Effective Resource Allocation Research Articles

An efficient synthetic minority oversampling technique-based ensemble learning model to detect COVID-19 severity

The COVID-19 pandemic has highlighted the importance of accurately predicting disease severity to ensure timely intervention and effective allocation of healthcare resources, which can ultimately improve patient outcomes. This study aims to develop an efficient machine learning (ML) model based on patient demographic and clinical data. It utilizes advanced feature engineering techniques to reduce the dimensionality of dataset and address the issue of highly imbalanced data using synthetic minority oversampling technique (SMOTE). The study employs several ensemble learning models, including XGBoost, Random Forest, AdaBoost, voting ensemble, enhanced-weighted voting ensemble, and stack-based ensembles with support vector machine (SVM) and Gaussian Naïve Bayes as meta-learners, to develop the proposed model. The results indicate that the proposed model outperformed the top-performing models reported in previous studies. It achieved an accuracy of 0.978, sensitivity of 1.0, precision of 0.875, F1-score of 0.934, and receiver operating characteristic area under the curve (ROC-AUC) of 0.965. The study identified several features that significantly correlated with COVID-19 severity, which included respiratory rate (breaths per minute), c-reactive proteins, age, and total leukocyte count (TLC) count. The proposed approach presents a promising method for accurate COVID-19 severity prediction, which may prove valuable in assisting healthcare providers in making informed decisions about patient care.

Exploring the influence of activity participation on the economic value of nature-based recreation in the Sierra Nevada

A more comprehensive understanding of how recreational values and forest visitation rates vary across different activities enables forest managers to tailor conservation and management strategies to align with preferences among visitors, ensuring more effective allocation of budgetary resources. However, current research often focuses on only a few recreational activities, resulting in limited insights for forest managers. This study aims to expand the nature-based activities considered so that management can better serve the broader public. We conduct a travel cost analysis using a large survey-based dataset to estimate the value of nature-based recreation in national forests in the Sierra Nevada region and assess how these values differ across main activities. We categorize recreational activities into five broad groups (Passive, Active, Camping, Winter, and Other) to offer a comprehensive view of recreational preferences. A truncated negative binomial regression accounting for endogenous stratification is used to analyze the relationship between the number of trips to the forests, travel cost, activity categories, and socio-demographic variables. Our results suggest a mean consumer surplus (CS) of $65 per visit per person to national forests in the Sierra Nevada. Aggregated over annual per person visits, the total CS is approximately $313.3 million per year. Our findings reveal variations in CS across activity groups, with winter activities (e.g., skiing, snowboarding) and active activities (e.g., hiking, fishing) attracting the highest number of visits, and the highest total CS. Our results provide valuable insights for national forest managers, facilitating the strategic allocation of limited resources to recreational activities that maximize societal welfare.

Health Insurance Cost Prediction Using Machine Learning

Abstract: Healthcare expenditure is a critical concern worldwide, and accurate prediction of health insurance costs can aid in effective resource allocation and risk management. In this project, we employ machine learning (ML) techniques to develop a predictive model forestimating health insurance costs. The dataset used comprises various demographic, lifestyle, andmedical information of insured individuals,including lifetime, gender, QTI, smoking habits, area, and medical history.

Advancing rapid urban flood prediction: a spatiotemporal deep learning approach with uneven rainfall and attention mechanism

ABSTRACT Urban floods pose a significant threat to human communities, making its prediction essential for comprehensive flood risk assessment and the formulation of effective resource allocation strategies. Data-driven deep learning approaches have gained traction in urban emergency flood prediction, addressing the efficiency constraints of physical models. However, the spatial structure of rainfall, which has a profound influence on urban flooding, is often overlooked in many deep learning investigations. In this study, we introduce a novel deep learning model known as CRU-Net equipped with an attention mechanism to predict inundation depths in urban terrains based on spatiotemporal rainfall patterns. This method utilizes eight topographic parameters related to the height of urban waterlogging, combined with spatial rainfall data as inputs to the model. Comparative evaluations between the developed CRU-Net and two other deep learning models, U-Net and ResU-Net, reveal that CRU-Net adeptly interprets the spatiotemporal traits of rainfall and accurately estimates flood depths, emphasizing deep inundation and flood-vulnerable regions. The model demonstrates exceptional accuracy, evidenced by a root mean square error of 0.054 m and a Nash–Sutcliffe efficiency of 0.975. CRU-Net also accurately predicts over 80% of inundation locations with depths exceeding 0.3 m. Remarkably, CRU-Net delivers predictions for 3 million grids in 2.9 s, showcasing its efficiency.

