peer Feedback (PF) on the development of self-regulation in writing among Iranian English as a Foreign Language (EFL) learners. The participants consisted of 58 intermediate-level Persian-speaking English learners, aged 25 to 30, enrolled in two intact IELTS preparation classes at a private language institute in Tehran. The learners were assigned into two experimental groups: one group (n = 30) received peer feedback, while the other (n = 28) received automated feedback through ProWritingAid. To assess the impact of these interventions, data were collected using the Writing Strategies for Self-Regulated Learning Questionnaire (WSSRLQ), semi-structured interviews, and classroom observations. Quantitative analysis using independent samples t-tests revealed significant improvements in self-regulation scores for both groups post-intervention, with the AWEF group showing a significantly greater increase. Qualitative analysis of interview and observation data further supported these findings, indicating that AWEF participants engaged in more structured and proactive revision practices, while PF participants developed greater reflective awareness and emotional resilience through collaborative interaction. Triangulated results confirmed that both feedback types positively influenced self-regulated writing behaviors, with AWEF demonstrating a stronger overall impact.

English for Specific Purposes (ESP) education has gained prominence as a specialized field designed to address the distinct linguistic needs of learners. Although AI-enhanced instruction has been investigated within general English language learning, its implementation in ESP contexts particularly concerning vocabulary recall and retention has remained insufficiently explored. This study sought to evaluate the impact of AI-supported instruction on vocabulary recall and retention among Iranian ESP learners. Based on non-random convenience sampling, a total of 48 undergraduate students in an ESP course from a university in a southeastern province in Iran participated in the present quasi-experimental study. The participants were non-randomly assigned into two different groups: an experimental group (n=24) receiving AI-powered vocabulary instruction through Diffit, and a control group (n=24) receiving traditional print-based vocabulary instruction. Data were collected during a twelve-week intervention through AI-powered instruction of ESP vocabulary. To address the research questions, a univariate repeated measures analysis of covariate (ANCOVA) was employed. Findings revealed that AI-enhanced instruction significantly improved vocabulary recall and retention among ESP learners. The results offer robust evidence supporting the efficacy of AI-based instructional approaches in enhancing vocabulary learning outcomes within ESP settings.

The present explanatory mixed methods study was designed to investigate the difference between the effects of using the pyramid model of Willingness to Communicate (WTC) and Communicative Tasks (CTs) on reducing Iranian English as a Foreign Language (EFL) learners' speaking barriers. The participants were 57 Iranian EFL learners selected based on convenience sampling from a language institute in Tehran. The Preliminary English Test (PET) results verified their homogeneity. To foster a stronger spirit of cooperation among the participants, they were placed in three different classes based on their tendencies; hence, the researchers could consider them as three groups: the Pyramid Model Group (PMG), the Communicative Tasks Group (CTsG), and the Conventional Approach Group (CAG) each including 19 participants. The PMG received instructions pertaining to the six levels of PM in willingness to communicate (WTC), while the CTsG received instructions based on information gap, reasoning gap, and opinion gap activities. The CAG relied on the Audio-Lingual Method (ALM), which the institute regularly followed. Accordingly, the three groups went through pretesting, intervention, and post-testing. The participants completed a speaking barriers survey as pre- and post-tests. Then, ten participants from the three groups were randomly selected and interviewed about the impact of the methods they had experienced on their speaking barriers. The Analysis of Variance (ANOVA) revealed the priority of the pyramid model of WTC over communicative tasks and conventional teaching in reducing learners' speaking barriers. The interview results also confirmed the quantitative findings indicating that anxiety, learners' low self-confidence, along with linguistic and instructional barriers could be reduced through being exposed to the pyramid model instructions. The results can be helpful for ELT professionals, EFL teachers and learners, and other stakeholders to hold more thriving L2 speaking classes.

The preservation of invaluable cultural heritage assets, including ancient artifacts and historical sites, remains a critical global concern. Existing perimeter protection methods, such as CCTV systems, infrared sensors, and conventional surveillance technologies, often exhibit significant limitations in terms of spatial coverage, sensitivity to subtle intrusions, and adaptability to varying environmental conditions. This paper presents an innovative and highly effective solution utilizing fiber optic sensor (FOS) technology, which offers numerous advantages, including lightweight construction, immunity to electromagnetic interference, ease of installation, high sensitivity, and reliable performance across a broad range of applications. Specifically, the study introduces novel designs for distributed acoustic FOS systems based on Michelson, Mach-Zehnder, and Sagnac interferometric configurations. Through comprehensive theoretical analysis, simulation modeling, and experimental validation, the research explores their practical utility in protecting cultural heritage. Two detailed case studies—focused on the Louvre Museum in France and the ancient archaeological site of Persepolis in Iran—demonstrate the implementation, functionality, and real-world efficacy of the proposed systems. The findings highlight how these FOS networks, particularly when combined with advanced artificial intelligence and machine learning algorithms, can effectively detect, classify, and localize human-induced acoustic disturbances such as walking or digging, while minimizing false alarms caused by environmental noise. This enhanced discrimination capability not only improves overall security but also significantly reduces the need for extensive human surveillance. Moreover, these systems enable precise, continuous, and real-time 24-hour monitoring over vast indoor and outdoor areas. In conclusion, the integration of FOS technology offers a scalable, intelligent, and transformative approach to addressing the complex and evolving challenges associated with the long-term preservation and protection of cultural heritage sites around the world.

