Vol. 8 No. 07 (2026): Volume 08 Issue 07

This study examined the relationship between financial literacy components and entrepreneurial intentions among Social Studies students in Ethiopia. Specifically, the study investigated the relationship between budgeting literacy, saving literacy, digital finance literacy, and entrepreneurial intentions among Social Studies students in the Oromia Region of Ethiopia. The study adopted a correlational survey research design. The population of the study comprised 4,860 undergraduate Social Studies students from selected public universities in the Oromia Region during the 2025/2026 academic session. A sample size of 486 students was selected using proportionate stratified random sampling and simple random sampling techniques. Data were collected using a structured questionnaire titled Financial Literacy Components and Entrepreneurial Intentions Questionnaire (FLCEIQ). The instrument was validated by experts in Entrepreneurship Education, Measurement and Evaluation, and Financial Literacy Studies. Cronbach’s Alpha reliability coefficients of 0.82, 0.85, 0.88, and 0.86 were obtained for budgeting literacy, saving literacy, digital finance literacy, and entrepreneurial intentions respectively, with an overall reliability coefficient of 0.85. Data collected were analyzed using mean, standard deviation, and Pearson Product Moment Correlation Coefficient at 0.05 level of significance. The findings revealed that budgeting literacy had a strong positive and significant relationship with entrepreneurial intentions (r = 0.672, p < 0.05). Saving literacy also showed a strong positive and significant relationship with entrepreneurial intentions (r = 0.714, p < 0.05). Furthermore, digital finance literacy demonstrated a strong positive and significant relationship with entrepreneurial intentions (r = 0.756, p < 0.05). The study concluded that financial literacy components significantly influence entrepreneurial intentions among Social Studies students in Ethiopia. The study recommended the integration of practical financial literacy training into university entrepreneurship programmes to improve students’ entrepreneurial preparedness and self-employment readiness.

This study explores which eye-tracking and face-reading indicators differentiate respondents with higher and lower tendencies toward compulsive buying when processing advertising leaflets. The objective was to examine whether compulsive buying tendencies are reflected in real-time visual attention and facial-emotional responses to specific leaflet elements. Data were collected from 177 respondents using an online panel. Participants viewed standardized advertising leaflets while eye-tracking and automated facial expression data were recorded. Compulsive buying tendencies were measured using a short questionnaire scale, and respondents were compared according to their CBB scores. The analysis focused on areas of interest including product image, product name, price, discount symbol, product description, and country-of-origin information. The results show that respondents with higher compulsive buying tendencies differed mainly in visual-attentional processing. The strongest differentiating indicator was shorter time to first fixation on the product image, suggesting faster orientation toward the product visual among higher-CBB respondents. Secondary patterns indicated weaker processing of country-of-origin information and slightly greater repeated attention to the product name. Face-reading indicators provided weaker differentiation, as facial responses were predominantly neutral. Although discount symbols generated relatively higher engagement and joy overall, they did not clearly distinguish higher- from lower-CBB respondents. The findings suggest that compulsive buying tendencies are expressed less through strong facialemotional reactions and more through subtle shifts in attention toward product-related cues. Eye-tracking and facereading should therefore be viewed as complementary exploratory tools rather than diagnostic substitutes for validated CBB questionnaires.  

