Resource Management Framework Research Articles

Geoelectrical resistivity mapping for sustainable groundwater management in Umuahia South: Insights from vertical electrical sounding

The study of geoelectrical resistivity provides critical insights into subsurface characteristics and aquifer dynamics, particularly in regions with varying geological formations. This research investigates the geoelectrical properties of the aquifer system in Umuahia South, highlighting its significance for sustainable groundwater management. The primary aim of this study is to characterize the geoelectrical layers, assess aquifer thickness and resistivity, and evaluate the implications for groundwater resource management. Vertical Electrical Sounding (VES) was conducted across ten locations to measure the resistivity and thickness of subsurface layers. The data collected were analyzed to identify distinct geoelectrical layers, their respective resistivity values, and thicknesses. The study reveals significant variations in aquifer properties across the region, with VES 1 showcasing the largest thickness of 69.2 m and VES 9 the smallest at 5.2 m. Aquifer thickness decreases from the Benin Formation toward the Ameki and Ogwashi-Asaba Formations. Hydraulic conductivity ranges from 0.207 to 0.954 m/day, indicating varying groundwater flow potential, while transmissivity values vary from 4.965 to 30.441 m²/day, with 60% of aquifers classified as intermediate. Groundwater Potential Index (GWPI) highlights approximately 70% of the area as high potential, underscoring the need for targeted management strategies to optimize water resource extraction and sustainability. The variability in resistivity and thickness across the study area suggests significant subsurface heterogeneity. High resistivity values indicate favorable conditions for groundwater flow, particularly in areas like VES 6. Conversely, lower resistivity in locations such as VES 9 indicates potential challenges for groundwater storage. This study provides vital information on the aquifer system in Umuahia South, emphasizing the need for targeted groundwater management strategies based on the geoelectrical characteristics identified. The research contributes to a deeper understanding of the aquifer dynamics in the region and offers a framework for sustainable groundwater resource management, highlighting the importance of geoelectrical assessments.

The Effect of a Parcel-Aggregated Cropping Structure Mapping Method in Irrigation-Water Estimation in Arid Regions—A Case Study of the Weigan River Basin in Xinjiang Province

Effective management of agricultural water resources in arid regions relies on precise estimation of irrigation-water demand. Most previous studies have adopted pixel-level mapping methods to estimate irrigation-water demand, often leading to inaccuracies when applied in arid areas where land salinization is severe and where poorly growing crops cause the growing area to be smaller than the sown area. To address this issue and improve the accuracy of irrigation-water demand estimation, this study utilizes parcel-aggregated cropping structure mapping. We conducted a case study in the Weigan River Basin, Xinjiang Province, China. Deep learning techniques, the Richer Convolutional Features model, and the bilayer Long Short-Term Memory model were applied to extract parcel-aggregated cropping structures. By analyzing the cropping patterns, we estimated the irrigation-water demand and calculated the supply using statistical data and the water balance approach. The results indicated that in 2020, the cultivated area in the Weigan River Basin was 5.29 × 105 hectares, distributed over 853,404 parcels with an average size of 6202 m2. Based on the parcel-aggregated cropping structure, the estimated irrigation-water demand ranges from 25.1 × 108 m3 to 30.0 × 108 m3, representing a 5.57% increase compared to the pixel-level estimates. This increase highlights the effectiveness of the parcel-aggregated cropping structure in capturing the actual irrigation-water requirements, particularly in areas with severe soil salinization and patchy crop growth. The supply was calculated at 24.4 × 108 m3 according to the water balance approach, resulting in a minimal water deficit of 0.64 × 108 m3, underscoring the challenges in managing agricultural water resources in arid regions. Overall, the use of parcel-aggregated cropping structure mapping addresses the issue of irrigation-water demand underestimation associated with pixel-level mapping in arid regions. This study provides a methodological framework for efficient agricultural water resource management and sustainable development in arid regions.

