Effective Resource Management Research Articles

The Role of Job Satisfaction in Mediating the Effect of Work Environment on Employee Performance

The increasingly intense competition in the business world encourages companies to optimize employee performance in order to achieve the desired goals. Product and service innovation, as well as effective human resource management, are important to improve the company's competitiveness. This study examines the effect of the work environment on employee performance through the role of job satisfaction variables as mediators. This research uses an associative quantitative approach, with a research sample of 100 respondents calculated using the Slovin formula. The research data is primary data obtained through interviews and distributing questionnaires. The data analysis technique used in this research is path analysis with SPSS, Sobel test, and VAF test. The results of this study indicate that the work environment has a positive and significant effect on employee performance, the work environment has a positive and significant effect on job satisfaction, job satisfaction has a positive and significant effect on employee performance, and job satisfaction significantly mediates the effect of the work environment on employee performance.

Symmetry-Aware Multi-Dimensional Attention Spiking Neural Network with Optimization Techniques for Accurate Workload and Resource Time Series Prediction in Cloud Computing Systems

Cloud computing offers scalable and adaptable resources on demand, and has emerged as an essential technology for contemporary enterprises. Nevertheless, it is still challenging work to efficiently handle cloud resources because of dynamic changes in load requirement. Existing forecasting approaches are unable to handle the intricate temporal symmetries and nonlinear patterns in cloud workload data, leading to degradation of prediction accuracy. In this manuscript, a Symmetry-Aware Multi-Dimensional Attention Spiking Neural Network with Optimization Techniques for Accurate Workload and Resource Time Series Prediction in Cloud Computing Systems (MASNN-WL-RTSP-CS) is proposed. Here, the input data from the Google cluster trace dataset were preprocessed using Multi Window Savitzky–Golay Filter (MWSGF) to remove noise while preserving important data patterns and maintaining structural symmetry in time series trends. Then, the Multi-Dimensional Attention Spiking Neural Network (MASNN) effectively models symmetric patterns in workload fluctuations to predict workload and resource time series. To enhance accuracy, the Secretary Bird Optimization Algorithm (SBOA) was utilized to optimize the MASNN parameters, ensuring accurate workload and resource time series predictions. Experimental results show that the MASNN-WL-RTSP-CS method achieves 35.66%, 32.73%, and 31.43% lower Root Mean Squared Logarithmic Error (RMSLE), 25.49%, 32.77%, and 28.93% lower Mean Square Error (MSE), and 24.54%, 23.65%, and 23.62% lower Mean Absolute Error (MAE) compared with other approaches, like ICNN-WL-RP-CS, PA-ENN-WLP-CS, and DCRNN-RUP-RP-CCE, respectively. These advances emphasize the utility of MASNN-WL-RTSP-CS in achieving more accurate workload and resource forecasts, thereby facilitating effective cloud resource management.

Hybrid Effects of Cooperative Hunting and Inner Fear on the Dynamics of a Fishery Model With Additional Food Supplement

ABSTRACTFish resources are indispensable natural resources for human beings. The 2020 report of FAO emphasized the critical need for sustainable development of fishery resources and effective resource management strategies. This study aims to investigate the exploitation of fishery resources from theoretical aspect. Firstly, a fishery prey–predator model involving cooperative hunting, fear effect, and additional food supplement is proposed. The impact of the triple effects on the population dynamics is deduced. Secondly, in order to meet human needs, rationally develop fishery resources, and maximize economic benefits, a weighted threshold feedback fishing strategy is adopted, and the complex dynamic behaviors induced by the weighted fishing strategies are discussed, including the existence and stability of the boundary periodic solution and the interior order‐ periodic solutions. Finally, computer simulations are presented step by step to illustrate the theoretical results. The results provide a theoretical reference for scientific planning on exploitation and sustainable development of fishery resources.

