Western Governors University

ABSTRACT Financial inclusion entails ownership of a formal account, such as a bank account, fintech account, or mobile money account, which can be used to access basic financial services and to send or receive digital payments. This essay analyzes the global and regional trends in financial inclusion of the older population. A descriptive analysis of the World Bank Global Findex 2021 microdata for the older age group was conducted. The findings reveal that financial inclusion of older adults, in terms of formal account ownership of any type, is greater in high-income OECD countries (98%), North America (96%), and the Euro Area (99%). In contrast, financial inclusion of older adults is much lower in low-income countries (25%), Arab countries (44%), Sub-Saharan Africa (44%), and Middle Eastern and North African countries (57%). There is disproportionate progress in financial inclusion of older people across regions, particularly in the “financial institution account” and “use of digital payments” categories. This disparity calls for more coordinated effort to expand access to financial services for older adults and ensure that they have access to old age–appropriate credit, savings, payment, and insurance products that enable them to live a meaningful life even in old age. Notable challenges to financial inclusion of the aging population include low income, discrimination, and poor formal saving culture. However, policymakers can address these challenges by encouraging financial service providers to offer tailored financial products, provide financial education and awareness, introduce digital literacy programs, and increase the financial capability of older adults.

Abstract Reliable pore-scale particle tracking is pivotal for understanding non-Fickian transport in dual-porosity media, yet the computational burden of deep-learning pipelines can limit their practical use. This study systematically quantifies how two fundamental hyper-parameters—image resolution and training batch size—jointly shape accuracy and efficiency in a state-of-the-art tracker that couples SimVP video prediction with Trackpy-derived ground truth. Fluorescence microscopy movies (1328 frames, 3672 × 4100 px) of colloid migration through a PDMS micromodel were down-scaled to 64–400 px and trained with batch sizes of 4 or 8. Point-wise errors (MSE, MAE, RMSE), structural fidelity (SSIM, PSNR), and perceptual quality (LPIPS) were evaluated on validated trajectories, 1000 unseen pairs, and a blind hold-out set. Increasing resolution from 64 to 300 px raises pixel-based errors (MSE × ≈700) and inference time (0.26 s → 5 s batch⁻ 1 ) but unlocks a 20-fold rise in detected particles, while perceptual metrics remain above high-quality thresholds (SSIM > 0.999). Reducing the batch from 8 to 4 consistently halves to tenfold improves all error metrics, boosts PSNR by ~ 8 dB, and lowers LPIPS by up to 85%, with minimal variance penalties. Recommended operating points are: batch 4 @ 300 px for maximum fidelity, batch 4 @ 400 px for the densest particle fields, and batch 8 @ 128–256 px where real-time throughput dominates. These results provide concrete guidelines for balancing accuracy, particle yield, and computational cost in next-generation pore-scale imaging studies.

Polysubstance use is prevalent in the population but remains understudied in preclinical models. Alcohol and opioid polysubstance use is associated with negative outcomes, worse treatment prognosis, and higher overdose risk; but underlying mechanisms are still being uncovered. Examining factors that motivate use of one substance over another in different contexts in preclinical models will better our understanding of polysubstance use and improve translational value. Here we assessed baseline anxiety-like and locomotive behavior and then measured voluntary consumption of multiple doses of alcohol and fentanyl in group housed male and female mice using our novel Socially Integrated Polysubstance (SIP) system. Fifty-six male (n=32) and female (n=24) adult mice were housed in groups of 4 for one week with continuous access to food, water, two doses of ethanol (5% and 10%) and two doses of fentanyl (5 ug/ml and 20 ug/ml). Our analyses revealed sex differences across multiple domains – female mice consumed more liquid in the dark cycle, had higher activity, a higher preference for both ethanol and fentanyl over water, and their fentanyl preference increased over the seven days. Furthermore, both male and female mice displayed polysubstance consumption patterns, with female mice displaying more prolonged polysubstance use across days in the SIP chambers. We then used machine-learning techniques to reveal underlying relationships between baseline behavioral phenotypes and subsequent polysubstance consumption patterns, where anxiety- and risk-taking-like behavioral phenotypes mapped onto discrete patterns of polysubstance use, preference, and escalation. By simulating more translationally relevant substance use and improving our understanding of the motivations for different patterns of consumption, this study contributes to the developing preclinical literature on polysubstance use with the goal of facilitating better treatment outcomes and novel therapeutic strategies.

This study presents a comprehensive multi-criteria decision-making (MCDM) framework for evaluating and selecting optimal non-destructive testing (NDT) techniques for oil and gas facility maintenance operations. This research used a Fuzzy Analytic Hierarchy Process (FAHP) integrated with multiple MCDM methods to assess eight NDT techniques including radiographic testing, ultrasonic testing, and thermographic testing. The evaluation framework incorporated seven technical criteria and seven economic criteria. The FAHP results revealed spatial resolution (0.175) as the most critical technical criterion, followed by depth penetration (0.155) and defect characterization (0.143). For economic criteria, downtime costs (0.210) and operational costs (0.190) emerged as the most significant factors. This study used TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), PROMETHEE (Preference Ranking Organization Method for Enrichment of Evaluations), and VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) methods to rank NDT techniques, with results consolidated using the CRITIC (CRiteria Importance Through Intercriteria Correlation) method. The final techno-economic analysis identified radiographic testing as the most suitable NDT method with a score of 0.665, followed by acoustic emission testing at 0.537. Visual testing ranked lowest with a score of 0.214. This research demonstrates the effectiveness of combining fuzzy logic with multiple MCDM approaches for NDT method selection in offshore welding operations.

