Optimal Resource Allocation Research Articles

Evolution of policy-driven ecosystem of original innovation talents

In an era of unprecedented global challenges, the cultivation, attraction, and retention of original innovation talent have become critical determinants of national competitiveness and sustainable development. This study introduces a novel multi-agent original innovation talent ecosystem model (MOITEM) to systematically analyze the effects of different talent policies on the ecosystems of original innovation talent across various regions in China. The simulations reveal that while direct talent policies drive short-term talent growth, they often fall short of addressing the long-term needs for resource allocation and environmental optimization, thereby limiting sustained competitiveness. In contrast, indirect and combined talent policies prioritize the overall quality of the ecosystem, providing more enduring support for original innovation. Furthermore, this study explores the interactions among agents within the ecosystem and their synergistic relationships with the external environment, highlighting the necessity of targeted policy formulation and multi-agent collaboration. The findings not only elucidate the strengths and limitations of various talent policies but also offer tailored recommendations based on regional characteristics, emphasizing the importance of continuous evaluation and adaptive policy adjustment. These insights hold substantial theoretical and practical implications for the development and optimization of global policies aimed at fostering original innovation talent.

Strategic investment and program effectiveness: a roadmap to ending AIDS in Bangladesh by 2030

ABSTRACT The journey towards ending AIDS epidemic in Bangladesh by 2030 is ambitious yet achievable. Although Bangladesh has always had a low rate of HIV among its general population, it remains one of seven countries in Asia and the Pacific where new HIV cases are rising. This study evaluates the effectiveness of HIV programmatic strategies and investment scenarios using the AIDS Epidemic Model (AEM) from 2023 to 2030, focusing on optimizing resource allocation and interventions. The findings indicate that without improved program effectiveness, new HIV infections will increase to 1,382 by 2030, failing to meet the targets of the Global AIDS Strategy 2021–2026. If Bangladesh improves its HIV program effectiveness according to the Global AIDS Strategy 2021–2026, the NSP and Global AIDS Strategy targets could significantly lower new infections and AIDS-related deaths and increase treatment coverage to meet Ending AIDS targets. The NSP targets could reduce new HIV infections to under 275 annually and achieve treatment goals by 2030. The study reveals that NSP targets are the most cost-effective, offering the highest benefit-cost ratio, highlighting the urgent need to enhance HIV prevention program effectiveness, particularly among key populations, to achieve both public health and economic benefits.

Predicting anxiety treatment outcome in community mental health services using linked health administrative data

Anxiety disorders is ranked as the most common class of mental illness disorders globally, affecting hundreds of millions of people and significantly impacting daily life. Developing reliable predictive models for anxiety treatment outcomes holds immense potential to help guide the development of personalised care, optimise resource allocation and improve patient outcomes. This research investigates whether community mental health treatment for anxiety disorder is associated with reliable changes in Kessler psychological distress scale (K10) scores and whether pre-treatment K10 scores and past health service interactions can accurately predict reliable change (improvement). The K10 assessment was administered to 46,938 public patients in a community setting within the Western Australia dataset in 2005–2022; of whom 3794 in 4067 episodes of care were reassessed at least twice for anxiety disorders, obsessive–compulsive disorder, or reaction to severe stress and adjustment disorders (ICD-10 codes F40–F43). Reliable change on the K10 was calculated and used with the post-treatment score as the outcome variables. Machine learning models were developed using features from a large health service administrative linked dataset that includes the pre-treatment K10 assessment as well as community mental health episodes of care, emergency department presentations, and inpatient admissions for prediction. The classification model achieved an area under the receiver operating characteristic curve of 0.76 as well as an F1 score, precision and recall of 0.69, and the regression model achieved an R2 of 0.37 with mean absolute error of 5.58 on the test dataset. While the prediction models achieved moderate performance, they also underscore the necessity for regular patient monitoring and the collection of more clinically relevant and contextual patient data to further improve prediction of treatment outcomes.

