Volume 8.
Issue 10.

This research examines the strategic role of international trade relations in promoting sustainable economic growth in Uzbekistan during 1997–2024. Uzbekistan has implemented trade liberalization, foreign investment attraction, macroeconomic stabilization, and market-oriented reforms to strengthen economic development. The study analyzes the relationship between exports, imports, foreign direct investment (FDI), inflation, and GDP growth using secondary macroeconomic data. Empirical analysis was conducted in Stata 16 through descriptive statistics, Pearson correlation, ordinary least squares (OLS) regression, and diagnostic tests.

The findings show that Uzbekistan achieved an average GDP growth rate of 6.19% during the study period. Regression results reveal that exports have a positive and statistically significant effect on economic growth, while inflation negatively affects GDP growth at the 5% significance level. FDI demonstrates a positive but marginally significant contribution, whereas imports have a negative and statistically insignificant impact. The model explains 35.33% of GDP growth variation.

Diagnostic tests confirm the absence of serious multicollinearity, heteroskedasticity, and autocorrelation issues. The study concludes that effective strategic management of international trade relations, export-oriented policies, and macroeconomic stability are key drivers of sustainable economic growth in Uzbekistan. The research also recommends strengthening export diversification, improving the investment climate, and enhancing trade management mechanisms to support long-term economic sustainability and global competitiveness.

The emergence of autonomous, self-optimizing algorithmic systems in finance, governance, and defence has introduced a novel form of systemic risk: Algorithmic Wars. Unlike traditional conflicts, these arise from misaligned, competing algorithms executing at superhuman speeds, amplifying fragility and destabilizing critical systems. From high-frequency trading-induced flash crashes to AI-driven disinformation campaigns and autonomous weaponry, early signals of such wars are already observable (Kroll et al., 2017; Mökander & Floridi, 2022). Yet global governance responses remain fragmented, aspirational, and reactive, anchored in static principles ill-suited for dynamic escalation risks.

This paper formally defines Algorithmic Wars, identifies their core drivers and pathways, and highlights the catastrophic potential of leaving such systems unchecked. It critically assesses the inadequacy of existing AI governance frameworks, which fail to prevent escalation, enforce accountability, or embed sovereign-calibrated deterrence. As a doctrinal response, the paper introduces the DFAS-PostObjective Ethical Response, an enforceable governance framework derived from Dynamic Financial Applied Science (DFAS) and its PostObjective philosophy (Alaali, 2025a; 2025b). The response operationalizes ethical foresight, authorship integrity, predictive auditability, and escalation governance through DFAS-FEP, DFAS-AAP, DFAS-DAIF, and DFAS-IFRS Manuscripts. Finally, the paper calls for global adoption of doctrinal deterrence mechanisms to pre-empt full-scale Algorithmic Wars.

This study examined the influence of credit mobilization on the development of cooperative businesses in Imo State, Nigeria. Three objectives guided the study: to assess the influence of credit mobilization on financial sustainability, examine the effect of member savings mobilization on capital base expansion, and investigate the influence of loan repayment ratio on investment capacity. The study was anchored on Resource Mobilization Theory and adopted a descriptive survey design. The study drew on information from 15 purposively selected agricultural cooperative societies across the three senatorial zones of Imo State. A sample of 377 respondents was determined, and 368 completed questionnaires were returned, representing a response rate of 97.6%. Data were collected with a structured questionnaire reported to have a Cronbach’s alpha of 0.89. Descriptive statistics and simple ordinary least squares regression were used for analysis. The reported ANOVA results indicated statistically significant relationships between credit mobilization and financial sustainability (F = 6,134.021, p < .001), member savings mobilization and capital base expansion (F = 6,828.277, p < .001), and loan repayment ratio and investment capacity (F = 34,306.981, p < .001). The findings suggest that access to mobilized credit, regular member savings, and timely loan repayment are important financial processes associated with cooperative development. The study recommends transparent credit procedures, consistent savings programmes, and stronger loan appraisal and recovery systems. The statistical tables in the source document contain inconsistencies in some reported model-summary values; these should be checked against the original SPSS output before journal submission.

The food processing industry in Vietnam represents a critical pillar of economic development, export competitiveness, and industrial modernization. However, disparities in provincial administrative efficiency continue to influence innovation outcomes across firms, limiting the sector’s full potential. This study develops a policy-oriented modeling framework to assess the relationship between provincial governance efficiency and innovation capacity within Vietnam’s food processing industry. Drawing on innovation systems theory, organizational capability frameworks, and policy-driven economic development models, the research integrates multi-dimensional indicators of administrative performance, regulatory support, and firm-level innovation behavior. The study synthesizes empirical insights from international food industry literature and adapts them to the Vietnamese provincial governance context. The proposed model highlights how governance quality, stakeholder collaboration, R&D investment, and institutional support mechanisms jointly shape innovation outcomes. Findings suggest that administrative efficiency acts as a catalytic enabler of innovation diffusion, particularly when combined with structured policy instruments and open innovation ecosystems. The study contributes a structured analytical framework for policymakers to optimize governance structures and enhance regional innovation capacity in emerging economies.

