Search Results (685)

Accelerated electric vehicle (EV) adoption imposes complex requirements on grid integration and energy dispatch. Current Vehicle-to-Vehicle (V2V) trading research frequently utilizes monolithic forecasting architectures that fail to account for the stochastic nature of mobility data. Furthermore, traditional optimization strategies often prioritize financial yields at the expense of user-centric utilities, hindering global system optimality. To resolve these limitations, this paper proposes a hierarchical optimization framework, designed to reconcile the interests of stakeholders. The approach first employs a hybrid deep learning architecture, integrating long short-term memory (LSTM), gated recurrent unit (GRU), and Transformer architectures, dynamically weight predictions and refine available dwell time estimations. Then, a multi-objective optimization model is formulated to identify Pareto-optimal solutions that balance economic efficiency with user convenience. Finally, a dynamic greedy matching algorithm is introduced to facilitate rapid transaction pairing for large-scale, real-time V2V requests under multiple constraints. Simulation results demonstrate that this hierarchical framework improves trading success rates, optimizes resource distribution, and enhances overall user satisfaction. Full article

The growing demand for sustainable energy carriers has intensified interest in hydrogen production from renewable resources and waste-derived substrates. In this context, microbial electrolysis cells (MECs) have emerged as a promising technology for the simultaneous treatment of organic waste and biohydrogen generation. This review provides an overview of recent advances in MEC systems, focusing on reactor configurations, performance indicators such as hydrogen production rate, coulombic efficiency, and chemical oxygen demand removal. Attention is given to the valorization of real waste streams, including municipal and agro-industrial effluents, highlighting the differences between laboratory- and pilot-scale applications. While numerous studies have demonstrated the technical feasibility of MECs, several bottlenecks still limit their large-scale implementation, including challenges associated with the use of complex substrates. In particular, untreated wastewater often leads to reduced process efficiency due to its variable composition and the occurrence of competing microbial pathways. To overcome these limitations, integrated approaches are also discussed, with emphasis on the coupling of dark fermentation, capable of enhancing substrate biodegradability through the production of volatile fatty acids, with MEC systems. Overall, MEC technology represents a promising pathway for sustainable hydrogen production within circular waste management frameworks, although further advancements are required to enable its practical application. Full article

Mohs micrographic surgery (MMS) is a highly specialized skin cancer procedure that combines complete microscopic margin assessment with maximal preservation of uninvolved tissue. The technique is based on staged excision of the tumor with systematic horizontal sectioning and real-time examination of the entire peripheral and deep surgical margins, allowing further tissue removal only in areas where residual tumor is identified. Its unique strength lies in the ability to detect subclinical tumor extensions that may be missed by conventional excision and standard vertical sectioning, thereby improving local control while minimizing unnecessary tissue sacrifice. Since its introduction in the 1930s by Frederic E. Mohs, the technique has evolved into a cornerstone of modern dermato-oncology, particularly for tumors arising in anatomically critical areas, recurrent neoplasms, and histologically aggressive malignancies. MMS is now widely regarded as the treatment of choice for high-risk basal cell carcinoma and cutaneous squamous cell carcinoma because of its superior cure rates and tissue-sparing approach. Beyond its oncologic advantages, MMS allows precise clinicopathologic correlation and immediate reconstruction tailored to the final defect, contributing to favorable functional and cosmetic outcomes. As experience with the technique has expanded, so too has interest in adjunctive tools for preoperative tumor delineation and margin control, further refining patient selection and surgical accuracy. Overall, MMS represents an essential advance over conventional excision for selected cutaneous malignancies, offering an optimal balance between radical tumor clearance and preservation of normal tissue. Full article

COVID-19, caused by the SARS-CoV-2 virus, has become a global public health crisis with diverse clinical manifestations affecting multiple organ systems, including the integumentary system. One notable cutaneous manifestation, referred to as “COVID toes,” involves the development of pernio-like chilblains, characterized by red-to-violet macules, plaques, or nodules, primarily on toes and fingers. This characteristic clinical feature gained significant attention due to its apparent association with COVID-19, especially during the early stages of the pandemic when individuals with mild or asymptomatic cases exhibited these symptoms. Concurrently, digital platforms such as Google Trends have emerged as tools for tracking public interest in health-related topics, offering insights into real-time patterns of disease awareness. Previous research has demonstrated that Google Trends data may correlate with the incidence of infectious diseases, suggesting that search interest can be a proxy for disease outbreaks. In this study, we sought to explore the potential relationship between public interest in COVID toes, as reflected in Google Trends, and the incidence and mortality rates of COVID-19. Specifically, we examined whether peaks in search interest for “COVID toes” corresponded with surges in COVID-19 cases and deaths. By analyzing trends in search data, we aimed to assess the utility of digital platforms as an epidemiological tool for monitoring disease progression and public awareness. Our findings provide insights into the potential role of digital search data in forecasting outbreaks and highlight the interplay between public perception and the clinical burden of COVID-19, emphasizing the importance of real-time data in public health surveillance and response. Full article