Integrated geophysical and GIS approaches for groundwater potential assessment: a case study of Aladja, Delta State, Nigeria

ABSTRACT This study explores the groundwater potential in Aladja, Udu, Local Government Area of Delta State, Nigeria, using an integrated approach combining electrical resistivity techniques with Remote Sensing (RS) and Geographic Information System (GIS) methods. The primary aim is to delineate aquifers and comprehensively assess groundwater resources. Methods include Vertical Electrical Sounding (VES) to gather resistivity data, RS and GIS for analyzing geological and hydrological parameters such as lineament density, drainage density, slope, soil type, and land use. The resistivity values ranged from 147 to 460.50 Ωm, with aquifer thicknesses varying between 6.50 and 36.30 m. The RS and GIS analysis indicated high groundwater potential in regions characterized by significant lineament density and moderate slopes. The study highlights the complementarity of VES and RS/GIS in groundwater exploration, revealing substantial groundwater resources in the northwestern regions of the study area. This approach underscores the importance of integrating these methods for a more accurate assessment of groundwater potential. The novelty of this study lies in validating RS and GIS findings with VES data, ensuring reliable groundwater potential mapping and effective resource management. This integrated methodology offers a robust framework for sustainable groundwater resource assessment and management in similar geological settings.

Coupling Relationships and Driving Mechanisms of Water–Energy–Food in China from the Perspective of Supply and Demand Security

The rapid increase in population and economy, coupled with accelerated urbanization, is placing immense pressure on the water–energy–food (WEF) system. In this context, the water–energy–food nexus framework has emerged, recognizing the interdependencies and interactions among water, energy, and food systems, with the aim of optimizing resource management through cross-sectoral collaboration to promote sustainable development. Understanding the spatio-temporal differentiation patterns of the WEF nexus and elucidating the driving mechanisms behind changes in their coupling relationships is essential. This knowledge is crucial for ensuring the security of each subsystem and enhancing the overall sustainability of interconnected systems through coordinated efforts. To address these challenges, this study first established evaluation indicators for water, energy, and food security to quantify their levels and spatio-temporal dynamics. Subsequently, the degrees of coupling coordination within the WEF nexus were calculated. Finally, the WEF nexus’s spatial correlations were analyzed by using a spatial autocorrelation model. Spatial econometric models then identified key factors affecting its coordination. The results revealed significant spatial heterogeneity in water, energy, and food security across mainland China’s provinces. From 2002 to 2022, water security improved substantially in 87% of the provinces, while energy security began to improve in the eastern regions following a phase of high consumption. Food security saw significant enhancements, particularly in Inner Mongolia and the northeastern provinces. The overall coupling coordination of the WEF nexus improved across 30 provinces, progressing toward primary coordination. However, Henan and Anhui provinces experienced fluctuations in WEF nexus coordination. Spatial correlation analysis showed upward trends and increased clustering in WEF nexus coordination. Factors such as economic development and population positively influenced coordination, while economic agglomeration, education, and effective irrigation area had negative effects. This study elucidates the complex interconnections and key influencing factors within the WEF nexus, providing a reference framework and practical recommendations for equitable resource management.

A Bibliometric Analysis of Green Human Resource Management

The purpose of this investigation is to use bibliometric analysis to examine publication trends, citations, top authors, top journals, and top study fields in the framework of Green Human Resource Management (HRM). This study analysed the data from 362 research studies between 2014 and 2024 using the bibliometric review approach. The VOS viewer program was used to analyse the data once it was gathered from the Dimensions database. Only two papers were published in 2014; by 2024, there will be sixty papers, according to the research. Moreover, there has been a significant increase in publications on Green HRM between 2019 and 2023. According to citation analysis, the study has determined the following information: the most significant studies on green human resource management (HRM), the most prestigious journals that publish articles on HRM, the most well-known authors who publish articles on HRM, and the top subject areas that publish research on HRM. This study show that the issue of green human resource management (HRM) is interdisciplinary in nature, as it is linked to fields such as biological sciences, engineering, psychology, and neuroscience, in addition to management and commerce. The review aims to provide scholars with an overview of Green HRM and highlight relevant areas for additional study. Keywords: Green HRM, Green Human Resource Management, Bibliometric, Citation analysis, Top journals

