Resource Allocation Decisions Research Articles

Drought Monitoring and Prediction in Agriculture: Employing Earth Observation Data, Climate Scenarios and Data Driven Methods; a Case Study: Mango Orchard in Tamale, Ghana

The study focused on the prediction of the Temperature Vegetation Dryness Index (TVDI), an agricultural drought index, for a Mango orchard in Tamale, Ghana. It investigated the temporal relationship between the meteorological drought index, Standardized Precipitation Index (SPI), and TVDI. The SPI was calculated based on utilizing precipitation data from the World Meteorological Organization (WMO) database (2010–2022) and CMIP6 projected precipitation data (2023–2050) from 35 climate models representing various Shared Socioeconomic Pathway (SSP) climate change scenarios. Concurrently, TVDI was derived from Landsat 8/9 satellite imagery, validated using thermal data obtained from unmanned aerial vehicle (UAV) surveys. A comprehensive cross-correlation analysis between TVDI and SPI was conducted to identify lag times between these indices. Building on this temporal relationship, the TVDI was modeled as a function of SPI, with varying lag times as inputs to the Wavelet-Adaptive Neuro-Fuzzy Inference System (Wavelet-ANFIS). This innovative approach facilitated robust predictions of TVDI as an agricultural drought index, specifically relying on SPI as a predictor of meteorological drought occurrences for the years 2023–2050. The research outcome provides practical insights into the dynamic nature of drought conditions in the Tamale mango orchard region. The results indicate significant water stress projected for different time frames: 186 months for SSP126, 183 months for SSP245, and 179 months for both SSP370 and SSP585. This corresponds to a range of 55–57% of the projected months. These insights are crucial for formulating proactive and sustainable strategies for agricultural practices. For instance, implementing supplemental irrigation systems or crop adaptations can be effective measures. The anticipated outcomes contribute to a nuanced understanding of drought impacts, facilitating informed decision-making for agricultural planning and resource allocation.

MAXIMIZING RETURNS: UNVEILING THE IMPACT OF LIQUIDITY, EFFICIENCY, AND DIVIDEND POLICY ON ASSETS

With an emphasis on Return on Assets (ROA), this study examines the interdependent dynamics of liquidity, efficiency, and dividend policy on business financial performance. With a quantitative research design, the study seeks to clarify how these components together affect organizational effectiveness. Regression analysis is used in the study to look at the associations between the dependent variable, ROA, and the important independent variables, current assets, quick ratio, liquidity, and dividend, using data from ITC Ltd. that was obtained from Moneycontrol over a five-year period (2019–2023). The findings show a strong relationship between liquidity, efficiency, dividend policy, and return on assets (ROA), with liquidity standing out as the main factor and efficiency and dividend policy following closely behind. The results highlight how crucial these variables are in determining financial performance and provide insightful information for organizational resource allocation and strategic decision-making. The study does, however, admit certain limitations, such as the possibility of wider generalizability and the dependence on data from a single organization. Further research is recommended to increase the sample size, carry out longitudinal studies, and investigate qualitative techniques in order to gain a deeper comprehension of and identify the causal factors behind the dynamics of financial success.

A structured process for the validation of a decision-analytic model: application to a cost-effectiveness model for risk-stratified national breast screening.

Decision-makers require knowledge of the strengths and weaknesses of decision-analytic models used to evaluate healthcare interventions to be able to confidently use the results of such models to inform policy. A number of aspects of model validity have previously been described, but no systematic approach to assessing the validity of a model has been proposed. This study aimed to consolidate the different aspects of model validity into a step-by-step approach to assessing the strengths and weaknesses of a decision-analytic model. A pre-defined set of steps were used to conduct the validation process of an exemplar early decision-analytic-model-based cost-effectiveness analysis of a risk-stratified national breast cancer screening programme [UK healthcare perspective; lifetime horizon; costs (£; 2021)]. Internal validation was assessed in terms of descriptive validity, technical validity and face validity. External validation was assessed in terms of operational validation, convergent validity (or corroboration) and predictive validity. The results outline the findings of each step of internal and external validation of the early decision-analytic-model and present the validated model (called 'MANC-RISK-SCREEN'). The positive aspects in terms of meeting internal validation requirements are shown together with the remaining limitations of MANC-RISK-SCREEN. Following a transparent and structured validation process, MANC-RISK-SCREEN has been shown to have satisfactory internal and external validity for use in informing resource allocation decision-making. We suggest that MANC-RISK-SCREEN can be used to assess the cost-effectiveness of exemplars of risk-stratified national breast cancer screening programmes (NBSP) from the UK perspective. A step-by-step process for conducting the validation of a decision-analytic model was developed for future use by health economists. Using this approach may help researchers to fully demonstrate the strengths and limitations of their model to decision-makers.

