Local Resource Allocation Research Articles

Local adaptation shapes functional traits and resource allocation in black spruce

Climate change is rapidly altering weather patterns, resulting in shifts in climatic zones. The survival of trees in specific locations depends on their functional traits. Local populations exhibit trait adaptations that ensure their survival and accomplishment of growth and reproduction processes during the growing season. Studying these traits offers valuable insights into species responses to present and future environmental conditions, aiding the implementation of measures to ensure forest resilience and productivity. This study investigates the variability in functional traits among five black spruce (Picea mariana (Mill.) B.S.P.) provenances originating from a latitudinal gradient along the boreal forest, and planted in a common garden in Quebec, Canada. We examined differences in bud phenology, growth performance, lifetime first reproduction, and the impact of a late-frost event on tree growth and phenological adjustments. The findings revealed that trees from northern sites exhibit earlier budbreak, lower growth increments, and reach reproductive maturity earlier than those from southern sites. Late-frost damage affected growth performance, but no phenological adjustment was observed in the successive year. Local adaptation in the functional traits may lead to maladaptation of black spruce under future climate conditions or serve as a potent evolutionary force promoting rapid adaptation under changing environmental conditions.

Social care data in the UK; current landscape, challenges, and future recommendations

ABSTRACT Objectives Social care in the United Kingdom (UK) refers to care provided due to age, illness, disability, or other circumstances. Social care provision offers an intermediary step between hospital discharge and sufficient health for independent living, which subsequently helps with National Health Service (NHS) bed capacity issues. UK Health Technology Assessments (HTAs) do not typically include social care data, possibly due to a lack of high-quality, accessible social care data to generate evidence suitable for submissions. Methods We identified and characterized secondary sources of UK social care data suitable for research (as of 2021). Sources were identified and profiled by desk research, supplemented by information from custodians and data experts. Results We identified twenty-one sources; six high potential (three national, three regional data sources), five future potential, seven limited potential, and three not considered further (outdated or lacking social care data). Conclusion Despite identifying numerous sources of social care data across the UK, opportunities and access for researchers appeared limited and could be improved. This would facilitate a deeper understanding of the clinical and economic burden of disease, the impact of medicines and vaccines on social care, enable better-informed HTA submissions and more efficient allocation of NHS and local council social care resources.

Indexing blood banking performance in India: A retrospective cross-sectional analysis of states and districts

BackgroundMultiple international assessments have highlighted gaps in blood banking globally. However, there is a dearth of subnational assessments. We applied a thematic framework with multiple composite indices to assess blood bank performance across Indian states and districts. MethodsIn this retrospective cross-sectional analysis, data for 2493 blood banks in 35 states/union territories (UTs) and 616 districts was extracted from the National Blood Transfusion Council for 2016. The framework involved seven themes (accreditation, ownership, safety, volume, infrastructure, regulation, and workforce) with several indicators nested under them. Composite thematic indices and an overall index (0-100, with 100 being the best performance) were constructed using the adjusted Mazziotta-Pareto index method that can provide composite indices that are partially non-compensatory and easily interpretable. ResultsThe state-level median [interquartile range] value of the overall index was 59.61 [46.35, 71.67]. Chandigarh had the highest values for safety, regulation, workforce, and ownership indices, Maharashtra for volume and infrastructure indices, and Manipur for accreditation index. Districts in southern and western states performed well on the overall index with inter- and intra-state variations for themes. District-level correlations depicted positive associations among indices with the strongest correlation between ownership and accreditation indices (n=616, R=0.92, p<0.001). ConclusionBlood banking in India is fragmented, with variations in themes across geographies. The northern and northeastern states require more attention for volume, accreditation, infrastructure, and ownership. While the southern and western-central regions need to prioritize safety. The framework with thematic indices, when applied to routine data, can be useful for monitoring and evaluation to decide local policies and resource allocations.

