Effective Resource Management Research Articles

Ensuring Ethical Standards in an Organization: A Comprehensive Guide on How the Human Resource Manager can Create an Ethical Organization

In each and every organization, Effective and efficient Human Resource Management is a key component that enables employees and managers to contribute effectively and productively to the overall business direction and the accomplishment of the organization’s goals and objectives through various functions such as employee motivation, effective hiring, performance management, organizational development, safety, wellness, benefits, communication, career development and training.

Hydrogeochemical characterization and water quality evaluation for drinking and irrigation purposes of coastal aquifers of Middle Andaman

A comprehensive study to characterize the hydrogeochemistry of the region and the impact of saltwater intrusion on groundwater was conducted along the eastern coast of Middle Andaman of Andaman and Nicobar (A&N) Islands. The escalating population growth and intensified tourism activities have resulted in the over-extraction of groundwater. Seismic activities led to the opening of lineaments to the sea and the dissolution of limestone in the influence of seawater. 24 groundwater samples and 1 reference sample from sea were taken from various locations of middle Andaman. The analysis involved the determination of major cations, anions, and heavy metals using Inductively Coupled Plasma Optical Emission spectroscopy (ICP), spectrophotometry, and flame photometry. Furthermore, X-ray diffraction analysis, binary diagrams, Chloro-alkaline indices (CAI), Gibbs Plot, correlation matrix, Piper plot, Chadha’s plot and Principal Component Analysis (PCA) to the major ions data indicated rock-water interactions, strong correlations among alkali and alkaline earth metals, and interactions between seawater and carbonate minerals respectively. The water quality index indicated “very poor to unsuitable for drinking purposes” in 24% of the samples. Additionally, indices for irrigation suitability; total hardness (TH), residual sodium carbonate (RSC), and Magnesium adsorption ratio (MAR) were found to be detrimental for irrigation in 80%, 08%, and 12% of the water samples, respectively. These results highlight the importance of implementing effective water resource management techniques, such as groundwater extraction rates, adopting appropriate water treatment technologies to mitigate the adverse impacts of heavy metals, saltwater intrusion and maintain water quality for human consumption and agricultural use in the region.

Hybrid modeling of karstic springs: Error correction of conceptual reservoir models with machine learning

Abstract The simulation of spring discharge is of high interest for water supply management. Accurate modeling and prediction of spring discharge can help water authorities and managers optimize water use, face seasonal variability, and prepare for possible drought conditions. Therefore, developing reliable simulation models and forecasting tools for spring discharge is essential for the effective management of groundwater resources relying on karstic springs. While the use of complementary modeling approaches has been explored in various hydrological contexts, its application to spring discharge modeling remains relatively unexplored. Previous studies on spring discharge modeling have predominantly focused on either conceptual/distributed models or data-driven models separately, without fully investigating the potential advantages of combining these two approaches. Consequently, there is a research gap in understanding the potential benefits and effectiveness of integrating these models for spring discharge modeling. This study developed and evaluated a hybrid modeling approach integrating a conceptual hydrological model with a machine learning technique to simulate spring discharge from the largest karst aquifer supplying the city of Bordeaux, France. Specifically, the study combined the five-parameter GR5J reservoir model with a random forests data-driven model in a hybrid framework. Model performance was assessed through comparison with….

Manajemen Efektif Pendidik dan Tenaga Kependidikan untuk Menciptakan Pembelajaran yang Berkualitas

Management of educators and education personnel is a key aspect of personnel management in educational institutions, with strategies for managing teacher and educator staff being the main focus for achieving quality learning. Professional development is also crucial in improving the competence and quality of trainers, while evaluation of their activities is necessary to monitor and improve performance. This study uses the literature method, with data collected through library data, reading, and managing research materials. The findings of this study show that effective human resource management strategies involve careful planning, thorough training and development, and regular performance evaluation. The implementation of education policies that focus on quality learning requires cross-departmental cooperation and active participation from various stakeholders. Environmental and organizational factors, including management, regulation, human resources, infrastructure, relationships, collaboration and changes in the external environment, also influence the effectiveness of HRM in educational institutions. By understanding the importance of educator and education personnel management and implementing effective HRM strategies, educational institutions can create a quality and competitive learning environment. This will positively impact the quality of education provided to students and help achieve broader educational goals.