Abstract PO3-10-04: Palliative Care, Healthcare Utilization, and Disparities in Deceased Breast Cancer Patients in U.S. Hospitals: Analysis of National Inpatient Sample

Abstract Objective: This study aims to explore the characteristics of receiving palliative care and the patterns of utilization among breast cancer patients using a large-scale representative population-based sample. Methods: A retrospective analysis was performed on hospitalization data obtained from the National Inpatient Sample (NIS) covering the period from January 2016 to December 2019. The objective of the study was to investigate the characteristics and disparities associated with the provision of palliative care to breast cancer patients who had passed away, and to evaluate its impact on healthcare utilization as indicated by discounted hospital charges and length of stay (LOS). To achieve this, multivariate linear and logistic regression analyses were conducted, with the data stratified according to age, race, Charlson comorbidity index, insurance status, patient's zip code, and hospital characteristics. The classification and identification of the study population were performed using ICD-10 codes. Results: In the study, a total of 6,325 deceased breast cancer patients were identified from hospitalization records. Among them, 59.16% (n=3,742) received palliative care during their hospital stay. Multivariate linear regression analysis revealed that patients who received palliative care experienced a significant reduction in LOS by an average of 0.67 days (-1.246 to -0.898, p = 0.02) compared to those who did not receive palliative care. Furthermore, the palliative care group exhibited lower total charges, with a mean decrease of $21,392 (-28205 to -14578, p=0.00) compared to the non-palliative care group. Multivariate logistic regression analysis indicated that patients with black race had lower odds of receiving palliative care, while those with higher household income and patients admitted to larger hospitals had higher odds of receiving palliative care. Additionally, patients with private insurance had a higher probability of receiving palliative care compared to other insurance groups. Conclusion: Palliative care has demonstrated its benefits in reducing healthcare utilization costs and length of stay for breast cancer patients. However, disparities exist in access to palliative care, particularly among Black patients and those with lower household income. Larger hospitals and patients with private insurance show higher rates of palliative care utilization. Addressing these disparities is crucial to ensure equitable access, optimize patient outcomes, and improve resource allocation. Integrating palliative care into breast cancer hospitalizations requires targeted interventions. Further research and implementation efforts are needed to enhance the availability and delivery of palliative care services for all patients, regardless of their demographics or socioeconomic backgrounds. Health policymakers should consider these findings in their resource allocation decisions as palliative care plays an increasingly important role in cancer treatment. Odds of Receiving Palliative care among deceased breast cancer patients XX XX Length Of Stay and Total Charges XX XX Citation Format: Rushin Patel, Zalak Patel, Mrunal Patel, Darshil Patel, Afoma Onyechi, Akshit Chitkara, Femina Patel. Palliative Care, Healthcare Utilization, and Disparities in Deceased Breast Cancer Patients in U.S. Hospitals: Analysis of National Inpatient Sample [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-10-04.

A budgeting resource allocation model for capacity expansion

The reconfiguration of the global supply chain has created opportunities for newly industrialized economies (NIEs) to strengthen and expand their production capacities. We develop a dynamic modeling framework to study a firm’s resource allocation decisions while building capacity for a portfolio of production lines over multiple decision periods. Our model adds to the literature on versions of this problem in which distributions of market demands are incorporated into the investment-return function, and where no partial order fulfillment is possible. We consider two specific cases: one involving exogenously defined demand distributions, and one where the distributions of the parameters of the demand distribution are updated based on Bayesian inference. For each case, we first identify optimal capacity levels as a function of unit investment cost, holding cost, and the demand distribution of each production line. We then characterize the optimal path to reach these levels under different market conditions by deriving closed-form optimal resource allocation policies.

