Resource Allocation Scheme Research Articles

Stochastic geometry-based resource allocation scheme over cellular shotgun systems

Stochastic geometry-based resource allocation scheme over cellular shotgun systems

Efficient resource allocation scheme for dually connected non-orthogonal multiple access based LIFI-RF networks

Efficient resource allocation scheme for dually connected non-orthogonal multiple access based LIFI-RF networks

An exploration of the service and crime prevention performance of the municipal police departments of Taiwan’s counties: the perspective of broken windows theory

PurposeThis study aims to evaluate the service and crime prevention performance of law enforcement agencies in Taiwan’s municipalities. By analyzing these performances, the study seeks to improve the efficiency of police services through appropriate resource allocation, ultimately achieving enhanced crime prevention.Design/methodology/approachThe study utilized a two-stage data envelopment analysis (DEA) to construct performance metrics. In the first stage, the service performance of the police force was measured using the number of police officers and police stations as inputs and the number of patrols and temporary patrol boxes as outputs. In the second stage, crime prevention performance was assessed with the number of patrols and temporary patrol boxes as inputs and cases of violence, injury, intimidation and theft as outputs. Additionally, a truncated regression model was employed to analyze the impact of environmental variables such as per capita income, population density, proportion of the population with higher education and the unemployment rate on crime prevention performance.FindingsThe study found significant variations in both service and crime prevention performance across different municipalities. By identifying the most efficient and inefficient units, the analysis revealed critical insights into resource utilization and areas needing improvement. The regression analysis indicated that environmental factors such as higher per capita income, population density, education level and unemployment rate significantly affect crime prevention performance.Originality/valueThis research provides a novel approach to assessing police service efficiency and crime prevention effectiveness using a two-stage DEA model. By incorporating environmental variables through truncated regression, the study offers a comprehensive evaluation framework that can inform policy decisions and resource allocation strategies. This methodology not only highlights the performance metrics of law enforcement agencies but also underscores the influence of socioeconomic factors on crime prevention, contributing to the broader literature on public safety and resource management.

The interplay of capability and complexity in military context: definitions, challenges, and implications

ABSTRACT This paper explores the interplay between capability and complexity within the context of military forces. The findings here describe how these two concepts intersect and highlight the capability definitions, themes, challenges, opportunities, and applications. By running a thorough review of 314 articles, 10 key research themes have been identified, including: 1) Capability Development and Assessment, 2) Regional Defence Studies, 3) Technological Advancements in Military, 4) Strategic Analysis, 5) Operational Tactics, 6) Defence Industry Dynamics, 7) Organisational and Management Aspects, 8) Cybersecurity and Network Capabilities, 9) Military Training and Exercises, and 10) Policy and Political Implications. Among these themes, the literature highlights the importance of a subtle understanding of capability and complexity for well-informed decision-making in military and defence domains. Moreover, this study identifies gaps in existing research and emphasises the need for a coherent theoretical framework to guide decision-makers for military capability acquisitions. The findings have implications for enhancing national security, defence capability planning and resource allocation strategies.

Analyzing disruptions in post-pandemic population return patterns: A network perspective on Chinese cities

The COVID-19 pandemic has fundamentally reshaped global socio-economic structures, precipitating a profound transformation in people's lifestyles. An in-depth analysis of the disruptions in post-pandemic population returns patterns and the evolving driving factors can facilitate socio-economic recovery, development, and macro-control. This paper employs social network analysis to examine the spatial patterns and evolving urban locational attributes of the Population Mobility Network (PMN) during post-holiday returns in major Chinese cities amid the pandemic, assessing the impact of varying proximities on the PMN. The results indicate that: (1) During the pandemic, the geographic patterns of population mobility in China underwent significant fluctuations, with distinct regional and temporal variations, while intercity connections increasingly shifted toward shorter trips. (2) From 2019 to 2022, major Chinese cities experienced notable changes in status and connectivity, evolving into a multi-centric structure within the national population mobility network. (3) Community clusters predominantly adhere to provincial demarcations, maintaining stable structures within major urban agglomerations despite intense intra-regional competition, with clear distinctions in community stability between northern and southern regions. (4) Beyond traditional socio-economic factors, the level of digital finance has emerged as a new driver of population mobility, with the varied attributes of inter-city relationships also significantly influencing the PMN. Studying spatial patterns and urban location from the perspective of population mobility can inform post-pandemic optimization of national resource allocation and urban recovery strategies.