Exploring the usefulness of the INLA model in predicting levels of crime in the City of Johannesburg, South Africa

Crime prediction serves as a valuable tool for deriving insightful information that can inform policy decisions at both operational and strategic tiers. This information can be used to identify high-crime areas, and optimise resource allocation and personnel management for crime prevention. Traditionally, techniques such as the Poisson model and regression analysis have been widely used for crime prediction. However, recent statistical advancements have introduced Integrated Nested Laplace Approximations (INLA) as a promising alternative for spatial and temporal data analysis. This study focuses on crime prediction using the INLA model. Specifically, the first-order autoregressive model under the INLA modelling framework is employed on longitudinal data for crime predictions in different regions of the City of Johannesburg, South Africa. The model parameters and hyperparameters considering space and time are estimated through the INLA model. In this work, the suitability and performance of the INLA model for crime prediction is assessed, which effectively captures spatial and temporal patterns. This study contributes to research by first introducing a novel approach for South African crime prediction. Secondly, it develops a model using no demographic information other than clustering attributes as an exogenous variable. Thirdly, it quantifies prediction uncertainty. Finally, it addresses data scarcity through demonstrating how INLA can provide reliable crime predictions, where conventional methods are limited. Based on our findings, the INLA model ranked areas by crime levels, obtaining a 29.3% Mean Absolute Percentage Error (MAPE) and 0.8 R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} value for crime predictions. These findings and contributions presents the potential of INLA in advancing evidence-based decision-making for crime prevention.

Examining the evolving landscape of liver cancer burden in the United States from 1990 to 2019

IntroductionLiver cancer (LC) is frequently preceded by cirrhosis and poses a significant public health challenge in the United States (US). Recent decades have seen notable shifts in the epidemiological patterns of LC, yet national data guiding the optimal allocation of resources and preventive efforts remain limited. This study aims to investigate the current trends, risk factors, and outcomes of LC in the US.MethodsThis study utilized the Global Burden of Disease (GBD) dataset to collect data on the annual incident cases, deaths, Disability-Adjusted Life Years (DALYs), age-standardized incidence rates (ASIR), age-standardized death rates, and age-standardized DALY rates of primary LC and its etiologies and risk factors, between 1990 and 2019. Percentage changes in incident cases, DALYs, and deaths and the estimated annual percentage change (EAPC) in ASIR and deaths rates of LC were calculated to conduct temporal analysis. Linear regression was applied for the calculation of EAPCs. Correlations of EAPC with socio-demographic index (SDI) were separately evaluated by Pearson correlation analyses.ResultsWe observed a marked increase in the ASIR of LC, increasing from 2.22 (95% CI: 2.15–2.27) per 100,000 people in 1990 to 5.23 (95% CI: 4.28–6.29) per 100,000 people in 2019, a percentage change of 135.4%. LC due to hepatitis C followed by alcohol use were the primary factors driving this increase. The ASIR and age-standardized death rates of LC showed a significant average annual increase of 3.0% (95% CI: 2.7–3.2) and 2.6% (95% CI: 2.5–2.8), respectively. There was a significant negative correlation between the SDI and the EAPC in ASIR (ρ = -0.40, p = 0.004) and age-standardized death rates (ρ = -0.46, p < 0.001). In 2019, drug and alcohol use, followed by elevated body mass index (BMI) were the primary risk factors for age-standardized DALY rates attributable to LC.ConclusionThe increased burden of LC in the US highlights the need for interventions. This is particularly important given that LC is mostly influenced by modifiable risk factors, such as drug and alcohol use, and elevated BMI. Our findings highlight the urgent need for public health interventions targeting socio-economic, lifestyle, and modifiable risk factors to mitigate the escalating burden of LC.

