Reasonable Resource Allocation Research Articles

Marine Mammal Conflict Avoidance Method Design and Spectrum Allocation Strategy

Underwater wireless sensor networks play an important role in underwater communication systems. Communication through collaborative communication is an effective way to solve critical problems in underwater communication systems. Underwater sensors are often deployed in spaces that overlap with those of marine mammals, which can adversely affect them. For this reason, in this paper, a marine mammal conflict avoidance method that can be dynamically adjusted according to the channel idle time duration and sensor node demand is designed, and the derivation of the maximum occupancy time duration is performed. Meanwhile, in addition, combining the potential of reinforcement learning in adaptive management, efficient resource optimization, and solving complex problems, this study also proposes a reinforcement learning-based relay-assisted spectrum switching method (R2S), which aims to achieve a reasonable allocation of spectrum resources in relay collaborative communication systems. The experimental results show that the method proposed in this study can effectively reduce the disturbance to marine mammals while performing well in terms of conflict probability, interruption probability, and quality of service.

Equal allocation, demand priority or negotiated allocation? How to allocate water resources in the Tigris and Euphrates River Basin efficiently

The Tigris and Euphrates River Basin is an important water supply, but it suffers from water scarcity. It is necessary to carry out reasonable allocation of water resources in this region. Since water resources issues in this region are of multinational interest, international cooperative distribution efforts are needed. Common water resources allocation modes include equal allocation, demand priority or negotiation allocation. In order to derive the applicable range of various water resources allocation modes, this article constructs three differential game models and compares and analyzes the equilibrium results obtained by the models. Finally, the study shows that when the cost of developing water resources is small and the revenue obtained from developing water resources is large, the water-scarce region can obtain the maximum benefit by adopting the demand priority mode. Otherwise, the water-scarce region can obtain the maximum benefit by adopting the negotiation allocation mode. This study can inform the allocation, strategic interaction and cooperation of dynamic water resources in the two river basins.

Impact of social welfare finance on institutional financial performance: Cross-country evidence

This study explores the nuanced impact of social welfare finance on institutional financial performance, focusing solely on its direct effects. Utilizing fixed effect, feasible generalized, and quantitative approaches alongside the dynamic generalized method of moments (GMM), we conduct a thorough analysis using data from 20 upper-middle-income economies spanning 2000–2023. Our findings highlight the pivotal role of social welfare finance, including its dynamic and lagged impacts, as a significant determinant of institutional financial performance. Quantile analysis reveals varying effects across different quantiles, enriching our understanding. The results emphasize the direct influence of social welfare finance and unveil its indirect impact through Corporate Social Responsibility (CSR), a novel contribution to the literature. Moreover, we stress the critical importance of reasonable resource allocation, highlighting the indispensable role of social welfare finance resources. We advocate for prioritizing public-private partnerships to optimize financial targeting, offering a pragmatic strategy for fostering a resilient financial economy.

A networking-economic model to enhance the cultural value in small towns

Purpose The paper aims to provide a decision-support model to ensure a proper use of the limited resources, financial and not, for the enhancement of the cultural heritage and comprehensive development of small towns from sustainable perspective.Design/methodology/approach The assessment model is set up using a multi-criteria method that combines elements of linear planning with a performance indicators system that may represent the complexity of the territory’s cultural identity as a result of existing cultural-historical assets.Findings The model reliability is tested in a case study in a Municipality in southern Italy. The case study’s findings highlight the advantages for the public/private operators, who can consciously choose which preservation and restoration projects to fund while taking into account the effects those decisions will have on the economic, social and environmental context of reference.Research limitations/implications Due to the suggested operational approach and the selection of variables for accounting economic, social and environmental impacts by the renewal project, the research findings may not be generalizable. Therefore, it is recommended that researchers look into the suggested theories in more detail.Practical implications The study offers implications for designing a user-friendly tool to help decision-making processes from a private–public viewpoint in a reasonable allocation of financial resources among investments for cultural property asset enhancement.Originality/value The suggested operational approach provides a reliable information apparatus to depict the decision-making process under small-town development in accordance with sustainability dimensions.