A dynamic pricing scheme for secure offloading and resource allocation based on the internet of vehicles

With the development of sixth-generation (6G) wireless communication technology, billions of vehicles will access the network in the future, and the number of vehicle applications and user data will also increase dramatically. Traditional cloud computing faces serious problems of latency and energy consumption in handling massive data. Mobile edge computing (MEC) has emerged to dramatically improve computing efficiency and reduce energy consumption by placing computing servers at network edge locations close to vehicles. However, the service range of MEC servers is limited and cannot fully satisfy user requirements. In addition, the task offloading process has security risks. The current research focuses on how to reduce the energy consumption and latency overhead of task offloading and neglects the economic cost or data transmission security of vehicles. To solve the above problems, we propose a cooperative security offloading (CSO) scheme for auxiliary vehicles and MEC servers. Firstly, we propose a dynamic pricing mechanism for computing resources by considering the credibility of MEC servers and auxiliary vehicles, the urgency of the task, and the number of users competing for auxiliary vehicles. Secondly, to prevent malicious MEC servers and eavesdroppers from attacking, we employ homomorphic encryption to protect user privacy. Meanwhile, efficient and secure computing services are achieved by optimizing user selection decisions, offloading decisions, and resource allocation decisions. Finally, the optimal decisions are obtained by the dueling DQN-based resource allocation and pricing strategy (DDRP) and the cost-minimizing security offloading (CMSO) algorithm, which minimizes the economic cost of users while maximizing security. Simulation results show that, compared with some existing schemes, the CSO scheme effectively reduces the economic cost of users while ensuring the security of data transmission.

Data-driven decision making: Shaping the future of business efficiency and customer engagement

Data-driven decision-making (DDDM) is a critical approach that organizations are adopting to enhance their operational efficiency and customer engagement. This abstract explores the significance of DDDM in shaping the future of businesses. Data-driven decision-making (DDDM) is revolutionizing the way businesses operate and engage with customers. By leveraging data analytics, organizations can extract valuable insights to inform strategic decisions, improve operational efficiency, and enhance customer experiences. This abstract examines the role of DDDM in shaping the future of business efficiency and customer engagement. DDDM enables organizations to optimize their processes and resources through data analysis. By identifying patterns and trends in data, businesses can streamline operations, reduce costs, and improve productivity. For example, predictive analytics can help forecast demand, allowing businesses to adjust their inventory levels accordingly and avoid stockouts or overstock situations. Additionally, data-driven insights can inform resource allocation decisions, ensuring that resources are allocated efficiently to maximize returns. DDDM also plays a crucial role in enhancing customer engagement. By analyzing customer data, businesses can gain a deeper understanding of customer behavior, preferences, and needs. This enables businesses to tailor their products and services to better meet customer expectations, leading to higher customer satisfaction and loyalty. For example, personalized marketing campaigns based on customer data can significantly improve engagement and conversion rates. As the volume and complexity of data continue to grow, DDDM will become even more essential for businesses. Advancements in technology, such as artificial intelligence and machine learning, will further enhance the capabilities of DDDM, allowing businesses to derive even more value from their data. However, organizations must also address challenges such as data privacy and security to ensure that DDDM is implemented ethically and responsibly. In conclusion, DDDM is reshaping the future of business efficiency and customer engagement. By embracing DDDM, organizations can unlock the full potential of their data and gain a competitive edge in today's data-driven world.

Leveraging Emerging Technologies for Enhanced Airport Ground Movement Management Efficiency and Reliability

The Executive summary serves to give the highlights of the research paper and an overview of the objectives, methods used, key findings and implications. It is a brief rundown of the whole research document and provides time-pressed readers with some insights onto the study without having to go into deeper details. Introduction: This research paper examines how blockchain, edge computing and autonomous systems can be integrated inside airports in order to enhance performance and reliability of airport ground movement management. There is need for innovative approaches towards optimizing ground movement in highly complex airports to ensure that they operate seamlessly in a growing air traffic scenario. Objectives: The main aim of this study is to explore how blockchain, edge computing and autonomous systems could change airport ground movement management entirely. These technologies will be exploited for improving data sharing, enhancing real-time processing capabilities as well as enabling automatic decision support services on the ground operations. Methodology: In this case a mixed method approach has been used which involves qualitative and quantitative analysis. Qualitative methods like surveys and questionnaires are done so as researchers get insights from different stakeholders, Integrating various stakeholders, including airlines, airports, and regulators, regarding their perspectives on integrating new technologies into ground movement management!!! Quantitative methods involve data analysis to assess the effectiveness and efficiency of proposed technological solutions. Additionally, simulations and case studies are conducted to evaluate the impact of these technologies on ground movement optimization. Key Findings: The research yields promising results, indicating significant improvements in routing efficiency, reduced taxiing times, and increased operational reliability. The integration of blockchain facilitates secure data sharing, while edge computing enables real-time processing, leading to faster decision-making in ground movement management! Autonomous systems contribute to optimizing resource routing and allocation decisions, further enhancing operational efficiency. Implications and Recommendations: The findings of the research carry both theoretical and practical implications. Theoretical implications include advancing knowledge in the field of airport ground movement management by exploring the potential of emerging technologies. On a practical level, the research provides valuable insights for airport stakeholders seeking to modernize their ground movement control systems?? Recommendations include addressing the complexity of integrating multiple emerging technologies and overcoming challenges related to data security and system interoperability. Conclusion: In conclusion, the research underscores the significance of integrating blockchain, edge computing, and autonomous systems into airport traffic management to enhance efficiency and reliability!!! By addressing key challenges in ground movement management, the study paves the way for more streamlined and resilient airport operations, ultimately benefiting the aviation industry as a whole.