Evaluation of water shortage and instream flows of shared rivers in South Korea according to the dam operations in North Korea

Abstract The Korean Peninsula's mountainous terrain poses challenges to effective water resource management. Notably, two significant river basins, North Han River and Imjin River basins, are essentially shared rivers originating in North Korea. After the construction of various dams in North Korea, billions of tons per year of water annually decreased from the upper reaches of these rivers of North Korea to South Korea. This study conducted an impact analysis on two major river basins affected by dam operations in North Korea. Both before and after the Imnam Dam operation, significant reduction (27.7%) in the average monthly inflow was observed, and the total release of the Hwacheon Dam in South Korea decreased by 40.2%. The analysis of instream flow indicated that the operation of dams in North Korea had a substantial impact on fulfilling instream flow requirements for dams located in North Han River and Imjin River basins. To ensure instream flow, this study proposed two plans. The first plan involved the utilization of existing dams in the North Han River basin, while the second plan suggested connecting the dams in North Korea, taking into account the shared rivers.

THEORETICAL FRAMEWORKS SUPPORTING IT AND BUSINESS STRATEGY ALIGNMENT FOR SUSTAINED COMPETITIVE ADVANTAGE

In today's dynamic business environment, the alignment between information technology (IT) and business strategy is critical for organizations seeking sustained competitive advantage. This paper presents a comprehensive examination of theoretical frameworks supporting this alignment, aiming to provide insights into how organizations can effectively integrate IT capabilities with strategic objectives. The theoretical foundations explored include the Resource-Based View (RBV), which emphasizes leveraging unique resources and capabilities to achieve competitive advantage. Additionally, the Dynamic Capabilities Framework underscores the importance of an organization's ability to adapt and innovate in response to changing market conditions, highlighting the role of IT in facilitating agility and flexibility. The Strategic Alignment Model offers a structured approach to ensuring coherence between business strategy, IT strategy, and organizational infrastructure, enabling seamless integration and alignment. Value Chain Analysis provides a framework for understanding how IT can add value across different stages of the value chain, informing strategic decision-making and resource allocation. Moreover, IT Governance Frameworks, such as COBIT and ITIL, offer guidelines for effective management and governance of IT resources to support strategic objectives. Business Process Reengineering (BPR) is also examined as a methodology for redesigning business processes to align with strategic goals and leverage IT capabilities. Through an exploration of these theoretical frameworks, this paper aims to equip organizations with the knowledge and tools necessary to achieve sustained competitive advantage through the strategic alignment of IT and business strategies. Keywords:  Theoretical Frameworks, IT, Business Strategy Alignment, Sustained Competitive Advantage.

Water-Level Prediction Analysis for the Three Gorges Reservoir Area Based on a Hybrid Model of LSTM and Its Variants

The Three Gorges Hydropower Station, the largest in the world, plays a pivotal role in hydroelectric power generation, flood control, navigation, and ecological conservation. The water level of the Three Gorges Reservoir has a direct impact on these aspects. Accurate prediction of the reservoir’s water level, especially in the dam area, is of utmost importance for downstream regions’ safety and economic development. This study investigates the application and performance of four distinct deep-learning models in predicting water levels. The models evaluated include the Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM), and Convolutional Neural Network–Attention–Long Short-Term Memory (CNN–Attention–LSTM). The performance of these models was assessed using several metrics, namely the Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The findings indicate that the CNN–Attention–LSTM model outperforms the others in all metrics, achieving an R2 value of 0.9940, MAE of 0.5296, RMSE of 0.6748, and MAPE of 0.0032. Moreover, the CNN–LSTM model exhibited exceptional predictive accuracy for lower water levels. These results underscore the potential of deep-learning models in water-level forecasting, particularly highlighting the efficacy of attention mechanisms in enhancing predictive accuracy. Precise water-level predictions are instrumental in optimizing hydropower generation and providing a scientific basis for effective flood control and water resource management.

