Law enforcement officers, particularly patrolmen, face chronic occupational stressors that disrupt work-life balance and contribute to burnout—a syndrome marked by emotional exhaustion, depersonalization, and diminished personal accomplishment. This study examined the correlation between work-life balance and burnout among patrolmen (n=18) in the City of Naga and San Fernando, Southern Cebu, Philippines. A quantitative-correlational design was employed using a validated survey grounded in the Maslach Burnout Inventory (MBI) and Spearman’s rank correlation. Most respondents (94.4%) rarely disconnect from work outside working hours. Emotional exhaustion registered a moderate weighted mean of 3.08. Depersonalization showed zero variance, precluding correlation analysis. Work-life balance showed no significant correlation with emotional exhaustion (ρ = -0.219, p = 0.383) or personal accomplishment (ρ = -0.157, p = 0.533). Findings reveal notable emotional exhaustion and work-life imbalance. Organizations should implement workload redistribution, stress management, and wellness-supportive policies to protect officer well-being.

The digital transformation of construction increasingly depends on the ability to integrate artificial intelligence (AI), data-driven analytics, and interconnected digital systems into operational environments characterized by heterogeneous data, distributed stakeholders, and complex information flows. However, technological integration alone does not guarantee effective digital transformation because organizational processes, human decision-making, information governance, and system interoperability influence how computational capabilities are translated into practical outcomes. This research develops a conceptual socio-technical framework for AI-driven digital integration in sustainable construction by synthesizing the methodological and technical insights contained in seven provided studies concerning encrypted traffic classification, deep learning, convolutional neural networks, representation learning, and anonymous traffic identification. Although the source literature is primarily situated in network-security contexts, its underlying principles concerning data characterization, deep feature extraction, end-to-end learning, and traffic identification provide transferable foundations for understanding secure and intelligent information infrastructures. The proposed framework integrates five layers: data acquisition and characterization, AI-based representation and classification, secure digital integration, socio-technical decision coordination, and sustainability-oriented operational application. The analysis indicates that AI-driven construction integration should be treated as an adaptive information system rather than as an isolated algorithmic deployment. The framework emphasizes the importance of reliable data representation, contextual classification, secure information exchange, human oversight, and continuous learning. The study contributes a conceptual bridge between AI-enabled information processing and socio-technical digital integration while identifying limitations arising from the indirect transfer of findings from cybersecurity research to construction management.

Real estate investment remains a critical component of urban economic development; however, it is inherently exposed to multiple forms of risk that vary across spatial and institutional contexts. In emerging cities such as Ibadan, Nigeria, macroeconomic instability, weak regulatory frameworks, and infrastructural deficiencies continue to shape the risk profile of property investments. This study assessed the nature, severity, and determinants of risks associated with real estate investment in Ibadan, Oyo State, with specific focus on developing areas within Oluyole Estate. The study population comprised registered Estate Surveyors and Valuers and registered real estate developers operating within the study area. The sampling frame consisted of 217 professionals; from whom a sample size of 160 respondents was determined using Taro Yamanes formula and selected through simple random sampling. Descriptive statistics, the Relative Importance Index (RII), and multiple regression analysis were used to analyze the data, which were obtained by distributing structured questionnaires proportionately among the two professional groups. The findings show that the biggest danger to the profitability of real estate investments is economic risk, specifically inflation, interest rate volatility, and growing construction costs, which score highest with a RII value of 0.92. The second most important risk variables were found to be legal and regulatory concerns, such as unstable land titles and delays in planning permissions; construction and market-related risks also had a substantial impact on investment performance. Further evidence that economic and legal concerns have statistically significant positive effects on perceived investment risk is provided by regression results, which together account for almost 69% of the variation in investment risk within the research area. The results show that Ibadan's real estate investment risk is multifaceted, systemic, and heavily influenced by institutional and macroeconomic factors. In order to encourage sustainable real estate investment in Ibadan, the study recommends thorough professional risk assessment, increased regulatory transparency, and improved land administration efficiency. It concludes that investors' exposure to investment failure is increased by inadequate risk assessment and mitigation practices.

