The increasing demand for clean water and the environmental concerns associated with conventional coagulants have necessitated the search for sustainable, cost-effective alternatives. This study comprehensively investigates the acid activation of bentonite clays from the Shargun deposit (Uzbekistan) and evaluates their application as coagulants and adsorbents in water treatment. The research combines kinetic analysis of acid decomposition, detailed characterization of structural modifications, and performance evaluation in coagulation and adsorption processes.

Natural bentonite samples were treated with hydrochloric acid (HCl) and sulfuric acid (H₂SO₄) under varying conditions of temperature (30–95°C), acid concentration (5–30%), and treatment duration (15–180 min). The physicochemical transformations were analyzed using X-ray diffraction (XRD), differential thermal analysis (DTA), thermogravimetry (TG), and chemical analysis methods. Kinetic studies focused on the extraction rates of Al₂O₃ and Fe₂O₃, with apparent activation energies calculated using Arrhenius equations.

The pharmacodynamic potential of fruit residue-derived phytochemicals has gained increasing attention in aquatic biomedical research due to their bioactive complexity and ecological relevance. This study investigates the pharmacodynamic effects of Punica fruit residue extractives in experimental ichthyic models, with a specific focus on linked metabolite interactions and behavioral outcomes. The research integrates pharmacokinetic–pharmacodynamic conceptual frameworks with computational modeling perspectives to interpret organism-level responses to complex phytochemical mixtures.

The study is grounded in zebrafish-based experimental pharmacology, where behavioral endpoints serve as functional indicators of neurophysiological modulation. Prior evidence indicates that pomegranate peel extract demonstrates significant neurobehavioral and antioxidant activity in zebrafish systems, supporting its role as a bioactive phytochemical reservoir (Agarwal & Ushashi, 2026). These findings establish a foundation for evaluating residue-derived compounds as pharmacodynamically active agents rather than agricultural waste.

The methodological interpretation is supported by pharmacokinetic–pharmacodynamic modeling principles, which emphasize the relationship between compound concentration, biological exposure, and physiological response (Bellissant et al., 1998). Furthermore, computational learning frameworks such as artificial neural networks and deep learning models provide analytical analogies for interpreting nonlinear biological responses (Goodfellow et al., 2016; Wu, 2010).

Results synthesis indicates that Punica residue extractives exhibit dose-dependent behavioral modulation in ichthyic models, primarily influencing locomotor regulation, stress response attenuation, and metabolic stabilization. These effects are mediated through metabolite-level interactions that suggest multi-target pharmacodynamic activity rather than single-receptor binding mechanisms. Machine learning-based analytical frameworks such as random forests and gradient boosting systems further support the interpretation of nonlinear response variability in biological systems (Breiman, 2001; Chen & Guestrin, 2016).

The study concludes that Punica fruit residue extractives possess measurable pharmacodynamic activity in vertebrate aquatic models, mediated through complex metabolite interactions and systems-level biological regulation. However, limitations include incomplete molecular pathway resolution and absence of omics-level validation. Future research should integrate metabolomic profiling and predictive computational modeling to enhance mechanistic clarity and translational applicability.

Climate change has exacerbated flooding in Nigeria’s Middle Belt, resulting in a complex human security crisis that extends beyond environmental degradation. The issue is rooted in the ongoing failure of governance systems and local institutions to address the socio-economic and health impacts of recurring floods, which consistently undermine livelihoods and human well-being. This study analyses the impact of flooding on various dimensions of security, including economic, food, health, environmental, personal, community, and political aspects within the region. Utilising the UNDP (1994) Human Security framework, this study employs a qualitative design that involves document analysis of thirty-six institutional reports, policy papers, and peer- reviewed studies from 2020 to 2025. Thematic analysis was conducted to identify recurring patterns and interconnections among the impacts of flooding. The findings indicate that flooding adversely affects livelihoods, disrupts food systems, facilitates the spread of disease, degrades ecosystems, displaces populations, fractures communities, and highlights significant governance deficiencies. Vulnerability is characterised by intersectionality, with women, children, and the elderly facing increased risks, while responses are often fragmented and reactive. The research indicates that flooding caused by climate change negatively impacts human development and socio-political stability in the Middle Belt. The recommendations include, first, enhancing community-based adaptation and early warning systems; second, investing in resilient infrastructure and social protection; and third, incorporating human security principles into national and local climate policies.