Effect of Urban Household Registration Controls on Labor Mismatch in China

This study examines the effect of the degree of household registration control on labor mismatch in China. Based on Chinese urban panel data, we construct a dynamic panel model to empirically examine the effect of the degree of household registration control on labor mismatch in China. The study finds that labor mismatch relatively exists in all regions of China, which differ significantly. Moreover, liberalizing the household registration system in cities with under- and over-allocated labor force can reduce and aggravate the degree of local labor mismatch, and improve and reduce resource allocation efficiency, respectively. This study uses the labor distortion coefficient to replace the original dependent variable, and the labor mismatch coefficient. The authors further conduct a robustness test using the system generalized moment estimation method. These results are consistent. The implication of this study is that, in the context of China’s shift from high-speed to high-quality development, local governments should tailor their household registration system reform programs to the local resource allocation situation to improve the degree of labor mismatch in China, to optimize resource allocation, and to promote economic transformation.

Examination of critical factors influencing ruminant disease dynamics in the Black Sea Basin.

Ruminant production in the Black Sea basin (BSB) is critical for national economies and the subsistence of rural populations. Yet, zoonoses and transboundary animal diseases (TADs) are limiting and threatening the sector. To gain a more comprehensive understanding, this study characterizes key aspects of the ruminant sector in nine countries of the BSB, including Armenia, Azerbaijan, Belarus, Bulgaria, Georgia, Moldova, Romania, Türkiye, and Ukraine. We selected six priority ruminant diseases (anthrax, brucellosis, Crimean Congo haemorrhagic fever (CCHF), foot-and-mouth disease (FMD), lumpy skin disease (LSD), and peste des petits ruminants (PPR)) that are present or threaten to emerge in the region. Standardized questionnaires were completed by a network of focal points and supplemented with external sources. We examined country and ruminant-specific data such as demographics, economic importance, and value chains in each country. For disease-specific data, we analysed the sanitary status, management strategies, and temporal trends of the selected diseases. The shift from a centrally planned to a market economy, following the collapse of the Soviet Union, restructured the ruminant sector. This sector played a critical role in rural livelihoods within the BSB. Yet, it faced significant challenges such as the low sustainability of pastoralism, technological limitations, and unregistered farms. Additionally, ruminant health was hindered by informal animal trade as a result of economic factors, insufficient support for the development of formal trade, and socio-cultural drivers. In the Caucasus and Türkiye, where diseases were present, improvements to ruminant health were driven by access to trading opportunities. Conversely, European countries, mostly disease-free, prioritized preventing disease incursion to avoid a high economic burden. While international initiatives for disease management are underway in the BSB, there is still a need for more effective local resource allocation and international partnerships to strengthen veterinary health capacity, protect animal health and improve ruminant production.

Integrated Sensing, Communication, and Computing for Self-Powered UAV-Assisted Corona Detection in High-Voltage Substations

Unmanned aerial vehicle (UAV) provides a cost-efficient solution for corona detection in high-voltage substations. In this article, we address the integrated sensing, communication, and computing joint optimization problem. The objective is to maximize the average amount of data sensed by UAV through jointly optimizing sensing, i.e., acquisition frequency and image depth selection, communication, i.e., task splitting and power control, and computing, i.e., local computation resource allocation. We propose a dueling deep Q network-based integrated sensing, communication, and computing joint optimization algorithm for self-powered UAV named DESCANT to solve the problem. DESCANT explores dueling DQN to intelligently learn the optimal strategy under complex electromagnetic environment of high-voltage substations. The historical information of electromagnetic interference (EMI) is incorporated into the state construction to improve convergence and learning optimality. DESCANT also realizes energy awareness through the dynamic adjustment of resource management based on energy consumption as well as harvested energy. DESCANT is compared with state-of-the-art algorithms and its superiority is verified through simulations.

Understanding the spatio-temporally heterogeneous effects of built environment on urban travel emissions

