Resource Allocation Issues Research Articles

Global renewable energy transition in fossil fuel dependent regions

The worldwide shift towards renewable energy, as defined by the United Nations' Sustainable Development Goals (SDGs) in 2015, is a crucial milestone in achieving a sustainable future. The core focus of SDG 7 is to advance the accessibility, dependability, and environmentally-friendly nature of energy, aiming to substantially enhance the proportion of renewable energy sources and enhance energy efficiency. Although there have been significant developments in renewable energy, notably in solar and wind technologies, the rate of transition is still inadequate to achieve climate objectives. This study examines the imperative of implementing policy changes and integrated strategies to align global and local goals, while tackling the issues of resource allocation and the pressing need to transition away from fossil fuels in order to mitigate global temperature increase. Case studies showcase effective national endeavors, such as Brazil's Proalcool, Germany's Energiewende, and India's ambitious renewable energy objectives, illustrating diverse strategies for bolstering renewable energy capability. The report also analyzes the economic consequences for emerging nations that heavily rely on fossil fuels and the expected effects of reduced fossil fuel consumption. This article offers a detailed analysis of the current advancements, difficulties, and variations across different regions in the field of renewable energy. It gives valuable information on the future of global renewable energy plans, highlighting the importance of unified worldwide endeavors to attain a sustainable energy future.

Contribution-Based Resource Allocation for Effective Federated Learning in UAV-Assisted Edge Networks.

This paper considers UAVs as edge computing nodes and investigates a novel network resource allocation method for federated learning within a three-layer wireless network architecture containing cloud, edges (UAVs), and clients. To address the issue of fair bandwidth resource allocation among clients participating in federated learning, a contribution calculation strategy based on the Shapley value (SV) used as the weight for model aggregation is proposed. On this basis, a client selection and wireless resource allocation method based on model contribution is further designed. By reducing the training and aggregation frequency of the low-contribution clients during the asynchronous aggregation phase, the limited bandwidth resources are allocated to high-contribution clients, thus improving the convergence speed and accuracy of the global model. Simulation experiments demonstrate that the proposed method can significantly reduce the system delay and total energy consumption with gains between 15% and 50% while also improving the final accuracy of the global model by 0.3% and 2% on both short-term and long-term perspectives, respectively.

Behavioral Nudging With Generative AI for Content Development in SMS Health Care Interventions: Case Study.

Brief message interventions have demonstrated immense promise in health care, yet the development of these messages has suffered from a dearth of transparency and a scarcity of publicly accessible data sets. Moreover, the researcher-driven content creation process has raised resource allocation issues, necessitating a more efficient and transparent approach to content development. This research sets out to address the challenges of content development for SMS interventions by showcasing the use of generative artificial intelligence (AI) as a tool for content creation, transparently explaining the prompt design and content generation process, and providing the largest publicly available data set of brief messages and source code for future replication of our process. Leveraging the pretrained large language model GPT-3.5 (OpenAI), we generate a collection of messages in the context of medication adherence for individuals with type 2 diabetes using evidence-derived behavior change techniques identified in a prior systematic review. We create an attributed prompt designed to adhere to content (readability and tone) and SMS (character count and encoder type) standards while encouraging message variability to reflect differences in behavior change techniques. We deliver the most extensive repository of brief messages for a singular health care intervention and the first library of messages crafted with generative AI. In total, our method yields a data set comprising 1150 messages, with 89.91% (n=1034) meeting character length requirements and 80.7% (n=928) meeting readability requirements. Furthermore, our analysis reveals that all messages exhibit diversity comparable to an existing publicly available data set created under the same theoretical framework for a similar setting. This research provides a novel approach to content creation for health care interventions using state-of-the-art generative AI tools. Future research is needed to assess the generated content for ethical, safety, and research standards, as well as to determine whether the intervention is successful in improving the target behaviors.