Optimized Resource Allocation for Sustainable Development in Beijing: Integrating Water, Land, Energy, and Carbon Nexus

Chinese megacities face significant challenges in reducing carbon emissions while balancing economic growth and social welfare. This study constructs an innovative multi-objective optimization model, the SD-NSGA-III model, integrated with a System Dynamics (SD) model and using the Non-dominated Sorting Genetic Algorithm III (NSGA-III) to optimize resource allocation in Beijing. The model targets environmental, economic, and social goals by establishing a water–land–energy–carbon (WLEC) nexus for analyzing resource allocation strategies and carbon reduction pathways under various constraints. Scenario simulations under the efficiency-oriented scenario indicated a potential reduction in energy carbon emissions of 81.4% by 2030. The fairness-oriented scenario revealed significant trade-offs between equity and emission reductions, emphasizing the need for balanced strategies. Introducing constraints on resources and economic growth significantly reduced median energy carbon emissions to 80 million tons by 2030. These findings demonstrate the effectiveness of the SD-NSGA-III model in providing actionable strategies for achieving carbon neutrality and sustainable development in cities.

Molecular spectral line data preprocessing container load grouping prediction algorithm based on EMD-LSTM

Unbalanced allocation of container resources in cluster environments is an urgent problem to be solved. Aiming at container load prediction and resource allocation strategy, this paper designs an astronomical data processing container load grouping prediction algorithm based on empirical mode decomposition-long short-term memory network, and proposes an adaptive recommendation value generation algorithm based on predicted load information, which can automatically allocate container computing resources according to the degree of load fluctuation. The load prediction accuracy is verified using simulated data and real astronomical observation data. The experimental results show that the algorithm proposed in this paper has higher prediction accuracy than the triple index method and the single long short-term memory network model. In the real-time preprocessing test of astronomical data, the recommendation value generation algorithm proposed in this paper can effectively improve the utilization efficiency of computing resources compared with the default strategy.

Bolman과 Deal의 네 가지 프레임으로 바라본 대학의 학과 통폐합 사례 연구

Objectives This study aims to analyze cases of merging and phasing out of university departments using Bolman and Deal's four-frame model, focusing on structural, human resource, political, and symbolic changes. Methods To conduct a case study on one engineering department at a private university and one arts and physical education department at a national university, a preliminary investigation was conducted through a review of relevant literature and in-depth written interviews. Based on the results of this preliminary research, Focus Group Interviews (FGIs) were carried out with with faculty, current students, teaching assistants, and alumni to supplement the data collected.. Subsequently, changes in merged and phased-out departments were analyzed according to Bolman and Deal's four frames: structural, human resource, political, and symbolic changes. Results In the structural frame, positive changes such as curriculum flexibility, modernization, and student choice were observed. However, issues such as ensuring choice, securing expertise, and alleviating teaching burdens remained unresolved. Additionally, initial administrative and student organization dualities created issues of belonging and highlighted the need for increased support. In the human resource frame, department consolidation expanded career choices and positively impacted new student enrollment, though initial promotional efforts were insufficient, leading to limited short-term effects. Despite increased career diversification and satisfaction through integrated education, the need for enhanced communication regarding department reorganization and feedback collection was identified. The political frame examined stakeholder position differences and resource allocation strategies for successful integration. In the symbolic frame, while the necessity of consolidating departments to address declining student populations and societal demands was emphasized, it was also confirmed that the reasons for actual consolidations are varied. Conclusions The case analysis of merging and phasing out of university departments provides an in-depth understanding of phenomena that may arise within universities during such processes and offers insights. Recommendations are provided on key considerations for universities when deciding on consolidation targets, supporting consolidated departments, and preparing for and responding to consolidations.