Transportation has become one of the fastest-growing fields for greenhouse gas emissions. It is important to promote the coordinated development of cities and transportation. To deeply understand the emission distribution for urban travel, this study first applies a map matching algorithm to correct the vehicle Global Positioning System (GPS) trajectories on the road network and calculates the travel emissions by the COmputer Programme to calculate Emissions from Road Transport (COPERT) model. Most studies on the impact of built environment on travel emissions only consider the spatially heterogeneity of variables. Although the traditional GTWR can consider the spatio-temporally heterogeneous effects, the spatio-temporal bandwidth is selected at the same scale in the operation process, which limits the relationship analysis among diverse variables. Therefore, this study adopts a unilateral geographically and temporally weighted regression model (UGTWR) and its multiscale extended model (MUGTWR) to estimate the spatio-temporally heterogeneous effects of the built environment on urban travel emissions. In addition, considering the scale effect and zoning effect of the spatial geographical unit selection on the results, we conducted experiments separately on three spatial units: sub-district scale, Traffic Analysis Zones (TAZs) and grid scale to compare and obtain the most appropriate partitioning schemes. The results show that, the fitting effects of UGTWR and MUGTWR are better than that of GTWR, indicating that considering flexible bandwidth selection can effectively improve the accuracy of simulation. Moreover, the temporal and spatial bandwidth values of each independent variable calculated by MUGTWR reflect the heterogeneous characteristics in space and time of different built environment factors, which can provide scientific suggestions for formulating urban planning. The research results can provide planning strategies for optimizing the allocation of local transportation resources and guiding low-carbon travel behaviors.

Computation Efficiency Optimization for Millimeter-Wave Mobile Edge Computing Networks With NOMA

In this paper, by improving the computation efficiency (CE) and ensuring the fairness among users, we study the CE optimization for millimeter-wave mobile edge computing (mmWave-MEC) Networks with NOMA, where both the analog beamforming (ABF) and hybrid beamforming (HBF) architectures under the partial offloading mode are considered. Firstly, according to the max-min fairness criterion, the CE maximization problem is formulated to jointly optimize the ABF and the local resource allocation of each user. An efficient CE optimization algorithm based on the penalized successive convex approximation is proposed to solve this non-convex problem. Then, the max-min CE optimization problem in mmWave-MEC with HBF is studied, where the joint design of the HBF and the local resource allocation of each user is carried out. By using the penalty function and the inexact block coordinate descent method, a feasible CE optimization algorithm is developed to tackle this challenging problem. Simulation results verify the convergence of the proposed algorithms and show that the proposed computation-efficient resource allocation schemes can improve the CE effectively, and mmWave-MEC with HBF can obtain higher CE than that with ABF. Besides, the NOMA scheme exhibits superior performance over the conventional orthogonal multiple access scheme in terms of CE.

A new community-based algorithm based on a “peak-slope-valley” structure for influence maximization on social networks

Influence Maximization (IM) is a key algorithmic problem that has been extensively studied in social influence analysis, but most of existing researches either make sacrifices in solution accuracy or suffer high computational complexity. In this paper, we propose a new Community-based Influence Maximization (CIM) algorithm for identifying a set of seed spreaders in a social network to maximize the expected number of influenced nodes. In CIM, the initial candidate seeds are first selected based on the proposed topological potential “peak-slope-valley” structure framework. Then, we propose a recursive clustering approach and a similarity indicator based on local resource allocation to partition communities. Finally, we design a community-based regional influence indicator to select seed nodes without using any prior knowledge. Experiment datasets include three artificial benchmarks with varying community strengths, as well as nine representative networks drawn from various fields. Extensive numerical simulations on both artificial and real networks indicate that (i) community-based techniques enrich the toolbox for addressing the IM problem and (ii) the derivative algorithm outperforms recent high-performing influence maximization algorithms in terms of influence propagation and coverage redundancy of the seed set with an acceptable complexity. Furthermore, our algorithm exhibits good stability on networks of varying scales and structural characteristics.

Study on an Equilibrium Water Price System Based on Cooperative Game Technology

In this study, the theory of equilibrium water prices is constructed and practiced based on previous research on cooperative game technology and the water price system. The equilibrium water price utilized in this study emphasized solving the unbalanced and unsustainable water supply system, including the reclaimed water resources and the current water price used in numerous Chinese cities. Safety, affordability, and sustainability become the key factors of the equilibrium water price system, which is operated and analyzed in a typical city in southeastern China. The cooperative game model of equilibrium water price is solved by game technology, considering various factors such as the price strategy, the water supply quantity, and the local water demand. The practical application shows that the theoretical system can effectively solve the problem of water price formation mechanisms after the unified allocation of different types of water resources. It not only enhances the enthusiasm and initiative of the public in the utilization of reclaimed water, but also plays an important role in the rational utilization of multiple water resources for different purposes by introducing a reasonable water price ratio and local water resource allocation.