Optimal resource allocation in micro-organisms under periodic nutrient fluctuations

Although microorganisms often live in dynamic environments, most studies, both experimental and theoretical, are carried out under static conditions. In this work, we investigate the issue of optimal resource allocation in bacteria growing in periodic environments. We consider a dynamic model describing the microbial metabolism under varying conditions, involving a control variable quantifying the protein precursors allocation. Our objective is to determine the optimal strategies maximizing the long-term growth of cells under a piecewise-constant periodic environment. Firstly, we perform a theoretical analysis of the resulting optimal control problem (OCP), based on the application the Pontryagin’s Maximum Principle (PMP). We determine that the structure of the optimal control must be bang–bang, with possibly some singular arcs corresponding to optimal equilibria of the system. If the control presents singular arcs, then these can only be reached and left through chattering arcs. We also use a direct optimization method, implemented in the BOCOP software, to solve the studied OCP. Our study reveals that the optimal solution over a large time horizon is related to the one over a single period of the varying environment with periodic constraints. Moreover, we observe that the maximal average growth rate attainable under periodic conditions can be higher than the one under a constant environment. We further extend our analysis to conduct a qualitative comparison between the predictions from our model and some recent biological experiments on E. coli. This analysis particularly highlights the mechanisms of action of the ppGpp signaling molecule, thus providing relevant explanations of the experimental observations. In conclusion, our study corroborates previous research indicating that this molecule plays a crucial role in the regulation of resource allocation of protein precursors in E. coli.

Resource Allocation and Security Threat in Cloud Computing: A Survey

Cloud computing is a cutting-edge technology with enormous commercial and enterprise potential. Apps and related data can be accessed from any location thanks to clouds. Companies can drastically lower the cost of their infrastructure by renting resources from the cloud for storage and other processing needs. They can also use pay-as-you-go application access available to the entire firm. Therefore, obtaining licenses for specific products is not necessary. However, allocating resources as efficiently as possible is one of the main challenges in cloud computing. Because the model is unique, resource allocation is done to lower its expenses. Meeting application and customer requirements and consumer needs presents additional issues in resource allocation. This study provides a detailed discussion of several resource allocation systems, security threats, and the difficulties they face. This work is anticipated to help researchers and cloud users overcome obstacles.

Perceived outcomes of medical teaching institute reforms: insights from management, faculty, and administration in Pakistani tertiary health care

ObjectiveThe study aims to explore the perceived outcomes of Medical Teaching Institution (MTI) reforms on autonomy and overall performance within tertiary healthcare institutions in Khyber Pakhtunkhwa (KP) province, Pakistan.MethodologyA cross-sectional study was carried out from September 2023 to March 2024, involving interviews with frontline staff, administrative personnel, and senior management within MTI-affiliated institutions. The methodology employed, using both qualitative and quantitative data analysis techniques.ResultsThe study showed that institutional staff members’ knowledge and understanding of the MTI changes differed. Some observed very minor adjustments, while others saw advances in hospital operations and service delivery. Administrative complexity, political meddling, and resource allocation problems were noted as challenges. Positive results were also observed, though, and they included improved infrastructure, possibilities for staff training, and decision-making procedures.ConclusionDespite significant improved, there are still challenges, such as inconsistent staff comprehension, mixed impacts on service delivery, resource allocation issues, and political meddling. Addressing these issues necessitates improved communication, continuous evaluation, and coordinated efforts to improve administrative systems and obtain consistent funding.

Transforming Public Health Data Management in IoMT Networks Based on Innovative Offloading Scheme

Healthcare platform monitoring IoT-oriented technologies constitute the idea of Internet of Medical Things (IoMT) in public health and medical services. The amount and quality of created data have a substantial influence on data management and privacy compute offloading solutions are unable to keep up with the growing needs of the health industry, especially when fast and dependable communication was needed. The study suggests a unique approach called Mutated Barnacles Mating Optimization (MBMO) for assisting the data management issues in IoMT systems. The suggested MBMO framework successfully addresses problems that are common in the medical industry by using a data offloading technique. To overcome the issues of discrete tasks and resource allocation, guarantees the needs of dependable and efficient communication. We implemented Java software. The evaluation of the performance step encompasses several measures, such as energy consumption (J/ms) and Time delay (ms)model to assess the efficiency of the suggested forecasting algorithm. We performed an assessment of comparison with other established approaches results indicate that the suggested model produces superior results for assisting the data management issues in IoMT systems.