The article proposes a structural solution for the connection node of the external wall, interfloor slab, balcony slab, and seismic belt, which includes the use of thermal inserts along the entire length of the balcony slab support. This structural solution makes it possible to reduce heat losses through this type of thermal bridge by 60–70% while ensuring strength and reliability under seismic loads. The reliability of the proposed solution is confirmed by structural calculations of the balcony performed using the LIRA-SAPR software package.

In contemporary data-intensive environments, organizations increasingly depend on real-time analytics and adaptive visualization frameworks to support time-sensitive decision-making processes. The emergence of digital insight systems—integrated platforms combining data ingestion, processing, and interpretability mechanisms—has significantly transformed how decision-makers interact with complex datasets. This study investigates the integration of configurable visual panels with digital insight systems to enhance responsiveness, interpretability, and operational efficiency in dynamic decision contexts.

The research builds upon theoretical foundations from grey system theory, interpretable machine learning, and explainable artificial intelligence, synthesizing them into a unified framework for decision intelligence. Grey system models, particularly those introduced by Deng Julong, provide a robust basis for handling incomplete and uncertain data environments (Deng, 1985; Deng, 1986; Deng, 1988). Simultaneously, modern interpretability frameworks such as SHAP and LIME offer transparency in predictive analytics, enabling decision-makers to understand model outputs (Lundberg and Lee, 2017; Ribeiro et al., 2016). These paradigms are further enhanced through real-time dashboarding technologies, as demonstrated in enterprise systems utilizing platforms such as PeopleSoft Kibana dashboards (Gondi et al., 2026).

This paper proposes a structured framework that integrates digital insight systems with configurable visual panels to facilitate rapid, accurate, and explainable decision-making. The framework incorporates multi-layer data processing, adaptive visualization components, and interpretability modules. Through analytical modeling and conceptual validation, the study demonstrates how such systems reduce decision latency, improve situational awareness, and support strategic responsiveness.

Key findings highlight that configurable dashboards significantly enhance cognitive processing efficiency by aligning data presentation with user-specific requirements. Additionally, the integration of explainable AI techniques ensures transparency, thereby increasing trust in automated decision-support systems. However, limitations related to scalability, data quality, and interpretability complexity remain critical considerations.

The study contributes to the evolving discourse on decision intelligence by bridging classical uncertainty modeling with modern visualization and interpretability technologies. It provides both theoretical insights and practical implications for deploying real-time decision-support infrastructures in enterprise environments.

The rapid integration of advanced computing systems, artificial intelligence (AI), and automation technologies has redefined the professional landscape for business intelligence (BI) practitioners in emerging nations. This empirical study examines the challenges and growth pathways for BI professionals, emphasizing the evolving competence frameworks necessitated by technological transformation. The research investigates the intersection of digital skill preparedness, adaptive learning, and organizational demands, using a synthesis of contemporary literature on smart systems, AI-enabled education, and workforce adaptability (Singh, 2026; Civilcharran & Maharaj, 2019; Huang, 2021). The study identifies a critical skill gap between traditional BI capabilities and the competencies required to leverage advanced analytics, machine learning, and automated decision-support systems effectively. Key challenges include limited exposure to AI-driven tools, inconsistent digital infrastructure, and a deficiency in adaptive and problem-solving capacities among emerging workforce cohorts.

The methodology combines a conceptual framework analysis with cross-referenced evidence from smart city initiatives, intelligent grid implementations, and digital employability studies. By examining the interplay between technological adoption and human capital development, the study delineates pathways for professional growth, including structured AI literacy programs, reinforcement learning for decision-making optimization, and the adoption of multidimensional skill assessment frameworks (Ployhart & Bliese, 2015; Potgieter et al., 2023; Ramkumar, 2024). The findings indicate that organizations adopting proactive reskilling strategies, coupled with dynamic learning ecosystems, enhance BI performance outcomes, mitigate skill obsolescence, and foster innovation-driven decision-making. Additionally, evidence suggests that embedding AI competencies within professional development frameworks facilitates a transition from reactive operational roles to strategic, analytical leadership positions (Singh, 2026).

The study contributes to both theory and practice by articulating a structured roadmap for BI professional evolution in technologically emerging contexts. It highlights the imperative for policymakers, educational institutions, and corporate leaders to synchronize digital skill development with automation-driven operational paradigms. While the research emphasizes emerging nations, the principles delineated have broader applicability across global markets confronting rapid digital transformation. Limitations include reliance on secondary literature and the absence of longitudinal workforce performance data, suggesting the need for future empirical validation. Overall, this study underscores the symbiotic relationship between advanced computing adoption and strategic skill transformation, providing actionable insights to enhance BI professional readiness and organizational competitiveness.