Groundwater mapping of urban and coastal areas in Brunei

Groundwater mapping plays an important role in effective water resource management, sustainable development, and environmental protection. In the tropical Brunei Darussalam (north Borneo Island), groundwater mapping is yet to be provided. The aim of the study is to provide groundwater mapping of Brunei, particularly for the urban and coastal areas of the Brunei-Muara capital district. The study uses a GIS interpolation technique to generate a groundwater contour map based on groundwater data from 572 sacrificial boreholes located in the study area. Remote sensing data and published maps from secondary sources were digitised in ArcGIS software to produce thematic layers for further hydrological evaluations. Results showed that groundwater levels in the study area are generally high and shallow, ranging from 0 to 18 m below ground level with a mean value of 2.9 m. According to the evaluation of geo-thematic layers and groundwater contours, groundwater flows towards the South China Sea in the coastal areas and towards the Brunei River further inland. Hydraulic gradients towards the South China Sea also vary between 0.004 and 0.08. Thus, assuming surface aquifer thickness in the weathered zone between 10 to 20 m, hydraulic conductivities ranges from 1∙10−5 to 1∙10−4 m∙s−1, a submarine groundwater discharge (SGD) flux between 4.7∙10−7 to 4.0∙10−4 m3∙s−1 per unit width can be estimated for the shallow aquifer. This study provides valuable insights into the groundwater system dynamics so important, which are critical for its future utilisation and protection, aiming to contribute to the national water security in Brunei Darussalam.

Deep reinforcement learning-based methods for resource scheduling in cloud computing: a review and future directions

With the acceleration of the Internet in Web 2.0, Cloud computing is a new paradigm to offer dynamic, reliable and elastic computing services. Efficient scheduling of resources or optimal allocation of requests is one of the prominent issues in emerging Cloud computing. Considering the growing complexity of Cloud computing, future Cloud systems will require more effective resource management methods. In some complex scenarios with difficulties in directly evaluating the performance of scheduling solutions, classic algorithms (such as heuristics and meta-heuristics) will fail to obtain an effective scheme. Deep reinforcement learning (DRL) is a novel method to solve scheduling problems. Due to the combination of deep learning and reinforcement learning (RL), DRL has achieved considerable performance in current studies. To focus on this direction and analyze the application prospect of DRL in Cloud scheduling, we provide a comprehensive review for DRL-based methods in resource scheduling of Cloud computing. Through the theoretical formulation of scheduling and analysis of RL frameworks, we discuss the advantages of DRL-based methods in Cloud scheduling. We also highlight different challenges and discuss the future directions existing in the DRL-based Cloud scheduling.

Investigation of changes in land use/land cover using principal component analysis and supervised classification from operational land imager satellite data: a case study of under developed regions, Pakistan

Monitoring and understanding Land Use/Land Cover (LU/LC) is critical for sustainable development, as it can impact various environmental, social, and economic systems. For example, deforestation and land degradation can lead to soil erosion, loss of biodiversity, and greenhouse gas emissions, affecting the quality of soil, air, and water resources. The present research examined changes in (LU/LC) within the underdeveloped regions of Balochistan and Sindh provinces, which are situated in Pakistan. In order to monitor temporal variations of LU/LC, we employed Geographic Information System (GIS) technique, to conduct an analysis of satellite imagery obtained from the Landsat 8 Operational Land Imager (OLI) during the time period spanning from 2013 to 2023. In order to obtain an accurate LU/LC classification, we used principal component analysis (PCA) and a supervised classification approach using the maximum likelihood algorithm (MLC). According to the results of our study, there was a decrease in the extent of water bodies (− 593.24 km2) and vegetation (− 68.50 km2) by − 3.43% and − 0.40% respectively. In contrast, the area occupied by settlements in the investigated region had a 2.23% rise, reaching a total of 385.66 square kilometers. Similarly, the extent of barren land also expanded by 1.60%, encompassing a total area of 276.04 square kilometers, during the course of the last decade. The overall accuracy (94.25% and 95.75%) and K value (91.75% and 93.50%) were achieved during the year 2013 and 2023 respectively. The enhancement of agricultural output in Pakistan is of utmost importance in order to improve the income of farmers, mitigate food scarcity, stimulate economic growth, and facilitate the expansion of exports. To enhance agricultural productivity, it is recommended that the government undertake targeted initiatives that aimed at enhancing water infrastructure and optimizing land use to foster a sustainable ecological framework. Integrating the sustainable ecological framework provides a foundation for informed decision-making and effective resource management. By identifying areas of urban expansion, agricultural intensification, or alterations in natural LU/LC, stakeholders can design targeted conservation strategies, mitigating potential environmental degradation and promoting biodiversity conservation. In conclusion, the integration of GIS and Remote Sensing (RS) may effectively facilitate the monitoring of land use patterns over a period of time. This combined approach offers valuable insights and recommendations for the judicious and optimal management of land resources, as well as informing policy decisions.