Needs assessment in long-term care: expression of national principles for priority setting in service allocation

BackgroundLong-term care services for older adults are characterised by increasing needs and scarce resources. Political strategies have led to the reorganisation of long-term care services, with an increased focus on “ageing in place” and efficient use of resources. There is currently limited research on the processes by which resource allocation decisions are made by service allocators of long-term care services for older adults. The aim of this study is to explore how three political principles for priority setting in long-term care, resource, severity and benefit, are expressed in service allocation to older adults.MethodsThis qualitative study uses data from semi-structured individual interviews, focus groups and observations of service allocators who assess needs and assign long-term care services to older adults in Norway. The data were supplemented with individual decision letters from the allocation office, granting or denying long-term care services. The data were analysed using reflexive thematic analysis.ResultsThe allocators drew on all three principles for priority setting when assessing older adults’ long-term care needs and allocating services. We found that the three principles pushed in different directions in the allocation process. We identified six themes related to service allocators’ expression of the principles: (1) lowest effective level of care as a criterion for service allocation (resource), (2) blanket allocation of low-cost care services (resource), (3) severity of medical and rehabilitation needs (severity), (4) severity of care needs (severity), (5) benefit of generous service allocation (benefit) and (6) benefit of avoiding services (benefit).ConclusionsThe expressions of the three political principles for priority setting in long-term care allocation are in accordance with broader political trends and discourses regarding “ageing in place”, active ageing, an investment ideology, and prioritising those who are “worse off”. Increasing attention to the rehabilitation potential of older adults and expectations that they will take care of themselves increase the risk of not meeting frail older adults’ care needs. Additionally, difficulties in defining the severity of older adults’ complex needs lead to debates regarding “worse off” versus potentiality in future long-term care services allocation.Trial registrationNot applicable.

Multi-objective comprehensive container scheduling and resource allocation for container cloud using tuna swarm optimization algorithm

The paper addresses the optimization challenges in cloud resource task execution within the container paradigm, introducing the Multi-Objective Comprehensive Container Scheduling and Resource Allocation (MOCCSRA) scheme. It aims to enhance cost-effectiveness and efficiency by utilizing the Tuna Swarm Optimization (TSO) technique to optimize task planning and resource allocation. This novel approach considers various objectives for task scheduling optimization, including energy efficiency, compliance with service level agreements (SLAs), and quality of service (QoS) metrics like CPU utilization, memory usage, data transmission time, container-VM correlation, and container grouping. Resource allocation decisions are guided by the VM cost and task completion period factors. MOCCSRA distinguishes itself by tackling the multi-objective optimization challenge for task scheduling and resource allocation, producing non-dominated Pareto-optimal solutions. It effectively identifies optimal tasks and matches them with the most suitable VMs for deploying containers, thereby streamlining the overall task execution process. Through comprehensive simulations, the results demonstrate MOCCSRA’s superiority over traditional container scheduling methods, showcasing reductions in resource imbalance and notable enhancements in response times. This research introduces an innovative and practical solution that notably advances the optimization field for cloud-based container systems, meeting the increasing demand for efficient resource utilization and enhanced performance in cloud computing environments.