This mixed-methods study examines how organizations develop AI-first HR functions by fundamentally reimagining people operations rather than merely adopting new technologies. Drawing on survey data from 152 HR leaders, 24 in-depth interviews, and longitudinal case studies of 8 organizations during 2022-2024, the research identifies three foundational pillars of successful AI-HR integration: augmented capabilities, data-driven culture, and ethical governance. Organizations implementing AI-first approaches demonstrate significant improvements in talent acquisition efficiency (38%), performance management effectiveness (27%), and learning personalization (41%). However, implementation success is hindered by data quality issues (74% of organizations), capability gaps (68%), and employee resistance (61%). Statistical analysis reveals that organizations emphasizing augmentation over automation achieve significantly higher implementation success rates (p < .01), with change management effectiveness emerging as the strongest predictor of successful adoption (β = .42, R² = .53). The resulting evidence-based implementation framework provides practical guidance for organizations navigating this complex transformation, highlighting the critical importance of strategic alignment, process redesign, data architecture, capability development, change management, and ethical governance in realizing the potential of AI-HR integration.

To manage cloud resources efficiently, reduce costs, and improve service delivery in dynamic cloud environments, accurate forecasting is essential. The highly variable and nonlinear nature of cloud workloads often makes traditional prediction models ineffective. In this paper, Recurrent Neural Networks (RNNs) are integrated with evolutionary optimisation algorithms, specifically Genetic Algorithms (GAs) and Particle Swarm Optimisation (PSO), to develop a hybrid forecasting methodology. Combining GA-PSO enhances the prediction accuracy for both univariate and multivariate resource usage, with a focus on CPU and memory usage. According to experiments, FLPSONN outperforms FLGANN, FLNN, and FLPSONN in terms of mean absolute error (MAE) and percentage improvement when used with real-world cloud workload datasets. Hybrid models enable scalable and adaptive forecasting of cloud resource demands, thereby improving the efficiency and reliability of cloud service provisioning.

This research article examines the imminent transformation of business and labor structures driven by artificial intelligence, critically analyzing adoption timelines and organizational impacts. Drawing on both primary survey data from 127 organizations and secondary research from institutional reports, the study quantifies workforce disruption projections while interrogating their methodological limitations. Through mixed-methods analysis combining quantitative industry data with qualitative case studies, the research presents an evidence-based three-dimensional strategic framework for organizational response: comprehensive upskilling that fosters behavioral change rather than mere tool adoption; an all-organization approach enabling distributed innovation; and strategic integration aligning systems and structures across departments. The study acknowledges significant implementation barriers including regulatory uncertainty, organizational resistance, and ethical considerations. It concludes that organizational adaptation capacity—not merely technological investment—will determine which organizations successfully navigate this period of transformation, while recognizing that adoption timelines may vary significantly across geographic and industry contexts.

This study explores the critical role of trust as an accelerant for organizational and social change. Drawing on mixed-methods research conducted across 27 organizations, this investigation conceptualizes trust as a "wormhole" that creates pathways for change to propagate rapidly through social systems, bypassing traditional barriers of resistance and institutional inertia. The findings demonstrate significant correlations between trust levels and change implementation velocity (r = .61, p < .001), with high-trust environments showing 42% faster implementation timelines. Using structural equation modeling, the research identifies key mechanisms through which trust operates as a change catalyst, including reduced resistance, enhanced information flow, and improved decision-making efficiency. The study presents a multi-level framework for understanding trust as a change accelerant and provides evidence-based strategies for practitioners navigating complex organizational transformations. Importantly, this analysis acknowledges contextual factors that moderate the trust-change relationship, including power dynamics, cultural contexts, and organizational structures. These findings contribute to both theoretical understanding and practical approaches to leveraging trust as a strategic asset in change management.

This research examines the dynamic relationship between job satisfaction and professional commitment among university professors, synthesizing empirical evidence and theoretical frameworks to provide actionable insights for academic leaders. This proposed multi-dimensional model addresses significant gaps in existing literature by clarifying the bidirectional and reciprocal relationships between environmental determinants, faculty satisfaction, professional commitment, and institutional outcomes. Drawing on established research and validation data from 347 faculty members across diverse institutional types, the analysis reveals that faculty satisfaction—influenced by autonomy, collegial environment, work-life balance, resource adequacy, and value alignment—demonstrates a strong positive correlation with professional commitment (r = .63, p < .001), with important variations by career stage, appointment type, and discipline. This relationship is further moderated by institutional context, with stronger effects in research-intensive settings (β = .71) compared to teaching-focused institutions (β = .58). The article outlines evidence-based strategies for enhancing both satisfaction and commitment, including developing supportive department leadership, implementing transparent workload policies, creating meaningful governance participation opportunities, and establishing robust mentoring systems. Through contextual applications tailored to different institutional environments, this research argues that systematic attention to faculty satisfaction and commitment represents not merely a human resources concern but a strategic imperative for institutional effectiveness, resilience, and long-term sustainability in today's challenging higher education landscape.