Two-timescale joint service caching and resource allocation for task offloading with edge–cloud cooperation

Task offloading with edge–cloud cooperation has emerged as a pivotal solution for meeting the intricate array of application coupled with dynamically evolving business demand in 6G business scenarios, such as traffic sensing, environmental monitoring, and video surveillance in smart cities. Nonetheless, effectively leveraging heterogeneous edge–cloud network resources for effective task offloading presents substantial challenges. Additionally, the inherent differences in system decision cycles escalate the complexity of the task offloading problem to a new dimension. In this study, we delve into a two-timescale joint service caching and resource allocation optimization for task offloading within edge–cloud cooperation aiming to maximize long-term network performance while adhering to energy constraints. We propose a novel edge–cloud cooperation task offloading scheme that supports both edge–cloud and edge–edge cooperation to effectively balance the edge–cloud and edge–edge loads, promoting the efficient co-utilization of all edge–cloud system resources. Furthermore, we devise an online two-timescale Lyapunov-based joint optimization framework for service caching, task offloading, and computing resource allocation. Our two-timescale decision-making framework can flexibly accommodate the inherent differences in the sensitive decision optimization periods, thereby mitigating the degradation of task offloading performance caused by frequent service caching updates. Finally, theoretical analysis confirms that our proposed algorithm can converge to an approximate optimal solution in polynomial time, and the superiority of our scheme is validated by extensive simulation experiments.

Channel Allocation in mobile multimedia network using artificial neural networks

Capital asset control systems may enhance network performance, reduce congestion, and optimise resource allocation with the use of artificial intelligence. Using machine learning models like decision trees and neural networks allows for more informed and adaptive admission control options. These models can precisely predict how the network's quality of service (QoS) will be affected by accepting a new call. Exploring AI-enhanced CAC schemes requires a thorough analysis of various machine learning methods and their potential applications to real-time network management. To ensure that AI-based CAC implementations are actually possible in resource-limited mobile situations, it is crucial to consider their computational complexity and resource requirements. Accuracy and computational efficiency are two competing goals, and this study examines both. Finding a happy medium that can meet the stringent requirements of mobile multimedia networks is the driving force behind this research. Also covered in this study is the possibility of mixing AI-powered CAC with cutting-edge tech like 5G networks and cloud computing. Collectively, these technology and artificial intelligence (AI) have to open up new opportunities for situationally-conscious, dynamic admission manipulate. As a result, mobile multimedia networks might be a lot more efficient and adaptable. An exploration of CAC schemes is provided in this article, which draws attention to the potential game-changing impact of AI-based methods for mobile multimedia network optimisation. The increasing demand for multimedia services is creating new challenges, yet these challenges may be amenable to artificial intelligence integration into CAC systems. More adaptive and intelligent network management solutions would be possible as a result.

Balancing allocative and dynamic efficiency with redundant R&amp;D allocation: The role of organizational proximity and centralization

AbstractResearch SummaryResource‐based‐view scholars have mainly examined two resource allocation approaches for competitive advantage in multiunit firms: resource sharing and resource redeployment. These approaches emphasize allocative efficiency—the optimal allocation of resources to maximize their current value. In technology‐intensive industries, firm success also requires achieving dynamic efficiency to increase its future value‐creation. We propose that the redundant allocation of resources—the parallel deployment of non‐scale‐free resources towards the same objective—although allocatively inefficient, increases dynamic efficiency by stimulating inter‐unit competition. Firms' structural features moderate these effects. An analysis of large pharmaceutical firms reveals that redundant R&amp;D increases innovations with high firm‐specific value but simultaneously increases project terminations to reduce wastage. Organizational proximity increases the former effect and decreases the latter. Firm's R&amp;D centralization amplifies the effect of unit proximity.Managerial SummaryIn technology‐intensive industries, multiunit firms often employ redundant allocation of R&amp;D resources, that is, the parallel deployment of scientists and equipment in different units towards realizing the same business objective. Although common, there is little managerial guidance on how this practice impacts firms' R&amp;D outcomes, and how organizational characteristics influence this relationship. An analysis of large pharmaceutical firms reveals that redundant allocation of R&amp;D resources across units increases wastage but also stimulates competing units to create innovations with high firm‐specific value. Organizationally proximate units are less likely to have their redundant projects terminated, while creating more high‐value‐innovations. Centralization of the firm's R&amp;D amplifies the effect of unit proximity.