Resource allocation and route planning under the collection price uncertainty for the biomass supply chain

The escalating severity of global warming has drawn worldwide attention to ecological problems. Nevertheless, with the introduction of environmental policies, biomass resources have been effectively developed as a renewable energy source. This paper investigates an advanced biomass supply chain design wherein biomass resources are initially converted into bio-oil through widely distributed fast pyrolysis facilities, and are subsequently transported to a centralised biorefinery for further refining into biofuels. This novel biomass supply chain addressed three key issues: (1) The number of fast pyrolysis facilities, (2) The allocation of resources, and (3) The routes of resources transport. In respect of these problems, a two-stage stochastic mixed integer programming model is established to minimise the total cost of the biomass supply chain considering the uncertainty collection price of fast pyrolysis facilities. A hybrid simulated annealing algorithm which incorporates the sample average approximation method is proposed to solve the stochastic model and is effectiveness for large-scale examples. Finally, a sensitivity analysis is performed using the proposed algorithm and the results show that the proposed stochastic model outperforms the deterministic model under uncertain collection price. The model allows optimising the biomass supply chain economic performances and minimise financial risk on investment by determining the fast pyrolysis facility locations, reasonable resource allocation and optimal transport routes under uncertain collection price.

Reducing the length of stay for patients stranded in the emergency department: A single-center prospective study of 18,631 patients in China.

The purpose of this study was to reduce the length of stay (LOS) for patients stranded in the emergency department (ED) of a Grade III A hospital in China, and to improve patient flow and increase bed capacity. We utilized a pre-/postintervention design and employed the Six Sigma methodology, which is based on the DMAIC cycle (define, measure, analyze, improve, and control), to evaluate and improve the existing process. Data from 18,631 patients who were stranded in the ED were collected and analyzed. The median LOS for stranded patients decreased from 17.21 (6.22, 27.36) hours to 13.45 (5.56, 25.85) hours (P < .05). Similarly, the median LOS for admitted patients decreased from 19.64 (7.77, 27.68) hours to 15.92 (6.19, 26.24) hours (P < .05). The median LOS for patients with an ED triage Level IV decreased from 16.15 (5.80, 26.62) hours to 12.59 (5.20, 24.97) hours (P < .05). In addition, the average hospitalization days of hospitalized patients decreased from 0.92 days to 0.82 days (P < .05). Furthermore, the bed utilization rate increased from 66.79% to 72.29% (P < .05). The number of bed turnovers in the ED resuscitation room increased from 20.30 to 21.96 (P < .05). We had effectively met our goal of minimizing ED patient LOS. Six Sigma method can effectively shorten patient LOS by measuring and analyzing the key factors affecting patient LOS, and by implementing measures such as strict implementation of emergency classification and triage system, establishment of multidisciplinary cooperative team, reasonable allocation of human resources, information management of bed resources, and improvement of performance appraisal scheme to improve and control the effectiveness of patient LOS.

Resource Scheduling in URLLC and eMBB Coexistence Based on Dynamic Selection Numerology

This paper focuses on the resource allocation problem of multiplexing two different service scenarios, enhanced mobile broadband (eMBB) and ultrareliable low latency (URLLC) in 5G New Radio, based on dynamic numerology structure, mini-time slot scheduling, and puncturing to achieve optimal resource allocation. To obtain the optimal channel resource allocation under URLLC user constraints, this paper establishes a relevant channel model divided into two convex optimization problems: (a) eMBB resource allocation and (b) URLLC scheduling. We also determine the numerology values at the beginning of each time slot with the help of deep reinforcement learning to achieve flexible resource scheduling. The proposed algorithm is verified in simulation software, and the simulation results show that the dynamic selection of numerologies proposed in this paper can better improve the data transmission rate of eMBB users and reduce the latency of URLLC services compared with the fixed numerology scheme for the same URLLC packet arrival, while the reasonable resource allocation ensures the reliability of URLLC and eMBB communication.

Productivity Decomposition of Chinese Energy Generation Enterprises: an Index Calculation Approach Embedded in the Data Envelopment Analysis Framework

To explore the development of China's energy power generation enterprises, undertake reasonable resource allocation and optimization, and promote sustainable development. We apply an index calculation approach within the data envelopment analysis framework, collecting data from 50 enterprises in 2007-2022, resulting in a total of 800 observations (16*50) for the in-depth analysis of productivity decomposition. The results highlight that during the study years, the sample enterprises experienced the following changes on average per year: an increase of 0.244% in efficiency change (effch), a decline of 0.293% in technical progress (techch), an increase of 0.119% in pure efficiency change (pech), an increase of 0.134% in scale efficiency change (sech), and a decline of 0.057% in total factor of productivity (tfpch). These findings suggest an ongoing improvement in overall efficiency, indicating that energy generators are achieving better results in terms of operations, resource utilization, and scale effects, but need to focus on research and adoption of new technologies while seeking opportunities to further enhance productivity.

