Qubahan Academic Journal

A Systemic-Functional Linguistic Analysis of Saki’s “The Image of the Lost Soul”

Howard Aries V. Ronquillo — Sat, 10 Jan 2026 00:00:00 +0000

This paper used Systemic-Functional Linguistics (SFL) to analyze Saki's short story “The Image of the Lost Soul” through Halliday's transitivity framework to reveal the linguistic methods of creating emotional detachment and existential loneliness with unreturned feelings. Multiple research investigations on transitivity have concentrated on traditional novels while bypassing short story analysis alongside the complexities of verbal and existential dynamic processes. The systematic process of categorizing clauses into material, mental, relational, verbal, behavioral, and existential categories demonstrates how such choices create meaning in the narrative. Behavioral clauses in the study demonstrate the absence of defined recipients because they primarily showcase separated emotional states and deep existential challenges. The existential processes that contain “a cry that could never be answered” created narrative spaces free of emotions, which reflect feelings of emotional severance and separation. The study used qualitative clause-based analysis to explain the language strategies Saki uses in constructing both character psychological states and social critiques. This paper shows how Saki employs process types to exceed narrative structure by generating existential meanings that saturate the grammatical framework. This analysis of short fiction demonstrates how brief narratives generate powerful emotions through their strategic wording to expand SFL’ application range. The research enhances both literary stylistics and systemic-functional literary studies through its analysis of language and emotional connection to ideology, so researchers should continue interdisciplinary examinations of early twentieth-century literary solitude models and emotional breakdowns.

Intelligent Tutoring Systems for French Language Learning in Police Training: A Big Data-Driven AI Approach

Hue Nguyen Thi — Wed, 04 Feb 2026 00:00:00 +0000

Communication challenges are frequently encountered by police due to need to interact with French-speaking communities. Law enforcement duties often occur at a fast pace and do not fall under typical language acquisition programs. The proposed research aims to provide law enforcement agencies, through the development of an Intelligent Tutoring System (ITS) based on Big Data, a means for French language acquisition for police-related uses. The ITS will consist of real-time interactive language learning tasks, proficiency assessments and simulated feedback based on the learner's performance, to develop an adaptive and goal-based learning program. Enhanced pre-processing techniques, including cleaning, tokenization or lemmatization, will ensure that any data entered into the ITS is of high quality. The ITS will include a Light Gradient Boosting Machine (LightGBM) that utilizes the predicted performance of learners and categorizes language-learning errors. The ITS will also include a Deep Q-Network (DQN) to automatically change the content of language lessons based on learners' engagement and progress. This represents a new ITS design model for the police training environment that integrates language with operational needs, a consideration that has thus far not been addressed in existing models. Assessment data of the ITS indicate substantial improvements in learner attention, rate of error correction and speed of learning. This research offers insight into the establishment of infrastructure as well as the implementation of strategies surrounding real-time, adaptively learned systems in the high-stakes context. In addition, it presents an opportunity through a modular design for police and multilingual communities to better communicate with one another, thereby enhancing the exchange of trust and increased citizen participation, through training opportunities for law enforcement personnel, in a manner that allows them to become better prepared for a more positive outcome.

Policy Implications for Improving Operational Risk Management of Commercial Banks: A Case Study in Vietnam

Phan Dien Vy , Phan Thanh Tam — Tue, 06 Jan 2026 00:00:00 +0000

This study investigates the impact of organizational factors and digital transformation on operational risk management (ORM) in Vietnamese commercial banks. Employing a mixed-methods approach, qualitative interviews with 25 senior risk experts helped refine measurement scales, followed by a large-scale quantitative survey with 958 valid responses. Structural equation modeling (SEM) reveals that the internal environment is the most influential driver of ORM, while the awareness and operation of the ERM framework (AOF) play a critical mediating role. Results emphasize that effective ORM requires a strong risk-aware culture, integrated digital systems, and clear risk communication. The study contributes theoretically by demonstrating a dual-channel mechanism, direct and mediated, through which organizational and technological factors shape ORM effectiveness. Practically, the findings highlight that sustainable ORM requires fostering a risk-aware culture, enhancing communication, aligning technology with ERM frameworks, and integrating risk management into strategic objectives. Policy recommendations emphasize the importance of leadership commitment, ethical governance, and systematic training to strengthen the internal environment and institutionalize ERM maturity, thereby enabling Vietnamese banks to build resilience and competitiveness in an increasingly digitalized financial landscape. Finally, enhancing leadership accountability, strengthening risk culture, and embedding ERM into daily operations and strategy.