GenesisRM: A state-driven approach to resource management for distributed JVM web applications

Reducing resource waste while maintaining end-to-end latency service-level objective (SLO) by simultaneously managing CPU bandwidth, memory allocation, and pod number of web applications running on Java virtual machine (JVM) is challenging. The challenges stem from the complexity of the multi-type resource allocation optimization problem, the high sensitivity of JVM performance to resource scaling actions, and the lack of low-level resource scaling mechanisms. We present GenesisRM, a resource management framework with a novel state-driven architecture. Specifically, we design a state control model for JVM web applications that encompasses seven pod states. This model serves as an abstraction layer, decoupling the centralized resource management system into a global state manager and distributed pod managers. The state manager controls the state transitions of the pods based on the overall workload, while the pod managers dynamically allocate resources for each pod according to the state and local workload.Then, we propose a multi-frequency control model with two predictive state controllers and a reactive state controller to manage the state of pods based on the state control model. In addition, GenesisRM brings new mechanisms to scale JVM pods’ low-level resources without damaging their performance. We evaluate our work using a real-world JVM web application benchmark in three different scale server clusters of Pengcheng Laboratory Developer Cloud, and the 21-day experimental results show that GenesisRM saves 31.70% CPU and 17.60% memory compared to the best-performing state-of-the-art solutions while guaranteeing the SLO imposed on end-to-end latency.

Groundwater potential mapping in arid and semi-arid regions of kurdistan region of Iraq: A geoinformatics-based machine learning approach

Groundwater (GW) is a crucial and increasingly scarce natural resource, that is affected by climate change and mismanagement. To manage GW resources effectively, it is crucial to accurately identify GW potential zones (GWPZs) using modern techniques. This study aimed to employ and assess geoinformatics-based machine learning (ML) models to delineate GWPZs in the Akre district, Kurdistan region of Iraq. Six nonparametric ML models were used: a support vector machine (SVM), k-nearest neighbours (KNN), decision tree (DT), random forest (RF), gradient boost DT (GBDT), and extreme gradient boosting (XGBoost). These models were trained on diverse GWPZ-favourable influencing factors, encompassing topographic, hydrological, geological, and environmental aspects. The findings of this study revealed that the XGBoost model outperformed the other nonparametric models in terms of best-fit performance and accuracy in generating a GW potential map (GWPM), achieving a R2 of 0.88, a root mean square error (RMSE) of 11.348, and a mean absolute error (MAE) of 6.623. Notably, over half of the study area (53%) was categorised as having high or very high GWPZs, primarily in the low-lying Rovia Plain. The study identified rainfall, elevation, lineament density (LD), drainage density (Dd), topographic wetness index (TWI), and slope as the most significant factors influencing GWPZ modelling. This study provides a comprehensive framework for GW resource management, ecological conservation, and urban development planning. These insights are crucial for stakeholders, policymakers, and local authorities in strategic resource planning and environmental stewardship.

Digital transformation, agile practices, and strategic human resource management: Basis for enhanced human resource management framework

Digital transformation, agile practices, and strategic human resource management: Basis for enhanced human resource management framework

A framework for natural resource management with geospatial machine learning: a case study of the 2021 Almora forest fires