Fairness mechanism and stackelberg strategy for multi-agent systems: A case study of legends of the three kingdoms

Alongside the rapid advancement of artificial intelligence technologies, the complexity of society continues to escalate, and the design of multi-agent system rules becomes increasingly crucial, optimize participant engagement and addressing the issue of fairness and equilibrium win rates. The card game Legends of the Three Kingdoms (LTK), as a complex multi-agent system, represents an important abstraction of real-world scenarios, requiring the implementation of proper incentive mechanisms. Based on the mechanism and Stackelberg, this paper optimizes player strategies and system fairness. Firstly, we constructed a multi-agent Stackelberg game model for team battles. Then, we analyzed the impact of decision-making factors on players. Subsequently, we defined three kinds of fairness and improved the incentive mechanism. The results indicate that our mechanism optimizes participants' game behaviors, enhancing fairness among team players. Win rates were improved from 56.2% VS 43.8% to 51.4% VS 48.6%. With three different fairness measures, the win percentage fairness increased by 72 %, and the first elimination fairness increased by about 79 %. Our research provides a reference for understanding and analyzing complex computational models and facilitates the resolution of various resource allocation and system design issues.

Gaining influence: Developing a Healthy Public Policy Unit at Vancouver Coastal Health.

Regional Public Health Unit (Office of the Chief MHO, Vancouver Coastal Health Authority, BC, Canada). Recognizing the need to invest resources that strategically move public health work "upstream" where policies and investments can shape the determinants of health, the Office of the Chief MHO for Vancouver Coastal Health embarked on a process in 2019 to develop a population health team designed to engage in Healthy Public Policy (HPP) and Health in All Policies (HiAP) approaches to health promotion. The mission and design of this unit was informed by internal and external research and engagement which continued even through the COVID-19 pandemic. Inthe fall of 2022, this new unit, the first of its kind in British Columbia, was launched. In 2023, it embarked on strategic planning, engaged in key public policy processes, and implemented priority areas of work to help advance healthy public policy in the region, including the development of processes, tools, and practices to support a more systematic and rigorous approach to this emerging area of public health practice. The creation of this new Healthy Public Policy Unit invites discussion within Canada's public health community on the issue of resource allocation to support upstream interventions seeking to build the capacity and effectiveness of HPP and HiAP approaches.

An adaptive MLP-based joint optimization of resource allocation and relay selection in device-to-device communication using hybrid meta-heuristic algorithm

Enhancement in both energy efficiency and spectral efficiency in cellular networks is made possible by means of an advanced technique called device-to device (D2D) communication. The enhancement of these efficiencies is done by utilizing the cellular user (CU) resources once again to make communication with nearby cellular devices in an effective manner by spectral means. As the D2D communication technology is capable of providing a direct communication link with nearby devices effectively with enhanced spectral efficacy, this approach is considered as an ideal solution for the futuristic cellular communication network. A flexible and reliable relay-assisted communication by means of D2D technology is required that acts as an intermediate relay when the attenuation between the channels across the D2D devices becomes high. The throughput of the system is increased by utilizing D2D communication technology as it uses direct data transmission within cellular devices. When the cellular user is far apart from one another, the data loss in the D2D communication system is minimized with the utilization of the relay. When the channels are good, then the relay nodes (RNs) serve the cellular user. However, it is noted that the D2D systems are affected by issues such as higher consumption of energy and spectral sharing. Also, the sum rate gets degraded as a result of mutual interference between resource-sharing cellular devices in the relay-assisted D2D communication system. The transmission of the data in a traditional relay-assisted D2D communication system is carried out between the D2D receiver (DR) and D2D transmitter (DT) only with the utilization of its own energy by the RN. The issues in relay selection and resource allocation in the conventional joint resource allocation schemes are tackled by executing a scheme for performing the task of optimal relay selection and joint resource allocation. The enhancement of the overall sum rate in the D2D communication is the main motive behind the implemented scheme. This goal is attained along with the minimization of the link rates in the cellular and D2D networks. The ideal selection of the relay and the execution of the joint resource allocation are done with the utilization of a new optimization scheme called the hybrid flow direction with the chameleon swarm algorithm (HFDCSA), in which the flow direction algorithm (FDA) is fused along with chameleon swarm algorithm. This optimal selection of the relays is assisted by considering constraints like the network’s sum rate and energy efficiency in the network to achieve high performance. The data obtained from distinct sources are given to the adaptive multi-layer perceptron (AMLP) in which optimal resource allocation and the relay selection are performed with the help of the suggested HFDCSA. The parameters in the MLP are tuned by the same HFDCSA. Finally, the performance validation is conducted in the stage to verify the working of the suggested approach.

