Allocation Issues Research Articles

“If government is saying the regulations are important, they should be putting in funding to back it up.”- An in-depth analysis of local authority officers’ perspectives of the Food (Promotion and Placement) (England) Regulations 2021

BackgroundAs part of the UK government’s obesity strategy, the Food (Promotion and Placement) (England) Regulations 2021 were implemented in October 2022 to restrict the prominent placement of products high in fat, sugar, or salt (HFSS) in most retail settings. Local authority (LA) officers have been tasked with enforcement of these regulations. This qualitative study examined the perspectives of LA officers including, trading standards, environmental health, and public health officers to understand enforcement approaches and requirements to optimise business compliance with the regulations.MethodsSemi-structured interviews were conducted via MS Teams with a purposive sample of LA officers across England. Data were analysed using inductive thematic analysis.ResultsThe 22 participants comprised 13 officers from Trading Standards, six from Environmental Health, and three from Public Health teams. The key messages include the following: (i) the regulations are complex and do not align with existing enforcement approaches, (ii) officers’ professional background will result in variable enforcement practices, and (iii) compliance assessment is an arduous task. LAs are facing resource and workforce constraints and have to prioritise regulations addressing high health risks (e.g., allergens). Therefore, officers will mostly apply a light touch approach to enforcement, raising awareness and engaging with businesses rather than issuing notices. To develop a consistent enforcement approach across LAs, officers asked for (i) further leadership from central government in the form of funding, training, and tools to determine in-scope businesses and products, (ii) cross-departmental collaboration to raise the regulations’ priority at local and regional levels, and (iii) greater consumer demand for healthier retail environments.ConclusionIt is crucial to address both structural challenges such as resource allocation, workforce, and prioritisation issues as well as the inherent complexity of the regulations to strengthen enforcement efforts. Our findings highlight the necessity of supporting enforcement activities at national and regional government levels to avoid potential false conclusions about ineffectiveness of regulations.

Machine Learning-Based Resource Allocation Algorithm to Mitigate Interference in D2D-Enabled Cellular Networks

Mobile communications have experienced exponential growth both in connectivity and multimedia traffic in recent years. To support this tremendous growth, device-to-device (D2D) communications play a significant role in 5G and beyond 5G networks. However, enabling D2D communications in an underlay, heterogeneous cellular network poses two major challenges. First, interference management between D2D and cellular users directly affects a system’s performance. Second, achieving an acceptable level of link quality for both D2D and cellular networks is necessary. An optimum resource allocation is required to mitigate the interference and improve a system’s performance. In this paper, we provide a solution to interference management with an acceptable quality of services (QoS). To this end, we propose a machine learning-based resource allocation method to maximize throughput and achieve minimum QoS requirements for all active D2D pairs and cellular users. We first solve a resource optimization problem by allocating spectrum resources and controlling power transmission on demand. As resource optimization is an integer nonlinear programming problem, we address this problem by proposing a deep Q-network-based reinforcement learning algorithm (DRL) to optimize the resource allocation issue. The proposed DRL algorithm is trained with a decision-making policy to obtain the best solution in terms of spectrum efficiency, computational time, and throughput. The system performance is validated by simulation. The results show that the proposed method outperforms the existing ones.

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.

A distributed simulation-optimization framework for many-objective water resources allocation in canal-well combined irrigation district under diverse supply and demand scenarios

To address the issues of both water resources allocation and sustainable management in agriculture areas with rising food demand, a simulation-optimization framework based on Flopy and Pymoo was proposed and developed for canal-well combined irrigation districts. The proposed framework first solved the many-objective water resources allocation problem which integrates groundwater simulation, crop production, and farmer income modules to quantitatively reveal the various trade-offs and synergies by using NSGA-III algorithm. The Entropy-TOPSIS method was then applied to recommend proper water allocation schemes. The proposed framework was further tested in Baojixia irrigation district considering various water supply and crop demand scenarios based on Copula-based uncertainty analysis. The Key findings are as follows: (1) the proposed framework could effectively optimize conjunctive water resources allocation problems of both surface water and groundwater; (2) low supply combined with high demand (p=0.17) is more likely to occur than high supply with high demand (p=0.02); (3) increased crop demand and restricted surface water negatively impact both water productivity and groundwater sustainability; and (4) the cumulative groundwater drawdown of recommend schemes is 36.9 % and 6.5 % higher under low to medium supply scenarios, while water productivity of recommend schemes decreases 28.2 % and 9.7 % with high and medium demand. This framework could provide useful insights for sustainable agricultural water management in canal-well combined irrigation district with various uncertainties in supply and demand scenarios.