Firearm violence and associated factors among young adults presenting to emergency departments in three cities: Baseline results from Project SPARK

ObjectiveRecent shifts in U.S. violence dynamics call for updated violence epidemiology among general emergency department (ED) samples of young adults. Using baseline data from a multi-site longitudinal study of firearm violence prediction, we describe violence rates and associated factors. MethodsStaff approached age 18–24 entrants to Level-1 trauma centers in three cities (Flint, Seattle, Philadelphia; 7/2021–5/2023). Consenting participants completed a survey including validated measures of violence experience, firearm-related behaviors, substance use, mental health symptoms, peer/parental/familial behaviors, community violence, and attitudes/norms. We described the sample and examined factors associated with firearm assault (victimization/aggression, including threats). ResultsAcross sites, 1506 participants enrolled (41.7. % Black; 33.6 % White; 61.4. % female). Half of participants self-reported past-six-month violent victimization and/or aggression; non-partner violence, and violent victimization were most common. Over half of participants self-reported high-risk substance use, and over half screened positive for post-traumatic stress disorder, depression, and/or anxiety. About 14.4 % self-reported past-six-month firearm assault, and 5.9 % self-reported firearm violence (excluding threats). Adjusted analysis showed community violence exposure was most strongly associated with firearm assault; each one-point-increase corresponded to a 13.7 % (95 %CI: 10.4 %–16.9 %) absolute increase in firearm assault prevalence. Drug misuse, mental health symptoms, firearm carrying, retaliatory attitudes, prosocial attitudes, and family conflict were also associated with firearm assault. ConclusionsViolence, including firearm assault, is common among young adults entering urban EDs, and is associated with several psychosocial factors. High rates of substance use and mental health symptoms underscore this as a high-need population. Leveraging this information could help tailor interventions and optimize resource allocation.

Crop Yield Prediction and Classification using the Agriculture Resources and Data Mining Techniques

This study investigates the application of data mining techniques in meteorological forecasting to enhance crop yield prediction accuracy Moreover, the integration of meteorological forecasting with agronomic models and geographical information systems (GIS) facilitates site-specific crop management and precision agriculture practices. By combining meteorological data with soil properties, crop phenology, and socio-economic factors, data mining techniques enable the development of predictive models that enhance crop yield potential and optimize resource allocation. By leveraging advanced data analytics and machine learning algorithms, agricultural stakeholders can make informed decisions, mitigate risks, and enhance productivity in the face of changing climatic conditions and environmental uncertainties.

Detecting the local characteristics from the iron ore import competition intensity among nations: A network-based resource allocation process method

Iron ore is an important industrial raw material, and its production and consumption have a significant impact on the world economy. As resource demand increases, competition among iron ore importing countries has intensified. To investigate the global iron ore competition landscape through the competition intensity among importing countries and to study the iron ore competition groups and key competition modes from a local perspective, this paper constructs the iron ore import competition intensity network (IOCIN) by employing the network decreasing-mode and resource allocation process (RAP). The empirical results are as follows. First, competition intensity is unevenly distributed. The unevenness of competition intensity eases slightly, and the IOCINs become tighter over time. Second, the composition of the community is unstable, and the competition groups show geographic dispersion. Third, unidirectional competition with an overwhelming advantage on one side is a common relationship in the global iron ore market. At the same time, a relatively stable bilateral competition pattern has promoted the evolution of the global iron ore competitive landscape. The value of this study includes not only the improvement of the method but also practical strategic recommendations for rational competition and resource allocation optimization among countries.

Multi-objective optimal allocation of water resources in Shule River Basin of Northwest China based on climate change scenarios

