Search Results (4,279)

This study offers an integrative and systematic examination of the relationship between the COVID-19 pandemic, digital payment systems, and financial inclusion. To achieve this, it adopts a dual methodological approach that combines a PRISMA 2020-based systematic literature review with bibliometric analysis. The analysis covers a set of peer-reviewed journal articles published between 2020 and 2025, using bibliometric mapping to explore the conceptual structure of the field, its main thematic clusters, and its temporal evolution. The findings indicate that COVID-19 acted as an external shock that accelerated the adoption of digital payment technologies. However, this acceleration did not automatically or uniformly lead to sustainable financial inclusion. Instead, digital payments emerge in the literature as an intermediate pathway linking the pandemic to financial inclusion outcomes under specific conditions. The strength and direction of this process depend on factors such as structural readiness, regulatory quality, digital infrastructure, levels of trust, and financial and digital literacy. Bibliometric results reveal strong conceptual convergence around three core themes—COVID-19, Digital Payments, and Financial Inclusion—forming a cohesive knowledge structure. Over time, the literature progresses from describing the crisis itself, to analyzing digital operational responses and finally to assessing longer-term inclusion and development outcomes. Overall, the study clarifies the interactive nature of the digital payments–financial inclusion nexus and proposes an integrative interpretive framework that can guide future research and support the design of more inclusive and resilient digital financial policies in post-crisis contexts. Full article

Community pharmacies in the United States (US) face an increasingly unsustainable future due to declining third-party reimbursement (remuneration) and ongoing cash flow challenges following the elimination of retroactive direct and indirect remuneration (DIR) fees. These pressures have contributed to widespread pharmacy closures, the emergence of pharmacy deserts, and reduced access to care for millions of patients. Despite these challenges, community pharmacy remains the most common employment setting for pharmacy school graduates in the US. However, currently required community pharmacy Advanced Pharmacy Practice Experience (APPE) student rotations may offer limited exposure to business, management, and entrepreneurial activities, potentially leaving students underprepared for practice in this setting. US colleges and schools of pharmacy are uniquely positioned to address this gap by partnering with their community pharmacy APPE rotation sites to intentionally integrate business- and practice-focused knowledge, skills, and attitudes (KSAs) into the APPE. Equipping students with these KSAs may enhance early career readiness while also supporting the financial sustainability of US community pharmacies through the development of innovative, revenue-generating services. These efforts further align with the 2025 Accreditation Council for Pharmacy Education (ACPE) Standards and may help advance the profession. Future research should examine optimal community pharmacy APPE structures, models, and assessment strategies to maximize student preparedness and long-term community pharmacy sustainability. Full article

Modern healthcare remains structurally and conceptually fragmented, with profound clinical and policy implications. At its root lies an ontological fracture: the prevailing biomedical model reduces patients to discrete biological systems (organs, biomarkers, and symptoms) detached from the psychological, social, and ecological contexts in which health and illness are experienced. This is compounded by epistemological fragmentation, where medical knowledge is compartmentalized into increasingly narrow specialties, limiting holistic understanding. These philosophical divisions manifest in downstream operational, informational, financial, and policy dysfunctions duplicative testing, misaligned incentives, disconnected care pathways, and population health failures. To address these multilevel fractures, we propose a unified architecture grounded in three interlocking components. First, the Precision and Personalized Population Health (P3H) framework offers a principle-based realignment toward care that is integrated, personalized, proactive, and population wide. P3H addresses the conceptual shortcomings of fragmented care by focusing on the full human trajectory across time, systems, and determinants. Second, General Purpose Technologies including artificial intelligence, biosensors, mobile diagnostics, and multimodal data systems enable the operationalization of whole-person care at scale, especially in low-resource settings. Third, the AI-WHOLE policy framework (Alignment, Integration, Workflow, Holism, Outcomes, Learning, and Equity) provides governance principles to guide ethical, equitable, and context-specific implementation. We argue that this triadic blueprint is particularly critical for Global South nations, where the lack of legacy infrastructure offers an opportunity for leapfrogging toward integrated, intelligent systems of care. Early models illustrate how policy-aligned, technology-enabled care rooted in whole-person principles can yield improvements in continuity, cost-efficiency, and chronic disease outcomes. This manuscript offers a systems-level strategy to overcome fragmentation and reimagine healthcare delivery, not only by refining clinical tools, but by redefining what it means to care for the human being in full. Full article