Distribution of hepatitis C virus in eastern China from 2011 to 2020: a Bayesian spatiotemporal analysis.

This study aimed to evaluate the spatiotemporal distribution of patients with hepatitis C virus (HCV) and the factors influencing this distribution in Jiangsu Province, China, from 2011 to 2020. The incidence of reported HCV in Jiangsu Province from 2011 to 2020 was obtained from the Chinese Information System for Disease Control and Prevention (CISDCP). R and GeoDa software were used to visualize the spatiotemporal distribution and the spatial autocorrelation of HCV. A Bayesian spatiotemporal model was constructed to explore the spatiotemporal distribution of HCV in Jiangsu Province and to further analyze the factors related to HCV. A total of 31,778 HCV patients were registered in Jiangsu Province. The registered incidence rate of HCV increased from 2.60/100,000 people in 2011 to 4.96/100,000 people in 2020, an increase of 190.77%. Moran's I ranged from 0.099 to 0.354 (P < 0.05) from 2011 to 2019, indicating a positive spatial correlation overall. The relative risk (RR) of the urbanization rate, the most important factor affecting the spread of HCV in Jiangsu Province, was 1.254 (95% confidence interval: 1.141-1.376), while other factors had no significance. The reported HCV incidence rate integrally increased in the whole Jiangsu Province, whereas the spatial aggregation of HCV incidence was gradually weakening. Our study highlighted the importance of health education for the floating population and reasonable allocation of medical resources in the future health work.

高质量发展背景下普惠性幼儿园面临的挑战及其发展策略

Under the background of high-quality development, the construction and development of inclusive kindergartens are particularly important. This is not only about educational equity, but also an important embodiment of social progress and population policy. Inclusive kindergartens aim to narrow the education gap between urban and rural areas and between regions and provide equal educational opportunities, especially for children from low- and middle-income families. The main challenges include the unbalanced distribution of resources between urban and rural areas, the structural imbalance of financial input, and the significant differences in the management system of parks. The key to solving these challenges lies in the fine allocation of resources, the establishment of early warning and cooperation mechanism between demand and supply, the optimization of fair and reasonable resource allocation mechanism, and the use of policy tools to build a sound incentive mechanism. In addition, it is essential to strengthen quality assessment and content development, and improve the level of education and childcare services.

Traffic prediction for 5G: A deep learning approach based on lightweight hybrid attention networks

The maturity of 5G technology provides a guarantee for increasingly large communication networks, while the resources required for communication and computation are also increasing, and reasonable resource allocation can improve the efficiency of network communication and reduce the consumption of communication resources. Existing deep learning methods have been able to predict network traffic to a certain extent, so as to solve the communication efficiency and resource consumption problems in the field of integrated sensing, communication and computation (ISCC) through rational resource allocation. However, the following problems still exist: (1) The feature learning ability of the prediction model is insufficient, and the prediction accuracy needs to be improved. (2) Powerful and complex deep learning methods lead to an increase in the prediction cost of the model. To address these problems, this paper proposes a deep learning method based on a lightweight hybrid attention network. In order to capture the key features of 5G data more effectively, an efficient hybrid attention mechanism (EHA) is proposed. After this attention is applied to convolution, the key information can be well enhanced. We use depthwise separable convolution in feature extraction, which greatly improves the efficiency of lightweight convolution layer (LC) in feature extraction. Combined with the efficient hybrid attention mechanism (EHA), the proposed model has better lightweight properties. Experimental results show that the model proposed in this paper has lower RMSE and MAE values on the three datasets, as well as fewer parameters and computational effort compared to the baseline scheme.

Global, regional and national burden of retinopathy of prematurity among childhood and adolescent: a spatiotemporal analysis based on the Global Burden of Disease Study 2019

BackgroundThis study aimed to provide a comprehensive assessment of burden estimates and the secular trend of vision loss due to retinopathy of prematurity (ROP) among people younger than 20 years,...