BackgroundWildfires have a substantial impact on air quality and ecosystems by releasing greenhouse gases (GHGs), trace gases, and aerosols into the atmosphere. These wildfires produce both light-absorbing and merely scattering aerosols that can act as cloud condensation nuclei, altering cloud reflectivity, cloud lifetime, and precipitation frequency. Uttarakhand province in India experiences frequent wildfires that affect its protected ecosystems. Thus, a natural resource management system is needed in this region to assess the impact of wildfire hazards on land and atmosphere. We conducted an analysis of a severe fire event that occurred between January and April 2021 in the Kumaun region of Uttarakhand, by utilizing open-source geospatial data. Near-real-time satellite observations of pre- and post-fire conditions within the study area were used to detect changes in land and atmosphere. Supervised machine learning algorithm was also implemented to estimate burned above ground biomass (AGB) to monitor biomass stock.ResultsThe study found that 21.75% of the total burned area burned with moderate to high severity, resulting in a decreased Soil Adjusted Vegetation Index value (> 0.3), a reduced Normalized Differential Moisture Index value (> 0.4), and a lowered Normalized Differential Vegetation Index (> 0.5). The AGB estimate demonstrated a significant simple determination (r2 = 0.001702) and probability (P < 2.2 10−16), along with a positive correlation (r ≤ 0.24) with vegetation and soil indices. The algorithm predicted that 17.56 tonnes of biomass per hectare burned in the Kumaun forests. This fire incident resulted in increased emissions of carbon dioxide (CO2; ~ 0.8 10−4 kg carbon h−1), methane (CH4; ~ 200 10−9 mol fraction in dry air), carbon monoxide (CO; 2000 1015 molecules cm−2 total column), and formaldehyde (HCHO; 3500 1013 molecules cm−2 total column), along with increased aerosol optical thickness (varying from 0.2 to 0.5).ConclusionsWe believe that our proposed operational framework for managing natural resources and assessing the impact of natural hazards can be used to efficiently monitor near-real-time forest-fire-caused changes in land and atmosphere. This method makes use of openly accessible geospatial data that can be employed for several objectives, including monitoring carbon stocks, greenhouse gas emissions, criterion air pollution, and radiative forcing of the climate, among many others. Our proposed framework will assist policymakers and the scientific community in mitigating climate change problems and in developing adaptation policies.

Psychological empowerment, work engagement and job satisfaction among hotel employees in CALABARZON: Basis for human resource management framework

Psychological empowerment, work engagement and job satisfaction among hotel employees in CALABARZON: Basis for human resource management framework

Containerized Microservices: A Survey of Resource Management Frameworks

Containerized Microservices: A Survey of Resource Management Frameworks

HRMF-DRP: A next-generation solution for overcoming provisioning challenges in cloud environments

The cloud computing infrastructure is a distributed environment and the existing research works have some provisioning problems such as suboptimal resource utilization and high execution time. The Heterogeneity Resource Management Framework for Dynamic Resource Provisioning (HRMF-DRP) is proposed for focusing on task scheduling and workload management. This framework incorporates advanced algorithms for dataset preprocessing, task clustering, workload prediction, and dynamic resource provisioning. For data preprocessing, the real-world workload traces were captured from the Planet Lab dataset that are taken as input for the preprocessing stage. The data preprocessing is responsible for ensuring data quality and reliability by using different models like missing data handling, outlier detection and removal as well as standardization and normalization. In this paper, the tasks are grouped into clusters by utilizing Density-Based Spatial Clustering of Applications with Noise (DBSCAN) model and this model categorizes the data points into border points, core points and noise points based on their density. The temporal dependencies are captured for the workload prediction by using Long Short-Term Memory (LSTM) neural network model. A Gaussian Mixture Model (GMM) model is responsible for estimating the number of Virtual machines (VMs) present in the workload prediction process. The Self-Adaptive Genetic Algorithm (SAGA) is implemented for task mapping that adjusts the parameters to change workload patterns for contributing adaptability and robustness. The different experimental evaluations are conducted based on the task completion time, workload balance index, resource utilization efficiency and workload prediction accuracy. The proposed model achieved the workload prediction accuracy of 98.5%, cost of $89.6, execution time of 125ms, Task Completion Time (TCT) of 40ms, Workload Balance Index (WBI) of 0.96 and Resource Utilization Efficiency (RUE) of 0.93. The quantitative results collectively position HRMF-DRP as a practical and efficient solution, promising advancements in dynamic resource provisioning for cloud computing, particularly within the Infrastructure as a Service (IaaS) cloud model.

Medical Resource Management in Emergency Hierarchical Diagnosis and Treatment Systems: A Research Framework.