Life Expectancy Gaps Among Black and White Persons and Contributing Causes of Death in 3 Large US Cities, 2018-2019

US cities have substantial, but varying, levels of racial mortality inequities, a consequence of structural racism. As committed partners increasingly pledge to eliminate health inequities, local data are required to focus and unify efforts. To analyze the contributions of 26 cause-of-death categories to Black to White life expectancy gaps within 3 large US cities. In this cross-sectional study, data were extracted from the 2018 and 2019 National Vital Statistics System Multiple Cause of Death Restricted Use data files for deaths by race, ethnicity, sex, age, place of residence, and underlying and contributing causes of death in Baltimore, Maryland; Houston, Texas; and Los Angeles, California. Life expectancy at birth was calculated for non-Hispanic Black and non-Hispanic White populations overall and by sex using abridged life tables with 5-year age intervals. Data analysis was performed from February to May 2022. Using the Arriaga method, the proportion of the Black to White life expectancy gap was calculated overall and by sex for each city that was attributable to 26 cause-of-death categories defined using the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes for underlying and contributing causes of death. A total of 66 321 death records from 2018 to 2019 were analyzed, with 29 057 individuals (44%) were identified as Black, 34 745 (52%) as male, and 46 128 (70%) as aged 65 years and older. Black to White life expectancy gaps were 7.60 years for Baltimore, 8.06 years for Houston, and 9.57 years for Los Angeles. Circulatory diseases, cancer, injuries, and diabetes and endocrine disorders were top contributors to the gaps, although the order and magnitude varied by city. The contribution of circulatory diseases was 11.3 percentage points higher in Los Angeles than in Baltimore (3.76 years [39.3%] vs 2.12 years [28.0%]). The contribution of injuries to Baltimore's racial gap (2.22 years [29.3%]) was twice as large as in Houston (1.11 years [13.8%]) and Los Angeles (1.36 years [14.2%]). By assessing the composition of Black to White life expectancy gaps for 3 large US cities and categorizing deaths at a more granular level than past studies, this study provides insight into the differing underpinnings of urban inequities. This type of local data can support local resource allocation that more effectively addresses racial inequities.

Federalization of Agriculture Sector: Issues and Challenges

The research examines the current issues and challenges of sectoral restructuring and assesses local governments' performance in agricultural service delivery at the local level under the federal system in Nepal. This study was conducted using a mixed method during 2019-2021 in three local governments at the local, Karnali Province, and federal levels. Qualitative methods such as KIIs, FGDs and field observations were used to analyze the issues and gaps in functions, institutions and policies in the agriculture sector under the federal system. Whilst qualitative methods and an empirical survey among 300 farming households were conducted to assess local governments' performance in agricultural service provisions. The qualitative data were analyzed using the thematic analysis method, and the quantitative data were analyzed using descriptive statistics such as frequency and percentage. Findings revealed that functional overlap, duplication and lack of coordination between the three tiers of the government as major issues in sectoral restructuring. Farming household surveys revealed that local governments are effective in the timely delivery of agricultural services. However, farmers indicated their lower level of agreement on the capacity of the local staff, policies, institutional mechanisms, participatory process in planning and financial resource allocation for agricultural services. The performance of these three local governments on agricultural service delivery was affected by leadership priority and commitment, local policies, local staff capacity, resource allocation and external support. Overlapping and duplication in service delivery roles, weak local capacity, higher conditionality of intergovernmental sectoral fiscal transfer and less priority of local governments in financial resource prioritization are current issues and gaps in agricultural service delivery. The findings suggest the demarcation of the roles and responsibilities between the three tiers of the government, strengthening local capacities and revisiting local governments' institutional arrangement for effective agricultural service delivery under the federal system.

TSIFIM: A three-stage iterative framework for influence maximization in complex networks

The problem of influence maximization is a classic issue that has been well-studied in the field of network science, but most of existing researches are compromising among computational complexity or result accuracy. In this work, a three-stage iterative framework for influence maximization (TSIFIM) is presented to find a set of seed spreaders in complex networks. In TSIFIM, the initial candidate seeds are first selected by considering the global communicability of each node and its importance in their local network. Then, in addition to the candidate seeds, other remained nodes are assigned to the specific communities based on the proposed local resource allocation similarity index, and the core node in each community which satisfies the local influence threshold condition are selected as the supplementary candidate seeds. Furthermore, we employ an adaptive search strategy to find the optimal solution among these candidates. The proposed algorithm is compared with eight popular influence maximization algorithms on nine real-world networks to verify the performance. Experimental results show that TSIFIM has better performance in terms of influence spreading, sensitivity analysis, seed dispersion and statistical test.

