Efficient Allocation Research Articles

Healthcare Value Assessment: A Critical Review of Economic Outcome Metrics and Future Directions

This paper provides a critical review of economic outcome metrics used in healthcare value assessment, emphasizing the evolving landscape of resource allocation, patient-centered approaches, and standardization efforts. With healthcare costs rising globally, the efficient allocation of limited resources is essential. Metrics like Quality-Adjusted Life Years (QALYs), Disability-Adjusted Life Years (DALYs), Incremental Cost-Effectiveness Ratios (ICERs), and Cost-Benefit Analysis (CBA) are central to guiding funding decisions, influencing insurance coverage, and shaping treatment prioritization. Emerging trends, such as the integration of artificial intelligence (AI) and big data, are enhancing the precision of these assessments, while patient-centered metrics underscore the importance of patient satisfaction and quality of life. Additionally, there is a growing push for the standardization of these metrics to create consistent frameworks across diverse health systems. This paper explores case studies, practical applications, and future directions to provide insights into how healthcare systems can adopt more effective, globally aligned economic assessments.

A novel two-stage game theory-based data-driven coordination control for efficient power allocation and EV charging management

A novel two-stage game theory-based data-driven coordination control for efficient power allocation and EV charging management

UAVs Revolutionizing Efficiency: Integrating DRL and Random Walrus for Enhanced Energy and Spectral Resource Management

The improvement of energy efficiency and spectral efficiency in networks is achieved by seamlessly integrating energy harvesting, cognitive radio technologies, and Non-Orthogonal Multiple Access (NOMA) techniques. These complementary strategies optimize resource usage and address challenges related to energy consumption. Additionally, the adaptability and versatility of Unmanned Aerial Vehicles (UAVs) offer innovative solutions for enhancing coverage performance, thereby improving connectivity, efficiency, and reliability. We introduce a novel approach called the Deep Reinforcement Learning-Random Walrus (DRL-RW) algorithm, which combines Deep Reinforcement Learning (DRL) with the Random Walrus optimization (RWO) technique for efficient spectrum resource allocation and energy harvesting management in dynamic environments. The DRL-RW algorithm enables UAVs to learn optimal spectrum-sharing strategies and energy harvesting policies, while the RWO enhances the algorithm's adaptability and speed in exploring diverse solutions. Simulation results demonstrate the effectiveness of the DRL-RW algorithm, showing significant improvements in several performance metrics, including reduced energy consumption, enhanced computation time, improved convergence, increased signal-to-noise ratio, higher throughput, extended network lifetime, and increased harvested energy. These findings underscore the efficacy of the DRL-RW approach in addressing challenges associated with energy management in cognitive radio networks. The integration of UAVs, NOMA networks, and this novel algorithm represents a promising direction for developing energy-efficient communication systems.

Maximizing energy efficiency and system throughput using a self-adaptive penalty function with a whale optimization algorithm in social-aware device-to-device communications

Device-to-device (D2D) communication is an emerging approach for 5 G networks. It allows for high capacity, low latency, and increased bandwidth. These features have prompted several research studies to focus on meeting customers’ expectations. This research concentrates on obtaining optimal resource and power allocation, better quality of service, and reliable connectivity between D2D clients through a whale optimization algorithm (WOA). The WOA possesses greater stability, faster integration speeds, a powerful global search ability, and greater accuracy than traditional methods. The WOA utilizes an unconstrained objective function obtained through a self-adaptive penalty function. This unconstrained objective function yields optimal solutions for the resource as well as power allocation, higher throughputs, and energy and spectral efficiencies in D2D communications. For D2D communications, the energy efficiency of the WOA is 47.12 % higher than a genetic algorithm (GA) and 70.33 % higher than particle swarm optimization (PSO), and it yields high spectral efficiency and system throughput. Obviously, the WOA in this proposal is more efficient than a GA or PSO and shows the advantages of combining physical and social parameters for improved wireless network performance.