Exploring Surface Water-Groundwater Interactions: A Multidisciplinary Approach

Surface water-ground water interaction plays a crucial role in hydrological systems, influencing water availability, quality and ecosystem sustainability. To maintain drinking water resources and ecosystem health, a mechanistic understanding of the underlying processes controlling the spatial patterns and temporal dynamics of groundwater–surface water interactions is crucial. Understanding these interactions requires a multidisciplinary approach that integrates insights from hydrology, geology, ecology, and geochemistry. Several software applications are available to model and analyze these interactions. This abstract presents the framework and key findings of a comprehensive study aimed at elucidating the complex dynamics of surface water-groundwater interactions through different software. Key finding highlights the significance of spatial and temporal variability in surface water-groundwater interactions, emphasizing the need for comprehensive monitoring networks and advanced modeling tools. It underscores the interconnected nature of surface water and groundwater resources, emphasizing the importance of integrated management strategies for sustainable water resource utilization and ecosystem health. It provides an overview of current research advances and innovative approaches in the broad field of surface water-ground water interactions. By adopting a multidisciplinary approach, we can understand the complex interactions shaping the surface water-groundwater systems, facilitating informed decision-making and effective resource management in a dynamic environment.

Validation Challenges in Data for Different Diesel Engine Performance Regimes Utilising HVO Fuel: A Study on the Application of Artificial Neural Networks for Emissions Prediction

Artificial neural networks (ANNs) provide supervised learning via input pattern assessment and effective resource management, thereby improving energy efficiency and predicting environmental fluctuations. The advanced technique of ANNs forecasts diesel engine emissions by collecting measurements during trial sessions. This study included experimental sessions to establish technical and ecological indicators for a diesel engine across several operational scenarios. VALLUM01, a novel tool, has been created with a user-friendly interface for data input/output, intended for the purposes of testing and prediction. There was a comprehensive collection of 12 input parameters and 10 output parameters that were identified as relevant and sufficient for the objectives of training, validation, and prediction. The proper value ranges for transforming into fuzzy sets for input/output to an ANN were found. Given that the ANN’s training session comprises 1,000,000 epochs and 1000 perceptrons within a single-hidden layer, its effectiveness can be considered high. Many statistical distributions, including Pearson, Spearman, and Kendall, validate the prediction accuracy. The accuracy ranges from 96% on average, and in some instances, it may go up to 99%.