The occurrence of major public health crises, like the COVID-19 epidemic, present significant challenges to healthcare systems and the management of emergency medical resources worldwide. This study, by examining the practices of emergency medical resource management in select countries during the COVID-19 epidemic, and reviewing the relevant literature, finds that emergency hierarchical diagnosis and treatment systems (EHDTSs) play a crucial role in managing emergency resources effectively. To address key issues of emergency resource management in EHDTSs, we examine the features of EHDTSs and develop a research framework for emergency resource management in EHDTSs, especially focusing on the management of emergency medical personnel and medical supplies during evolving epidemics. The research framework identifies key issues of emergency medical resource management in EHDTSs, including the sharing and scheduling of emergency medical supplies, the establishment and sharing of emergency medical supply warehouses, and the integrated dispatch of emergency medical personnel. The proposed framework not only offers insights for future research but also can facilitate better emergency medical resource management in EHDTSs during major public health emergencies.

Retraction Note: Towards an adaptive human-centric computing resource management framework based on resource prediction and multi-objective genetic algorithm

Retraction Note: Towards an adaptive human-centric computing resource management framework based on resource prediction and multi-objective genetic algorithm

OCL-MEC: An online CPU-core prediction based on load balancing framework for offloading resource management in mobile edge computing environment

Clients can increase or decrease the number of resources they use dynamically over time due to the elasticity of cloud resources. As a result, variations in resource demands and predefined VM sizes result in a lack of resource utiliation, load imbalances, and excessive power consumption. A framework of efficient resource management is proposed to address these issues, balancing the load accordingly and anticipating the resource utilization of the servers. By optimizing resource utilization and minimizing the number of active servers, this technique facilitates power savings. Under/overloaded servers reduce energy consumption, execution delay, and performance degradation through a resource prediction system that is deployed at the CPU. Moreover, OCL-MEC load-balancing and resource allocation algorithms are proposed to reduce data center network traffic and power consumption. Experiments on real-world workload datasets, namely Bitsbrain VM traces, are conducted to evaluate the proposed framework. Different performance metrics demonstrate the superiority of the proposed framework over state-of-the-art approaches. Power savings of up to 98 % can be achieved by the OCL-MEC framework using a decision tree load balancing model based on HMM prediction systems.

Intellectual property protection in the oil, gold sectors and mineral resources rent on indigenous populations: Preserving innovation and value

This study investigates into the intricate relationship between intellectual property protection and the wellbeing of indigenous communities within the oil, gold, and mineral sectors. Covering the period from 1990 to 2020 and focusing on nations rich in natural resources such as Russia, China, India, Indonesia, Nigeria, South Africa, Brazil, Canada, Mexico, Argentina, and Australia, the research employs advanced econometric models to dissect the dynamics between intellectual property protection, innovation within these sectors, and the preservation of economic and cultural values. The dependent variable in our analysis encompasses a composite measure that reflects the overall socio-economic and cultural impact of these sectors on indigenous communities. Our findings reveal that stronger intellectual property protection measures potentially foster innovation and value generation in these sectors. However, it also emerges that these measures could lead to adverse consequences for indigenous communities, including restricted access to resources and potential displacement. This work significantly contributes to understanding the complex interplay among intellectual property protection, indigenous communities, and the economic benefits derived from natural resource revenues, offering insights into optimizing policy frameworks for sustainable and equitable resource management.

Water Resources Management In Small Islands: A Review of Public Policy Implementation In The Development of Lekloor Reservoir, Nomaha Island

Water scarcity in small island regions, including Kisar Island in Maluku, presents a pressing issue due to inadequate water resource management. This article aims to describe a holistic approach to addressing the challenges of water resource management and water scarcity on Kisar Island, Maluku. The research methodology employed is a mixed-method approach, combining technical feasibility studies for reservoir technology, policy analysis through literature reviews, and field assessments to evaluate the topographic, hydrological, and environmental conditions of Kisar Island. In the technical context, the article analyzes reservoir technology as a solution to address water issues, supported by the topographic and hydrological characteristics of Kisar Island. Additionally, the article examines relevant public policies, such as Government Regulation No. 7 of 2013 on National Spatial Planning, Provincial Regulation No. 16 of 2013 on Provincial Spatial Planning in Maluku, and District Regulation No. 1 of 2023 on District Spatial Planning in Maluku Barat Daya. Through this review, the article highlights the crucial role of policy in establishing a supportive framework for water resource management. Furthermore, the importance of local community participation in water resource management on Kisar Island is emphasized, asserting that active engagement from the local community is key to achieving success in sustainable water management efforts.