A Practical Study of the Use of AI-Based E-Book Guides to Improve Early Childhood Mathematics Skills

Sat, 24 Jan 2026 00:00:00 +0000

Early mathematics is a crucial foundation for children's cognitive development, yet teaching in early childhood education often remains conventional and less engaging. This study aimed to evaluate the practicality of an AI-based e-book designed to support mathematics learning in preschool settings. Employing an explanatory sequential mixed-methods design, quantitative data were collected from 125 preschool teachers in Padang, Indonesia, followed by qualitative interviews with 15 participants. Findings indicated high practicality across all assessed dimensions: design and layout (91.0%), purpose and usefulness (91.6%), ease of use (88.4%), clarity (89.6%), assessment and evaluation (90.4%), and AI-based interactivity (87.6%), with an overall mean score of 89.8%, classified as "Highly Practical." Qualitative insights confirmed that the e-book enhanced engagement, facilitated instructional differentiation, and aligned well with pedagogical objectives, though infrastructural barriers limited its full use in resource-constrained schools. The study highlights that practicality, grounded in the Technology Acceptance Model, is essential for ensuring adoption alongside effectiveness. The findings contribute uniquely to the literature by shifting focus toward feasibility and teacher acceptance. The study concludes that the AI-based e-book is a highly practical tool for teachers, with its successful adoption contingent on addressing infrastructural barriers to ensure equitable implementation.

Leveraging Knowledge Management to Achieve Principled Performance: The Mediating Roles of Risk and Compliance

Subramaniam Anbanathan , Nury Effendi , Aldrin Herwany — Wed, 21 Jan 2026 00:00:00 +0000

This study examines the role of Knowledge (KM) as catalyst in realizing Principled Performance (PP) through the mediating effects of Risk Management (RM) and Compliance (CM). It elaborates on the specific nature of KM platform and aims to uncover the unique characteristics of PP in comparison to the conventional term of ‘performance. The study employs a quantitative approach, utilizing purposive sampling from 36 companies with a total of 252 managers from Indonesian airports, each representing seven observation units focusing on activities within Indonesian airport companies. Data were analysed using the Partial Least Squares Structural Equation Modelling (PLS-SEM) program. Initial findings indicated that KM had a significant direct effect on PP and an indirect effect through RM and CM. KM provided essential actions in addressing uncertainties through RM and increasing compliance awareness and commitment via CM. Furthermore, the direct relationship between CM and PP is significant due to the influence of compliance obligation, while RM and PP did not show a significant relationship. However, CM and RM had a notable simultaneous effect as mediating variables when KM influenced PP. These results positioned KM as a foundational element of PP, emphasizing its importance in promoting sustainable performance outcomes. This study significantly enhances theoretical understanding and enriches practical discourse by integrating insights from various disciplines through empirical methodologies. It clarifies the critical interplay among KM, RM, and CM, demonstrating how they promote a balanced approach that improves performance. The PP strengthens organizational resilience and ensures long-term sustainability. Moreover, the results introduce an alternative conceptual model that practitioners can explore and refine. The study highlights the pivotal role of knowledge in navigating risk and compliance to achieve PP, which can lead to positive outcomes and greater confidence in an organization's capabilities and success. This study positions KM as a fundamental aspect of PP, highlighting its significance in reinforcing sustainable performance outcomes. To the best of the authors’ knowledge about PP, no prior research has specifically used knowledge to enhance PP.