Reassessing water resource challenges in a changing climate in the Kagera transboundary river basin, Tanzania

The dynamic shifts in climatic patterns, particularly alterations in precipitation, have significantly impacted water resource availability and accessibility. These climate-induced changes affect hydrological processes, encompassing runoff, groundwater replenishment, water demand, and biophysical patterns within river basins. This study seeks to unravel the multifaceted implications encountered and challenges in water resource management within the river basin. It employed a survey-based approach for data collection, including Key informant interviews (KI), consultation methods and documentary reviews. The study utilized Statistical Package for the Social Sciences (SPSS) and Microsoft Excel for quantitative analysis and thematic synthesis for qualitative insights, including local community perspectives. The findings reveal substantial fluctuations in precipitation, protracted droughts, severe flooding, and other climatic extremes. The observed annual mean rainfall of 832 mm, with a standard deviation of 248, a standard error of 361, and a skewness of −945, accentuates the pronounced variability in mean precipitation, exhibiting a declining trend indicative of climatic shifts (R2 = 0.1797 and P = 0.002). The challenges are limited access to hydrological and meteorological data, undermining prediction accuracy, infrastructure planning, and response strategies for water resource allocation and management. The absence of unified approaches, influenced by the politics of riparian states, feeble coordination, conflicting sectoral policies, and ambiguous priorities, hinders effective transboundary water resource management. The study addresses these complexities and advocates for institutionalizing robust water management strategies that capitalize on existing resources and foster innovative capacities among critical stakeholders. Central to these efforts is harmonizing and enforcing policies, regulations, and laws governing water use, coupled with a concerted focus on inclusive governance to ensure the sustainability of water resource management. Such effort entails synchronized strategies, programs, plans, and livelihood practices underpinned by active stakeholder involvement in pivotal facets of water resources management.

Optimizing secure multimedia communication in embedded systems a parallel convolutional neural network approach to RIS and D2D resource allocation

With the rapid development of Internet of Things (IoT) services, technologies that leverage multimedia computer communication for information sharing in embedded systems have become a research focus. To address the challenges of low spectral efficiency and poor network flexibility in multimedia computer communications, this paper proposes a resource allocation scheme based on parallel Convolutional Neural Network (CNN). The scheme optimizes the base station beamforming vector and the Reconfigurable Intelligent Surface (RIS) phase shifts to maximize the secure transmission rate for cellular users (CUs), while ensuring normal and secure communication for device-to-device (D2D) users. First, to mitigate interference caused by D2D users reusing CU spectrum resources, the RIS phase shifts and beamforming vectors are optimized to suppress interference and enhance system secrecy rates. Second, to maximize the CU secrecy rate, the paper proposes a parallel CNN-based resource allocation model that considers base station transmission power, RIS reflection coefficients, and D2D communication rate constraints, incorporating multi-scale residual modules in the convolutional layers of the model. Simulation results demonstrate that the proposed CNN-based resource allocation scheme significantly improves the secrecy rate of embedded system communications, ensuring secure multimedia computing, and outperforms traditional methods.

Resource Allocation Approach of Avionics System in SPO Mode Based on Proximal Policy Optimization

Single-Pilot Operations (SPO) mode is set to reshape the decision-making process between human-machine and air-ground operations. However, the limited on-board computing resources impose greater demands on the organization of performance parameters and the optimization of process efficiency in SPO mode. To address this challenge, this paper first investigates the flexible requirements of avionics systems arising from changes in SPO operational scenarios, then analyzes the architecture of Reconfigurable Integrated Modular Avionics (RIMA) and its resource allocation framework in the context of scarcity and configurability. A “mission-function-resource” mapping relationship is established between the reconfiguration service elements of SPO mode and avionics resources. Subsequently, the Proximal Policy Optimization (PPO) algorithm is introduced to simulate the resource allocation process of IMA reconfiguration in SPO mode. The objective optimization process is transformed into a sequential decision-making problem by considering constraints and optimization criteria such as load, latency, and power consumption within the feasible domain of avionics system resources. Finally, the resource allocation scheme for avionics system reconfiguration is determined by controlling the probability of action selection during the interaction between the agent and the environment. The experimental results show that the resource allocation scheme based on the PPO algorithm can effectively reduce power consumption and latency, and the DRL model has strong anti-interference and generalization. This enables avionics resources to respond dynamically to the capabilities required in SPO mode and enhances their ability to support the aircraft mission at all stages.