Assessment of Human Resource Management Framework from Islamic Paradigm: Proposition of an Islamic Model of Human Resource Management

The study of Human Resource Management (HRM) in the context of Islamic philosophy has gained traction especially in the last two decades. This paper aims to assess the HRM purpose, processes and practices in the light of Islamic doctrine to eventually present an Islamic model of HRM that is hinged upon Islamic principles and values. The postulated model offers a unique perspective by incorporating both contemporary and spiritual dimensions to HRM framework in order to provide a holistic outlook to the study of Islamic Human Resource Management (IHRM). The proposed model named The “AF Islamic HRM Model” is driven by two key Islamic concepts of Adl (justice) and Falah (wellbeing or success), where Adl represents an essential underlying element for all HRM processes as per the proposed IHRM model and the conception of Falah implies an ultimate reward or success in this world as well as in the world hereafter, as perceived from the Islamic belief system. The paper entails a qualitative research approach by evaluating the existing HRM models as well as analyzing the already available literature regarding Islamic perspective to the HRM theory to ultimately posit a model which contributes value to the existing knowledge base of Islamic HRM. There is not much research regarding the application of Islamic ideology to various facets of HRM and this study endeavors to address this research gap by proposing an IHRM model which consolidates both the contemporary HRM philosophy as well as the Islamic tenets and guidelines, encompassing diverse aspects of both religion and worldly life by providing insights about the conventional HRM body of knowledge with a touch of ethics and spirituality.

Development of big data assisted effective enterprise resource planning framework for smart human resource management.

The planning of human resources and the management of enterprises consider the organization's size, the amount of effort put into operations, and the level of productivity. Inefficient allocation of resources in organizations due to skill-task misalignment lowers production and operational efficiency. This study addresses organizations' poor resource allocation and use, which reduces productivity and the efficiency of operations, and inefficiency may adversely impact company production and finances. This research aims to develop and assess a Placement-Assisted Resource Management Scheme (PRMS) to improve resource allocation and usage and businesses' operational efficiency and productivity. PRMS uses expertise, business requirements, and processes that are driven by data to match resources with activities that align with their capabilities and require them to perform promptly. The proposed system PRMS outperforms existing approaches on various performance metrics at two distinct levels of operations and operating levels, with a success rate of 0.9328% and 0.9302%, minimal swapping ratios of 12.052% and 11.658%, smaller resource mitigation ratios of 4.098% and 4.815%, mean decision times of 5.414s and 4.976s, and data analysis counts of 6387 and 6335 Success and data analysis increase by 9.98% and 8.2%, respectively, with the proposed strategy. This technique cuts the switching ratio, resource mitigation, and decision time by 6.52%, 13.84%, and 8.49%. The study concluded that PRMS is a solid, productivity-focused corporate improvement method that optimizes the allocation of resources and meets business needs.

Developing an integrated framework for Marine Cultural Heritage (MCH) and Marine Protected Areas (MPA) across Africa and the Middle East

There is currently a significant imbalance in existing marine resource management frameworks. Across Africa and the Middle East there is a developing network of marine protected areas focused on natural heritage but with very limited engagement with area-based management for cultural heritage. Yet both natural and cultural heritage resources are subject to the same considerable threat levels from both natural and anthropogenic processes of change. The MarEA project is engaged in an extensive programme of mapping and documentation of endangered cultural heritage sites and landscapes across the MENA region. During the course of this and aligned work, its engagement with coastal communities and international practice has identified a clear need for more informed and holistic approaches to marine resource management. This study presents a model for the integrated management of natural and cultural heritage and proposes a framework for the future identification and designation of Marine Cultural Heritage in Protected Areas.