A Balanced Mission Planning for Multiple Unmanned Underwater Vehicles in Complex Marine Environments

The collaboration of a multiple unmanned underwater vehicles (multi-UUVs) system has attracted widespread attention in recent years, as it can overcome the limitations of a single UUV and enhance mission completion efficiency. Oriented towards patrol and exploration missions with multiple waypoints, this paper proposes a balanced mission planning strategy, aiming to improve mission quality while reducing mission time for multi-UUVs. Firstly, due to the uneven performance of the two optimization objectives, a quick initialization screening method is employed specifically for mission quality to reduce the mission space. Secondly, to ensure mission load distribution and collaboration among multi-UUVs, and ease the difficulty in solving the issues of mission allocation and route planning, a balanced bi-level mission planning method based on regional segmentation is proposed. Finally, applicable weight evaluation criteria are utilized to evaluate the feasible solution set and determine the optimal solution. The efficacy of the balanced mission planning strategy is substantiated through comprehensive numerical simulations in a complex 2D marine environment.

Toward Equity in Global Access to SoHO-based Therapies: Recommendations for Action.

Therapies derived from substances of human origin (SoHOs) such as organs, cells, and tissues provide life-saving or life-changing treatment for millions of people worldwide each year. However, many people lack timely access to SoHO-based therapies because of insufficient supplies of these exceptional health resources and/or broader barriers in access to healthcare. Despite well-established governmental commitments to promote health equity in general and equity of access to SoHOs in particular, information about inequities in access to most SoHO-based therapies is scarce. Furthermore, the issue of equitable allocation of SoHO-based therapies has received little attention from policymakers and ethicists, except in the context of organ allocation for transplantation. Consequently, the extent and nature of potential inequities within and between countries are largely unknown, and few sources of guidance are available to support progress toward equity in global access to SoHO-based therapies. We present here the findings of an international ethics working group convened in preparation for the 2023 Global Summit on Convergence in Transplantation, organized in Santander, Spain. The group sought to assess potential gaps in knowledge about inequities involving SoHO-based therapies, to elucidate systemic factors that may influence access to these therapies, and to consider how policies and frameworks governing access to and allocation of SoHO-based therapies may promote equity when it is necessary to define boundaries in access because of insufficiency of supply. In discussing these challenges, we also outline several recommendations for action by governments and health authorities.

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.

A Narrative Review: Optimization Algorithms for The Problem of Allocating Shelf Space in Retailing

The Shelf Space Allocation Problem (SSAP) is of great significance in the retail industry since it directly impacts the effectiveness of product placement and organization within a shop. This issue arises from a deficiency in comprehending the strategic arrangement of products, resulting in the wasteful utilization of space. Notably, SSAP has a significant negative impact on store operations and customer satisfaction, resulting in wasted space and a reduction in the number of products that can be displayed to customers. Hence, the careful selection of an optimization method is crucial for effectively managing the allocation of shelf space in retail operations. This study aims to examine optimization algorithm techniques for addressing the issue of shelf space allocation in a particular environment, such as enhancing sales, minimizing congestion, or optimizing space utilization. Several studies have considered several optimization algorithm methods that have been used in solving the SSAP, including Genetic Algorithm (GA), Linear Programming (LP), Dynamic Programming (DP), Simulated Annealing (SA), Particle Swarm Optimization (PSO), and Variable Neighborhood Search (VNS). These methods are selected based on problem characteristics, including nonlinearity, integer requirements for variables, and constraints such as limited rack space. This optimization method helps retailers to effectively optimize shelf space allocation and maximize sales and profits. The study thus reviewed previous papers examining optimization algorithm methods to solve the problem of shelf space allocation. Those methods are crucial in improving product organization efficiency, customer satisfaction, and overall business performance.

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.

Research on Multiple AUVs Task Allocation with Energy Constraints in Underwater Search Environment

The allocation of tasks among multiple Autonomous Underwater Vehicles (AUVs) with energy constraints in underwater environments presents an NP-complete problem with far-reaching consequences for marine exploration, environmental monitoring, and underwater construction. This paper critically examines the contemporary methodologies and technologies in the task allocation for multiple AUVs, with a particular focus on strategies that optimize navigation time with energy consumption constraints. By conceptualizing the multiple AUVs task allocation issue as a Capacitated Vehicle Routing Problem (CVRP) and addressing it using the SCIP solver, this study seeks to identify effective task allocation strategies that enhance the operational efficiency and minimize the mission duration in energy-restricted underwater settings. The findings of this research provide valuable insights into efficient task allocation under energy constraints, providing useful theoretical implications and practical guidance for optimizing task planning and energy management in multiple AUVs systems. These contributions are demonstrated through the improved solution quality and computational efficiency.