Gray Measure and Spatial Distribution Exploration of Local Emergency Resilience on Compound Disasters.

The complexity and uncertainty of compound disasters highlight the significance of local emergency resilience. This paper puts forward a framework, including the Projection Pursuit Model based on Real-coded Accelerating Genetic Algorithm and the Moran’s Index (Moran’s I), to measure the local emergency resilience and analyze its spatial distribution. An empirical test is conducted with the case of Hubei Province, China. The results show that: (1) the measurement indices related to infrastructure, material reserves, and resource allocation have a larger weight, while those related to personnel and their practice have a smaller weight. (2) The measurement value of local emergency resilience of sub-provincial regions in Hubei Province is vital in the eastern and weak in the western, and there are apparent east-west segmentation and north-south aggregation characteristics. (3) Although the sub-provincial regions do not show significant spatial correlation, the eastern regions centered on Wuhan are negatively correlated, and the western regions are positively correlated. Furthermore, this study provides theories and methods for local emergency resilience evaluation and spatial correlation exploration, and it has specific guidance recommendations for optimizing local emergency management resource allocation and improving local emergency resilience.

Computation Efficiency Optimization for RIS-Assisted Millimeter-Wave Mobile Edge Computing Systems

In this paper, we present the computation-efficient resource allocation (RA) schemes for millimeter-wave mobile edge computing (mmWave-MEC) system with the aid of reconfigurable intelligent surface (RIS), which is used to assist the uplink communication from the users to the base station (BS). By means of the theoretical analysis, the achievable rate and computation efficiency (CE) are derived. Then, the optimization problem for the CE maximization under the constraints of the minimum rate, maximum power consumption and local CPU frequency is formulated, where the joint design of the hybrid beamforming at the BS and the passive beamforming at the RIS as well as the local resource allocation of each user is carried out. An effective iterative algorithm based on the penalized inexact block coordinate descent (BCD) method is proposed to obtain the computation-efficient RA scheme. Next, a low-complexity suboptimal RA scheme based on the BCD method is proposed, and corresponding algorithm is presented. Simulation results show that the proposed schemes are effective, and high CE can be attained. Moreover, the second scheme can achieve the CE performance close to the first scheme but with lower complexity. Besides, it is effective to deploy the RIS scheme in mmWave-MEC system, which can strike a balance between the CE and energy consumption when compared to the conventional relay schemes.

Joint MIMO Precoding and Computation Resource Allocation for Dual-Function Radar and Communication Systems With Mobile Edge Computing

In this paper, an integrated communication, radar sensing, and mobile-edge computing (CRMEC) architecture is developed, where user terminals (UTs) perform radar sensing and computation offloading simultaneously at the same spectrum by using multiple-input and multiple-output (MIMO) arrays and dual-function radar-communication techniques. We formulate a multi-objective optimization problem to jointly consider the performance of multi-UT MIMO radar beampattern design and computation offloading energy consumption while jointly optimizing individual transmit precoding for radar and communication and computation resource allocation. To address the optimization problem, we first decompose the it into three subproblems and adopt an iterative optimization algorithm. Specifically, quadratic transform based fractional programming methods are used to minimize the offloading energy consumption. The design objective of MIMO radar beampattern is handled by the first-order Taylor expansion. Transmit precoding is designed to optimize radar sensing and computation task offloading. The local and edge computation resource allocation are obtained in closed-form. Numerical results verify the effectiveness of the proposed algorithms. The proposed CRMEC architecture can generate the desired multi-UT MIMO radar beampattern and perform computation offloading simultaneously.

Reporting behaviors and perceptions toward the National Healthcare Safety Network antimicrobial use (AU) and antimicrobial resistance (AR) modules.