Robust optimization method of emergency resource allocation for risk management in inland waterways

This study proposes a robust optimization method for waterborne emergency resource allocation in inland waterways that addresses the uncertainties and mismatches between supply and demand. To accomplish this, we integrate the risk evaluation of maritime with a robust optimization model and employ the Entropy Weighted Method (EWM)-Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)-Analytic Hierarchy Process (AHP) method to evaluate the risk of various areas. The approach enables exploration of the relationship between maritime risk and emergency resource allocation strategy. The robust optimization method is used to deal with uncertainty and derive the robust counterpart of the proposed model. We establish an emergency resource allocation model that considers both the economy and timeliness of emergency resource allocation. We construct an optimization model and transform it into an easily solvable robust counterpart model. The results demonstrate that the proposed method can adapt to real-world scenarios, and effectively optimize the configuration effect while improving rescue efficiency under reasonable resource allocation. Specifically, the proportion of rescue time saved ranges from 28.52% to 92.60%, and the proportion of total cost saved is 95.82%. Our approach has significant potential to provide a valuable reference for decision-making related to emergency resource allocation in maritime management.

Intensive care challenges for the next pandemic

Experience during the COVID-19 pandemic has highlighted the need for crisis preparedness, particularly in intensive care units (ICUs). Addressing key challenges is vital for improving readiness and response, as well as ensuring better protection for vulnerable populations. Our review focuses on the recent literature examining the challenges in acute care settings during the pandemic. We aimed to identify lessons that could improve our ability to handle similar situations. Studies have highlighted the critical role of intensive care, responsiveness, and preparedness. The key challenges identified include a shortage of trained personnel to meet high demand without compromising care quality or overburdening healthcare workers. Resource scarcity during the pandemic emphasizes the importance of reasonable resource allocation to ensure sufficient care provision. Additionally, addressing post-ICU syndrome and early rehabilitation is crucial, as ICU treatment can lead to long-term physical, psychological, and cognitive issues that can affect patients' quality of life. Finally, fostering a culture of data sharing and research collaboration is essential for knowledge exchange and improving crisis management across healthcare centers.

Investigation on evaluation of education effect based on deep learning algorithm

With the rapid development of society today, in the context of the impact of education on personal future development, people have increasingly attached importance to education. Various types of online and offline education institutions have mushroomed. However, the factors that affect the effectiveness of education are numerous and complex. As a very important part of early childhood learning, preschool education should receive more attention. The deep learning algorithm can reasonably classify and plan the factors affecting preschool education through massive data calculations to ensure the reasonable allocation of resources and save the time and energy costs wasted in allocating educational resources. The educational effectiveness of preschool education institutions and organizations can be reasonably evaluated. This article applied deep learning algorithms to the evaluation of preschool education effectiveness. Data related to the effectiveness of preschool education in preschool education institutions using convolutional neural networks and not using algorithms were compared. The experimental results showed that the expert recognition rates of the convolutional neural network algorithm and traditional manual measurement data were 98.8% and 92.55%, respectively. At the time level of teaching quality estimation, five algorithms and 100 sets of relevant experimental data were compared. In terms of the accuracy of feature search, the average accuracy of the convolutional neural network algorithm was 97.96%, while the accuracy of the traditional manual search was 53.9%. Therefore, the application of the convolutional neural network algorithm in deep learning to the evaluation of preschool education effectiveness was more efficient and efficient and could improve the evaluation effect from various aspects.

A Truthful Reverse Auction Mechanism for Federated Learning Utility Maximization Resource Allocation in Edge–Cloud Collaboration

Federated learning is a promising technique in cloud computing and edge computing environments, and designing a reasonable resource allocation scheme for federated learning is particularly important. In this paper, we propose an auction mechanism for federated learning resource allocation in the edge–cloud collaborative environment, which can motivate data owners to participate in federated learning and effectively utilize the resources and computing power of edge servers, thereby reducing the pressure on cloud services. Specifically, we formulate the federated learning platform data value maximization problem as an integer programming model with multiple constraints, develop a resource allocation algorithm based on the monotone submodular value function, devise a payment algorithm based on critical price theory and demonstrate that the mechanism satisfies truthfulness and individual rationality.

Identifying the Relationships between Landscape Pattern and Ecosystem Service Value from a Spatiotemporal Variation Perspective in a Mountain–Hill–Plain Region