Economic Cost of Secondary Postpartum Hemorrhage: A Case-Control Study at a Tertiary Hospital in Australia

(Aust N Z J Obstet Gynaecol. 2023;63:308–313) Secondary postpartum hemorrhage (PPH) is excessive vaginal bleeding occurring up to 12 weeks after childbirth. The incidence ranges from 0.2% to 3.0%, usually presenting 1 to 2 weeks postnatally and commonly caused by endometritis and retained products. Despite its impact on health, evidence for managing and understanding its cost burden is lacking compared with primary PPH. Australian public hospitals are subsidized, but reproductive care costs were $8.7B in 2018 to 2019. Effective resource allocation is vital amidst rising health care expenses. Limited evidence guides secondary PPH treatment, potentially leading to multiple presentations and increased costs. Consequences include resuscitation, transfusion, surgery, and hysterectomy, elevating health care expenses. This study sought to analyze the economic impact of secondary PPH.

Characteristics and risk management of urban surface flooding in Guangzhou, China: Insights from 2022 ground monitoring

Study RegionGuangzhou, ChinaStudy FocusOur research, based on the meticulous collection and rigorous statistical analysis of ground monitoring records for the entire year of 2022 in Guangzhou, delves into the spatio-temporal distribution patterns, evolution processes, flood formation mechanisms, and corresponding disaster mitigation strategies of urban surface flooding related to the capacity of drainage system in the region. New Hydrological InsightsThe urban surface flooding (USF) in Guangzhou in 2022 provides key insights for flood risk management. The overall spatial distribution of USF risk in Guangzhou is stable, but specific flood locations during rainstorms vary due to factors such as rainfall conditions and drainage efficiency. This complexity highlights the diverse nature of USF challenges. The frequency of USF events varies significantly across months and dates, with a higher occurrence during the flood season. However, there is a noticeable extension of flood risk into the non-flood season, challenging traditional flood season concepts. USFs peak within 50 minutes, demanding urgent action within 25 minutes, underscoring the need for an efficient emergency response. Certain areas, times, and processes contribute more to the overall flood risk, emphasizing the importance of focusing on these high-risk factors for effective management and resource allocation. The study also reveals the prevalence of Surface Runoff-type Floods, challenging existing USF simulation methods and necessitating a reevaluation and improvement in the maintenance and design of urban rainwater inlets. The study suggests vigorously promoting private proactive flood mitigation behavior and proposes a hierarchical, classification-based approach to urban flood risk control. Comprehensive intervention in the urban runoff formation process is emphasized for effective flood risk management.

A Novel Deep Learning Approach for Forecasting Myocardial Infarction Occurrences with Time Series Patient Data.