With the rise of Financial Operations (FinOps), cloud resource management requires the enforcement of strict budgetary guardrails rather than soft cost objectives. However, discrete Virtual Machine (VM) types often cause structural infeasibility, which existing methods fail to address. We formulate the Budget-Constrained VM Resizing problem under temporal hard constraints and establish the NP-hardness of the scalarized problem as a completeness result. To solve this, we propose the Budget-aware Dual (BD) solver, which utilizes a dual variable as a shadow price to dynamically steer candidate decisions toward budget feasibility without opaque penalty tuning. Extensive experiments demonstrate that BD significantly improves budget feasibility and operational stability compared to the baselines. In the run-rate setting, BD reduces candidate budget violations to zero once the budget enters feasible regimes at and substantially reduces operational churn, decreasing the change rate from 53.95% to 7.80% in an oscillatory workload scenario. BD also exhibits near-linear scalability and remains more than 100× faster than NSGA-II at large fleet sizes. This framework provides a theoretically grounded and scalable approach for balancing economic efficiency, operational stability, and strict budget compliance. Full article

Electric vehicle(EV) diffusion exhibits nonlinear, path-dependent dynamics shaped by interacting economic, technological, and social constraints. This paper develops a unified hybrid systems framework that captures these complexities by integrating microfounded household choice, capacity-constrained firm behavior, local network spillovers, and multi-level policy intervention within a Filippov differential-inclusion structure. Households face heterogeneous preferences, liquidity limits, and network-mediated moral and informational influences; firms invest irreversibly under learning-by-doing and profitability thresholds; and national and local governments implement distinct financial and infrastructure policies subject to budget constraints. The resulting aggregate adoption dynamics feature endogenous switching, sliding modes at economic bottlenecks, network-amplified tipping, and hysteresis arising from irreversible investment. We establish conditions for the existence of Filippov solutions, derive network-dependent tipping thresholds, characterize sliding regimes at capacity and liquidity constraints, and show how network structure magnifies hysteresis and shapes the effectiveness of local versus national policy. Optimal-control analysis further demonstrates that national subsidies follow bang–bang patterns and that network-targeted local interventions minimize the fiscal cost of achieving regional tipping. Beyond theoretical characterization, the framework is structurally calibrated to match the order-of-magnitude effects reported in leading empirical and simulation-based studies, including network diffusion models, agent-based simulations, bass-type specifications, and fuel-price shock analyses. The hybrid formulation reproduces short-run percentage-point subsidy effects, long-run forecast dispersion under alternative network assumptions, and policy-induced equilibrium shifts observed in the applied literature while providing a unified geometric interpretation of these heterogeneous results through explicit basin boundaries and regime switching. The framework provides a complex systems perspective on sustainable mobility transitions and clarifies why identical national policies can generate asynchronous regional outcomes. These results offer theoretical foundations for designing coordinated, cost-effective, and network-aware EV transition strategies. Full article

Reducing agricultural emissions is vital for climate mitigation, yet evidence on green finance’s potential to facilitate agricultural decarbonization—particularly in China—remains scarce. Leveraging China’s Green Finance Reform and Innovation Pilot Zones as a quasi-natural experiment, this study employs a staggered difference-in-differences design and complementary Callaway-Sant’Anna estimates. Using a balanced panel of 282 prefecture-level and above cities spanning 2012–2022—a window covering five pre-policy years before the initial 2017 pilot rollout and sufficient post-policy years to capture dynamic effects for the 2017, 2019, and 2022 cohorts—this study assesses the policy impact on agricultural carbon emission intensity. The findings reveal that the pilot policy reduces emission intensity by approximately 9.2% on average. This result is robust across event-study analyses, placebo tests, PSM-DID, policy interference checks, and alternative outcome specifications. Channel-consistent evidence suggests that the effect operates through three mechanisms: greener credit allocation, stronger green technological innovation, and lower-carbon adjustment of the agricultural production structure. The effect is larger in eastern China, major grain-producing regions, and cities with higher levels of financial development, and exhibits a strengthening trend over time. By analyzing China’s city-based pilot approach, this study demonstrates how financial policy can support agricultural decarbonization in settings characterized by dispersed emitters, imperfect environmental monitoring, and strong food-security constraints. The findings extend beyond China to inform other developing economies seeking non-price-based pathways to greener agriculture. Full article