The increasing integration of intelligent systems into human-centered performance evaluation has created a demand for adaptive computational models capable of analyzing complex relationships between cognitive capacity, task execution, and fitness-related factors. Traditional predictive approaches often struggle with uncertainty, nonlinear interactions, and dynamic variations in human performance characteristics. This research presents an Adaptive Fuzzy Neural Framework for Task Efficiency and Fitness Prediction Analysis, designed to combine the reasoning capability of fuzzy systems with the learning ability of neural networks for improved prediction accuracy and adaptability. The framework conceptualizes task efficiency as a multidimensional outcome influenced by individual resources, environmental conditions, technological interaction, and performance constraints. The proposed approach utilizes fuzzy inference mechanisms to manage imprecise behavioral and operational variables while neural learning techniques optimize predictive relationships from collected performance patterns. The study is theoretically supported by previous research on automation impacts, data-driven analysis, workplace psychological resources, and intelligent system adoption. Existing studies demonstrate that technological transformation changes task requirements and creates new demands for adaptive human performance models (Acemoglu & Restrepo, 2019). Similarly, algorithmic approaches in healthcare and behavioral analysis highlight the potential of computational models for extracting meaningful patterns from complex human data (Alonso et al., 2018). The proposed framework provides a structured pathway for predicting task performance efficiency while considering fitness-related indicators, offering potential applications in workforce optimization, intelligent training systems, and adaptive decision-support environments. The research contributes a conceptual model that bridges artificial intelligence, fuzzy reasoning, and human performance analytics while identifying limitations related to data dependency, interpretability, and real-world deployment challenges.

Our lifestyle is unsustainable. The planet's resources are being depleted by humans. All species are being impacted by climate change, which is destroying ecosystems and causing previously unheard-of environmental devastation. Finding answers and building a more sustainable world requires learning. We must start living for the earth! People who get education for sustainable development are equipped with the beliefs, attitudes, behaviors, and knowledge necessary to live in a way that benefits society, the economy, and the environment. It inspires people to make responsible, wise decisions that contribute to a brighter future for everybody.  The ESD for 2030 program of UNESCO will be covered in the paper, along with how it is bringing changes in a few developing nations. The purpose of this article is to ascertain the chances for ESD in Bangladeshi high school and university education. It will assist researchers and stakeholders in taking the required actions to create an ESD curriculum that works for Bangladeshi high schools and universities. The possibilities of ESD in Bangladesh will be briefly surveyed. Students in high school and college are the target demographic.

The increasing complexity, scale, and dynamic behavior of contemporary software systems have intensified the limitations of conventional rule-based testing approaches. Artificial intelligence (AI), particularly machine learning, deep learning, reinforcement learning, and intelligent decision-making techniques, provides a basis for developing adaptive test automation systems capable of selecting actions, prioritizing test scenarios, learning from execution outcomes, and responding to changing system conditions. This research and review paper examines how AI-enabled decision-making principles can be conceptually transferred to automated software quality engineering. The analysis is based exclusively on the supplied literature, which primarily investigates intelligent decision-making, reinforcement learning, deep reinforcement learning, autonomous maneuvering, trajectory planning, and adaptive control in unmanned aerial vehicle environments. Although these studies are not directly concerned with software testing, their methodological foundations provide useful analogies for intelligent test selection, adaptive execution, test prioritization, and autonomous quality decision-making. The paper develops a conceptual AI-enabled software testing framework consisting of software-state representation, intelligent test generation, reinforcement-based test selection, adaptive execution, defect-oriented prioritization, and continuous feedback. The findings indicate that reinforcement-learning-based decision mechanisms are particularly relevant for environments where testing decisions must be repeatedly optimized under changing conditions. However, the transfer of these techniques requires careful treatment of differences between physical autonomous systems and software environments. The paper concludes that AI-enabled test automation can improve testing efficiency and adaptability when learning mechanisms are combined with controlled validation, risk-based decision criteria, and human oversight.