The rapid expansion of the digital economy is transforming the landscape of sustainable entrepreneurship worldwide. This study examines how digital business models accelerate ESG-oriented transformation through the integration of data-driven technologies, platform solutions, and AI-enabled tools. Despite the growing relevance of green entrepreneurship, existing research lacks a systematic understanding of how digitalization enhances environmental performance, social inclusion, and governance transparency. The objective of this paper is to develop a comprehensive conceptual model of Green Digital Entrepreneurship (GDE) and empirically assess its contribution to ESG outcomes. A mixed-method approach was applied, combining quantitative analysis of ESG indicators with qualitative case studies of green digital startups. The findings reveal that digital technologies significantly improve carbon footprint measurement accuracy, reduce operational costs, and strengthen stakeholder engagement. Data-driven models demonstrate the highest ESG impact, particularly in emissions monitoring, circular resource management, and transparency of sustainability reporting. The study contributes to the theoretical development of digital entrepreneurship by linking digital capabilities to sustainable value creation. Practical implications include recommendations for green startups, policymakers, and investors on scaling digital solutions for the low-carbon transition. This research highlights the strategic importance of digital innovation in shaping resilient, climate-responsive, and socially inclusive entrepreneurial ecosystems. Future studies should explore the impact of emerging technologies such as digital twins, green fintech, and AI governance frameworks on long-term ESG transformation.

The article examines the role of behavioral segmentation in improving state management of entrepreneurship development. The study focuses on differences in entrepreneurs’ risk tolerance, innovation activity, investment intentions, and digital readiness. A differentiated approach to government support is proposed, based on identifying behavioral groups and matching them with appropriate financial, informational, and institutional instruments. The proposed mechanism includes diagnostics, segmentation, targeted support, monitoring, and policy adjustment. This approach can improve the effectiveness and targeting of government measures for entrepreneurship development.

This study investigates machine learning-based real-time fraud detection and risk prediction for secure banking software systems. We comparatively evaluated Logistic Regression, Random Forest, XGBoost, LightGBM, and Neural Network models using a highly imbalanced credit-card transaction dataset. The models were assessed using accuracy, precision, recall, F1-score, ROC-AUC, false-positive rate, and detection latency. The comparative results indicate that ensemble boosting approaches provide the most effective balance between fraud detection performance and operational efficiency. XGBoost achieved the strongest overall performance across the evaluated criteria, while LightGBM demonstrated competitive predictive capability with lower computational latency. Based on these findings, we propose an integrated risk-scoring architecture in which the machine learning model assigns transaction-level fraud risk and supports differentiated responses, including normal approval, step-up authentication, and fraud investigation or blocking. The study further highlights the importance of model governance, explainability, continuous monitoring, concept-drift detection, and secure integration when deploying machine learning within banking environments. Although the experimental evaluation is based on a publicly available benchmark dataset and therefore does not directly represent production-scale U.S. banking data, the findings demonstrate the potential of machine learning, particularly gradient-boosting methods, as a foundation for scalable and adaptive real-time fraud detection systems.

The food processing industry in Vietnam represents a critical pillar of economic development, export competitiveness, and Foreign direct investment (FDI) is an important mechanism through which economies may acquire capital, advanced technologies, managerial expertise, and access to international production networks. However, increased FDI inflows do not automatically translate into higher labour productivity, as the outcomes depend on institutional quality, domestic firms’ absorptive capacity, technological diffusion, workforce capabilities, and the structure of investment. This article develops an analytical framework for assessing the relationship between FDI inflows and labour productivity by integrating endogenous growth theory, productivity spillover mechanisms, institutional conditions, and dynamic econometric analysis. Drawing on the supplied literature, the study proposes a conceptual and methodological approach that distinguishes direct productivity gains from indirect spillovers and identifies the conditions under which FDI may produce sustained improvements in productive capacity. The framework combines labour productivity indicators, FDI measures, institutional variables, and relevant macroeconomic controls within a dynamic analytical structure. It also outlines an empirical strategy based on autoregressive distributed lag modelling and bounds testing to distinguish short-run adjustments from long-run relationships. The synthesis indicates that the productivity effects of FDI should be evaluated conditionally rather than assumed to be uniformly positive. The framework provides a basis for comparative research and evidence-informed investment policy, while recognizing that the proposed relationships require empirical validation using country-specific or panel data.

This reseаrch studies the оppоrtunities fоr develоping cоmmunity-bаsed ecоtоurism in the Urgut district, Uzbekistаn by аligning empiricаl findings with current perspectives. The study аnаlyzes the role оf institutionаl suppоrt, infrastructure, lоcаl speciаlization, and in shаping tоurism develоpment. The results shоw thаt sustаinable grоwth depends оn strаtegic аlignment, mоdernized infrаstructure, and regiоnаl ecоsystems. The pаper prоpоses а lоcаlized mоdel for ecоtоurism develоpment thаt suppоrts tо bоth аcаdemic discоurse аnd prаctical pоlicy implementatiоn.

This study examined the influence of selected Sustainable Development Goals (SDGs) on the welfare of rural households in Cross River State, Nigeria, with specific attention to three dimensions of welfare: household income, nutritional well-being, and healthcare outcomes. Guided by three objectives, the study investigated the influence of access to quality education on household income, the effect of employment opportunities on nutritional well-being, and the relationship between access to clean water and healthcare outcomes among rural households. The study adopted a descriptive survey research design, anchored on Social Capital Theory, and drew a sample of 317 respondents from a population of 1,524 registered rural households across the three senatorial districts of Cross River State, using a multistage sampling technique. A structured questionnaire was administered, out of which 304 copies were correctly completed and retrieved, representing a 95.9 per cent response rate. Data was analysed using descriptive statistics and Ordinary Least Squares (OLS) regression. Findings revealed that access to quality education, particularly teacher qualification and improvement in educational access over time, significantly influenced household income (R² = 0.976, F = 2980.164, p < 0.001). Employment opportunities, especially youth employment and the provision of regular income, significantly improved nutritional well-being (p < 0.001 for all indicators). Access to clean water was also very strongly and positively correlated with healthcare outcomes (r = 0.949 to 0.987, p < 0.001). The study concludes that SDG-aligned interventions in education, employment, and water provision are critical levers for improving rural household welfare, and recommends that policymakers prioritise teacher quality, youth-focused employment programmes, and reliable clean water provision as integrated strategies for sustainable rural development.