Compressed air leakages represent a major source of energy waste and financial loss in industrial facilities. However, accurately detecting and quantifying these leaks remains challenging due to the wide variation in the accuracy, cost, usability, and practical applicability of available methods. This paper presents a structured review and evaluation of leakage localization and quantification methods for compressed air systems (CASs), categorized into hardware-, software-, and non-technical-based approaches. Based on expert interviews and a comprehensive literature review, a set of evaluation criteria was defined and applied within a multi-criteria decision analysis (MCDA) framework. The Analytic Hierarchy Process (AHP) was used to derive criteria weights, while the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was employed to rank the alternatives separately for localization and quantification tasks. To enhance practical relevance, five expert interviews were conducted with industrial stakeholders from diverse professional backgrounds, including maintenance engineers and energy managers. A questionnaire was also distributed to assess the methods. The results provide illustrative insights into the relative suitability of different methods. Within the scope of this exploratory study, from a practical industrial perspective, the compressor duty cycle method and non-intrusive load monitoring (NILM) appear to be promising approaches to leakage quantification, while ultrasonic detection is preferred for localization. Notably, discrepancies between questionnaire-based rankings and expert interview insights highlight the limitations of purely survey-driven evaluations. The proposed framework supports industrial decision-makers in selecting leakage detection and quantification methods by balancing technical performance, implementation effort, and operational constraints, thereby contributing to reduced energy losses and improved system efficiency. Full article

Objectives: Despite vaccination being the most effective way of preventing infections and vaccination rates recovering worldwide after the COVID-19 pandemic, vaccine hesitancy persists. Some factors, such as psychological and social barriers, can negatively impact views on vaccines and can contribute to vaccine hesitancy. The primary objective of this structured literature review is to investigate the available evidence relating to factors affecting vaccine hesitancy within several countries in Southeastern Europe. Methods: An electronic database search was conducted to identify studies assessing the public and healthcare professionals’ (HCPs) attitudes towards vaccination in Southeastern Europe. These searches were supplemented with grey literature searches. Included studies were conducted in Bulgaria, Croatia, Romania, Serbia, and Slovenia between 1 January 2012 and 31 December 2022. Results: Of the 35 studies identified from the database searches, the most prominent theme observed across Romania, Croatia, and Bulgaria was low confidence in COVID-19 vaccines. Across all age groups, COVID-19 vaccine confidence in these regions was highly dependent on whether individuals thought vaccines were safe and effective, as well as their general trust in vaccines. Confidence in COVID-19 vaccines was seen as relatively high, with attitudes towards routine and elective vaccines being generally positive amongst the general public and HCPs, in Romania, Croatia, Serbia and Slovenia. However, uncertainty around the effectiveness of the vaccine still exists. In Bulgaria, trust in routine and elective vaccines remained low in the general public. Complacency and financial constraints were also identified as underlying causes of vaccine hesitancy. Conclusions: The main cause behind vaccine hesitancy in several countries in Southeastern Europe is distrust in vaccine effectiveness and safety. These key findings can be utilised to support evidence-based decisions regarding where to focus resources to improve public and HCP perception of vaccines in Southeastern Europe. Full article

This study’s purpose is to explore the factors that support and impede women’s economic empowerment in rural settings of Saudi Arabia. A qualitative study was conducted in particular rural settings of Saudi Arabia. Data were collected through semi-structured interviews with 18 rural women. A thematic analysis was performed to analyze and present the findings. The findings reveal the challenges that women face that limit their engagement in economic activities, such as low levels of education, limited access to finance, and societal and cultural barriers. Moreover, there are opportunities for women’s empowerment via technical training, policy reforms and infrastructural development. Hence, women’s engagement in economic activities is vital for their personal development as well as overall economic growth. It is necessary to uplift the economy with women’s engagement in economic activities by emphasizing community-based programs, redesigning gender-sensitive policy and providing initial finance to start activities. Thus, policymakers should focus on creating environments that provide access to technical education, financial inclusion, and startup initiatives. Moreover, tailored programs based on women’s needs in rural settings could be highly supportive in empowering them economically. Full article