Deep Reinforcement Learning-Based Task Offloading and Load Balancing for Vehicular Edge Computing

Vehicular edge computing (VEC) effectively reduces the computational burden on vehicles by offloading tasks from resource-constrained vehicles to edge nodes. However, non-uniformly distributed vehicles offloading a large number of tasks cause load imbalance problems among edge nodes, resulting in performance degradation. In this paper, we propose a deep reinforcement learning-based decision scheme for task offloading and load balancing with the optimization objective of minimizing the system cost considering the split offloading of tasks and the load dynamics of edge nodes. First, we model the mutual interaction between mobile vehicles and Mobile Edge Computing (MEC) servers using a Markov decision process. Second, the optimal task-offloading and resource allocation decision is obtained by utilizing the twin delayed deep deterministic policy gradient algorithm (TD3), and server load balancing is achieved through edge collaboration using a server selection algorithm based on the technique for order preference by similarity to the ideal solution (TOPSIS). Finally, we have conducted extensive simulation experiments and compared the results with several other baseline schemes. The proposed scheme can more effectively reduce the system cost and increase the system resource utilization.

Verification report: Egalitarianism in young children

AbstractThe present study sought to evaluate the reproducibility of prominent findings stated by Fehr et al. in their developmental resource allocation experiment "Egalitarianism in Young Children", published in 2008. The experiment involved children making decisions about distributing sweets between themselves and either an in‐group or an out‐group recipient. Fehr et al. found that (1) inequity aversion develops with age; (2) 3‐ to 4‐year‐old children are inclined toward self‐advantageous allocations, whereas 7‐ to 8‐year‐olds distribute sweets more evenly in divisions, and (3) the influence of group status increases as children age. The original article stated that 229 Swiss school students aged 3 to 8 years (102 boys, 127 girls) participated in the study. However, no further demographics were reported. In our attempts to reproduce Fehr et al.'s original analyses and reanalyse the raw dataset, we found that one of the key variables was miscoded. After rectifying the miscoded variable, the reproduction results revealed only one ambiguously irreproducible result regarding a group status main effect in the sharing mini‐game—with three other tests exhibiting either strong reproducibility or ambiguous reproducibility. Reanalysis results indicated that Fehr et al.'s conclusions are robust when tested with alternative analytical tests.Highlights We evaluated the reproducibility of Fehr et al.'s (2008) highly cited developmental resource allocation findings. After identifying an inversely coded variable, we found all tests—bar one probit regression not related to major findings—to be reproducible. Reanalysis confirmed the robustness of Fehr et al.'s conclusions when using alternate analytical methods. Fehr et al.'s developmental resource allocation findings are reproducible and have theoretical implications for understanding inequity aversion and group status effects on resource allocation decisions in young children.

A Review: A Comparative Study of Branded and Generic Anti- Diabetic Drugs

This comprehensive review investigates the market landscape of antihypertensive medications, undertaking a detailed comparative analysis of branded and generic options. In the face of rising global prevalence of hypertension, the study aims to provide a holistic perspective on the efficacy, safety, and market dynamics shaping the use of branded and generic drugs in the management of these chronic conditions. Utilizing data derived from market trends, sales analytics, and consumer preferences, the review scrutinizes the market behaviors and forces influencing the adoption of branded and generic antihypertensive drugs. Factors such as cost-effectiveness, patient adherence, and healthcare provider preferences are explored to shed light on the complexities associated with medication selection in the context of chronic disease management. Through an in-depth examination of industry strategies, regulatory frameworks, and the role of healthcare policies, the review aims to uncover the interplay between market dynamics and clinical considerations. It seeks to inform healthcare professionals, policymakers, and industry stakeholders about the evolving landscape of choices in antihypertensive therapies, fostering a nuanced understanding that can guide optimal decision-making in patient care and resource allocation.

A Review: A Comparative Study of Branded and Generic Anti-Hypertensive Drugs

This comprehensive review investigates the market landscape of antihypertensive medications, undertaking a detailed comparative analysis of branded and generic options. In the face of rising global prevalence of hypertension, the study aims to provide a holistic perspective on the efficacy, safety, and market dynamics shaping the use of branded and generic drugs in the management of these chronic conditions. Utilizing data derived from market trends, sales analytics, and consumer preferences, the review scrutinizes the market behaviors and forces influencing the adoption of branded and generic antihypertensive drugs. Factors such as cost-effectiveness, patient adherence, and healthcare provider preferences are explored to shed light on the complexities associated with medication selection in the context of chronic disease management. Through an in-depth examination of industry strategies, regulatory frameworks, and the role of healthcare policies, the review aims to uncover the interplay between market dynamics and clinical considerations. It seeks to inform healthcare professionals, policymakers, and industry stakeholders about the evolving landscape of choices in antihypertensive therapies, fostering a nuanced understanding that can guide optimal decision-making in patient care and resource allocation.