Collaborative computation offloading and wireless charging scheduling in multi-UAV-assisted MEC networks: A TD3-based approach

Computation offloading, resource allocation, and endurance issues in unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) networks have always been a research focus. UAV-aided MEC allows mobile users’ (MUs’) tasks to be offloaded to drones for processing in special scenarios, such as natural disasters or military attacks. However, as the number and size of offloaded tasks continuously increase, it is difficult for a single UAV to meet all computational demands, result in the decline of QoS. In order to address this issue, this paper presents a collaborative computation offloading scheme where multiple UAVs can cooperate to handle massive tasks. Firstly, considering that battery-limited UAVs cannot complete all tasks and sustain flight without recharging, we incorporate charging stations (CS) into multi-UAV-assisted MEC networks. Subsequently, we design a price-based incentive mechanism to encourage the adjacent UAVs with unused computation resources to cooperate with busy UAVs, so as to maximize the total revenue obtained from UAVs’ collaborative computation. Then, we formulate the joint optimization problem of computation offloading, resource allocation and charging scheduling as a Markov Decision Process (MDP), and propose a Twin Delayed Deep Deterministic policy gradient (TD3) algorithm to find optimal strategies. Finally, extensive simulations demonstrate that the proposed TD3 algorithm outperforms other benchmark methods, achieving the maximum overall system utility under different scenarios.

AI-based resource allocation techniques in D2D communication: Open issues and future directions

The fifth-generation (5G) mobile network enhances network connectivity between many mobile devices by utilizing higher bandwidth with lower communication delay. This massive connectivity generates huge network traffic while burdening the base station (BS). Hence, it is required to offload the BS and enhance the system’s overall performance. Device-to-device (D2D) communication is the prominent solution to offload the network traffic and enhance spectrum efficiency. However, D2D communication significantly disadvantages resource allocation while operating in underlay mode. A prominent solution is to embrace artificial intelligence (AI) in the D2D environment to tackle resource allocation issues. This paper exhaustively surveys all AI-based resource allocation schemes researchers have proposed globally. We found that no exhaustive literature surveys discuss the role of AI in resource allocation in a D2D environment. Thus, this motivated us to prepare a comprehensive survey and propose an AI-based resource allocation taxonomy comprising channel, power, and spectrum allocation. We also discuss the various real-time applications of AI-driven schemes for efficient resource allocation in D2D communication. Further, we presented a case study highlighting AI’s role in efficient resource allocation in D2D communication. Here, we used a novel channel quality indicator (QI) that uses signal-to-noise ratio (SINR) and signal-to-noise-and-distortion Ratio (SNDR) to decide the best D2D users for the resource allocation process. Then, we discussed the open research challenges that point to the future research direction in AI-based efficient resource allocation in D2D communication.

Game Theoretic Approaches to Modelling Cooperative and Competitive Dynamics for Enhanced Routing and Resource Allocation

The goal of the field of game theory is to simulate scenarios in which decision-makers must choose between courses of action that may or may not conflict and in which the choices made by one element may affect the choices made by other elements. Techniques from game theory have been widely used to solve a variety of engineering design issues. Game theory may be applied to wireless networks to create cooperative strategies between nodes, terminals and network providers, among other organizations. The majority of the time, game theory has been used in networking to address routing and resource allocation issues in competitive settings. Because radio communication channels are typically shared in wireless networks, the actions of one wireless device may have an impact on the communication capabilities of a neighboring device. Game theory may be used to simulate situations such as these. A multi-layered viewpoint is presented in Applications of Game Theory in Wireless Networking, highlighting the domains in which game theory might find practical application. This explains how different wireless network interactions may be represented as games and how game-theoretical methods can accurately mimic or forecast genuine user behavior in cooperative or competitive settings. Because of these similarities, it is possible to grasp the intricate relationships between nodes in this highly dynamic and dispersed environment by using a strong mapping between classical game theory components and network parts