This study set out to investigate the interactions between business cycles, financial cycles and monetary policy in South Africa. Explicitly, the study aims to examine the role of financial factors in business cycle models and the possibility of a unified macroeconomic framework in South Africa. Further, the study assesses the effects of demand shocks, supply shocks, interest rate shocks, and financial shocks on macroeconomic fluctuations. The study applied an analytical approach integrating the Generalised Method of Moments and System Generalised Method of Moments with a Structural New Keynesian Dynamic Stochastic General Equilibrium framework. Accordingly, it was concluded that the financial cycle plays a significant role in business cycle models and is a main driver of macroeconomic fluctuations in South Africa. Further, a unified macroeconomic framework for monetary policy analysis that links the financial system to the real economy in South Africa possibly exists. This study contributes to the South African Reserve Bank’s efforts by deepening understanding of the interactions between the financial system and the real economy and their implications for monetary policy in South Africa. By comparing the standard Taylor rule with a finance-augmented Taylor rule in a DSGE framework, the study helps answer the question of whether financial stability should be adopted as a second objective of monetary policy. Full article

Export supply chains (ESCs) for perishable fruits, such as mangoes and avocados, are shaped by complex supply–demand dynamics and macroeconomic conditions. However, limited forecasting of these dynamics constrains strategic planning and investment in Australia’s horticultural sector. This study assesses the longitudinal growth and future potential of mango and avocado exports. To achieve this, the study identifies influential supply–demand dynamics and applies time-series forecasting to understand the export trends. Historical export–import data were analysed for mango and avocado from 1992 to 2024, including volume, value, per capita GDP (Australia and key importing nations), real exchange rate, and real interest rate. Holt’s exponential smoothing was used to forecast export trends, supported by unit root testing in RStudio 4.2.3 and model execution in SPSS version 30. ARIMA and ARIMAX models were applied to stationary variables to improve mango export forecasts. The results show that avocado exports follow a strong upward trajectory, while mango exports remain volatile due to logistical inefficiencies and informal trade disruptions. ARIMAX modelling confirmed that production and consumption volumes significantly enhance forecast accuracy. Macroeconomic trends, rising GDP, declining real interest rates, and stable real exchange rates further reinforce Australia’s competitive position in the destination markets. The long-run trends in export volume and value suggest that both the mango and avocado sectors hold potential for further export growth, although the higher volatility observed in the avocado series indicates that expansion should be approached cautiously. To sustain this growth, maintaining a balanced relationship between production capacity and export demand, particularly for commodities exhibiting higher volatility, will be essential for ensuring stable and efficient export performance over time. Full article

This study examines how income inequality conditions the effectiveness of monetary policy in delivering sustainable economic growth in Saudi Arabia over 1980–2024, a question of direct relevance to the Kingdom’s Vision 2030 agenda and to Sustainable Development Goals 8 and 10. We apply an Autoregressive Distributed Lag (ARDL) bounds-testing framework to four monetary policy instruments—the Saudi Central Bank (SAMA) repo rate, broad money supply (M2), domestic credit to the private sector, and the real effective exchange rate (REER)—with the Gini coefficient introduced as a moderator through mean-centered interaction terms. The bounds test confirms a robust long-run cointegrating relationship, and the error-correction term indicates rapid adjustment to equilibrium. In the long run, interest rates exert a significant negative effect on growth and on trade openness, a positive effect, while income inequality significantly moderates the growth effects of broad money supply and private-sector credit. Diagnostic tests support the adequacy of the specification. The findings indicate that financial inclusion is not only a distributional objective but a macroeconomic prerequisite for effective monetary policy transmission, with direct implications for integrating inclusive-finance policy into the Vision 2030 framework. Full article

Science, Technology, Engineering, and Mathematics (STEM) education and workforce participation are growing increasingly important for economic and social development, yet STEM disengagement and participation challenges persist. Existing research has predominantly focused on the aspirations of young people, leaving the retrospective perspectives of STEM professionals relatively underexplored, particularly in non-metropolitan contexts. This study examines how 79 STEM professionals, educated in non-metropolitan settings, rated and described influential factors in their STEM career journeys. A predominantly female sample working in health fields was obtained through convenience and snowball sampling approaches. Using a convergent mixed-method design, participants quantitatively rated 10 STEM career influence factors and qualitatively reflected on their educational and professional pathways. Repeated-measures analyses indicated personal interest/ability was rated as the most influential factor, followed by secondary/high school education and teachers. Qualitative cluster analysis identified a bifurcated pattern in which proximal personal, social, and school-based influences contributed to STEM trajectory establishment, while broader economic and cultural influences were associated with STEM trajectory resilience and maintenance. Participants also described circumstantial impediments, alternate pathways, and real-world experiences that challenged linear pipeline assumptions about STEM careers. The findings suggest that rural STEM participation involves both the establishment and maintenance of career trajectories shaped by related, but distinct, influences. Full article