A Blueprint for Multi-use Disease Modeling in Health Economics: Results from Two Expert-Panel Consultations.

The current use of health economic decision models in HTA is mostly confined to single use cases, which may be inefficient and result in little consistency over different treatment comparisons, and consequently inconsistent health policy decisions, for the same disorder. Multi-use disease models (MUDMs) (other terms: generic models, whole disease models, disease models) may offer a solution. However, much is uncertain about their definition and application. The current research aimed to develop a blueprint for the application of MUDMs. We elicited expert opinion using a two-round modified Delphi process. The panel consisted of experts and stakeholders in health economic modelling from various professional backgrounds. The first questionnaire concerned definition, terminology, potential applications, issues and recommendations for MUDMs and was based on an exploratory scoping review. In the second round, the panel members were asked to reconsider their input, based on feedback regarding first-round results, and to score issues and recommendations for priority. Finally, adding input from external advisors and policy makers in a structured way, an overview of issues and challenges was developed during two team consensus meetings. In total, 54 respondents contributed to the panel results. The term 'multi-use disease models' was proposed and agreed upon, and a definition was provided. The panel prioritized 10 potential applications (with comparing alternative policies and supporting resource allocation decisions as the top 2), while 20 issues (with model transparency and stakeholders' roles as the top 2) were identified as challenges. Opinions on potential features concerning operationalization of multi-use models were given, with 11 of these subsequently receiving high priority scores (regular updates and revalidation after updates were the top 2). MUDMs would improve on current decision support regarding cost-effectiveness information. Given feasibility challenges, this would be most relevant for diseases with multiple treatments, large burden of disease and requiring more complex models. The current overview offers policy makers a starting point to organize the development, use, and maintenance of MUDMs and to support choices concerningwhich diseases and policy decisions they will be helpful for.

Energy-Delay Joint Optimization for Task Offloading in Digital Twin-Assisted Internet of Vehicles

Vehicle edge computing (VEC) provides efficient services for vehicles by offloading tasks to edge servers. Notably, extant research mainly employs methods such as deep learning and reinforcement learning to make resource allocation decisions, without adequately accounting for the ramifications of high-speed mobility of vehicles and the dynamic nature of the Internet of Vehicles (IoV) on the decision-making process. This paper endeavours to tackle the aforementioned issue through the introduction of a novel concept, namely, a digital twin-assisted IoV. Among them, the digital twin of IoV offers training data for computational offloading and content caching decisions, which allows edge servers to directly interact with the dynamic environment while capturing its dynamic changes in real-time. Through this collaborative endeavour, edge intelligent servers can promptly respond to vehicular requests and return results. We transform the dynamic edge computing problem into a Markov decision process (MDP), and then solve it with the twin delayed deep deterministic policy gradient (TD3) algorithm. Simulation experiments demonstrate the adaptability of our proposed approach in the dynamic environment while successfully enhancing the Quality of Service, that is, decreasing total delay and energy consumption.

A quality appraisal of economic evaluations of community water fluoridation: A systematic review.

To critically appraise the methodological conduct and reporting quality of economic evaluations (EE) of community water fluoridation (CWF). A systematic literature search was conducted in general databases and specialist directories of the economic literature. The Consensus on Health Economic Criteria list (CHEC) appraised the methodological quality while the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) assessed the reporting quality of included studies. A total of 1,138 records were identified, of which 18 met the inclusion criteria. Cost analysis emerged as the most prevalent type of EE, though a growing trend towards conducting full EEs is observed. CHEC revealed the items most frequently unfulfilled were the study design, measurement and valuation of costs and outcomes, while CHEERS also identified reporting deficiencies in these aspects. Furthermore, the review highlights subtleties in methodological aspects that may not be discerned by CHEC, such as the estimation of the impact of fluoridation and the inclusion of treatment savings within cost estimates. While numerous studies were conducted before publication of these assessment instruments, this review reveals that a noteworthy subset of studies exhibited good methodological conduct and reporting quality. There has been a steady improvement in the methodological and reporting quality over time, with recently published EEs largely adhering to best practice guidelines. The evidence presented will assist policymakers in leveraging the available evidence effectively to inform resource allocation decisions. It may also serve as a resource for researchers to enhance the methodological and reporting standards of future EEs of CWF.