Proportional-fair uplink resource allocation with statistical QoS provisioning for RAN slicing

This paper investigates the uplink resource allocation issue for radio access network (RAN) slicing with a focus on three primary fifth generation (5G) use cases, namely ultra-reliable low-latency communications (uRLLC), enhanced mobile broadband (eMBB) and massive machine-type communications (mMTC). Most of the existing resource allocation schemes for RAN slicing were designed to address the use cases in downlink communications. However, these schemes are not applicable to the uplink RAN slicing, even though the primary use cases also entail stringent requirements in the uplink. To this end, we aim to develop a new, fairness-aware uplink resource allocation scheme for RAN slicing, catering to the three 5G use cases. Firstly, we formulate a new uplink resource allocation optimization problem that maximizes a sum logarithmic utility for ensuring proportional-fair effective capacity distribution and resource allocation among RAN slices, subject to the distinct quality of service (QoS) requirements of the uRLLC, eMBB and mMTC slices. Then, an efficient, hierarchical resource allocation framework is then proposed to solve the problem deterministically, whereby resource block assignment is first performed at the base station via a demand-oriented greedy algorithm, followed by power allocation using a bisection method at individual user equipment. Results show that the proposed scheme outperforms the baseline schemes with fairness and QoS provisioning performance gains of up to 44.3% and 19.17% respectively.

Revamping Village Governance based on Insights from Law 6/2014 in Indonesia

This study explores the implementation of Law Number 6 of 2014 in Gempol Village, Pasuruan Regency, focusing on factors hindering and supporting village officials' performance in orderly village administration. Using a descriptive qualitative approach with interviews, observation, and documentation, the research reveals challenges in communication, resource management, bureaucratic structure, and organizational culture among officials. Findings indicate a need for improved communication, resource allocation, and organizational culture to enhance Law Number 6 of 2014's effectiveness and overall village administration performance. Highlights: Communication gaps hinder Law 6/2014's effectiveness in Gempol Village. Resource allocation issues impact orderly administration in Gempol Village. Cultural change needed for better Law 6/2014 implementation in Gempol. Keywords: Implementation, Law Number 6 of 2014, Gempol Village, Performance, Orderly Administration

Exploring the Confluence of Risk Management, Project Quality, and Project Performance in the Jordanian Context in Construction Industry

The construction sector in Jordan has experienced significant growth in recent years, driven by government initiatives for economic diversification. However, the industry has faced challenges such as project delays, inadequate time management, and resource allocation issues. This paper presents a comprehensive review of the intricate connections between risk management, project quality, and project performance within Jordan's construction landscape. It reviews the interplay between risk management, project quality, and project performance in this context. The literature review emphasizes themes like effective communication, critical success factors, and timely project delivery. It also highlights risk management's role in mitigating communication-related risks and addressing time delays. The abstract mentions the Resource-Based View (RBV) theory's application, emphasizing how unique resources contribute to competitive advantage. Overall, the review provides insights for stakeholders to navigate challenges and excel in the construction industry.

Dynamics of resource allocation–firm performance relationship: Panel autoregressive distributed lag approach

AbstractThe present study aims to unravel the intricacies of the resource allocation–firm performance relationship by examining the impact of resource allocation to research and development (R&D), advertising, inventories, and wages and salaries on firm performance, considering both short‐term and long‐term dynamics This work is conducted using a sample of 240 listed Indian manufacturing firms over a time span of 18 years, that is, from 2006 to 2023. Based on the empirical findings from the dynamic panel autoregressive distributed lag (ARDL) model, it is revealed that, in the short term, allocating resources to areas such as R&D, advertising, inventories, and wages and salaries has a detrimental effect on sales. However, in the long term, these resource allocations show a positive impact on sales. Regarding stock market performance, both R&D and advertising exert a negative influence on Tobin's Q in the short term, but their impact becomes positive in the long term. In the case of inventories and wages and salaries, their effects evolve over time: Initially, in the short term, both have a positive impact on Tobin's Q, but this effect switches to negative and becomes significant in the long term. The key contribution of this study revolves around a model‐driven approach aimed at tackling the complex issue of resource allocation and its dynamic impact on performance outcomes.