Myocardial Infarction (MI) commonly referred to as a heart attack, results from the abrupt obstruction of blood supply to a section of the heart muscle, leading to the deterioration or death of the affected tissue due to a lack of oxygen. MI, poses a significant public health concern worldwide, particularly affecting the citizens of the Chittagong Metropolitan Area. The challenges lie in both prevention and treatment, as the emergence of MI has inflicted considerable suffering among residents. Early warning systems are crucial for managing epidemics promptly, especially given the escalating disease burden in older populations and the complexities of assessing present and future demands. The primary objective of this study is to forecast MI incidence early using a deep learning model, predicting the prevalence of heart attacks in patients. Our approach involves a novel dataset collected from daily heart attack incidence Time Series Patient Data spanning January 1, 2020, to December 31, 2021, in the Chittagong Metropolitan Area. Initially, we applied various advanced models, including Autoregressive Integrated Moving Average (ARIMA), Error-Trend-Seasonal (ETS), Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal (TBATS), and Long Short Time Memory (LSTM). To enhance prediction accuracy, we propose a novel Myocardial Sequence Classification (MSC)-LSTM method tailored to forecast heart attack occurrences in patients using the newly collected data from the Chittagong Metropolitan Area. Comprehensive results comparisons reveal that the novel MSC-LSTM model outperforms other applied models in terms of performance, achieving a minimum Mean Percentage Error (MPE) score of 1.6477. This research aids in predicting the likely future course of heart attack occurrences, facilitating the development of thorough plans for future preventive measures. The forecasting of MI occurrences contributes to effective resource allocation, capacity planning, policy creation, budgeting, public awareness, research identification, quality improvement, and disaster preparedness.

The burden of working time lost to compensable occupational injury and disease in Australia, 2012-17: a retrospective population-based study.

To determine the national burden of working time lost to occupational injury and disease in Australia compensable by workers' compensation schemes; to characterise the distribution of time lost by age, sex, and injury and disease type. Retrospective population-based study; analysis of National Dataset for Compensation-based Statistics (NDS) data. Granted workers' compensation claims by people aged 15-100 years including payment of wage replacement benefits for time off work lodged in Australia, 1 July 2012 - 30 June 2017. Working years lost (WYL) per annum (total number of years of wage replacement benefits paid to injured and ill workers), overall and by sex, age, and injury and disease type; WYL per 10 000 fulltime equivalent (FTE) years worked. A total of 755 330 eligible claims with complete data for analysis variables by people aged 15-100 years were identified, for compensable injuries and disease that led to 41 194 (95% confidence interval [CI], 41 020-41 368) WYL/year. The annual WYL number and rate were each higher for men (25 367 [95% CI, 25 230-25 503] WYL/year; 42.6 [95% CI, 42.1-43.1] WYL/10 000 FTE years) than for women (15 827 [95% CI, 15 720-15 935] WYL/year; 38.8 [95% CI, 38.2-39.4] WYL/10 000 FTE years). Workers aged 45-100 years made 66 742 claims per year (44.1% of all claims) but incurred 21 763 WYL/year (52.8% of all WYL). Traumatic joint and muscle injuries led to 16 494 WYL/year (40.0% of all WYL), musculoskeletal disorders to 8547 WYL/year (20.7%), mental health conditions to 5361 WYL/year (13.0%), fractures to 4276 WYL/year (10.4%), and wounds and lacerations to 3449 WYL/year (8.4%). Occupational injury and disease covered by workers' compensation result in lost working time in Australia equivalent to more than 41 000 fulltime jobs. Distribution of the burden reflects the greater labour force participation of men, slower recovery of older workers, and the impact of common occupational injuries and diseases. Population-based monitoring of lost working time could support effective occupational health surveillance and allocation of resources for protecting the health of Australian workers.

Forecasting U.S. Fossil Energy Consumption: Advancing Accuracy with Multilayer Perceptron and Residual Learning

Accurate midterm forecasts of fossil fuel consumption are essential for effective energy planning, economic management, and resource allocation. While machine learning models have demonstrated their efficacy in handling large-scale nonlinear datasets, many, including Multilayer Perceptrons (MLPs), suffer from performance degradation with increased depth. Fortunately, recent studies have revealed that Residual Networks (ResNets) can mitigate or even overcome this challenge. In this paper, we propose a Weighted Residual Network based on MLP to enhance predictive performance. We employ the Adam algorithm for model training and utilize the Gridsearch algorithm for hyperparameter tuning. In the application section, we develop predictive models using three case studies: natural gas, petroleum, and total fossil fuel consumption. We validate the effectiveness of our proposed model and compare it with ten other machine learning models. Our findings demonstrate that our proposed model consistently outperforms others in all three cases, underscoring its superior performance in midterm forecasting of fossil fuel consumption.