Dynamic monitoring of surface area and water volume of reservoirs using satellite imagery, computer vision and deep learning

The development of surface area and estimated volume measurements for water reservoirs using advanced computational techniques has become an increasingly important task for reservoir operation. Thus, the objective of this study is to determine the surface area and estimate the volume of water reservoirs through the segmentation of multispectral satellite images using a Fully Convolutional Neural Network (FCN). This paper introduces a universally applicable framework that utilizes deep convolutional neural networks for segmenting water bodies, particularly focusing on areas in Paraíba State, Northeast Brazil, susceptible to severe droughts. The study utilized Sentinel-2 satellite data obtained from Google Earth Engine for the period between January 2019 and September 2023. The extraction of water surface area from satellite images was achieved using the U-Net model, a deep learning framework based on FCN. The accuracy of water surface area extraction was analyzed using the Intersection-Over-Union (IoU) metric, also known as Jaccard Index. Subsequently, reservoir volumes were calculated using elevation-area-volume curves. The evaluation metrics for the results included root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R2), and Pearson correlation coefficient (r). The network demonstrated high accuracy, achieving a 95% IoU on the test set. This methodology was applied to four reservoirs, yielding promising results that closely aligned the monitored reservoir curves derived from satellite imagery with observed field data. The findings of this study are significant for research on semiarid region changes, extending their relevance to a broader understanding of global environmental dynamics. This work not only contributes to the field of remote sensing in hydrology but also offers practical insights for effective water resource management in drought-prone regions.

Oily wastewater detector based on superhydrophobic liquid marbles

Rapid detection of oily wastewater plays a vital role in safeguarding public health, protecting the environment, and supporting effective resource management. It serves as an indispensable tool for early warning, surveillance, and response to various challenges that can affect communities and ecosystems. Here, activated carbon was ground and modified to realize superhydrophobicity, and superhydrophobic activated carbon liquid marbles were prepared with special droplet repulsion properties. The robustness and stability of the liquid marbles were investigated. We demonstrated that the prepared liquid marbles break up immediately once contact with the oils. In addition, we developed a self-made oily wastewater detector based on the high sensitivity of liquid marbles to oily wastewater. Compared with the traditional oily wastewater detection methods, our oily wastewater detector does not require a large amount of chemical reagents, thereby reducing the environmental impact effectively. We believe that this study has a wide range of applications in pollution prevention and liquid treatment.

Effective management of human resources in enterprises: example of information technologies

Effective management of human resources in enterprises: example of information technologies

Optimizing selection based on BLUPs or BLUEs in multiple sets of genotypes differing in their population parameters

Key messageSelection response in truncation selection across multiple sets of candidates hinges on their post-selection proportions, which can deviate grossly from their initial proportions. For BLUPs, using a uniform threshold for all candidates maximizes the selection response, irrespective of differences in population parameters.Plant breeding programs typically involve multiple families from either the same or different populations, varying in means, genetic variances and prediction accuracy of BLUPs or BLUEs for true genetic values (TGVs) of candidates. We extend the classical breeder's equation for truncation selection from single to multiple sets of genotypes, indicating that the expected overall selection response (ΔGTot)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$({\\Delta G}_{{\ ext{Tot}}})$$\\end{document} for TGVs depends on the selection response within individual sets and their post-selection proportions. For BLUEs, we show that maximizing ΔGTot\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\Delta G}_{{\ ext{Tot}}}$$\\end{document} requires thresholds optimally tailored for each set, contingent on their population parameters. For BLUPs, we prove that ΔGTot\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\Delta G}_{{\ ext{Tot}}}$$\\end{document} is maximized by applying a uniform threshold across all candidates from all sets. We provide explicit formulas for the origin of the selected candidates from different sets and show that their proportions before and after selection can differ substantially, especially for sets with inferior properties and low proportion. We discuss implications of these results for (a) optimum allocation of resources to training and prediction sets and (b) the need to counteract narrowing the genetic variation under genomic selection. For genomic selection of hybrids based on BLUPs of GCA of their parent lines, selecting distinct proportions in the two parent populations can be advantageous, if these differ substantially in the variance and/or prediction accuracy of GCA. Our study sheds light on the complex interplay of selection thresholds and population parameters for the selection response in plant breeding programs, offering insights into the effective resource management and prudent application of genomic selection for improved crop development.