An iterative price-based combinatorial double auction for additive manufacturing markets

The increasing adoption of additive manufacturing (AM) in the industrial sector is leading to an imbalance between supply and demand of additively manufactured subcomponents: companies demanding AM services require very specific products and AM suppliers differ widely in their capabilities. Some existing proposals aim to help match supply and demand by merely making customer–supplier allocations. Only a few recent works go beyond allocation issues and propose market mechanisms to also address pricing aspects. However, we observe that these mechanisms do not fully exploit the potential of additive manufacturing techniques. The aim of this paper is to design a market mechanism that considers the particularity of AM techniques, wherein suppliers can benefit from manufacturing multiple heterogeneous parts from multiple customers in the same build area to increase production throughput. This market mechanism has been implemented as an iterative combinatorial double auction that adapts to this feature of the AM market: customers will bid to get their orders produced and suppliers will submit asking quotes to win the production of combinations of those orders. The mechanism solves the allocation and pricing of AM orders while seeking the maximization of social welfare. The procedure is simulated in a theoretical environment to evaluate its performance and to identify the most appropriate conditions for its implementation in a real environment. Unlike other existing proposals for client-supplier allocation mechanisms in additive manufacturing, the proposed mechanism allows a single supplier to produce a combination of orders from different clients, leading to a pricing system that maximizes social welfare without participants disclosing sensitive information.

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.

The Changing Unpolitics of Covid‐19 Vaccine Procurement

Populist governments engage in “unpolitics” when the electoral incentives for doing so outweigh the distributive risks from policy failure. Studying the joint procurement of vaccines against Covid-19, I show that a group consisting of mostly populist governments led by Austria negotiated in bad faith, rejected compromise solutions, and obstructed joint problem-solving. They deployed these “unpolitical” tactics only once the legal framework for joint procurement was in place and the roll-out of the jointly ordered vaccines had begun. At this point, populist governments no longer faced the distributive risk of having limited access to affordable vaccines. By contrast, the electoral incentives for hard-nosed bargaining in bad faith increased, as the distributive issue of vaccine allocation became more salient and as populist governments came under pressure to deflect responsibility for having ordered insufficient vaccine doses.

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.

Clinical decision-making process and distributive justice: The mediating role of economic analysis. Empirical evidence from Italy.

The COVID-19 pandemic has not only tested the resilience of public health systems but also underscored the criticality of allocative choices on health resources. These choices, however, are not confined to health emergencies but are integral to public health decisions, which inherently grapple with limited resources. In this context, physicians play a pivotal role as the architects of clinical actions in various scenarios. Therefore, doctors are called upon to make their decisions by considering not only the criteria of clinical appropriateness but also the ethical aspects linked, in particular, to the principle of justice. Indeed, the assessment of the effectiveness of a treatment for a particular patient must be balanced against criteria of equity and justice for the whole. To be fully applied, the principle of justice presupposes the use of economic evaluation techniques designed to drive the organisation decisions by effectiveness and efficiency. The present paper aims to empirically analyse whether and to what extent economic evaluation is known and used by doctors in healthcare decision-making and, therefore, what the most widespread approaches are used in such processes. In particular, this paper intends to present the results of an empirical study on a sample of doctors registered with the Order of Physicians in Lombardy (Italy), one of the areas most affected by the COVID-19 pandemic. The research reveals a particular awareness of the criticality of allocation issues accompanied by a lack of knowledge of the economic evaluation techniques or, more broadly, by an almost total disuse of financial criteria. The main reasons are doctors' need for more knowledge of these tools and insufficient availability of economic information at the country system level. In the conclusion, we propose some suggestions to facilitate the transition to more current decision-making models consistent with the characteristics of more advanced national healthcare contexts.

Անուղղակի ծախսումների մասին տեղեկատվության կառավարման մեխանիզմների կատարելագործումը շինմոնտաժային աշխատանքներ կատարող կազմակերպություններում / Improving indirect cost allocation in construction organisations

The issue of the indirect cost allocation is not new, but it is becoming more relevant due to the increase in the relative weight of indirect cost in the production cost in organizations. This is due to automation of production processes and other reasons. Often, a single basis is used for the indirect cost allocation in organizations, but the choice of a single allocation basis for all resources and calculation objects cannot accurately reflect all causal relationships, that is why the process is conditional. Based on the professional literature and in accordance with the characteristics of the studied organizations, an indirect cost grouping was made for the organizations performing construction works and an approach of costing based on Activity-Based Costing was proposed. According to the author, the proposed approach can improve the financial information management mechanisms, and especially information about indirect cost, therefore also cost, and other indicators calculated based on it, in organizations.

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.