Regional Healthcare Resource Allocation and Decision-Making: Evaluating the Effectiveness of the Three-Stage Super-Efficiency DEA Model

This study addresses the challenge of achieving a more rational allocation of medical resources at the regional level, using Guangxi Province, China, as a case study. A three-stage super-efficiency Data Envelopment Analysis (DEA) model is employed to assess and analyze the effectiveness of resource allocation. The research methodology involves identifying input, output, and environmental variable indicators to construct a healthcare resource allocation index system. The indicator data are processed using Excel software. The three-stage super-efficiency DEA model is then applied to evaluate the healthcare system in Guangxi Province, focusing on Pure Technical Efficiency Change (PEC), Scale Efficiency Change (SEC), Efficiency Change (EC), Technological Change (TC), and Total Factor Productivity (TFP). Finally, the Malmquist index method is utilized to measure and dynamically analyze the efficiency of healthcare resource allocation. The study's results show that, from a static perspective, the average comprehensive efficiency is 1.067 before adjustment and 1.054 after adjustment, indicating relatively high overall efficiency in healthcare resource allocation in Guangxi Province. However, environmental factors and random errors have led to an overestimation of healthcare resource allocation efficiency, which the three-stage super-efficiency DEA model effectively corrects. Additionally, the average SEC and PEC values are 0.997 and 0.998, respectively, both below 1. This indicates that both scale efficiency and pure technical efficiency contribute to a decline in technical efficiency. Based on the results of the sensitivity analysis, the conclusions regarding the efficiency of healthcare resource allocation in Guangxi are deemed highly reliable. Despite the influence of uncertain factors, the model consistently provides stable and coherent assessment results in most scenarios. Therefore, special attention is needed to improve scale efficiency in healthcare resource allocation within the region, alongside enhancing management and technological capabilities in the healthcare sector. Overall, this study provides valuable reference and guidance for researchers and practitioners in related fields and offers scientific decision support for healthcare resource allocation.

Variation Across Organ Procurement Organizations in Deceased-Donor Kidney Offer Notification Practices.

How offer notifications are distributed early in the kidney allocation timeline, including how widely they are offered, is unclear. A better understanding of offer notification practices across organ procurement organizations (OPOs) may identify opportunities for more efficient allocation. We merged the Scientific Registry of Transplant Recipients potential transplant recipient file with additional offer notification time stamps to identify 54631 deceased-donor kidney match runs from 2017 to 2023. Offer notifications for a given match run are sent to candidates/centers in "batches." We quantified the number of offers in the initial batch-which theoretically reflects the OPO's initial estimate of how widely a kidney should be offered-and compared this metric across OPOs. Kidneys were offered to a median of 14 candidates (IQR 9-38) in the first batch of notifications, and this varied across OPOs from 3 to 746 candidates per initial batch. Batch size at the OPO-level did not correlate with rank at kidney placement or OPO nonuse rate. OPOs in the highest quartile of batch size sent more offers (median 100) than presumably necessary to place kidneys (median rank at placement 21), and OPOs in the lowest quartile of batch size sent fewer offers (6) than needed to place kidneys (rank at placement 19). Offer notification practices vary widely across OPOs, and many OPOs offer kidneys far more widely than necessary for placement. Optimization of offer notification practices may reduce unnecessary communications. Further research into allocation processes is needed to identify opportunities to improve efficiency of allocation for OPOs and transplant centers.

The effects of promotional narratives in entrepreneurial financing: Evidence from pre-sale crowdfunding

Based on a sample of pre-sale crowdfunding projects launched on the Kickstarter platform between 2009 and 2021, we find that using promotional words in project descriptions positively affects crowdfunding performance and strengthens the effect over time. We also find that, when lying cost is high, when the proportion of naïve investors is high, or when there exists a contemporaneous signal, the positive association between the use of promotional words and crowdfunding performance is more pronounced, implying that investors discount founder's promotional narratives less in these cases. Finally, we find that the use of promotional words is positively correlated with a higher probability of delayed delivery and requests for refunds, suggesting the employment of promotional narratives reduces the efficiency of resource allocation. The above findings are inconsistent with the classical signaling theory but consistent with the theory of cheap talk communication with lying costs. Overall, this study expands the research on signaling behavior in entrepreneurial financing by incorporating the theory of cheap talk communication with lying costs into this field.