A general task offloading and resources allocation strategy for multi-RSUs with load unbalance and priority awareness

Vehicular Edge Computing is a new computing paradigm that enables real-time response to vehicular applications and servers by performing data processing on edge computing devices near the vehicle. However, on the one hand, the random distribution and the mobility of vehicles may lead to load unbalance among different Roadside Units (RSUs), and some tasks may not be able to get timely response due to inadequate computing resources and communication resources in the high-load RSU areas. On the other hand, considering the different urgency of the tasks, the service quality of the system will be seriously affected if these tasks are not treated indistinguishably. To address the above challenges, this paper constructs a priority-aware task offloading and computing&communication resources allocation problem in a general scenario of unbalanced load among multi-RSUs, aiming at minimizing the average delay. In the problem, considering the absence of communication resources, the relay vehicle is used to offload the subtasks of splittable tasks to the RSUs that are in the neighbouring and low-load. Moreover, to take full advantage of computing resources, the task can be reasonably split into at most four parts and processed in parallel on a relay vehicle, a current RSU, a neighbouring RSU and a local vehicle. To solve the problem, a Split-Hop Offloading and Resources Allocation Strategy (SHORAS) based on an improved particle swarm optimisation algorithm is proposed, which uses a penalty function to incline resources towards high priority tasks. Simulation results show that SHORAS improves 24% in terms of the total system delay and effectively reduces the processing delay in the high-load areas compared to other strategies, while ensuring the delay requirements of high priority tasks.

Male Reproductive Traits Display Increased Phenotypic Variation in Response to Resource Quality and Parental Provisioning in a Tropical Rainforest Dung Beetle, Onthophagus c.f. babirussa.

Reproductive traits that mediate differential fitness associated with mate acquisition and fertilisation success are often strongly linked to the overall condition. We investigated the effects of resource quality and parental provisioning in the phenotypic expression of sexual and non-sexual traits in a rainforest dung beetle, Onthophagus c.f. babirussa (Eschscholtz, 1822) from Singapore. F1 individuals were reared from wild-caught beetles and paired up to produce offspring (F2), and F2 larvae from the same F1 parents were reared on two dung substrates (herbivore and omnivore) in a full-sib design. Sexual traits displayed greater phenotypic variation in response to dung resource quality, with the precopulatory trait (horn length) responding more than the postcopulatory trait (testes weight). Notably, genotype-by-environment interactions between parental lines (genotype) and dung type (environment) affected male body size and horn length only, suggesting sex-specific variance in plasticity associated with sexually selected precopulatory traits. Dung type had significant effects on all measured traits. Offspring that were provisioned higher quality resource (omnivore dung) had larger absolute and relative trait values. Parental lines only significantly affected female body size but none of the male traits, suggesting an important role of environment and resource partitioning in determining precopulatory success of male offspring. Parental provisioning of larval resource varied with resource quality and brood sequence. Parents provisioned more dung when herbivore dung was presented than when they were given omnivore dung and provisioned more dung for their earlier broods when using herbivore dung but not omnivore dung. This suggests a trade-off between early offspring fitness and resource quality. We tested directly for genotype-by-environment (G × E) interactions in the expression of several morphological traits relevant to dung beetle fitness and documented that offspring with similar phenotypes may result from completely different parental resource allocation strategies. We discuss the importance of studying parental investment on trait variation and its implications on dung beetle ecology.