This paper investigates the role of Zen philosophy, as derived from haiku poetry, in the architectural design process. It explores the nature of Zen, its unique perspective on art and literature, and how this perspective is reflected in architectural design processes. The paper also examines how the creation of architectural spaces can be integrated with the poetry and (haiku) influenced by this philosophy. This research seeks to answer the question of how Zen thought can be applied in architectural design through the utilization of haiku poetry. A descriptive-analytical approach, combining inductive and deductive reasoning, is employed. Initially, Zen theories based on haiku are deductively applied to case studies. Subsequently, Zen architectural theories based on haiku are extracted from these case studies. Finally, the results are obtained by combining inductive and deductive methods. This paper aims to introduce haiku, elucidate the role of Zen philosophy in haiku poetry, and explore the influence of Japanese Buddhism on the architectural design process. The result of this research reveal that haiku is the product of immediate, intuitive experience and cannot be evaluated based on conventional poetic criteria. Haiku is a form of enlightenment or illumination, in which the poet perceives the vitality of objects and grasps the meaning of the ordinary and commonplace. Haiku poetry presents viewers with perspectives on nature, humanity, and life. By integrating literary concepts into the architectural design process, haiku can serve as a powerful cultural and artistic medium, fostering audience engagement with literature and cultivating a unique, philosophical understanding of phenomena.

Temporal databases, quickly rising in size, are distinguished by their capacity to maintain the older version of data objects against actions on them, allowing logical deletions. Queries for historical data are particularly costly due to the linear scanning of temporal versions. Temporal data structures like time-split B-Tree or multiversion B-Tree are working underlying the state-of-the-art temporal databases. So far, most efficient temporal data structures are partially persistent or fully persistent, but none of them support retroactive queries. On the other hand, efficient temporal indexing is required to address bulk loading in a real-life application. To the best of our knowledge, there is no efficient solution for bulk loading and updating retroactive index structures. This article seeks to offer a new data structure, the Retroactive B-Tree (RBT), to facilitate retroactive operations in temporal databases as well as bulk loading. It presents theoretical and empirical research and analysis of the suggested data structure and its relevant operations. The experiments were conducted to demonstrate the performance of the proposed retroactive B-Tree in terms of execution time, I/O complexity, space complexity, and bulk loading. The obtained results show that indexing with a buffer is the most powerful model for existing temporal databases for implementing a retroactive B-Tree. The tree of lists architecture is observed as an I/O efficient data structure for all variants of temporal indexing for large databases.

The proliferation of fake news, characterized by the dissemination of inaccurate information to deceive audiences, has become a pressing concern in recent times. Traditional approaches to phony news detection, often focused on analyzing Twitter content, are susceptible to noise and variations in input sequences, leading to suboptimal performance. To address these challenges, this study proposes a novel method called Multi-Head Attention-Hierarchical Bidirectional Long Short-Term Memory (MHA-HBiLSTM) Networks. Our approach involves two phases: training and testing, wherein we employ tweet pre-processing techniques such as stemming, punctuation removal, stop-word elimination, URL handling, and Twitter control removal. Features are represented using the Glove word embedding technique for experimental evaluation and comparison. The MHA-HBiLSTM model integrates multi-head attention and hierarchical concepts, allowing meaningful information extraction from Twitter data. Notably, our model utilizes dual-level attention mechanisms and a hierarchical structure, reflecting the inherent hierarchy in documents and prioritizing key material during document representation. The effectiveness of the proposed MHA-HBiLSTM algorithm is evaluated using the Whale & Multi-Verse (W-MVO) Optimizer approach, with tests conducted on Kaggle and FakeNewsNet datasets. Comparative analysis with traditional machine learning approaches and deep learning models demonstrates the superior performance of the MHA-HBiLSTM approach in fake news detection.

Objective: This study aimed to evaluate the effectiveness of a strength-based parenting program in enhancing family cohesion and reducing marital conflicts among parents. Methods: A randomized controlled trial design was employed with 30 participants (15 in the intervention group and 15 in the control group), all of whom were parents residing in Italy. The intervention group participated in a nine-session, strength-based parenting program delivered over nine weeks, while the control group received no intervention during the study period. Both groups completed assessments at three time points: pretest, posttest, and a five-month follow-up. The standardized tools used included the Family Adaptability and Cohesion Evaluation Scales IV (FACES IV) to measure family cohesion and the Marital Conflict Questionnaire (MCQ) to assess marital conflicts. Data were analyzed using repeated measures ANOVA and Bonferroni post-hoc tests in SPSS version 27. Findings: The repeated measures ANOVA indicated significant time × group interaction effects for both family cohesion (F(2,56) = 16.63, p < .001, η² = .39) and marital conflicts (F(2,56) = 18.38, p < .001, η² = .41). Post-hoc comparisons revealed that the intervention group experienced significant increases in family cohesion from pretest to posttest (Mean Difference = 6.87, p < .001) and follow-up (Mean Difference = 6.14, p < .001). Similarly, marital conflicts significantly decreased in the intervention group from pretest to posttest (Mean Difference = -10.85, p < .001) and remained lower at follow-up (Mean Difference = -11.19, p < .001), with no significant changes observed in the control group. Conclusion: The strength-based parenting intervention proved effective in promoting family cohesion and reducing marital conflict over time, with sustained benefits evident at a five-month follow-up. These findings highlight the value of strengths-focused approaches in enhancing family relationships and emotional well-being.