Flexible electricity market clearing zones

Zonal markets and nodal pricing are the dominant designs for liberalized electricity markets. We propose an alternative design that changes zones in each bidding period according to the estimated most efficient dispatch. These flexible electricity market clearing zones consider the grid’s physical constraints to a larger degree than zonal markets but maintain their bidding simplicity and few price areas. We propose a proof-of-concept framework for flexible electricity market clearing zones, including a method to enumerate all zonal configurations. We illustrate the performance of this framework on a case study in the Nordic countries using flow-based market clearing (FBMC), considering a model for the day-ahead market and a real-time balancing market. Our results suggest that flexible electricity market clearing zones on sequential day-ahead and real-time balancing markets achieve costs slightly above nodal stochastic clearing. But, contrary to stochastic clearing, it can guarantee short-term revenue adequacy and cost recovery. Moreover, the flexible market design increases day-ahead market price levels and price variability at the nodal level, particularly in scenarios with high renewable generation, demonstrating its capacity to align price signals with network congestion and real-time supply conditions. Flexible electricity market clearing zones can thus facilitate the integration of renewables by enhancing system adaptability and promoting more efficient resource allocation.

Dynamic energy efficient resource allocation in multi-user multi-IRS mmWave 6G networks

Dynamic energy efficient resource allocation in multi-user multi-IRS mmWave 6G networks

Efficient UAV Hovering, Resource Allocation, and Trajectory Design for ISAC With Limited Backhaul Capacity

Efficient UAV Hovering, Resource Allocation, and Trajectory Design for ISAC With Limited Backhaul Capacity

Searching Across Realities: Investigating ERPs and Eye-Tracking Correlates of Visual Search in Mixed Reality.

Mixed Reality allows us to integrate virtual and physical content into users' environments seamlessly. Yet, how this fusion affects perceptual and cognitive resources and our ability to find virtual or physical objects remains uncertain. Displaying virtual and physical information simultaneously might lead to divided attention and increased visual complexity, impacting users' visual processing, performance, and workload. In a visual search task, we asked participants to locate virtual and physical objects in Augmented Reality and Augmented Virtuality to understand the effects on performance. We evaluated search efficiency and attention allocation for virtual and physical objects using event-related potentials, fixation and saccade metrics, and behavioral measures. We found that users were more efficient in identifying objects in Augmented Virtuality, while virtual objects gained saliency in Augmented Virtuality. This suggests that visual fidelity might increase the perceptual load of the scene. Reduced amplitude in distractor positivity ERP, and fixation patterns supported improved distractor suppression and search efficiency in Augmented Virtuality. We discuss design implications for mixed reality adaptive systems based on physiological inputs for interaction.

Rhythms in insect olfactory systems: underlying mechanisms and outstanding questions.

Olfaction is a critical sensory modality for invertebrates, and it mediates a wide range of behaviors and physiological processes. Like most living organisms, insects live in rhythmic environments: the succession of nights and days is accompanied by cyclic variations in light intensity and temperature, as well as in the availability of resources and the activity of predators. Responding to olfactory cues in the proper temporal context is thus highly adaptive and allows for the efficient allocation of energy resources. Given the agricultural or epidemiological importance of some insect species, understanding olfactory rhythms is critical for the development of effective control strategies. Although the vinegar fly Drosophila melanogaster has been a classical model for the study of olfaction and circadian rhythms, recent studies focusing on non-model species have expanded our understanding of insect olfactory rhythms. Additionally, recent evidence revealing receptor co-expression by sensory neurons has brought about an ongoing paradigm shift in our understanding of insect olfaction, making it timely to review the state of our knowledge on olfactory rhythms and identify critical future directions for the field. In this Review, we discuss the multiple biological scales at which insect olfactory rhythms are being analyzed, and identify outstanding questions.

Research on Whether Quality Policies Can Promote the High-Quality Development of China’s Manufacturing Industry and Its Configuration Paths in the Context of Sustainable Development

High-quality development enhances economic quality and resource allocation efficiency through various effective and sustainable means, with particularly notable results in the manufacturing sector. Quality policies, through establishing standards, norms, and incentive measures, aim to foster technological innovation, efficient resource utilization, and quality improvement in manufacturing, thereby supporting its high-quality development. This study explores the impact of quality policies on high-quality development in manufacturing and their configurational pathways, providing theoretical foundations and practical guidance for optimizing policy combinations and promoting sustainable development. This analysis is based on quality policies released from 2013 to 2022 and sample data from 30 Chinese provinces. The findings reveal that quality policies significantly promote high-quality development in manufacturing, particularly through incentive, capacity building, and system changing instruments, while command instruments may exert certain pressures on high-quality development. Configuration analysis further indicates that a single instrument is insufficient to support high-quality development in manufacturing fully; the synergistic effect of multiple policy instruments is essential, and regions should optimize policy combinations according to their characteristics. This study provides valuable insights for sustainable development, offering a deeper understanding of the complex causal relationship between quality policies and high-quality development in manufacturing.