The optimal allocation of water resources is crucial for addressing regional water scarcity, adapting to climate change, and promoting sustainable development. This study focused on the Shule River Basin and considered three climate scenarios (SSP1–2.6, SSP2–4.5, SSP5–8.5), which is combinations of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). A water resources optimal allocation model including water supply and demand prediction, multi-objective optimization, decision-making and analysis on optimal water allocation schemes was constructed. This model aims to analyze the optimal allocation of water resources in the Shule River Basin over three planning years (2025, 2030, 2035), considering guarantee rates of 50 % and 75 % under various climate scenarios. The obtained results indicate that: (1) An increasing trend in water demand in the planning year. For instance, under the SSP1–2.6 scenario, the basin's water demand is projected to reach 1.920×10^9 m3, 2.037×10^9 m3, and 2.103×10^9 m3 in 2025, 2030, and 2035, respectively. (2) The total water supply with a guarantee rate of 50 % (75 %) in the planning years is projected to be 1.939 (1.483)×10^9, 1.957(1.502) ×10^9, and 1.974 (1.519) ×10^9 m3, respectively. (3) In the three scenarios of SSP1–2.6, SSP2–4.5, and SSP5–8.5, the water demand for domestic and ecology is met in each planning year. There is a slight water shortage in the industrial sector, with a shortage rate ranging from 0 % to 13.41 %. The agricultural sector experiences the highest water shortage, with a rate of 1.93–44.77 %. (4) The water supply structure of each optimal allocation scheme of water resources was optimized. The economic benefits mainly depend on the industrial and ecological sectors. These findings offer valuable insights for optimizing the distribution of regional water resources in response to changing climatic conditions.

The Role of Artificial Intelligence Technology in Promoting Socio-Economic Development

This paper comprehensively examines the pivotal role of artificial intelligence (AI) technology in promoting socio-economic development. It commences by introducing the rapid advancements and current status of AI technology, which has permeated diverse industries through breakthroughs in big data, cloud computing, and deep learning. The paper highlights AI’s contributions to economic growth by enhancing production efficiency (e.g., 45% increase in efficiency through automation), optimizing resource allocation (25% raw material utilization improvement), and fostering innovative business models such as personalized customization and intelligent supply chain management. AI also fosters the emergence of new industries (intelligent medicine, education, finance) and creates substantial job opportunities directly for data scientists and algorithm engineers, and indirectly through driving related industries. Furthermore, AI facilitates industrial upgrading, transforming manufacturing into smart manufacturing, enhancing service quality in industries like retail, and modernizing agriculture with smart equipment. A case study on Chongqing Soft River Turing AI Technology Co., Ltd. showcases AI’s practical applications and corporate competitiveness enhancement. Overall, the paper underscores AI’s significance in economic growth, job creation, and industry transformation, while acknowledging challenges like data privacy and job displacement, emphasizing the need for strategic policy guidance and regulation.

Research on Vegetable Product Replenishment Decision Based on K-means Algorithm

The decision-making and pricing of supermarket restocking is a complex task that requires considering factors such as market demand, supply changes, and operating costs, making scientific and accurate decisions, providing customers with rich and fresh vegetable products, and promoting sustainable development. The pricing of vegetables in supermarkets generally adopts the "cost plus pricing" method, and supermarkets will formulate reasonable sales prices based on comprehensive considerations such as procurement costs, transportation costs, and operating costs. For products that may be damaged during transportation and have poor product quality, supermarkets usually offer discounts to meet customer needs and reduce food waste. This article first uses Excel software to describe and statistically analyze the data, transpose and visualize the data. Then, using the K-means algorithm in cluster analysis, the distribution patterns and interrelationships of vegetable categories and single product sales were analyzed in more detail. Finally, using Excel software, calculate the cost plus pricing for each vegetable category based on the formula "cost plus pricing=unit cost (1+cost profit margin)", and reasonable analysis is carried out according to the calculation results.This study on the calculation of cost-plus pricing for each vegetable category is of great significance to ensure the economic benefits of vegetable suppliers, improve the sustainability of agricultural production, optimize resource allocation, enhance market competitiveness, and promote the healthy development of the vegetable industry.