Synthesis of SiC─SiO2─Si Nanocomposites from Natural Resources and Waste for Corrosion Resistance and Circular Economy

AbstractSiC─SiO2─Si (SCSOS) nanocomposites have recently significantly improved the corrosion resistance of metals through different treatments. The most promising aspect of SCSOS nanostructures is their ability to reduce corrosion due to their chemical inertness behavior, including high thermal stability, unique optical and electronic characteristics, and good mechanical strength and hardness. This review provides a comprehensive overview of different SCSOS powders products from natural resources and waste. It illustrates each compound's synthesis mechanisms and optimization parameters, such as carbon thermal reduction, sol–gel processes, chemical vapor deposition, laser synthesis, and laser ablation. It is observed that the optimization parameters are crucial in controlling the final product type. SCSOS nanocomposites, whether used together or individually, can effectively protect metals from corrosion in various environments, including seawater. This protection can be achieved through several methods: electrochemical, etching, dip coating, or spin coating. Finally, the synthesis of SiC from marine sponge‐derived silicon presents an innovative and sustainable strategy aligned with the principles of the circular economy, offering effective management of aquatic resources while reducing waste and minimizing environmental impact.

Future propagation characteristics of meteorological drought to hydrological drought in the Yellow River basin

Future propagation characteristics of meteorological drought to hydrological drought in the Yellow River basin

Differences in the Impact of Extreme ENSO-IOD on Sea Surface Temperature (SST) Variability in Open and Semi-enclosed Water

Abstract The Makassar Strait and Bone Bay are regions with high potential for biodiversity and marine management, where sea surface temperature (SST) serves as a crucial parameter for various activities and as an indicator of water phenomena. SST is particularly influenced by the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). This study investigates the differences in SST variability influenced by seasonal cycles, IOD, and ENSO in the Makassar Strait (Open Waters) and Bone Bay (Semi-Enclosed Waters). Data analysis employs remote sensing-based anomaly and cross-correlation analysis methods along with spatial-temporal techniques for monthly time series data. The findings reveal distinct characteristics of SST variability between open and semi-enclosed waters during the extreme El Niño event in 2015 and the positive IOD event in mid-2019. Specifically, SST in semi-enclosed waters shows a low correlation with IOD and no correlation with ENSO. These results highlight the importance of understanding the differential impacts of ENSO and IOD on various water characteristics, which is crucial for formulating effective marine resource management policies.

Lignocellulose biosorbents: Unlocking the potential for sustainable environmental cleanup.

Lignocellulose biosorbents: Unlocking the potential for sustainable environmental cleanup.

Assessing Machine Learning Models for Precipitation Prediction in the Upper Indus Basin: A Comparative Analysis

Precipitation plays a critical role in the effective management of water resources and the maintenance of reservoir water levels. However, climate change has significantly altered precipitation patterns, leading to extreme hydrological events such as droughts and floods, which have profound socioeconomic and environmental impacts. This study focuses on predicting precipitation events in the Upper Indus Basin (UIB) using machine learning models. In this study three widely used machine learning algorithms Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) were employed to forecast precipitation events in the UIB. The dataset was divided into training (80%) and testing (20%) subsets for model evaluation. Among the algorithms tested, KNN demonstrated the best predictive performance, yielding a mean absolute error (MAE) of 2.662, a root means square error (RMSE) of 16.3, and an R² score of 0.879, with an overall accuracy of 83.16%. The results indicate that the KNN algorithm is the most effective machine-learning model for precipitation prediction in the UIB. The findings of this study contribute to improving early warning systems and facilitating efficient water resource management in the face of climate variability and extreme weather events.

Hydrological Modelling of Rainfall-Runoff Dynamics in the Padas River Basin: Insights for Water Resources Management