DDQN-based online computation offloading and application caching for dynamic edge computing service management

Multi-access Edge Computing (MEC) reduces task service latency and energy consumption by offloading computing tasks to MEC servers. However, constrained by the limited bandwidth and computing resources, MEC servers often cannot parallelly process all computing tasks. Simultaneously, the high dynamism of service popularity necessitates MEC servers to dynamically update cached applications, under ensuring compliance with storage resource constraints and the system cache updating cost budget for service providers. In response to the above two issues, this paper firstly formulates computation offloading and application caching as a dual-timescale decision optimization problem, aiming to minimize the average service latency for users by obtaining optimal offloading decision, cache decision, transmission bandwidth, and computing resource. Then, we propose a Deep Reinforcement Learning (DRL)-based two-stage online computation offloading and application caching (DTSO2C) algorithm, effectively stabilizing application cache update costs and enhancing Quality of Service (QoS) for users. Furthermore, we utilize convex optimization algorithms to derive the optimal communication bandwidth and computing resource allocation strategy, further reducing the average service latency for users. Simulation results demonstrate that the DTSO2C algorithm outperforms the compared algorithms, achieving an average reduction in service latency of 66.2%, with an average cache update cost of only 0.15 USD per time frame.

Dual-timescale resource management for multi-type caching placement and multi-uer computation offloading in Internet of Vehicle

In Internet of Vehicle (IoV), edge computing can effectively reduce task processing delays and meet the real-time needs of connected-vehicle applications. However, since the requirements for caching and computing resources vary across heterogeneous vehicle requests, a new challenge is posed on the resource management in the three-tier cloud–edge-end architecture, particularly when multi users offload tasks in the same time. Our work comprehensively considers various scenarios involving the deployment of multiple caching types from multi-users and the distinct time scales of offloading and updating, then builds a joint optimization caching placement, computation offloading and computational resource allocation model, aiming to minimize overall latency. Meanwhile, to better solving the model, we propose the Multi-node Collaborative Caching, Offloading, and Resource Allocation Algorithm (MCCO-RAA). MCCO-RAA utilizes dual time scales to optimize the problem: employing a Bellman optimization idea-based multi-node collaborative greedy caching placement strategy at large time scales, and a computational offloading and resource allocation strategy based on a two-tier iterative Deep Deterministic Policy Gradient (DDPG) and cooperative game at small time scales. Experimental results demonstrate that our proposed scheme achieves a 28% reduction in overall system latency compared to the baseline scheme, with smoother latency variations under different parameters.

Power data analysis and mining technology in smart grid

This study proposes a smart grid model named “GridOptiPredict”, which aims to achieve efficient analysis and processing of power system data through deep fusion of deep learning and graph neural network, so as to improve the intelligent level and overall efficiency of power grid operation. The model integrates three core functions of load forecasting, power grid state sensing and resource optimization into one, forming a closely connected and complementary framework. Through carefully designed experimental scheme, the practical value and effectiveness of “Grid OptiPredict” model are fully verified from three aspects: accuracy of load forecasting, sensitivity of power grid state sensing and efficiency of resource allocation strategy. Experimental results show that the model has significant advantages in prediction accuracy, model stability and robustness, resource optimization, security, information security, social and economic benefits and user experience.

LINEAR PROGRAMMING FOR ALLOCATING TRANSPORTATION CAPACITIES IN FREIGHT PLANNING

Effective allocation of transport capacities is vital for motor transport enterprises engaged in freight transportation. As the demand for goods transportation rises, optimizing resource utilization becomes essential for ensuring timely deliveries, minimizing costs, and enhancing overall operational efficiency. Linear programming, a powerful tool in mathematical optimization, plays a key role in facilitating the rational distribution of limited resources. By framing the allocation problem as a linear program, enterprises can systematically identify the optimal use of their transport capacities, taking into account critical factors such as vehicle availability, delivery schedules, and associated costs. In the realm of operational planning, linear programming offers a structured and analytical approach to addressing the complexities inherent in freight transportation. This study delves into both the theoretical underpinnings and the practical applications of linear programming, showcasing its potential to significantly improve resource allocation strategies, boost efficiency, and reduce operational costs. Through the analysis of real-world examples and empirical data, the study provides valuable insights that can aid practitioners and researchers alike in refining logistics optimization practices, ultimately contributing to more efficient and cost-effective freight transport operations. Keywords: linear programming, transportation capacities, operational planning, freight transportation, resource optimization.