This study examines the impact of technology on residential property management in Oyo State, Nigeria, with comparative insights from Lagos, Abuja, and Ogun States. It examines the obstacles to professional adoption and looks into how technology integration affects cost savings, tenant happiness, and management effectiveness. According to the ESVARBON Directory (issue of April 10, 2025), all 177 Registered Estate Surveyors and Valuers (ESVs) in Oyo State were included in the research population. Quantitative data from structured questionnaires and qualitative data from expert interviews were combined in a mixed-method study approach. Quantitative data was analyzed using descriptive and inferential statistical methods, while qualitative responses were subjected to theme analysis. With an overall mean acceptance rate of 57%, the results showed that 91% of respondents use at least one type of technology in property management operations. The most popular technologies were cloud-based systems (75%) and communication platforms (84%), while the least popular ones were smart/IoT systems (15%) and online rent payment (46%). According to a comparative research, Ogun (61%) and Oyo (57%) lagged behind Lagos (83%) and Abuja (79%) in terms of technological adoption, indicating the impact of infrastructural, policy, and economic environments. The study comes to the conclusion that although technology greatly improves efficiency, transparency, and service delivery, Oyo State's use of it is hampered by inadequate infrastructure, a lack of training, and uneven governmental support. In order to enhance ICT infrastructure, boost digital literacy, and create favorable regulatory frameworks, it suggests cooperative efforts between governmental organizations, trade associations, and commercial technology suppliers. The results add to the expanding conversation in emerging economies on the digital transformation of property management.

Authorial interior design in the United States often loses fidelity between the studio concept and the finished space, and this study examines why that loss occurs and how it can be reduced. The aim is to characterize the realizability gap and to analyze how computer-generated imagery (CGI), technical detailing, and coordination with general contractors operate together to close it. The work combines a structured review of peer-reviewed sources, comparative analysis of market and labor data, and a single case study of a cross-border studio that serves United States clients with a European design language and a remote production team. Results show that the visualization market grows faster than design services, that construction rework and a verified labor shortage raise the cost of misaligned documentation, and that treating CGI as the binding reference rather than a sales asset lowers rework risk. The conclusion confirms the aim by presenting an alignment proposal and practical recommendations. These findings are useful for interior designers, CGI studios, general contractors, real-estate developers, and workforce-policy stakeholders.

Residential HVAC systems consuming up to 48% of household energy operate predominantly under single-point thermostat control, a configuration that cannot differentiate thermal demand across zones. This article presents a distributed IoT climate management framework in which per-zone smart controllers form a self-healing wireless mesh, jointly optimize heating and cooling via a two-tier reinforcement learning architecture, and share thermal models through a federated aggregation protocol that preserves occupant-data privacy. Under a multi-zone residential testbed spanning six independently actuated zones over 90-day seasonal trials, the framework achieves 31.4% reduction in HVAC energy consumption relative to a single-thermostat baseline, of which 17-20 percentage points are attributable to the RL policy rather than to the occupancy instrumentation, with thermal comfort violation rates of 3.2% across occupied hours. The article describes system mechanics, experimental protocol, results across three building archetypes, and the specific conditions under which the performance gains do and do not generalize.

The increasing application of artificial intelligence (AI) has created new opportunities for improving agricultural extension services through intelligent advisory systems such as ChatGPT. Despite its potential, little empirical evidence exists regarding the factors influencing its adoption among smallholder farmers in developing countries. This study examined the influence of trust and perceived accuracy on the adoption of ChatGPT for agricultural extension among smallholder farmers in Nigeria. A cross-sectional survey research design was adopted. The study was conducted across the six geopolitical zones of Nigeria using a sample of 316 registered smallholder farmers selected through stratified and simple random sampling techniques. Data were collected using the Trust, Perceived Accuracy and ChatGPT Adoption Questionnaire (TPACAQ). Descriptive statistics were used to summarize respondents' characteristics, while simple linear regression and multiple linear regression were employed to answer the research questions and test the hypotheses at the 0.05 level of significance. The findings revealed that trust significantly influenced the adoption of ChatGPT for agricultural extension (β = 0.591, p < .001), accounting for 34.9% of the variation in adoption. Perceived accuracy also had a significant positive influence (β = 0.638, p < .001), explaining 40.7% of the variation. Furthermore, trust and perceived accuracy jointly exerted a significant influence on ChatGPT adoption (R² = 0.530, F(2,313) = 176.35, p < .001), with perceived accuracy contributing more strongly than trust. The study concludes that farmers' adoption of ChatGPT depends largely on their confidence in the trustworthiness and accuracy of AI-generated agricultural recommendations. The study recommends improving the accuracy, transparency, and contextual relevance of ChatGPT responses while strengthening farmers' digital literacy and confidence through extension training programmes. These measures will facilitate the effective integration of generative AI into agricultural extension services in Nigeria.