Fixed-wing unmanned aircraft are widely used for aerial mapping because they can acquire high-resolution data at relatively low cost, but maintaining both energy efficiency and image quality in the presence of wind and flight-performance limits remains challenging. In practice, operators introduce buffer regions and extended waypoints outside the area of interest to cope with deviations during turning, which increases flight distance and energy use; yet, this approach can still degrade image overlap near the boundary. This paper presents a path-planning framework that designs turning maneuvers compatible with bank-angle, stall-margin, and roll-rate constraints while aligning mapping lanes directly with the area of interest. The framework combines analytically structured turn patterns, an energy-based metric that accounts for increased aerodynamic load in banked flight, and a two-stage path-angle selection procedure that uses a fast, simplified model to guide a more detailed optimization. Simulation studies on both idealized and real survey geometries indicate that, within the considered maneuver families and assumptions, the proposed method can reduce the integrated aerodynamic energy metric and improve coverage compliance relative to a conventional path-following approach that relies on overshoot points. Full article

Shared immersive environment sports venues, virtual classrooms, and collaborative workspaces require multiple users to stream 360° videos simultaneously over the same edge network, yet every existing adaptive bitrate system optimises each viewer in isolation. This self-interested behaviour triggers a bandwidth auction that chronically starves the most uncertain viewers: Jain’s Fairness Index for ten independently optimised agents routinely falls below 0.85. We present FairEdge360, a hierarchical multi-agent reinforcement learning framework that reformulates multi-user 360° streaming as a Decentralised Partially Observable Markov Decision Process (Dec-POMDP) and proves, formally, that fairness and quality are complementary rather than competing objectives. Three tightly coupled innovations make this possible. First, a Lightweight Uncertainty Estimator (LUE) a compact 8385-parameter four-layer MLP evaluates per-device viewport prediction confidence $c_t^i=\sigma \left(w_4^{\top }{h}_3\right)$ in under approximately 2.1 ms on commodity smartphones (95th percentile, iPhone 12 A14 Bionic), enabling selective edge offloading that reduces device energy consumption by 38.9%. Second, a variational Graph Neural Network compresses each agent’s 256-dimensional GRU state into a 32-byte INT8 latent, transmitted over a dynamic RTT-gated neighbourhood graph at 96 bytes per agent per 500 ms 75% less overhead than competing approaches. Third, the edge coordinator maximises the Nash social welfare objective $\mathrm{NSW}={\left({\prod }_{i=1}^N{Q}_i\right)}^{1/N}$, whose gradient $\partial \mathrm{NSW}/\partial Q_i\propto 1/Q_i$ automatically prioritises the most disadvantaged viewer; a formal proof guarantees that every Pareto-optimal policy satisfies $Q_i/{\sum }_j{Q}_j\ge 1/N$. Counterfactual advantage estimation correctly attributes each agent’s marginal contribution to the global reward, eliminating the credit-assignment ambiguity inherent in standard multi-agent baselines. Evaluated on 284 users, 52 omnidirectional videos, and 10,000 real network traces spanning 4G LTE, 5G mmWave, HSDPA, and campus WiFi, FairEdge360 raises Jain’s Fairness Index from 0.934 to 0.976 (+4.5%), improves worst-case user quality-of-experience from MOS 2.54 to MOS 3.21 (+26.4%), and halves rebuffering rate from 2.1% to 1.1%, all within a 20 ms motion-to-photon budget on a commodity smartphone. Full article

This study develops a composite index of financial repression to overcome persistent gaps and inconsistencies in financial data across the CFA franc zones. The index aggregates proxies such as interest rate spreads, real interest rates, domestic credit to the private sector as a percentage of GDP, broad money supply as a percentage of GDP, and bank liquid-reserves-to-assets ratio, with inversion applied to align higher values with greater repression. Fixed-effects panel regressions reveal a significant negative impact of repression on gross capital formation, indicating a 2.8 percentage point reduction per unit increase, robust to controls including GDP per capita growth, trade openness, population growth, public debt, and inflation. Findings underscore repression’s role in impeding investment activity in CFA franc zones, where centralized controls crowd out private allocation amid fiscal dependencies. Policy implications advocate for gradual liberalization to enhance intermediation, while future research could extend to dynamic interdependencies via vector autoregression. This contribution advances repression measurement in African contexts, bridging theoretical distortions with empirical evidence for sustainable growth. Full article