This paper introduces Historical State Reconstruction, a novel framework for low-latency financial decision-making using Large Language Models. While agentic systems have demonstrated potential in synthesizing complex financial narratives, they typically rely on Retrieval-Augmented Generation or memory-based architectures. These paradigms introduce significant latency and risk look-ahead bias during real-time inference, rendering them unsuitable for high-frequency trading environments where milliseconds determine profitability. This proposed framework resolves this bottleneck by decoupling the heavy computational cost of context acquisition from the latency-sensitive critical path of decision-making. We propose a system that proactively compiles unstructured regulatory filings (10-K, 10-Q, 8-K) into a structured, bitemporal database. By pre-computing complex state facets, such as financial health ratios, governance structures, and insider trading signals offline, the system allows trading agents to “time travel” to a reconstructed state at any historical moment t with $O(1)$ snapshot retrieval plus $O(k)$ delta application complexity. We implement this approach on the top 50 companies in the S&P 500 ranked by market capitalization, processing over 12,000 filings to demonstrate a pipeline that transforms high-dimensional financial narratives into compact, prompt-ready context. Our evaluation shows that the system reduces context retrieval latency by over 97% compared to traditional baselines while achieving a 300:1 compression ratio for financial health data. Furthermore, the bitemporal architecture guarantees strict temporal integrity, eliminating the risk of data leakage in backtesting and satisfying the reproducibility requirements of regulatory frameworks like SR 11-7. Full article

This study examines the dynamic interactions between oil price shocks, monetary policy, and non-oil output in Saudi Arabia using Vector Autoregressive Model (VAR), and quarterly data spanning 2010: Q1–2025: Q3. The study aims to provide policy-relevant insights through which external oil price shocks and domestic monetary policy shocks affect inflation and non-oil economic activity in the context of Saudi Arabia’s structural transformation under Vision 2030. The results show that global oil prices behave largely as exogenous shocks, with limited feedback from domestic monetary conditions, implying that monetary policy effectiveness operates primarily through inflation and domestic demand channels rather than through oil prices directly. The findings underscore the importance of gradual and predictable monetary tightening, coordinated with fiscal and macroprudential policies, to mitigate the indirect spillovers of oil price volatility on the non-oil sector. While monetary policy plays a stabilizing role by containing inflation and supporting macroeconomic balance, sustaining diversification and non-oil growth under Vision 2030 requires complementary measures, including targeted credit support, financial market deepening, and structural reforms that enhance productivity and private-sector investment. Full article

This study investigates the determinants of capital structure in Moroccan agricultural SMEs, with particular emphasis on the distinction between interest-bearing debt and non-interest-bearing liabilities in a context characterized by persistent credit constraints. While traditional capital structure theories typically treat debt as a homogeneous aggregate, such an approach may obscure important financing dynamics in financially constrained environments. Using a panel dataset of 52 agricultural SMEs observed over the period 2017–2022, the analysis employs a correlated random effects model to control for unobserved heterogeneity. The results indicate a negative relationship between profitability and both total and short-term debt, consistent with the predictions of the Pecking Order Theory. Liquidity, asset tangibility, and firm size are negatively associated with non-interest-bearing current liabilities, suggesting that trade-based financing may serve as an adjustment mechanism when access to formal credit is limited. In contrast, long-term debt is only weakly explained by firm-level characteristics, pointing to potential supply-side constraints in agricultural credit markets. Overall, the findings suggest that financing patterns in agricultural SMEs appear to be more closely associated with credit market imperfections than with optimal trade-off considerations. By distinguishing between different debt components, this study contributes to the literature by highlighting the importance of debt composition when analyzing capital structure in emerging and financially constrained economies. Full article