Dynamic Management of Flexibility in Distribution Networks through Sensitivity Coefficients

Due to a rising share of renewable energy sources on the production side and electrification of transport and heating on the consumption side, the efficient management of flexibility in distribution networks is crucial for ensuring optimal operation and utilization of resources. Nowadays, the sensitivity-based approach is mainly used in medium-voltage (MV) networks for regulating voltage profiles with reactive power of distributed energy resources (DER). The main disadvantage of the simplified sensitivity-based method is its inaccuracy in case of a high deviation of the network voltage from the nominal values. Furthermore, it was also noted that despite the fact that the method is well described in the literature, there is a lack of systematic approach to its implementation in real-life applications. Thus, the main objective of this paper is to address this disadvantage and to propose an algorithm designed to calculate required consumer flexibility in near real-time to ensure distribution grid operation within operational criteria. In the first part of the paper, network state, including line loading and node voltages, is assessed to determine distribution network node capacity. By analyzing the sensitivity of network busbars to changes in consumption and production, our algorithm effectively identifies the most efficient nodes and facilitates strategic decision-making for resource allocation. We demonstrate the effectiveness of our approach through simulations of real-world distribution network data, highlighting its ability to enhance network flexibility and improve resource utilization. Leveraging sensitivity coefficients, the algorithm enables flexible consumption and production management across various scenarios, supporting the transition to a more dynamic and efficient power system.

The influence of farm resources and management decisions on the severity of Striga infestation and maize productivity in western Kenya

AbstractThe control of Striga is of critical importance for smallholder farmers cultivating maize and other cereals in the western region of Kenya. Therefore, advanced knowledge and analysis is needed to understand farmers' resource endowment and decision making with regards to crop management, and the possible effect on Striga infestation and crop productivity. The overall objective of the study was to understand the role of resource endowment and allocation decisions on Striga infestation and maize productivity and offer effective and locally adapted options in western Kenya. The study used semi‐structured questionnaires, focus group discussions and resource flow mapping to identify farm resources and key management decisions that are critical in management of Striga on smallholder farms. Soil was sampled from fields of selected farms and taken to the laboratory for analysis including Striga seed counts. In the fields, Striga shoots counts, and maize yield were recorded. Socio‐economic variables such as land size, household income, labour availability, were used to construct a farm typology and assign farmers to different resource groups (RGs). Our results showed that household diversity leads to different land‐use patterns, resource‐use intensity and agricultural management practices which affect soil fertility and Striga infestation. Poor‐resourced farmers tend to be associated with poor crop and soil management which results in maize fields with low soil fertility and high level of Striga infestation. Soil organic carbon showed a strong negative association with Striga infestation. Improving soil fertility is, therefore, the central component of any integrated and sustainable Striga management approaches, and technological solutions to address Striga and soil fertility issues, and they must be tailored to the farm and field diversity. The inclusion of legumes is highly recommended either as rotation or intercropping depending on land availability. Where possible, farmers may use mulch from previous crop residues to suppress weed emergence and prevent flowering and seed setting. Hand weeding though tiresome, remains a low‐cost effective method to control Striga especially in areas where fields are small as in western Kenya.

Joint Service Caching, Resource Allocation and Task Offloading for MEC-Based Networks: A Multi-Layer Optimization Approach

To provide reliable and elastic Multi-access edge computing services, one feasible solution is to federate geographically proximate edge servers to form a logically centralized resource pool. Optimization of such systems, however, becomes challenging. In this paper, we study the problem of maximizing users' QoE in a MEC-based network, through jointly optimizing service caching, resource allocation and task offloading decisions. We formulate a mixed-integer nonlinear programming (MINLP) problem for the task and establish its NP-hardness. To tackle it efficiently, we propose a novel two-stage algorithmic solution based on approximation and decomposition theory. The proposed algorithm achieves high system performance while at the same time, ensures all constraints from different layers are satisfied. Meanwhile, the structure of the algorithm also fits the multi-layer optimizing feature, making it suitable to be implemented at different layers. In addition, we propose a distributed and online version of our mechanism with very limited information exchange between MEC servers, and further demonstrate how the cost of service switches from real MEC systems can be incorporated into our framework. We evaluate our mechanisms through simulations with both synthetic and real-world traces, and results indicate they are effective as compared to representative baseline algorithms.