This article focuses on analyzing the system of international and national legal foundations concerning Vietnam's sovereignty, sovereign rights, and jurisdiction over the East Sea (or South China Sea). By comparative analysis between the 1982 United Nations Convention on the Law of the Sea (UNCLOS 1982) and the 2012 Law on the Sea of Vietnam, the research clarifies the legal substance, boundaries, and administrative-security management regimes for each specific maritime zone: internal waters, the territorial sea, the contiguous zone, the exclusive economic zone, and the continental shelf. Furthermore, the article analyzes the practical value of the 2016 Arbitral Award by the Permanent Court of Arbitration (PCA) in invalidating China's baseless claims over the "nine-dash line," as well as Vietnam's consistent strategic position in protecting national interests and upholding the rule of law across the oceans.

African higher education systems face a persistent and widening crisis: graduates possess credentials yet remain unprepared for the labor market demands of a rapidly transforming economy. Despite decades of curriculum reform rhetoric, the structural misalignment between what universities teach and what employers require continues to drive youth unemployment, underemployment, and skills obsolescence across the continent. This paper addresses this gap by proposing the ADAPTIVE Curriculum Alignment Model, a conceptual framework that reimagines curriculum-employability bridges as dynamic, data-informed, and equity-centered systems rather than static, periodic revision exercises. Drawing on curriculum development theory, competency-based education scholarship, and emerging literature on artificial intelligence in education, the ADAPTIVE model integrates eight interconnected components: Assess, Design, Align, Partner, Train, Include, Validate, and Evaluate. Each component is grounded in established curriculum traditions; Tyler's objectives model, Stenhouse's process model, and context-responsive curriculum theory while being updated for the digital era through AI-assisted labor market scanning, predictive analytics, and continuous feedback mechanisms. The framework explicitly centers equity and gender responsiveness, recognizing that curriculum alignment without attention to structural barriers reproduces rather than resolves employability inequalities.

The paper argues that the prevailing approach to curriculum revision in African universities typically episodic, top-down, and disconnected from real-time labor market intelligence cannot keep pace with the velocity of economic transformation. By contrast, the ADAPTIVE model treats curriculum alignment as an institutional capability that requires technological infrastructure, stakeholder governance, faculty development, and inclusive design. The framework is intentionally designed for adaptability across diverse institutional resource contexts, offering scalable pathways for well-resourced universities and phased implementation strategies for resource-constrained institutions. The conceptual contribution lies in synthesizing curriculum theory, employability scholarship, and AI-education research into a unified model that is theoretically robust, practically actionable, and contextually grounded for African higher education. The paper concludes with implications for institutional policy, faculty development, and future empirical research.

The contemporary fashion industry increasingly relies on multidisciplinary backstage teams operating under strict production schedules, high creative expectations, and rapidly changing visual requirements. Professional makeup has therefore evolved beyond an artistic procedure into a structured operational process requiring standardized planning, coordinated communication, technical consistency, and quality assurance [1–3].

 Despite the central role of makeup departments in fashion productions, limited scholarly attention has been devoted to organizational models capable of integrating management principles with standardized professional makeup methodologies.

This study presents the Professional Makeup Organizational Model (PMOM), an interdisciplinary organizational framework developed from professional observational practice in international fashion productions, educational activities, and large-scale beauty project coordination [4,5].

 The model standardizes the organizational structure of professional makeup teams through sequential stages including project planning, team organization, model assessment, workflow management, quality assurance, interdisciplinary communication, and post-production evaluation.