Spatial and temporal dynamics of livestock grazing intensity in the Selinco region: Towards sustainable grassland management

Grazing pressure in the Selinco region exhibits significant complexity and variability. To optimize sustainable grassland management in the region, understanding the spatial and temporal dynamics of grazing intensity is crucial. Previous studies have shown the substantial potential of machine learning algorithms in accurately modeling grazing intensity. Consequently, this study predicted grazing intensity for the period 2001–2020 on a 500 × 500 m grid, using variables such as climate, topography, vegetation, anthropogenic disturbances, water sources, and roads as inputs to the machine learning model, with livestock density at the township level in suitable grazing areas as the output variable. Subsequently, the spatial and temporal dynamics of grazing intensity from 2001 to 2020 were synthesized and analyzed. Of these, the Support Vector Machine Regression (SVMR) model exhibited the best fit, with a mean absolute error (MAE) of just 0.068 SU/km2 and a coefficient of determination (R2) of 0.98. The results indicate that grazing intensity shows a spatial pattern of being high in the southeast and low in the northwest, with significant regional differences. The average annual trend declined by −0.006/10a, indicating a continuous downward trend; however, a substantial increase is anticipated in the future, as suggested by a mean Hurst index value of 0.39. Additionally, the grazing pattern in the northern region, including the Qiangtang Nature Reserve, remains relatively stable. However, it is worth noting that the southeastern region exhibited a significant exacerbation of grazing patterns during 2015–2020, necessitating focused attention. These findings are crucial for making informed decisions regarding grassland-livestock balance management and can aid in optimizing resource allocation and ecological conservation strategies. Future research will utilize higher resolution geographic data to enhance the accuracy and efficiency of grazing intensity models.

Surgical control time estimation variability: Implications for medical systems and the future integration of AI and ML models

BackgroundAccurate estimation of surgical procedure times, crucial for optimizing healthcare access, patient outcomes, and cost-effectiveness, is essential for operating room efficiency. Surgical control time (SCT) is a preoperative estimate by surgeons representing their predicted time to complete the surgery, spanning from completion of anesthesia induction to surgical site closure. MethodsIn this within-subjects, longitudinal study, we examined the differences between predicted surgical control times versus actual SCTs and determined variability by surgical specialty. We included cases regardless of classification (i.e., outpatient or inpatient), type of surgery (i.e., elective, urgent, or emergent), or level of complexity (i.e., major or minor). We ran Shapiro–Wilk tests to assess the normality of the difference in actual versus predicted surgical control times (dSCT) by surgical specialty. We used a generalized linear model (GLM) with robust clustered variance and pairwise comparisons of surgical specialties (with Bonferroni adjustment for family-wise error rate) to assess differences in the prediction accuracy of SCTs by specialty. ResultsWe analyzed 14,438 surgical cases performed by 168 surgeons across 13 specialties from January 2019 to January 2023. 11 of 13 specialties had higher actual than predicted times, suggesting an overall pattern of underestimating SCTs. On average, surgeries took 12.3 % longer than predicted, with surgeons underestimating SCTs by an average of 10.4 min. SCTs comprised 78 % of the total operative time. The four specialties with the largest underestimations of SCTs were neurosurgery (27.04 min), orthopedics (22.75 min), urology (19.4 min, and plastic surgery (18.67 min), while two specialties exhibited overestimations, namely ear nose and throat (11.14 min) and pediatrics (–3.21 min). GLM results and pairwise comparisons showed that surgeons significantly differed in their SCT prediction by surgical specialty. ConclusionsOur findings showed significant differences across surgical specialties in the accuracy of predicting surgical control times. These results have implications for integrating evolving technologies such as artificial intelligence and machine learning models to assist surgical administrators in accurately predicting surgical case durations and optimizing resource allocation.

Resource Allocation Optimization in Mobile Multiuser Molecular Communication by Deep Neural Network

Resource Allocation Optimization in Mobile Multiuser Molecular Communication by Deep Neural Network

Joint Optimization of Task Offloading and Resource Allocation for UAV-Assisted Edge Computing: A Stackelberg Bilayer Game Approach

Joint Optimization of Task Offloading and Resource Allocation for UAV-Assisted Edge Computing: A Stackelberg Bilayer Game Approach

Joint Optimization of Trajectory and Resource Allocation for Multi-UAV-Enabled Wireless-Powered Communication Networks

Joint Optimization of Trajectory and Resource Allocation for Multi-UAV-Enabled Wireless-Powered Communication Networks

Distributed Optimal Time-Varying Resource Allocation for Networked High-Order Systems

Distributed Optimal Time-Varying Resource Allocation for Networked High-Order Systems