Water resources are highly dependent on climatic variations and accurately quantifying the impacts of climate change on surface water availability is crucial for effective water resources management. This study explores the estimation of rainfall-runoff dynamics within the Padas River basin, an essential aspect of water resources planning and management. Utilizing the MIKE 11 NAM model, the research delves into the intricate processes governing catchment runoff, focusing on calibration and validation against observed streamflow data. The study encompasses flow duration curve analysis to elucidate flow rate distributions and river flow recurrence interval analysis to assess the likelihood of different flow magnitudes occurring over varied timeframes. Results reveal significant year-to-year variations in streamflow, emphasizing the dynamic nature of the basin's hydrological regime. Peak streamflow levels during the study period reached approximately 934.82 m³s-1, while the lowest flow averaged around 43 m³s-1. The recurrence intervals associated with specific flow rates, such as 5418.3 m³s-1 for a 100-year interval, provide critical insights for flood risk assessment and infrastructure design. These findings underscore the importance of informed decision-making and adaptive management strategies to ensure resilience against potential flood events in the Padas River basin. Overall, this study contributes to a comprehensive understanding of hydrological processes and associated risks, laying a foundation for sustainable water resources management and resilient infrastructure development in the region.

Predicting Population Growth in Taraba State, Nigeria

Accurate forecasting of population is crucial for effective policy development, resource management, and sustainable growth. This research utilizes the Runge-Kutta-Fehlberg (RKF) technique to project the population increase in Taraba State, Nigeria. Conventional models, including the Malthusian and logistic growth approaches, provide basic insights but often do not adequately reflect the complexities of actual demographic behavior. The logistic model, which takes into account the limits of carrying capacity, presents a more plausible framework for forecasting population patterns. The rate of prediction and the carrying capacity were estimated using data through MATLAB analytics, yielding values of and . By applying the RKF method, this study improves the precision of numerical solutions to the logistic model, thereby minimizing errors in long-term forecasts. By utilizing census data from Nigeria for the years 1991 and 2006, alongside projected figures from the National Population Commission, Nigeria, from 2007 to 2022, the RKF method yields dependable estimates of population trends through 2050. By 2050, it is anticipated that Taraba State's population will approach 8 million individuals. The findings illustrate the RKF method’s capability to effectively capture demographic variations and enhance the accuracy of population forecasts. This study highlights the significance of sophisticated numerical approaches in demographic research and their potential to inform policy development and infrastructure planning in rapidly expanding areas like Taraba State.

Analysing the Balance of Human and Physical Resources in Outpatient Departments during the COVID-19 Pandemic

Abstract The article analyses studies on the impact of the COVID-19 pandemic on outpatient services in a large hospital, aiming to provide insights for resource management amidst disruptive events. The objectives include identifying challenges and proposing solutions to optimize service delivery and address spatial constraints using discrete-event simulation. Utilizing a case study approach, the research employs simulation as a key methodology to analyse outpatient service scenarios. Scenarios are generated by combining different probabilities of patient return to check-in with various team parameterizations. The researchers analysed historical data and key performance indicators from the simulation. The study focuses on a collaborative approach with the hospital team to ensure the relevance and applicability of proposed solutions. The research identifies bottlenecks induced by social distancing measures, particularly in patient reception and check-in areas. Uneven service distribution throughout the day leads to a misallocation of resources and reduction of available physical space. Telemedicine emerges as a significant response, effectively addressing both service optimization and physicians’ workload despite spatial constraints. Additionally, the study underscores the role of simulation in crisis decision-making for hospital operations management. Practical applications emanating from the study emphasize the need for healthcare institutions to adopt adaptable strategies and leverage simulation tools for effective resource management during disruptive events. Hospital administrators can draw insights to inform resource reallocation and workflow optimization, with a focus on negotiating flexible scheduling and exploring telemedicine to enhance service delivery.

From Nearly Zero Water Buildings to Urban Water Communities: The Need to Define Parameters to Support the New Paradigms

In the context of freshwater scarcity, effective water resource management is essential. This study explores techniques to optimise the use of local water sources and promote conservation, proposing a model to balance the supply and demand of non-potable water in urban environments. The model serves as an alternative or complement to public water supplies, particularly in drought-prone regions. Through a qualitative analysis of national and international regulations, specifications, and technical standards, this research identifies key trends in the use of alternative water sources and highlights knowledge gaps in urban water management, which are addressed through the proposed model. The study emphasises the transition from Nearly Zero Water Buildings (NZWBs) to Urban Water Communities (UWCs) as a sustainable and resilient solution, integrating decentralised water management approaches and resource recovery from wastewater. Additionally, a case study in a Portuguese urbanisation area illustrates the application of these principles and assesses their potential in real-world scenarios. The findings contribute to the development of comprehensive guidelines and public policies for sustainable urban water management, supporting the implementation of decentralised and integrated solutions that enhance resilience, water security, and resource efficiency.