Booster or Trapper? Corporate Digital Transformation and Capital Allocation Efficiency

Booster or Trapper? Corporate Digital Transformation and Capital Allocation Efficiency

The impact of artificial intelligence on corporate financial asset allocation: Moderating role of organizational dynamic capabilities

The integration of artificial intelligence (AI) into corporate operations has revolutionized financial decision-making processes, yet our understanding of how AI adoption specifically impacts financial asset allocation remains limited. While existing research has explored AI's role in various financial applications, there is a critical gap in empirically examining the relationship between AI adoption and corporate financial asset allocation, particularly in understanding the organizational capabilities that enable firms to effectively leverage AI technologies. This study investigates this relationship using panel data from 25,811 firm-year observations of Chinese A-share listed companies (2008–2022). Through comprehensive regression analyses, we find that AI adoption significantly enhances corporate financial asset allocation efficiency, with this relationship being distinctly moderated by organizational dynamic capabilities. Notably, absorptive capability exhibits the strongest moderating effect, followed by innovative and adaptive capabilities. These findings advance our understanding of AI's role in corporate finance by demonstrating that the success of AI implementation in financial decision-making is contingent upon firms' underlying organizational capabilities. The results provide valuable insights for managers and policymakers in developing targeted strategies to enhance the effectiveness of AI adoption in corporate financial management.

Implementation of Risk Management in Asset Management Governance Towards Optimizing Asset Utilization at Public Service Agency State Universities (Case Study of UPN Veteran Yogyakarta)

This study aims to analyze the application of risk management in asset governance at UPN Veteran Yogyakarta to achieve optimal asset utilization. As a Public Service Agency State University (PTN-BLU), UPN Veteran Yogyakarta faces various challenges in asset management, including budget constraints, lack of consistent monitoring, and the unavailability of formal risk evaluation standards. Qualitative descriptive research methods are used to gain in-depth understanding through interviews with asset managers and field observations. The results show that the implementation of risk management can have a significant impact on the efficiency of budget allocation, which includes more appropriate budget distribution based on priority needs and risks. Risk management also has the potential to extend the useful life of assets, with risk mitigation measures taken on assets that show signs of wear or early damage, thus preventing major repair costs due to serious damage. This research highlights that a technology-based monitoring system is an indispensable aspect to support the effectiveness of risk management in asset management. The implementation of structured risk assessment procedures helps in determining asset maintenance priorities, so that the assets that are most critical to the sustainability of campus operations receive greater attention. With the implementation of this comprehensive risk management, optimization of asset utilization can be achieved, which in turn supports the stability and sustainability of UPN Veteran Yogyakarta's operations as a quality higher education institution.

Measuring Going Concern Viability and the Effect of Interim Financing Under the Indian Insolvency and Bankruptcy Code, 2016

Abstract India’s insolvency framework mandates preserving viable firms as going concerns, but a critical gap remains – the lack of a standardised approach to assessing a firm’s viability during bankruptcy proceedings, resulting in inefficient allocation of interim finance to insolvent firms. This study sheds light on this issue by examining the performance of 21 firms during insolvency. We find that the absence of a clear viability assessment has led to suboptimal outcomes: viable firms being underfinanced and unviable firms being overfinanced. This has led to value deterioration, as many viable firms were ultimately pushed into liquidation due to insufficient interim financing. As a remedy, the authors propose a periodic viability assessment framework, for collecting and publishing information about the insolvent firm during insolvency proceedings. This would improve transparency and predictability for viable firms seeking interim finance, empowering lenders and investors to make better-informed decisions. By supporting viable distressed firms, this framework could foster a thriving market for distressed debt in India, rescuing valuable companies from the bankruptcy abyss.