To identify characteristics of US health systems and end users that report antimicrobial use and resistance (AUR) data, to determine how NHSN AUR data are used by hospitals and health systems and end users, and to identify barriers to AUR reporting. An anonymous survey was sent to Society of Infectious Diseases Pharmacists (SIDP) and Society for Healthcare Epidemiology of America (SHEA) Research Network members. Data were collected via Survey Monkey from January 21 to February 21, 2020. Respondent and hospital data were analyzed using descriptive statistics. We received responses from 238 individuals across 43 US states. Respondents were primarily pharmacists (84%), from urban areas, (44%), from nonprofit medical centers (81%), and from hospitals with >250 beds (72%). Also, 62% reported data to the AU module and 19% reported data to the AR module. Use of software for local AU or AR tracking was associated with increased reporting to the AU module (19% vs 64%) and the AR module (2% vs 30%) (P < .001 each). Only 36% of those reporting data to the AU module used NHSN AUR data analysis tools regularly and only 9% reported data to the AR module regularly. Technical challenges and time and/or salary support were the most common barriers to AUR participation cited by all respondents. Among those not reporting AUR data, increased local expectations to report and better software solutions were the most commonly identified solutions to increase AUR reporting. Efforts to increase AUR reporting should focus on software solutions and salary support for data-entry activities. Increasing expectations to report may incentivize local resource allocation to improve AUR reporting rates.

Does industrial intelligence improve resource misallocation? An empirical test based on China.

Resource misallocation has become a new threat to China's high-quality economic development, and industrial intelligence may play an important role in optimizing resource allocation. Using panel data of 30 Chinese provinces from 2010 to 2020, the relationship between industrial intelligence and resource misallocation is studied using the spatial Durbin (SDM) model. To further explore the indirect effect of environmental pollution on resource misallocation, the interaction term between industrial intelligence and environmental pollution is added to test how environmental pollution affects the relationship between industrial intelligence and resource. The research results show that industrial intelligence in China has significant spatial correlation. Local industrial intelligence can improve the local resource allocation, but the intelligence of neighboring industries will intensify the resource misallocation in the region. The interaction between environmental pollution and industrial intelligence also further affects resource allocation, and the interaction term can intensify local capital allocation to improve local labor allocation, and the interaction term in the surrounding area will also improve capital and labor misallocation in the region. Heterogeneous results find that industrial intelligence exacerbates resource misallocation in regions with insufficient capital allocation, and the mediating effect of environmental pollution is not significant. However, in places with excess capital allocation, the development of industrial intelligence helps improve resource misallocation, while the mediating effect of environmental pollution is also present, and industrial intelligence can improve resource misallocation through the indirect effect of improving environmental pollution. Therefore, the policies of local industrial development policies need to fully consider the resource allocation situation in the region. At the same time, it needs to smooth the capital circulation mechanism, reduce the cost of capital allocation, establish a sound employment mechanism, and improve labor misallocation.

Attracting Investment Capital to Help Develop the Economy of Countries in General and Attractive Localities in Particular

Attracting investment capital to help develop the economy of countries in general and attractive localities in particular. This study was carried out to evaluate the impact of the distribution of local attributes on attracting investment capital in the Thai Nguyen province of Vietnam. The research method is conducted through quantitative analysis with 150 enterprises surveyed in the locality. With multivariable analysis technique (reliability test, PLS-SEM model analysis) on Smart-PLS version 3 software. Research results from 150 business enterprises in Thai Nguyen (a province of Vietnam). Vietnam) shows that all four groups of local attributes influence investment decisions in the area as well as investor satisfaction: (1) investment incentives; (2) government support; (3) skill training; and (4) living environment. From the results of this study, the authors make some recommendations to help Thai Nguyen province better attract investment capital based on appropriate local resource allocation.

Distributed Self-Healing for Resilient Network Design in Local Resource Allocation Control

Many infrastructure networks are considered the backbone of our society; however, increasing disasters and terrors cause serious damage to energy, water, communication, and transportation systems. In this study, we proposed a distributed self-healing method for the damaged networks whose original structure is extremely vulnerable and scale-free. For reconstructing a sustainable network, the key ideas of our method are ring formation and enhancing loops by adding the resource of healing links between low-degree nodes inspired from the state-of-the-art edge rewiring methods. In emulating a healing algorithm asynchronously, we showed that the reconstructed network has both higher robustness and efficiency than the ones using the conventional self-healing methods. Our distributed self-healing method will be useful as a basic framework for sustainable network reconstruction.