Barriers and Challenges Affecting Quality Education (Sustainable Development Goal #4) in Sub-Saharan Africa by 2030

Education is a fundamental human right and a crucial tool for sustainable human capacity development, which can advance the economic growth of a country. Yet for many children in sub-Saharan Africa (SSA), quality education remains out of reach. This study aimed to identify the barriers and challenges to enacting Sustainable Development Goal 4: “Ensuring Inclusive, Equitable, and Quality Education and the Promotion of Lifelong Learning Opportunities for All”. Through a content analysis of the relevant literature, we identified three themes: funding constraints, access and inclusion, and teacher education. Regarding funding, issues of resource allocation, technical capacity, and accountability were identified as primary factors requiring intervention strategies to become fully realized in SSA. When exploring access and inclusion, we found that incorporating students with disabilities, gender disparities, physical barriers, and inadequate curriculum are essential factors limiting quality education in SSA. Finally, teachers’ conditions and training emerged as crucial challenges to reach quality pedagogy. Some SSA countries have improved their efforts for quality education, but these barriers and challenges continue to impact education for all children. A new perspective on mitigating these impediments can address several factors responsible for the exclusion of diverse groups from accessing quality education in the SSA region.

Optimizing production logistics through advanced machine learning techniques: A study on resource allocation for small-batch and multi-variety challenges

In light of increasing consumer purchasing power and the growing diversification of demands, the emergence of small-batch and multi-variety requirements has posed substantial challenges to production manufacturing, particularly in on-site production logistics management. This study aims to address the issues of effective material flow and resource allocation in production logistics management by investigating a method for the rapid and accurate allocation of supply logistics resources. Leveraging the extensive logistics resource allocation data available in the Plan For Every Part system, this research utilizes the Term Frequency-Inverse Document Frequency algorithm to filter out less discriminative data. Additionally, a recommender system based on machine learning, specifically the Recsys (Recommender System) employing similarity algorithms, is employed to construct a logistics resource allocation recommendation model. The effectiveness of this approach is validated through a case study that involves material resource allocation recommendations in a specific remote control production workshop. The validation results indicate that, compared to traditional logistics resource allocation processes, the proposed model optimizes resource allocation recommendations in production logistics, resulting in a significant improvement in material delivery punctuality and overall operational efficiency in enterprises.

Incorporating Morris' Design Thoughts for AI and Big Data-Enabled Coverage Optimization in China's Wireless Communication Network

Morris changes this study's China cellular network AI and Big Data Analytics. Scalability, regulatory compliance, and resource allocation efficiency are checked. Numerous methods seamlessly combine qualitative interview, document, and case study findings with quantitative network performance statistics. Qualitative study highlights industrial resource allocation, efficiency, and user-centric design issues. Innovative problem-solving emphasizes tech and regs. Researchers think Morris' designs improve China's wireless network. Explain and apply Morris' design concepts to problems. This comprehensive theoretical and practice study optimizes networks using Morris' design theories. Interdisciplinary research improves Morris' digital ideas. This research ingeniously integrates theory and practice to create network theory. Research employing mixed methods. Interviews, document analysis, and case studies increase efficiency, resource allocation, and user-centric design. Data quality and processing speed are investigated in quantitative network performance studies. Quantifying complex relationships with correlation and regression analysis strengthens the study's powerful method. Innovative regulatory compliance and scalability solutions demonstrate the study's cutting-edge approach. The paper then examines key findings and implications. Network optimization requires high-quality data, feature engineering, and user-centered design, according to research. Executives get proper network optimizing guidance. The essay emphasizes industry regulatory and technical improvements. Morris optimized networks theoretically. This integrated strategy boosts theory and digital relevance. Wireless network enhancements in China. Effectiveness, user experience, and data-driven accuracy help researchers optimize networks. This study addresses specific challenges and extends network theory to create future-ready networks utilizing Morris' design methods. Chinese wireless communication network optimization demonstrates this research's practical and theoretical benefits.