A Novel Centralized Allocation Data Envelopment Analysis Model for Carbon Emission Allocation Under a Heterogeneous Abatement Cost: Application Within the Chinese Industrial Sector

This paper presents a mathematical approach to analyzing carbon abatement costs and the allocation of carbon emission allowances in China’s industrial sectors. We utilize input–output data from 30 Chinese provinces between 2009 and 2018 to estimate carbon abatement costs by applying the slack-based measure (SBM) efficiency model and its dual form. The SBM model captures inefficiencies and offers a rigorous framework for measuring abatement costs. Using these costs, we develop a centralized allocation data envelopment analysis (DEA) model, which maximizes sectoral benefits through optimal reallocation. This DEA model is formalized as a linear programming problem, with the aim of determining the efficient allocations of carbon allowances while maintaining the system’s economic productivity. Furthermore, we construct intertemporal, interregional, and spatiotemporal allocation DEA models to examine the dynamics of carbon emission allowance allocation over time, space, and combined spatiotemporal dimensions. These models offer insights into the efficiency of carbon markets under varying conditions. Our proposed new mathematical formulations reveal optimal allocation strategies that can balance emission reductions with industrial productivity. This study also provides novel mathematical frameworks for analyzing the carbon allowance distribution and contributions to both the theory and application of mathematical optimization in environmental policy design. Our findings reveal that China’s industrial carbon abatement costs exhibit significant interprovincial and regional differences. Developed provinces with higher levels of industrial development have higher carbon abatement costs, while provinces with less-developed industrial sectors have lower costs. Under the interregional allocation scenario of carbon emission allowances that consider abatement costs, developed provinces have smaller industrial carbon emission reductions, whereas less-developed provinces have larger reductions. In the intertemporal allocation scenario, provinces with larger industrial economies face greater emission reduction tasks. Under the combined interregional and intertemporal allocation scenario, industrial sectors in coastal developed provinces have lower carbon emission reductions, while those in inland less-developed provinces have higher reductions, mirroring the spatial allocation results of carbon emission allowances.

Who takes the lead: Synergistic emission reduction effects of proactive government and efficient market in atmospheric pollution mitigation

In environmental protection, government and market forces must work together. However, existing literature often separates the two and discusses their roles independently. Therefore, this study incorporates both the market and government into a unified analytical framework. It redefines government force as environmental regulation and market force as allocation efficiency in environmental protection. The study empirically examines the synergistic emission reduction effects of active government and efficient market on air pollution, using data from 272 prefectural-level cities in China from 2003 to 2019. The results show: (1) Both government and market interventions can significantly reduce air pollution levels, and their combination achieves a synergistic emission reduction effect.(2) Within this synergy, government intervention remains dominant, while the market plays a supportive role. (3) Analyzing the heterogeneous effects under different scenarios reveals that collaboration and mutual monitoring between the government and the market can effectively counteract the adverse effects of economic policy uncertainty and corruption on the environment. Furthermore, when a single entity undertakes environmental protection, it may encounter issues of diminishing effectiveness over time. However, the synergistic effects of government and market cooperation can mitigate these challenges. (4) The synergy exhibits a threshold effect based on the varying strengths of government and market forces. (5) Public participation enhances the synergistic emission reduction effect of government and market, acting as a vital complement to their environmental protection efforts. This study provides new empirical evidence on the synergistic effect of current environmental protection measures in improving air pollution control.

Advanced Hybrid Optimization Algorithms for Energy Efficient Cloud Resource Allocation

AbstractCloud computing is highly sought after for its dynamic resource allocation capabilities and pay‐per‐use model. However, previous research has identified several challenges, such as lower coverage, high integration rates, longer computation times, and complex operators, all of which are associated with NP‐hard problems. These issues negatively impact the efficiency of resource allocation and scheduling, leading to slower processes, inefficiencies in multi‐objective optimization, lower throughput, and higher power consumption. To address these challenges, we propose a unique Hybridized Optimization Algorithm that integrates Crow Swarm Optimization (CSO), Cuckoo Search Optimization (CSO), and Cat Hunting Optimization (CHO). Initially, Particle Swarm Optimization (PSO) and the Crow Search Algorithm (CSA) handle the exploitation and exploration phases to balance task loads. Subsequently, Cuckoo Search Optimization (CSO) enhances resource utilization and addresses NP‐hard issues, while Cat Hunting Optimization (CHO) refines the search from global to local optimal spaces to achieve the best values. The results demonstrate that the proposed hybrid technique effectively reduces user request waiting times, lowers energy consumption, and decreases execution times on cloud servers compared to baseline approaches, thereby significantly improving overall system performance.