From Retrofitting to Renewables: Navigating Energy Transition Pathways for European Residential Space Heating

From Retrofitting to Renewables: Navigating Energy Transition Pathways for European Residential Space Heating

Building College Education Management in the New Era: Theoretical Discussion and Model Construction Based on New Quality Productivity

This study analyses the use of new quality productivity theory in higher education management and develops a framework called the "multi-dimensional collaborative innovation management model" to tackle the challenges of globalisation and information technology. This concept combines the three dimensions of scientific and technological innovation, management innovation, and system innovation to promote effective resource allocation, improve educational quality and efficiency, and increase the education system's adaptability. This study uses mixed-methods research to verify the model's effectiveness in practical educational management as well as to present fresh theoretical perspectives and practical tactics for cultivating high-qualified talents who will satisfy the needs of future society.

Snake swarm optimization‐based deep reinforcement learning for resource allocation in edge computing environment

SummaryMobile edge computing has become popular in the past few years as a means of creating computing resources close to end‐user nodes at the network edge. Nodes—end users—demand work offloading to improve service utilization. Furthermore, when the number of users in mobile edge computing increases, the minimal resources deployed at the edge become a problem. Develop the idea of reinforcement learning using a metaheuristic technique intended to achieve effective resource allocation and resolve offloading issues to handle this issue. The ideal way to manage the implementation of mobile edge computing with a cognitive agent's help is to request compensation for all client necessities. To complete the infrastructure type for the Internet of Things (IoT), the operator information is combined with its distinctive methodology. Neural caching during task execution is provided by reinforcement learning based on snake swarm optimization (SSO). Neural caching during task execution is provided by reinforcement learning based on SSO. In the process of creating the cost mapping table and incentive factor‐based optimal resource allocation, this suggested method is applied to a contract with effective resource allocation among the end manipulators. Using performance metrics like delivery ratio, energy consumption, throughput, and delay, the suggested approach is put into practice and examined. It is also contrasted with traditional methods like Gray Wolf Optimization (GWO) ant colony optimization (ACO) and genetic algorithms (GA).

Fiscal Devolution and Healthcare Accessibility A Case Study of Homa Bay County

Fiscal devolution has gained prominence as a policy approach in many developing countries, aiming to enhance healthcare accessibility. This study focused on the influence of Fiscal devolution on healthcare accessibility in Homabay County. The study employed a mixed-methods approach to assess the impact of fiscal devolution on public sector efficiency, combining quantitative data analysis and qualitative observations. With an emphasis on metrics like service quality, punctuality, and accessibility, quantitative data were gathered via questionnaires and administrative records. Key stakeholders, such as representatives of the local government, prominent members of the community, and recipients of services, were interviewed in-depth and participated in focus groups to get qualitative data. The data were analyzed using appropriate SPSS version 27 statistical techniques and thematic analysis to identify patterns and relationships. Results showed that fiscal devolution had both positive as well as negative effects on healthcare accessibility in Homabay County, underlining the importance of effective local government and resource allocation processes. Preliminary data indicate that fiscal devolution in Homabay County has had a mixed effect on healthcare accessibility. On one hand, decentralization has empowered local authorities to make decisions tailored to local needs, resulting in improved service responsiveness and community engagement. On the other hand, challenges related to inadequate financial resources, capacity constraints, and intergovernmental coordination have hindered the efficient delivery of public services. Effectiveness of fiscal devolution largely depends on the presence of effective local governance structures, transparent resource allocation mechanisms, and capacity building initiatives.