Rapid urbanization in Sub-Saharan Africa has intensified the challenges of achieving sustainable mobility, particularly in cities where informal public transport dominates. This 1 study examined the relationship between the use of minibuses, tricycles (keke), and bicycles (okada) and their effects on urban congestion levels and travel efficiency in Ibadan, Nigeria. Anchored on the Urban Mobility Theory, the study adopted a descriptive survey design using a structured questionnaire administered to 429 respondents drawn from major transport corridors across the city. Findings revealed a significant relationship between transport mode and urban congestion (R = 0.589, R² = 0.347, F(2,380) = 88.037, p < 0.001), indicating that minibuses and tricycles are primary contributors to congestion, while bicycles emerged as a congestion-mitigating alternative. Similarly, the analysis showed that public transport mode significantly affects travel efficiency (R = 0.652, R² = 0.425, F(2,380) = 87.945, p < 0.001), with minibuses (B = -0.421, p < 0.001) and tricycles (B = -0.193, p < 0.001) negatively influencing efficiency relative to bicycles. The results emphasize on the dual nature of informal transport systems in Ibadan. enhancing mobility access but simultaneously worsening congestion and travel delays. The study concludes that achieving sustainable urban mobility requires integrating non-motorized transport, particularly bicycles, into urban transport planning and regulating informal modes for improved efficiency. It recommends multimodal transport planning, investment in bicycle-friendly infrastructure, stricter regulation of informal operators, and institutional coordination through a metropolitan transport authority

The increasing digitization of financial services and migration of transaction-processing workloads to cloud environments have expanded the scale, velocity, and complexity of financial fraud. Conventional rule-based detection mechanisms frequently struggle with evolving fraud patterns, high-dimensional transaction behavior, and the requirement for timely alert prioritization. This research proposes a deep learning-driven financial fraud detection and alert management architecture designed for secure cloud computing environments. The proposed architecture integrates cloud-based data ingestion, preprocessing, deep representation learning, fraud-risk classification, alert prioritization, and automated notification into a unified analytical pipeline. Its conceptual foundation is informed particularly by the Deep Belief Network-based financial fraud detection and alerting mechanism proposed by Lankala et al. (2025), while insights from transfer learning, deep learning, conversational artificial intelligence, and intelligent recommendation systems are incorporated to strengthen the architectural design (Lankala et al., 2025). The methodology emphasizes layered security, scalable model execution, behavioral feature extraction, confidence-based alert generation, and feedback-driven adaptation. Analytical findings indicate that combining deep learning with cloud-native alert management can improve the consistency of fraud-risk assessment, reduce dependence on static rules, and support faster operational response. However, the architecture also introduces challenges related to false positives, computational cost, explainability, privacy, model drift, and cloud security. The study contributes a theoretically grounded architecture for integrating intelligent fraud analytics with automated alert management and identifies directions for empirical validation using real-world financial transaction datasets.

Financial markets are generally viewed as information-driven systems in which trading activity and price adjustments follow the release of new information. Yet market behavior does not always conform neatly to this sequence. This study investigates a notable event in the oil futures market that occurred in March 2026, where unusually concentrated trading activity and price movements were observed approximately fifteen minutes before an official political announcement concerning Iran.

The event raises a fundamental question: can observable market behavior emerge before formal information enters the public domain? Existing explanations would typically point to information asymmetry, insider trading, or informed order flow. While such mechanisms remain relevant, they do not fully capture situations in which substantial market activity develops despite the absence of clearly identifiable public signals.

To explore this issue, the study draws on insights from behavioral economics, behavioral finance, market microstructure, and price discovery research [7–11,16,19]. It introduces the concept of Pre-Announcement Behavioral Dislocation (PABD), a framework that focuses on the temporal relationship between expectations, uncertainty, trading behavior, and formal information disclosure. The central argument is that market participants may react to evolving expectations, contextual developments, and perceived signals before information becomes formally confirmed, creating observable market effects ahead of official announcements.

Rather than treating such episodes solely as anomalies or evidence of information leakage, the paper examines whether they may reflect a broader behavioral pattern that appears across different contexts. The findings suggest that pre-announcement activity deserves greater attention within financial research because it challenges announcement-centered assumptions about timing and market response. By emphasizing the role of expectation formation and behavioral dynamics under uncertainty, the study contributes to ongoing discussions regarding market efficiency, price formation, and the processes through which markets interpret emerging developments.