A distinctive component of the proposed framework is the integration of the AK Photoshop Face Method™ as the standardized technical protocol for makeup execution within PMOM. Rather than functioning solely as a corrective makeup methodology, the AK Photoshop Face Method™ provides a unified professional approach to facial assessment, skin preparation, texture refinement, individualized corrective techniques, and cosmetic balance. Integrating this methodology into organizational workflow reduces technical variability among team members while preserving individualized artistic adaptation according to facial anatomy, skin characteristics, and designer-directed visual concepts [20,21,25].

The proposed organizational model was developed through professional observations obtained during fashion productions involving multidisciplinary creative teams, international runway events, educational projects, and large-scale backstage coordination. Practical implementation demonstrated improvements in workflow efficiency, communication between creative departments, operational consistency, time management, and quality control while maintaining flexibility for individualized cosmetic decision-making [5,6].

The findings suggest that professional makeup should be recognized not only as an artistic discipline but also as a structured organizational system requiring standardized operational management supported by evidence-informed technical methodologies. The Professional Makeup Organizational Model (PMOM) may contribute to the development of standardized organizational practices in fashion productions, beauty education, commercial photography, television, and other multidisciplinary environments requiring coordinated professional makeup services.

The ‘self’ of an individual is the consciousness of the waking state. ‘Inner self’ or ‘pure consciousness’ is experienced during the state of deep sleep, when there is only ‘oneness’. ‘Pure consciousness’ is the ‘core reality’ of one’s being, as evidenced by one’s own daily experience of deriving bliss, clarity and freshness from this ‘oneness’. Collective intelligence studies have confirmed similarity in the oneness experience. The waking state consciousness can then be perceived to be a ‘derivative’ of this oneness within. The physical phenomena can be shown to fit into the domain of oneness. The universal physical constants appear to be consequent to the over-arching domain of oneness. The subjective and objective perceptions of physical, biological and sociological phenomena perceived in the waking state, can all be shown to fit into the ‘mold of oneness’. All human problems and conflicts should be eventually resolvable through dissemination of knowledge of ‘view from oneness’ and by being in tune with the ‘mold of oneness’.

Adaptive ensemble learning has emerged as a critical paradigm for addressing non-stationarity, heterogeneity, and scalability challenges in modern signal intelligence systems. Traditional signal processing frameworks rely heavily on static model assumptions, limiting their effectiveness in dynamic environments such as IoT networks, wireless communications, and cyber-physical systems. This paper proposes an Adaptive Ensemble Signal Intelligence Architecture (AESIA) designed to integrate meta-learning, bilevel optimization, and differentiable architecture search to enable robust, self-adjusting neural network systems capable of quantitative equity trend estimation in signal-driven domains.

The proposed architecture synthesizes key advances in model-agnostic meta-learning (Finn et al., 2017), robust few-shot learning frameworks (Killamsetty et al., 2022), and hyperparameter optimization techniques (Pedregosa, 2016), alongside bilevel optimization strategies (Ghadimi and Wang, 2018; Liu et al., 2020). These components collectively support adaptive model reconfiguration in response to evolving signal distributions and operational constraints. Furthermore, differentiable architecture search (Liu et al., 2018) is incorporated to automate neural architecture design under computational constraints, ensuring efficiency in deployment scenarios.

The study also incorporates insights from signal intelligence applications in IoT authentication and emitter identification (McGinthy et al., 2019; Zhang et al., 2019), as well as security-driven signal analytics frameworks (Sang and Jun, 2021). A key contribution is the formulation of a quantitative neural architecture equity trend estimation model, which evaluates model fairness, stability, and predictive consistency across heterogeneous signal environments. This is further aligned with multi-model forecasting principles demonstrated in large-scale temporal systems (Vollem et al., 2026), which provide foundational guidance for hybrid statistical-deep learning integration.

Results from theoretical modeling indicate that AESIA improves adaptability under distribution shifts, reduces optimization instability, and enhances ensemble robustness compared to conventional deep learning pipelines. The architecture demonstrates strong potential for deployment in real-time signal intelligence systems requiring continuous learning and structural adaptation.