An integrated optimization method to task scheduling and VM placement for green datacenters

In the realm of cloud computing, effective resource allocation can significantly enhance the energy efficiency of datacenters. Task scheduling and Virtual Machine Placement (VMP) are two pivotal aspects of resource allocation. However, in current research, they are often treated separately, overlooking the potential for integrated optimization. In this paper, we propose an integrated solution for task scheduling and VMP in energy-efficient datacenters, based on queueing theory and Deep Reinforcement Learning (DRL) methods. This novel and comprehensive approach provides an alternative perspective for resource scheduling strategies in datacenters. We construct a queueing theory model for task scheduling, aiming to minimize the number of VMs that need to be instantiated, while ensuring that Service Level Agreement (SLA) violation remains at a low level. Furthermore, we design a VMP algorithm based on DRL for real-time selection of Physical Hosts (PHs) for deploying VMs. Finally, we conduct a simulation evaluation using a small-scale datacenter. The experimental results demonstrate that our method consistently ensures a lower rate of SLA violation. Compared to existing algorithms, the DRL-based VMP algorithm enables a more balanced utilization of the various resources in the PHs and reduces the total power consumption of the datacenter by more than 10% on average.

A practical study on the near-zero discharge of rainwater and the collaborative treatment and regeneration of rainwater and sewage

Non-conventional water recovery, recycling, and reuse have been considered imperative approaches to addressing water scarcity in China. The objective of this study was to evaluate the technical and economic feasibility of Water Reclamation Plants (WRP) based on an anaerobic-anoxic-oxic membrane bioreactor (A2O-MBR) system for unconventional water resource treatment and reuse in towns (domestic sewage and rainwater). Rainwater is collected and stored in the rainwater reservoir through the rainwater pipe network, and then transported to the WRP for treatment and reuse through the rainwater reuse pumping station during the peak water demand period. During a year of operation and evaluation process, a total of 610,000 cubic meters of rainwater were reused, accounting for 10.4 % of the treated wastewater. In the A2O-MBR operation, the average effluent concentrations for COD (chemical oxygen demand), NH4+-N (ammonium), TN (total nitrogen), and TP (total phosphorus) were 14.23 ± 4.07 mg/L, 0.22 ± 0.26 mg/L, 11.97 ± 1.54 mg/L, and 0.13 ± 0.09 mg/L, respectively. The effluent quality met standards suitable for reuse in industrial cooling water or for direct discharge. The WRP demonstrates a positive financial outlook, with total capital and operating costs totaling 0.16 $/m3. A comprehensive cost-benefit analysis indicates a positive net present value for the WRP, and the estimated annualized net profit is 0.024 $/m3. This research has achieved near-zero discharge of wastewater and effective allocation of rainwater resources across time and space.

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

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

Unravelling the potential of Big Data-driven decision-making in sustainable water irrigation: An AI perspective

Big Data-driven initiatives have transformed several industries by using large databases to extract vital information and enhance operations. In the field of sustainable water irrigation, Big Data enables intelligent water management, allowing for exact resource allocation, monitoring and analysis. This combination enables smart decision-making, increasing productivity in agriculture while reducing water consumption and environmental impacts. The revolutionary influence of data-driven approaches on managing water resources for sustainable and efficient irrigation practices has significant potential. The research highlights the importance of Big Data-driven approaches in water management by examining their applicability. It explores the artificial intelligence (AI) techniques for water irrigation that is sustainable and determines significant affecting factors efficient water management. The study encourages AI in intelligent water management systems and explains the application of big data in irrigation. It illustrates how smart water management which includes hydrated irrigation and predictive water infrastructure can contribute to AI-driven water management. To handle complicated water supply problems, the research presents an estimated water level model, highlighting the integration of AI and indicating the revolutionary potential of these techniques in attaining sustainable water irrigation. We highlight the novel possibilities of big data-driven methods for sustainable water irrigation, illustrating the essential part of AI techniques in effective water resource allocation.

