Volume 8.
Issue 3.

The article uses regression analysis to process the results of experimental studies conducted on three parameters that determine the maximum cleaning efficiency of a peg-type screw cleaner: the peg placement pitch s, the mesh hole width b, and the screw rotation speed n. Based on the B3 second-order Boh plan, 14 experiments were conducted, and the results were summarized in the form of a complete quadratic regression equation. All ten coefficients of the equation turned out to be statistically significant (t > 2.048), and the coefficient of determination was R2 = 0.964. As a result of the canonical analysis of the response surface, it was found that the stationary point of the model is the saddle point (minimax); the most optimal combination of parameters within the studied region is s ≈ 216 mm, b ≈ 6.9 mm, n = 130 rpm, where ηo ≈ 84.2% is achieved according to the model. It is shown that the results obtained are in good agreement with the parameters of the experimental sample (b = 6 mm, n = 160 rpm) adopted on the basis of the theoretical model.

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 transition toward ecological business systems has intensified the need for sustainable financing structures capable of balancing environmental responsibility, economic resilience, and long-term operational viability. Ecological business networks, comprising interconnected enterprises, regulatory agencies, investors, and community stakeholders, increasingly rely on predictive analytical systems to optimize funding allocation, minimize financial uncertainty, and improve environmental performance. This study investigates how data-centric predictive methodologies can enhance sustainable funding optimization within ecological business networks through integrated analytical frameworks involving artificial intelligence, resource-based management, ecological economics, and predictive financial modeling. The paper develops a multidimensional research framework that combines sustainability metrics, network-based financial interactions, ecological performance indicators, and machine learning-oriented predictive mechanisms for strategic funding decisions.

The study synthesizes interdisciplinary literature from sustainability science, urban ecological economics, resource optimization, social network analysis, predictive systems, and environmental governance. Existing studies demonstrate significant advances in ecological assessment, carbon-constrained industrial optimization, environmental risk prediction, and neural-network-based forecasting systems; however, major gaps remain in integrating these dimensions into coherent sustainable financing architectures. The research therefore proposes a conceptual predictive funding optimization model that integrates ecological footprint analytics, investment risk forecasting, dynamic resource allocation, and sustainability-driven decision intelligence. Particular attention is devoted to predictive mechanisms that reduce uncertainty in green investment portfolios and ecological infrastructure financing. The study also incorporates recent insights regarding artificial intelligence and circular economy financing mechanisms developed by Mirza et al. (2026), emphasizing the role of predictive analytics in de-risking ecological investments.

Methodologically, the paper adopts a qualitative analytical research design supported by theoretical synthesis, comparative literature evaluation, conceptual framework development, and system-oriented modeling. The research identifies critical variables influencing sustainable funding optimization, including ecological risk exposure, resource efficiency, industrial diversification, environmental carrying capacity, stakeholder commitment, and predictive data integration. The findings indicate that predictive methodologies substantially improve ecological funding efficiency by enabling dynamic capital redistribution, environmental risk anticipation, adaptive governance, and sustainability-oriented financial prioritization. Furthermore, predictive systems facilitate stronger coordination among network participants, thereby increasing ecological resilience and long-term investment sustainability.

The study concludes that data-centric predictive methodologies represent a transformative mechanism for ecological business financing by integrating environmental intelligence with strategic funding optimization. The research contributes to sustainability finance literature through a unified conceptual framework connecting predictive analytics, ecological governance, and adaptive investment systems while offering implications for policymakers, financial institutions, ecological enterprises, and sustainability researchers.

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.

The promotion of gender equality has become one of the defining characteristics of democratic development in the twenty-first century. Women's legal activism plays a significant role in strengthening democratic institutions, protecting human rights, and ensuring equal opportunities for all citizens. This article examines the relationship between women's legal activism and gender equality within the framework of democratic state-building. Using comparative, historical, and socio-philosophical methods, the study analyzes international experiences and recent reforms implemented in Uzbekistan. The findings indicate that women's participation in legal processes, public administration, civil society institutions, and decision-making mechanisms contributes significantly to democratic governance and social justice. Furthermore, the research demonstrates that legal empowerment of women enhances institutional transparency, promotes social inclusion, and strengthens the rule of law. The article concludes that increasing women's legal activism remains one of the most important conditions for achieving sustainable democratic development and effective gender equality policies.

The increasing digitization of lending ecosystems has intensified the need for continuous monitoring of borrower financial behavior and accurate estimation of default probability. Traditional credit evaluation models, which rely on static historical data, are increasingly inadequate in dynamic financial environments characterized by real-time transactions, evolving borrower profiles, and digitally mediated financial interactions. This research investigates a continuous financial behavior assessment framework integrated with smart technologies to improve default probability evaluation in modern lending infrastructures.

The study synthesizes concepts from smart system architectures, financial information systems, and intelligent data-driven decision frameworks to propose an adaptive evaluation paradigm. Drawing from smart city theory and integrated digital infrastructure models (Harrison and Donnelly, 2011; Chourabi et al., 2012), the research positions lending systems as interconnected intelligent ecosystems where financial behavior data is continuously captured, processed, and analyzed. Prior studies on financial management digitization (Hao Chenyan, 2019; Tang Wenting, 2020) highlight the role of computational tools in improving financial decision-making accuracy, while ERP-based financial systems (Zhang Jie, 2018) demonstrate the importance of structured digital integration in enterprise financial operations.

A key contribution of this work is the integration of real-time analytics mechanisms inspired by advanced AI-driven credit scoring methodologies, where continuous behavioral signals are transformed into predictive risk indicators. In particular, the framework aligns with intelligent risk modeling approaches described in AI-based lending systems, where real-time credit scoring and data-driven decision-making significantly improve predictive accuracy and financial stability (Modadugu et al., 2025).

The findings suggest that continuous financial behavior monitoring significantly enhances early detection of default risks, reduces uncertainty in lending decisions, and improves adaptive responsiveness in credit allocation systems. However, challenges remain in data heterogeneity, system interoperability, and algorithmic bias. The study concludes that the integration of smart technologies in lending infrastructures enables a shift from static credit evaluation to dynamic, behavior-aware financial intelligence systems.

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.