Islamic Education Management, Sufism, and Digital Literacy: An Interdisciplinary Approach

Andi Warisno , M. Afif Anshori , Nur Hidayah — Wed, 21 Jan 2026 00:00:00 +0000

This article aims to examine the interdisciplinary relationship between Islamic education management, Sufism, and digital literacy in addressing the challenges of 21st-century education. Islamic education serves not only as a medium for knowledge transfer but also as a vehicle for character formation and spiritual development. Sufism, with its emphasis on inner dimensions, ethics, and transcendental awareness, provides an ethical and spiritual framework highly relevant in the era of digital disruption. Meanwhile, digital literacy has become an urgent competency for learners to adapt to the global flow of information while filtering hoaxes, hate speech, and harmful content. This study employs a qualitative approach through library research, drawing upon authoritative books, peer-reviewed journals, and international reports. The findings reveal that integrating Islamic education management with Sufi values and digital literacy fosters an educational model that is humanistic, spiritual, and technologically adaptive. Case studies from madrasahs, pesantrens, and Islamic higher education institutions demonstrate that this integrative model enhances learning effectiveness, strengthens digital ethics, and nurtures religious character relevant to the digital age. Nevertheless, this research has limitations due to the lack of empirical field data, meaning the findings are still conceptual and require testing through qualitative or quantitative research in Islamic educational institutions. Further studies are expected to develop more applicable instruments and implementation models for administrators and educators. This article concludes that the integration of Sufism, Islamic education management, and digital literacy forms a strategic foundation for cultivating Indonesia’s golden generation one that is character-driven, intellectually capable, and globally competitive.

Urban Victimization in Peruvian Cities: Influence of Demographic and Population Factors on Citizen Security

Wed, 21 Jan 2026 00:00:00 +0000

The rapid transformation of large urban concentrations in Latin America faces new challenges due to crime, especially in the most profound socio-economic contrasts between cities with high population density. This study sought to examine the features of urban victimization in Peru. The research used a quantitative, non-experimental and cross-sectional design, from data collected by the 2024 National Survey of Budgetary Programmes and analyzed with statistical software version 26.0. The use of this methodology facilitated the exploration of space, producing a more nuanced understanding and revealing unique spatial patterns of victimization across different sizes of urban areas. The results showed that victimization depends on demographic factors and the size of population centers, with higher levels of insecurity in large cities. In intermediate and small urban areas, population growth revealed a complex and even negative relationship with crime, where community cohesion may act as a protective factor. The most novel finding is that population size does not always increase criminality, as its effects vary according to social and territorial contexts. In addition, young people aged 15 to 29 were identified as the most vulnerable group, and property crimes had the highest incidence, while sexual assaults and frauds were notably underreported. t is concluded that urban victimization in Peru responds to differentiated dynamics according to the size and characteristics of population centers. The results show that population growth does not always increase crime, as in certain contexts it may be associated with protective effects. This highlights the need for citizen security policies adapted to each territory and especially focused on youth and the most recurrent crimes.

More than a Good Deal: The Interplay of Perceived Value, Multidimensional Engagement, and Intention to Buy in Live Platform

Wed, 07 Jan 2026 00:00:00 +0000

Live stream shopping has become popular alternative to traditional social media marketing, particularly in Indonesia. In this study, we adopted Perceived Value Theory to explore how financial, functional, individual, and social values influence three types of engagement, including affective, cognitive, and behavioral, and how these forms of engagement will shape purchase in live stream shopping environments. This research collects samples of 200 respondents via survey. Partial Least Squares Structural Equation Modeling was used to analysis the data. The output revealed that financial value strongly drives affective and cognitive engagement. Another finding is that functional value significantly influences cognitive and behavioral engagement, while individual and social values enhance users' emotional connections, that translate into direct purchasing behavior. Interestingly, the results suggest that functional benefits may not be sufficient to drive deep engagement or prompt purchasing action. The findings emphasize the need for a balanced value strategy one that effectively addresses both the emotional and practical needs of live stream audiences to foster meaningful engagement and influence purchase intention. Live streamers could emphasize financial and individual values in their contents to gain direct purchase intention. Furthermore, they need to highlight the financial, individual, and social values that could trigger emotional engagement in order to improve the purchase intention for longer term benefit.