“The Effectiveness of Integrating Spiritual Leadership and Skills Managerial in Improving Church Performance”

This study analyzes the effectiveness of integrating spiritual leadership and managerial skills in improving the performance of church organizations. Spiritual leadership integrates spiritual values with organizational goals, while managerial skills include the ability to plan, organize, execute, and control activities with a focus on efficiency and effectiveness in resource management. Using a literature review method and a descriptive qualitative approach, this study explores the principles of spiritual leadership rooted in Biblical values and modern management theories. The findings indicate that spiritual leadership, emphasizing role modeling, service, and a relationship with God, fosters harmony and intrinsic motivation within the organization. Meanwhile, managerial skills such as planning, organizing, executing, and controlling are essential for effective and efficient resource management. The study concludes that integrating spiritual leadership and managerial skills is key to creating an effective, ethical, and sustainable church amidst social, cultural, and economic changes.

Sustainable Digital Transformation: Leveraging HR Analytics for Efficient Workforce Management

The pursuit of sustainable digital transformation represents a strategic imperative in the contemporary business landscape. This research paper delves into the critical dimension of leveraging HR analytics for efficient workforce management for that both quantitative and qualitative analysis has done from the 100 respondents. As the field of HR analytics continues to evolve, this study investigates the ongoing challenges faced by organizations, even those with substantial HR analytics investments, in fully realizing its potential benefits (Wang et al., 2023). The transition to HRM 5.0 underscores a growing awareness among companies of the advantages conferred by technology adoption in human resources (Kiran et al., 2023). In today's world, responsible digital transformation is not just a business necessity but a societal imperative. It demands a delicate balance between technological advancements, environmental considerations, ethical responsibilities, and social consequences (Kiran et al., 2023). By adopting an interdisciplinary approach, this paper seeks to address these multifaceted complexities and harness the transformative power of digital technologies to foster inclusivity, equity, and sustainability. Furthermore, the digital transformation of HR practices, with a focus on technological innovation, has become paramount for organizations aiming to enhance their strategies and overall performance. This research contributes to the growing body of HR digitization literature by emphasizing the adoption of HR analytics, a potent tool for managing attrition, forecasting firm performance, and driving employee satisfaction (Pappas et al., 2023). In today's fiercely competitive corporate environment, effective human resource management is central to achieving corporate success (Bonilla-Chaves & Palos-Sánchez, 2023). This research highlights the importance of embracing HR analytics in the quest for efficient workforce management and sustainable digital transformation. By investigating these key dynamics, this study provides insights that can help organizations navigate the complicated terrain of digital transformation and HR analytics, ultimately driving sustainable success.

Integrating Otsu Thresholding and Random Forest for Land Use/Land Cover (LULC) Classification and Seasonal Analysis of Water and Snow/Ice

Accurate land use/land cover (LULC) classification and the detection of seasonal dynamics are crucial for effective environmental monitoring and resource management. To improve the precision and temporal resolution of regional LULC classification products, this study combined the Otsu threshold method and Random Forest algorithm to generate a 10 m-resolution land cover classification map for Wensu County based on Sentinel-2 imagery, with a particular focus on orchard categories, and investigated the seasonal dynamics of LULC between winter and summer. The results show that the overall accuracy (OA) of the water and snow/ice models was 85.50%, with a Kappa coefficient of 0.8088; for the vegetation model, the OA was 93.77%, with a Kappa coefficient of 0.8755. Feature importance analysis indicated that terrain features were key factors in improving classification performance. Seasonal dynamics analysis showed that the snow/ice coverage area in winter increased by 6379.18 square kilometers compared to that in summer, with 5252.85 km2 of bare land and 910.66 km2 of grassland being covered by snow/ice. Meteorological data analysis revealed that land cover changes caused by winter snowfall were primarily concentrated in areas where temperatures exceeded −8 °C, while land cover changes were smaller in areas with either low or high precipitation. These findings provide valuable data support for regional resource management and agricultural development.