Project Management Information Systems and Performance of Monitoring and Evaluation Systems in The State-Owned Enterprises, Kenya

This study investigated the relationship Project Management Information Systems (PMIS) and the performance of Monitoring and Evaluation (M&E) systems in Kenya's state-owned enterprises (SOEs). The efficient execution of state-owned projects was identified as critical for achieving national development goals; however, challenges such as inadequate integration of PMIS, ineffective M&E systems, inefficient resource deployment, and difficulties in measuring project outcomes often hindered success. A baseline survey indicated that while 40% of SOEs had begun to integrate PMIS into their M&E processes, only 25% reported significant improvements in project tracking. The study aimed to examine the relationship between PMIS utilization and M&E performance, focusing on resource allocation and technological development. A descriptive research design was employed, targeting 263 SOEs, with data collected from 384 respondents, primarily M&E managers. The results demonstrated a robust correlation between resource allocation, technological development, and M&E performance, with Pearson correlation coefficients of 0.653 and 0.763, respectively. Regression analysis revealed that PMIS accounted for 64.9% of performance variance in M&E systems, underscoring its critical role in enhancing project outcomes. The findings indicated that effective resource allocation and technological advancements were essential for improving organizational performance. Recommendations included investing in technology, enhancing resource allocation strategies, and fostering a culture of innovation within SOEs. Additionally, the study highlighted the need for continuous training to equip staff with the skills to leverage technological advancements. Areas for further research were identified, emphasizing the importance of exploring contextual factors affecting performance, the long-term impacts of PMIS implementation, and comparative studies with the private sector to enhance understanding of resource allocation and technological strategies across different organizational contexts. Overall, the research underscored the vital role of integrated PMIS and effective M&E in optimizing the performance of state-owned enterprises in Kenya

COVID-19 from symptoms to prediction: A statistical and machine learning approach

During the COVID-19 pandemic, the analysis of patient data has become a cornerstone for developing effective public health strategies. This study leverages a dataset comprising over 10,000 anonymized patient records from various leading medical institutions to predict COVID-19 patient age groups using a suite of statistical and machine learning techniques. Initially, extensive statistical tests including ANOVA and t-tests were utilized to assess relationships among demographic and symptomatic variables. The study then employed machine learning models such as Decision Tree, Naïve Bayes, KNN, Gradient Boosted Trees, Support Vector Machine, and Random Forest, with rigorous data preprocessing to enhance model accuracy. Further improvements were sought through ensemble methods; bagging, boosting, and stacking. Our findings indicate strong associations between key symptoms and patient age groups, with ensemble methods significantly enhancing model accuracy. Specifically, stacking applied with random forest as a meta leaner exhibited the highest accuracy (0.7054). In addition, the implementation of stacking techniques notably improved the performance of K-Nearest Neighbors (from 0.529 to 0.63) and Naïve Bayes (from 0.554 to 0.622) and demonstrated the most successful prediction method. The study aimed to understand the number of symptoms identified in COVID-19 patients and their association with different age groups. The results can assist doctors and higher authorities in improving treatment strategies. Additionally, several decision-making techniques can be applied during pandemic, tailored to specific age groups, such as resource allocation, medicine availability, vaccine development, and treatment strategies. The integration of these predictive models into clinical settings could support real-time public health responses and targeted intervention strategies.

Joint spectrum and power allocation scheme based on value decomposition networks in D2D communication networks

Device-to-device (D2D) communications allow short-range communication devices to multiplex cellular-licensed spectrum to directly establish local connections for ultra-high number of terminal connections and greater system throughput. However, spectrum sharing also brings serious interference to the network. Therefore, a reliable and efficient resource allocation strategy is important to mitigate the interference and improve the system spectral efficiency. In this paper, we investigated spectrum access and power allocation in D2D communications underlay cellular networks based on deep reinforcement learning with the aim of finding a feasible resource allocation strategy to maximize data rate and system fairness. We proposed a value decomposition network-based resource allocation scheme for D2D communication networks. Our proposed scheme avoids frequent information exchanges among D2D users by centralized training, while allowing D2D users to make distributed joint resource allocation decisions. Simulation results show that the proposed scheme has stable convergence and good scalability, and can effectively improve the system capacity.