Strategic Audit and Economic Efficiency of Tourism Development in Kazakhstan: International Practices and Policy Implications

Tue, 13 Jan 2026 00:00:00 +0000

The purpose of this study is to conduct a rapid audit of the effectiveness of government support measures in the tourism sector of the Republic of Kazakhstan and to develop recommendations for im-proving strategic control tools. The study employed methods of systems analysis, documentary re-search, induction, and statistical calculations. The informational basis included data from the tourism satellite account and reports from state programs. The analysis revealed a low level of budget fund utilization (in some subprograms less than 30%), a lack of correlation between the amount of funding and achieved results, and issues with the reliability and completeness of reporting. In 2024, actual funding of the State Program amounted to only 17.2% of the planned amount, while half of the target indicators were not achieved. Additionally, systemic shortcomings in the planning and evaluation of investment projects were identified. The conclusion proposes measures to update target indicators, integrate Sustainable Development Goals (SDGs) into efficiency audits, improve statistical accounting, and develop a methodology for assessing the effectiveness of subsidies. Implementation of these recommendations will enhance transparency and the efficiency of tourism sector development in Kazakhstan.

Suicide Prevention Among Adolescents: Integrating Psychological Assistance with Legal and Social Frameworks

Thu, 29 Jan 2026 00:00:00 +0000

Adolescent suicide remains one of the most pressing global public health challenges, reflecting a complex interplay of psychological vulnerability, social disconnection, and inadequate institutional responses. Despite advances in clinical psychology and psychiatry, prevention strategies often remain fragmented, failing to bridge mental health care with the legal and social protection systems necessary for sustainable impact. This paper examines adolescent suicide through a multidimensional framework integrating psychological, legal, and social components. Drawing on international research, case studies, and policy analyses from WHO, UNICEF, and national health ministries, the study emphasizes that early identification, confidential psychological counseling, and family-based interventions must operate alongside legally mandated protection protocols and community engagement programs. Empirical data were collected from three regions representing diverse socio-economic backgrounds, using a mixed-methods approach combining surveys, interviews, and psychological assessments. Empirical data were collected using a mixed-methods design from three Central Asian countries Uzbekistan, Kazakhstan, and Kyrgyzstan comprising survey responses from 612 adolescents aged 13–18, alongside interviews with parents, educators, psychologists, and legal or social-service professionals. The findings highlight critical gaps in legal accountability and the underutilization of social networks as preventive tools. The proposed integrative model the P-L-S framework aims to synchronize multidisciplinary responses to at-risk adolescents, fostering early detection and coordinated intervention. The article concludes by advocating for a cross-sectoral policy agenda that aligns educational institutions, healthcare systems, and justice departments within a unified prevention ecosystem.

Secure and Efficient Authentication Scheme in IOT Environments based OT

Shoroug Al-Hadlaa , Albandari Alsumayt , Majid Alshammari — Sat, 24 Jan 2026 00:00:00 +0000

The swift growth in the number of Internet of Things (IoT) devices and their deployment into Operational Technology (OT) settings has brought about difficult security issues, including authentication. Current procedures are deficient of scalability, theft of credentials and real-time dynamism, which expose the IoT-OT networks to threats. This paper will present a zero-trust authentication system that uses Artificial Intelligence (AI), blockchain, and Elliptic Curve Cryptography (ECC) to provide a decentralized, adaptive, and lightweight security system. The framework uses situational data, i.e. the location of a device, a history of behaviors, and access patterns to facilitate continuous identity checking without interrupting activities. AI actively identifies anomalies and prompt re-authentication, whereas blockchain allows managing identities transparently and without tampering by distributed devices. Lightweight ECC and XOR encryption are high security and low computation intensity algorithms that are used to overcome resource limitations. The 37 percent reduction in encryption time, 41 percent decreased energy usage and six-fold minimization of error rate over the traditional ECC techniques is shown by simulation and formal verification with ProVerif. The results verify the ability of the proposed approach to increase trust, performance, and resilience in industrial IoT-OT systems. This study makes a theoretical contribution by operationalizing continuous authentication on zero trust and offers a practical system design of secure and scalable industrial connectivity.