The growing demand for renewable energy, together with the need to mitigate climate change and promote more sustainable agriculture systems, has stimulated interest in energy crops. In this context, invasive alien plant species (IAPS), which have progressively colonized abandoned farmland, degraded ecosystems, and marginal areas, represent a key bioresource. IAPS have a dual nature combining high ecological invasiveness and fast growing rate with notable energetic potential. These aspects have generated a still ongoing debate among farm managers, ecologists, and policymakers regarding their role within the future bioeconomy. The present study provides a review of the IAPS black locust (Robinia pseudoacacia L.) on its real benefits as a source of bioenergy, ecological impact, and the management strategies adopted. We examine the trade-offs between containment efforts and use for renewable bioenergy production, particularly in marginal areas where few alternatives exist. This review highlights the need for stratified site-specific approaches that balance biodiversity conservation with bioresource exploitation. Finally, this study also contributes to the ongoing discussion on whether IAPS should be regarded primarily as a management challenge or a multifunctional bioresource, as in the production of bioenergy. Full article

For personalized treatments, including soft tissues repair, the use of in situ bioprinting is of increased interest. Many soft tissues, such as sphincters, have poorly known mechanical properties and a complex structure, with limited options for a medical practitioner to assess where the injections should be made and how much should be injected. The rate of injection and its variation have a direct implication on pain sensation for patients, but post-injection efficacy largely depends on the ability of the hydrogel to adapt to local loads and displacements, keeping the 3D structure compliant to the surrounding tissues. Such a method is known as ‘in situ bioprinting’. There are, however, limited data regarding hydrogels’ functionalities for such applications, and many commercial hydrogels, as medical devices, are used off-label. This study aims to introduce an innovative, robust, and reliable approach for evaluating the ejection-related mechanical properties of various commercial hydrogels. The ejectability of six clinically approved hydrogels was assessed through their rheological properties, characterized by measuring apparent viscosity using a mechanical testing device in a novel setup combined with the dynamic syringe pump analysis (for a pre-set constant ejection rate). It was shown that a well-established power-law approximation offers a straightforward, less computationally intensive approach than more complex models that attempt to account for viscosity, shear rate, and wall slip. It assesses hydrogel performance within an actual system, including the syringe and nozzle, rather than just characterizing the material in isolation, thus making it particularly valuable for predicting how gels will behave under real conditions. This method can be adapted for specific clinical bioprinting applications, including sphincter repair, lipoatrophy correction, or deep dermal/transdermal targets, optimizing speed, flow rate, and applied force. Full article

This paper examines the interaction between fiscal and monetary policies in Myanmar under ongoing political and economic uncertainty. We estimate a small open-economy New Keynesian DSGE model using Bayesian methods, combining the Kalman filter with Markov Chain Monte Carlo sampling on quarterly data from 2013Q1 to 2022Q1. The results show a persistent regime of monetary and fiscal policy conflict. While the central bank follows an active anti-inflationary interest rate rule that satisfies the Taylor principle, fiscal policy shows weak responsiveness to public debt, providing limited fiscal backing for monetary stabilization. As a result, monetary tightening aimed at controlling inflation exacerbates fiscal stress through the debt-service channel, undermining the overall effectiveness of macroeconomic stabilization. Political instability emerges as a key structural driver of macroeconomic fragility. Political shocks are highly persistent and are transmitted primarily through increases in the country risk premium, accounting for more than 50% of real exchange rate volatility and generating exchange rate depreciation, higher inflation, and output contraction. Overall, the findings indicate that monetary tightening alone is insufficient to restore macroeconomic stability in fragile and conflict-affected economies. Credible fiscal adjustment and improvements in political stability are necessary to contain external vulnerabilities and restore the effectiveness of monetary policy. Full article

Uncertain fractional differential equations model complex systems that exhibit memory effects and are influenced by human-based uncertainty. These equations provide a flexible and accurate framework for representing real-world phenomena, particularly in situations where traditional probabilistic methods are inadequate, such as modeling financial market systems where uncertainty and memory effects play a significant role. This research first presents an existence and uniqueness result for the uncertain fractional system with the $\phi$-Hilfer fractional derivative, obtained via the successive approximation approach. Then the analytical solution is derived using the Mittag–Leffler function, and sample continuity is demonstrated under Lipschitz and linear growth conditions. To illustrate the applicability of the theory, we consider an interest rate model and provide two numerical examples to support the theoretical results on existence and uniqueness. All results are developed using the $\phi$-Hilfer fractional derivative, which generalizes the Caputo, Hadamard, and Katugampola fractional derivatives. Consequently, the results are presented in a generalized form. Full article