Identifying the changes in landscape pattern and ecosystem service value (ESV) and clarifying their relationship in temporal changes and spatial variations can provide insight into regional landscape features and scientific support for regional landscape planning. Leveraging land use data from the Yihe River Basin, we quantitatively assessed the landscape pattern and ESV shifts spanning from 2000 to 2018 using the landscape pattern indexes and the equivalence factor method. We employed Pearson correlation metrics and the geographically weighted regression model to explore the interrelation of their spatiotemporal variations. Our results show the following: (1) Forestland represents the most expansive land cover category. Apart from construction land, all other types experienced a decline in area. The most notable change occurred in the area of construction land. (2) The aggregation of the overall landscape shows a downward trend. The levels of fragmentation, landscape diversity, and richness increased. (3) Throughout the entire study period, the overall ESV gradually decreased, and the land cover type with the greatest contribution to the ESV was forestland. (4) In terms of temporal changes, the patch density and edge density of the overall area are significantly negatively correlated with total ESVs. The largest values for the patch index, perimeter–area fractal dimension (PAFRAC), and aggregation are significantly positively correlated with total ESVs. (5) In terms of spatial variation, the contagion index (CONTAG), PAFRAC, and the Shannon diversity index (SHDI) were noticeably correlated with ESVs. The CONTAG is positively correlated with ESVs upstream, but negatively midstream and downstream. The SHDI is negatively correlated with ESVs upstream, but positively midstream and downstream. The PAFRAC exhibits a positive correlation with ESVs for the most part. The association between the landscape pattern indexes and ESVs exhibits temporal and spatial inconsistencies in most instances, suggesting a spatiotemporal scale effect in their relationship. This study recommends that the local government devises a long-term strategy for urban development and exercises stringent control over the unregulated expansion of construction land. Through reasonable territorial spatial planning, government departments could enhance the connectivity of the overall landscape pattern of the Yihe River Basin to achieve the reasonable allocation and sustainable development of regional resources.

Resource Allocation Method of Edge IoT Agent Based on Deep Reinforcement Learning

Reasonable allocation of resources is an important guarantee for efficient support of power business in edge IoT agents. Facing the above problems of the current power Internet of Things, this paper proposes a resource optimization allocation method based on deep Q-learning. This method first comprehensively considers the communication performance and network security. Involving indicators such as latency and service satisfaction, a complete and reliable mathematical model of the edge Internet of Things proxy network is constructed to achieve efficient and reliable modeling of the power Internet of Things (pIoT), aiming to better fit the practical interaction needs for efficient and secure communication. The Q-learning network model is optimized, and the method combining Reinforcement learning and deep learning is used to solve the model. Used by this network, the optimization and improvement of the deep network model is realized, so that the status, action and other parameters of the network model can be solved in a timely manner, so as to better support the reliable and efficient information interaction of the communication network. The test results prove that the delay of the proposed method can be maintained within 12[Formula: see text]ms in more complex scenarios, and the interaction success rate reaches 0.975, confirming that the proposed method can provide good information interaction guarantee services.

The Allocation Scheme of Software Development Budget with Minimal Conflict Attributes

During the process of software development, a significant challenge revolves around accurately estimating the associated costs. The primary goal of project managers is to ensure the delivery of a highly trustworthiness product that aligns with the designated budgetary constraints. Nonetheless, the trustworthiness of software hinges upon a range of distinct attributes. When implementing a budget allocation scheme to enhance these attributes, conflicts among them may arise. Thus, it becomes imperative to select an appropriate allocation scheme that effectively mitigates conflict-associated costs. In this paper, we will define the conflict costs and establish costs estimation models. The difficulty coefficient constraint for improving attributes is established. Subsequently, we will analyze the relative importance weights of these attributes. Drawing upon the conflict costs, importance weights, and difficulty coefficient constraint, we present an algorithm to determine an appropriate budget allocation scheme, which can minimize conflict-associated costs. Finally, we provide an illustrative example that demonstrates the practicability of our proposed algorithm. This research offers valuable insights to software managers, aiding them in the reasonable allocation of budgetary resources, thereby maximizing overall benefits.

Energy trading model for multi-microgrid energy storage alliance based on Nash negotiation

With the continuous development of the electricity market and the gradual expansion of the number and scale of participation in market transactions, the traditional energy trading model has limited the formation of a competitive pattern of multi-agents. In this paper, a new multi-microgrid energy storage alliance energy trading model based on Nash negotiation is proposed. This model takes energy storage, multi-microgrid, and superior power grid enterprises as the main participants and establishes an energy market trading model with “buy–sell” cooperation and competition coexisting within the alliance based on Nash negotiation theory. Through the interaction of electricity between different entities, energy conversion and complementary utilization are increased, achieving reasonable allocation of resources, enhancing the overall flexibility of the alliance, and promoting the local consumption of a high proportion of new energy. The simulation results of the example show that the energy trading model based on Nash negotiation can fully leverage the initiative of demand-side participation in scheduling and improve the utilization rate of energy storage systems while ensuring the payment benefits of all participating entities, which can provide technical support for energy complementarity among multiple entities and provide new technological paths for the sustainable development of energy sharing mechanisms.