Predicting Corporate Profitability in Morocco: Comparing Classical Regression and Machine Learning

Youssef Jamil , Imane EL Yamlahi , Nabil Bouayad Amine — Sun, 18 Jan 2026 00:00:00 +0000

To the best of our knowledge, this study provides the first systematic comparison between classical regression and advanced machine learning models for predicting the profitability of Moroccan firms listed on the Casablanca Stock Exchange. While prior research has largely focused on developed markets, profitability prediction in emerging economies such as Morocco remains underexplored, despite the market’s structural particularities (sectoral concentration, reliance on bank financing, and limited disclosure practices). This article provides the first systematic comparative analysis between regression and machine learning approaches applied to Moroccan listed companies, highlighting the advantages and limitations of each method in capturing complex and non-linear financial dynamics. Using a dataset covering ten years of financial statements, we evaluate multiple models, including OLS, Ridge regression, Random Forest, Gradient Boosting, Support Vector Regression, KNN, and XGBoost. Results show that machine learning models consistently outperform regression in predictive accuracy, while regression retains value in interpretability. Findings contribute to academic research by extending profitability forecasting studies to an under-explored emerging market, and to practice by offering investors, policymakers, and managers tools that improve risk assessment, capital allocation, and decision-making under conditions of uncertainty. These implications are particularly relevant for emerging economies, where informational asymmetries and structural heterogeneity complicate financial forecasting.

Impact of Pedagogical Management Training Program on Students' Professional Competencies and Management Abilities Development: Case of Kazakhstan

Sat, 17 Jan 2026 00:00:00 +0000

To perform their basic functions in professional practice, future teachers need not only general vocational pedagogical competencies but also specialized knowledge and abilities in management the effectiveness of management determines efficiency of education. The purpose of this study is to analyze the potential of improving students' professional competencies, management abilities, and leadership qualities through the introduction of a pedagogical management training program within the educational process. The research employs literature analysis and a pedagogical experiment conducted with 102 students at a pedagogical university in Kazakhstan. The experimental group participated in an elective pedagogical management course, which included elements of psychological training, while the control group followed the traditional education process. The findings reveal that the students exposed to the pedagogical management program showed a significant improvement in leadership qualities and academic performance compared to students in the control group. This study concludes that integrating pedagogical management programs into teacher training can effectively enhance future educators' managerial skills and professional competence.

Convergent Microfinance Strategy: Combining Fintech and Conventional Methods to Enhance Rural Financial

Wed, 07 Jan 2026 00:00:00 +0000

Financial inclusion in rural areas continues to face challenges due to poor infrastructure, low digital literacy, and resistance to financial technology. While both traditional microfinance and fintech have tried to address these issues, implementing them separately often leads to inefficiencies and low adoption. This study explores hybrid microfinance models that combine both approaches, using a system dynamics method with Causal Loop Diagrams (CLDs) to analyze interactions between technological, social, and operational factors. Data were gathered through stakeholder interviews and thematic analysis to map key feedback loops and intervention points. The findings highlight three core drivers of success: (1) Infrastructure Development, emphasizing connectivity and platform access; (2) Social Dynamics, focusing on financial literacy and trust to reduce resistance; and (3) Operational Innovation, which includes staff training, strategic resource use, and partnerships. Results suggest that a holistic strategy combining infrastructure investment, community engagement, and operational improvements is essential for advancing sustainable financial inclusion. Inclusive digital service design, targeted education, and supportive regulation are emphasized in the study as interventions to establish trust and improve accessibility in the rural contexts.

Sustainable Development of AI-Based Educational Tools: Addressing Affordability and Equity between Private and Public Schools in Saudi Arabia's Early Childhood Education