A non-cooperative game approach on isolated water-energy microgrids

The integration of renewable energies into water systems emerges as a crucial challenge and an opportunity within the water-energy nexus, particularly in isolated communities, emphasizing the importance of developing sustainable and self-sufficient isolated water-energy microgrids (IWEMGs). This study addresses a significant gap in existing research by proposing a comprehensive model that simultaneously considers operation and dispatch in IWEMGs. Our work introduces a framework based on game theory to model and optimize resource management in IWEMGs, where actors, including consumers, producers, or prosumers, engage in a non-cooperative game (Cournot competition), enabling an effective resource allocation mechanism for water and energy that considers strategic interactions among participants. The application of this model to a case study in a rural community, known as Ranchería, in La Guajira, Colombia, demonstrates the practicality and effectiveness of IWEMGs in meeting the water and energy demands of users. The model is implemented and solved using the GAMS software. Our quantitative results show optimal performance in resource management, highlighting the model’s ability to facilitate informed decision-making. Through a detailed analysis of water and energy transactions and the net benefits achieved, the study illustrates the potential of game theory to enhance the operational performance of IWEMGs. Furthermore, this research contributes to the broader discourse on sustainable resource management, offering insights that could guide the development of policies and strategies for implementing IWEMGs in similar contexts globally.

Crossover Boosted Grey Wolf Optimizer‐based framework for leader election and resource allocation in Intrusion Detection Systems for MANETs

AbstractMobile Ad hoc Networks (MANETs) is a self‐organizing networks without having a fixed infrastructure for making them susceptible to security threats. Intrusion Detection Systems (IDS) promotes security in MANETs by identifying malicious activities. Leader election is a fundamental aspect of IDS deployment, impacting resource allocation and system efficiency. This article presents a novel approach, the Crossover Boosted Grey Wolf Optimizer (CBGWO), for leader election and resource allocation in MANET‐based IDS. The proposed CBGWO algorithm integrates the Grey Wolf Optimizer (GWO) with innovative crossover operators that have an ability to enhance the capabilities of exploration and exploitation in the optimization process. The leader election problem is solved through applying multi‐objective optimization by considering energy consumption, reputation, and communication overhead. Objective functions are defined to maximize energy efficiency while maintaining a balanced distribution of leadership roles. Extensive simulations are conducted, varying network densities and the percentage of selfish nodes. Results demonstrate the effectiveness of the CBGWO‐based model in balancing energy consumption, prolonging network lifespan, and enhancing intrusion detection by comparing different state‐of‐the‐art models such as PCA‐FELM, CTAA‐MPSO, FLS‐RE, LEACH, DCAIDS, WOA‐GA, and VOELA. The proposed model achieved an energy consumption of 4.31 J, network lifetime of 560.482 ms, and average intrusion detection latency of 0.12 s, respectively. The proposed model outperforms than existing random and connectivity‐based leader election methods that is evaluated by taking main consideration of energy efficiency and network survivability. This research contributes to the field by introducing a robust algorithm for leader election in MANET‐based IDS, addressing challenges posed by network dynamics and resource constraints. The CBGWO‐based approach showcases its potential to achieve effective leader election and efficient resource allocation, thereby enhancing the security and sustainability of MANETs.

Resource Allocation for LoRaWAN Network Slicing: Multi-Armed Bandit-based Approaches

Wireless sensor networks have become increasingly popular in recent years due to the growing demand for Internet of Things (IoT) applications, including LoRaWAN networks. However, the effective allocation of resources remains a crucial challenge in LoRaWAN networks due to the limited bandwidth and the diverse demands for multiple services. This paper presents three novel resource allocation solutions for LoRaWAN network slicing to address this challenge. These solutions are based on the Multi-Armed Bandit (MAB) algorithm, which is known for balancing the exploration of available actions with the exploitation of optimal decisions. Our objective is to dynamically and efficiently allocate resources to network slices by treating the resource allocation as a MAB problem. This approach aims to maximize Packet Delivery Rate (PDR) performance while ensuring each service’s Service Level Agreement (SLA). The first solution, UCB-MAB, uses the Upper Confidence Bound (UCB) strategy to balance exploration and exploitation to improve network performance. The second solution, Q-UCB-MAB, continuously updates Q-values using the Q-learning update equation and incorporates the UCB strategy for further optimization. Finally, the third solution, ARIMA-UCB-MAB, leverages the predicted reward value from the Autoregressive Integrated Moving Average (ARIMA) model within the UCB framework to enhance network performance. Our results demonstrate that all three solutions offer efficient resource allocation in terms of PDR and SLA satisfaction. Specifically, the ARIMA-UCB-MAB solution outperforms the two other solutions.