Balancing structural safety and economic efficiency in super-tall building design remains a formidable challenge. To address this issue, this study proposes a genetic-algorithm-based multi-variable, multi-objective optimization method. The design variables include the member sizes and vertical layout positions of outrigger and belt trusses, as well as the cross-sectional dimensions of mega-columns. Total structural weight and maximum inter-story drift ratio are adopted as objective functions, while code-specified constraints, such as shear-weight ratio, stiffness-weight ratio, and axial compression ratio, are incorporated to formulate the fitness evaluation for optimization. Taking a 300 m baseline structure designed for 6-degree seismic intensity and equipped with two outrigger trusses and three belt trusses as an example, single-variable sensitivity analyses are first performed. The results show that optimizing any single parameter can yield certain local improvements, yet it cannot overcome the weight–deformation trade-off induced by strong variable coupling. By selecting representative feasible solutions from the multi-variable solution set that match the “optimal” values identified by single-variable optimization as benchmarks, the multi-variable optimum reduces the total structural weight by approximately 6.5–18.4% relative to these representative designs. Moreover, optimal layout strategies of outrigger and belt trusses are investigated for two typical building heights (200 m and 300 m) and two seismic intensity levels associated with design ground motions having a 10% exceedance probability in 50 years, namely 6-degree (0.05 g) and 8-degree (0.20 g). Finally, the proposed method is validated through a case study of a super-tall financial center in Chongqing, where the total structural weight is reduced by 12.3% after optimization while the inter-story drift ratio still satisfies relevant code requirements. The results demonstrate that the proposed framework can generate competitive feasible solutions and provide a systematic means to achieve a balanced trade-off between structural safety and economic efficiency for outrigger–belt-truss super-tall buildings. Full article

Systemic risk in banking systems arises from losses transmitted through networks of contractual exposures. Yet, most widely used measures rely on market-implied volatility and equity prices rather than structural balance sheet fragilities. This paper develops a flow network framework that models systemic risk as a capacity-constrained loss-diffusion process governed by flow conservation, contractual seniority, and interbank topology. Using regulatory balance sheet data for four major U.S. banks across six quarters of the 2007–2008 financial crisis, we simulate millions of unit-consistent cascade scenarios to characterize the distribution of bank failures and aggregate losses. Despite severe macro-financial stress, the system remains in a subcritical contagion regime, exhibiting frequent single-bank failures, virtually no multi-bank cascades, and quasi-stationary aggregate losses concentrated around USD 420–430B.We extend the model to a stochastic setting in which the initial shock magnitude is randomized while propagation mechanics remain deterministic. The resulting loss distribution remains tightly concentrated and scales approximately linearly with shock size, suggesting that uncertainty in shock realizations does not induce nonlinear cascade amplification. Applying an efficient network benchmark, we estimate that 10–23% of expected systemic loss is attributable to suboptimal network architecture, implying potential gains from structural policy intervention. A comparison with SRISK reveals early divergence and convergence only at peak stress, highlighting the complementary roles of structural and market-based systemic risk measures. Finally, a graph neural network trained on synthetic flow network data fails to reproduce threshold-driven cascade dynamics, underscoring the importance of considering network structures vis-à-vis data-driven approaches. Full article

The management of large and heterogeneous building stocks requires decision-support tools capable of prioritising interventions under limited technical and financial resources. In this framework, the role of structural deterioration is rarely integrated within a unified prioritisation framework. This study proposes a rapid deterioration-based assessment for prioritising maintenance within heterogenous portfolios. The assessment is articulated into two levels. A Project Level (PL) is based on visual inspections and component-level condition ratings, while a Network Level (NL) introduces contextual and functional modifiers related to the relevance of each structural unit within the building stock. A seismic assessment procedure is integrated in proposed decision-making system for optimising intervention planning. The two assessments are integrated through a decision-tree logic providing an overall classification of buildings within portfolios. The proposed framework is applied to an industrial-oriented building stock located in Italy, comprising 79 structural units characterised by significant typological heterogeneity, including masonry, reinforced concrete, precast reinforced concrete, and steel buildings. The application illustrates the internal consistency of the proposed framework and its ability to support a transparent and articulated prioritisation process for maintenance and risk mitigation within heterogeneous building portfolios. Further applications to different building stocks are required to explore the general applicability of the methodology. Full article