Khawlah Alhejii — Wed, 21 Jan 2026 00:00:00 +0000

The integration of artificial intelligence (AI) in early childhood education offers major opportunities for personalized learning but also exposes equity gaps between private and public institutions. This study investigates the affordability, accessibility, and sustainability of AI-based educational tools across Saudi Arabia’s early childhood education sector using an explanatory sequential mixed-methods design. Quantitative survey data were collected from 127 participants representing both private (68) and public (59) schools, followed by 14 in-depth interviews to capture contextual experiences. Results show clear disparities in AI adoption and readiness. Private institutions reported markedly higher use of adaptive learning platforms (78 % vs 12 %), educational robots (45 % vs 3 %), and AI-based assessment tools (52 % vs 8 %). They also benefited from dedicated technology budgets (73 % vs 23 %) and higher rates of teacher training (62 % vs 18 %), creating positive feedback loops that reinforce innovation. Public schools, however, cited affordability concerns (92 %), inadequate infrastructure (68 %), and limited professional development (86 %) as primary obstacles, widening the digital divide. Four major themes emerged: technological readiness, financial constraints, professional development gaps, and differing perceptions of educational effectiveness. The findings highlight that sustainable AI integration requires more than funding; it demands coordinated infrastructure investment, continuous teacher training, and equity-driven policy reform. The study recommends government subsidies for AI procurement in public schools, mandatory AI literacy programs for teachers, and strategic public-private partnerships to ensure long-term technological sustainability. This research contributes the first comprehensive evidence on AI equity in Saudi early childhood education, offering actionable insights for policymakers pursuing inclusive digital transformation under Vision 2030.

Effective Risk Analysis and Predictive Modeling in Motor Insurance in Saudi Arabia

Tue, 13 Jan 2026 00:00:00 +0000

Accurate prediction of motor insurance premiums that correspond with actual claims are critical to the sustainability of insurance companies. However, predicting premiums is a challenging task due to the complexity of risk factors. This study aims to identify significant risk factors and develop predictive models for motor insurance pricing within the Saudi context, using real data obtained from one of the leading insurance providers in Saudi Arabia. The dataset consists of 71,280 records and 26 features of insurance claims reported during the year of 2023. After preprocessing the data, significant risk factors are identified using Analysis of Variance (ANOVA), which are used later to build the prediction models. The findings reveal that vehicle body type and manufacturing country emerged as the most influential risk factors. The evaluation metrics (R², MAE, MSE) have been applied to evaluate the best-performing machine-learning pricing prediction model (Decision tree, Neural network, Generalized linear model, and Random Forest). The results of our evaluation show that the Random Forest model consistently outperformed the other models in terms of prediction accuracy. The study contributes to motor insurance industry in Saudi Arabia by supporting informed risk assessment within the Saudi Takaful insurance operations. It highlights the performance of prediction models for motor insurance pricing in Saudi Arabia.

Certification of Computer Applications in Customer Satisfaction: An Analysis of the Developer's Role

Marcelino P. Braga , Miguel A. Brito — Sun, 18 Jan 2026 00:00:00 +0000

This study examines the certification of software applications and the role of programmers in customer satisfaction. It seeks to evaluate the relationship between these two factors. Twenty-two articles were examined in order to identify trends and conclusions regarding the connection between software application certification and customer satisfaction. The results demonstrate that programmers play an essential role in ensuring the integrity of systems and user security, and that their qualifications and training are vital for the effectiveness of certification and customer satisfaction. The article concludes that certifications are formal processes that foster customer confidence and that collaboration between software development companies and customers is vital to make sure the quality of software applications.

Governance in Higher Education: Models, Institutional Transformations and Management Challenges

Sat, 10 Jan 2026 00:00:00 +0000

The governance of higher education institutions is the process of organizing and regulating institutional power in order to guide decision-making, policies, and resource allocation in a complex and diverse context. Confronted with mounting pressures from external sources, including internationalization and demands for greater transparency, academic institutions find themselves compelled to recalibrate their internal structures and redefine their mission. The present research employs the PRISMA 2020 methodology in order to systematically review the extant literature on governance models, recent transformations, and challenges in management, participation, and sustainability. Tensions have been identified between hierarchical and participatory approaches, which reflect the variety of contexts and the need for flexible responses. Transformations driven by social, political and technological factors require updated mechanisms that integrate strategic leadership and effective participation, in addition to considering the well-being of academic staff. The concept of governance is understood as an evolving system that articulates local and global levels, combines public and institutional policies, and applies ethical values to design resilient and inclusive models that facilitate innovation and position higher education as a driver of just and sustainable societies. The findings reveal the predominance of hybrid governance models and highlight governance fragmentation as the most pressing challenge for achieving quality and sustainability in higher education.