Simulation of national-scale groundwater dynamics in geologically complex aquifer systems: an example from Great Britain

ABSTRACT The national-scale British Groundwater Model (BGWM) is implemented to simulate groundwater dynamics and budgets in Great Britain. Notwithstanding the challenges of integrating a very large amount of data, finding a trade-off between computational efficiency and realism, performing automatic calibration, and addressing multiple sources of structural and parameter uncertainty, a quantitative–qualitive evaluation approach showed that the BGWM provides a reasonably accurate digital representation of groundwater systems and processes at a national scale. In this work, the model was applied to understand the variability of budget components across multiple spatial and temporal scales. Comparisons showed regional differences linked to lithological and climatic factors, which in turn can be associated with more or less groundwater resilience to extreme climatic events. There is confidence that the current and future versions of the BGWM can become valuable tools for effective water resources management and adaptation strategies under future climatic and population changes.

HUMAN RESOURCE MANAGEMENT SYSTEM(HRMS)

In today's fast-paced business environment, the effective management of human resources stands as a cornerstone for organizational excellence and competitiveness. This project is dedicated to introducing a state-of-the-art Human Resource Management System (HRMS) designed to modernize conventional HR practices and streamline operations through technological innovation. The primary goal of this endeavor is to amalgamate and revamp diverse HR functions, encompassing employee records management, payroll processing, leave administration, and recruitment. Through the utilization of automation and digitalization, the project endeavors to alleviate administrative burdens, mitigate errors, and elevate overall HR efficacy. Key Words: MySQL ,Thymeleaf, Spring Boot, CI/CD , Data Security

Simulating Tree Root Water Uptake in the Frame of Sustainable Agriculture for Extreme Hyper-Arid Environments Using Modeling and Geophysical Techniques

In order to ensure sustainability in the agricultural sector and to meet global food needs, a particularly important challenge for our time is to investigate the possibility of increasing agricultural production in areas with extreme hyper-arid environments. Warming air temperatures and sandy soils significantly influence tree root water uptake (RWU) dynamics, making accurate estimation of RWU depth distribution and magnitude crucial for effective resource management, particularly in the context of precision irrigation within agroecosystems. This study employed two non-invasive techniques, namely HYDRUS 1D and electrical resistivity tomography (ERT), to simulate RWU under controlled experimental conditions and under an extreme hyper-arid environment. The results revealed that the highest RWU rates occurred during the morning (08:00–11:00). RWU activity predominantly concentrated in the upper soil profile (0–30 cm), and the soil water content in this area was notably lower compared to the deeper soil layers. With increasing temperature, there was a tendency for the RWU zone to shift to lower depths within the soil profile. The findings of this study could have important implications for farmers, providing valuable insights to implement irrigation water management strategies.

Market transformations in the development of land relations (experience of Canada and Scotland)

The development of land relations today remains one of the most important tasks of Ukraine. The priority directions for the development of such relations are the creation of conditions for the inclusion of land to the economic circulation and the regulation of land relations in combination with the increase of budget revenues of all levels from the use of land.
 In modern conditions, the land policy of developed countries creates mechanisms for regulating the land market with a multifaceted socio-economic content. This complex process is accompanied by the reform of land relations. Effective management of land resources ensures the development of the land market. Discussions regarding the creation of a full-fledged land market in Ukraine are still ongoing. Analysis of foreign experience will allow to transform land relations in Ukraine.
 Natural resources are diverse, but land is becoming an increasingly valuable resource for most countries. They are trying to achieve high rates of economic growth, which, in turn, are related to the development of land resources. It often turns out that such «development» is connected with the seizure of a million hectares of agricultural land by rich corporations or even foreign states. Deprivation of rights to land claims the status of the most famous and, at the same time, little-researched form of deprivation of rights not only in modern literature devoted to «land grabbing», but also in classical theory in general. The article discusses the factors contributing to «land grabbing». Global factors causing similar processes in Canada and Scotland, current consequences, future risks are identified. It was established that the factors of «grabbing of land» for agricultural purposes are of different nature. This is a significant price differentiation on the land market, and the increase in the investment attractiveness of agricultural land, the consequences of privatization and land consolidation. The leading role of the state in the investment process and the existence of a strategy for spreading the influence of the countries in question, which is implemented in parallel with the purchase of land, are shown.