Enhanced water quality prediction model using advanced hybridized resampling alternating tree-based and deep learning algorithms.

Water quality modeling in riverine systems is crucial for effective water resource management and pollution mitigation planning. However, the intricate interplay of anthropogenic activities with hydrological, climatic, and fluvial processes presents significant challenges in developing robust models for predicting water quality parameters. This study develops novel deep learning (DL) models, leveraging bidirectional-LSTM (Bi-LSTM) networks and advanced ensemble-based approaches using bootstrap aggregating (BA) combined with alternating model tree (BA_AMT), to predict key water quality parameters, including daily turbidity (TU) and dissolved oxygen (DO). The proposed hybrid models were applied to the Clackamas River, USA, and their performance was benchmarked against standalone AMT models. The dataset comprised daily records of water discharge (Q), gage height (GH), water temperature (Tw), specific conductance (SC), and pH. Model performance was evaluated under six input combination scenarios to determine optimized input configurations. Results demonstrated the superior predictive accuracy of Bi-LSTM for both TU (Root mean square error-RMSE = 0.172mg/L, Nash-Sutcliffe efficiency-NSE = 0.985, Percent of IAS-PBIAS, 0.01% and ratio of RMSE to the standard deviation of observation (RSR)-RSR = 0.11) and DO (RMSE = 1.37mg/L, NSE = 0.713, PBIAS, 1.90% and RSR = 0.53). Sensitivity analysis revealed that models incorporating five input parameters, including Q, GH, SC, and Tw for TU, and Tw, SC, GH, PH, and Q for DO, yielded the best predictive performance. Among these, Q and GH showed the strongest correlation with TU, while Tw, SC, and GH were most influential for DO prediction. While Bi-LSTM outperformed BA-AMT in overall accuracy, the BA-AMT model demonstrated superior capability in capturing extreme values. These findings underscore the importance of optimizing Bi-LSTM models using metaheuristic techniques to enhance predictive performance. The proposed modeling framework offers a scalable and generalizable approach for water quality forecasting and environmental management in freshwater systems, providing a valuable tool for decision-makers.

The Role of Leadership in Effective and Efficient Human Resource Management Decision Making

This study examines the role of leadership in effective and efficient human resource management (HRM) decision-making. It analyzes leadership styles, including transformational, adaptive, ethical, and AI-driven leadership models; it explores how leadership strategies influence key HRM functions such as recruitment, talent retention, workforce planning, and performance management. Furthermore, the research addresses the integration of data-driven decision-making and AI technologies in optimizing HRM strategies while maintaining ethical and human-centered leadership principles. This study adopts a Systematic Literature Review (SLR) approach, synthesizing recent scholarly works from reputable sources to evaluate the effectiveness of leadership in HRM decision-making. A qualitative analysis of peer-reviewed journals, books, and empirical studies was conducted to identify patterns, challenges, and best practices in leadership-driven HRM. The study also examines the interplay between technological advancements and ethical governance in shaping leadership effectiveness within HRM frameworks. The findings reveal that leadership is crucial in shaping HRM policies, ensuring efficiency, strategic workforce planning, and organizational agility. AI-powered HR analytics and automation enhance efficiency but require ethical oversight to maintain fairness and transparency. The study also highlights challenges in leadership adaptation, including resistance to digital transformation, concerns over AI ethics, and the balance between efficiency and employee well-being. This research provides valuable insights for HR leaders, policymakers, and organizational managers, emphasizing the need for leadership development programs that integrate AI literacy, ethical governance, and strategic HR analytics. Future research should empirically validate leadership effectiveness and its contextual applications across different industries and workforce structures.

Geochemical characteristics and environmental implications of heavy metals and fluoride of the hot spring waters in China's Tengchong Geothermal Field.

Tengchong County, Southwestern China, is a renowned volcanic region with abundant geothermal resources. This study systematically investigates the geochemical characteristics of hydrothermal waters in Tengchong, utilizing major and trace elements as well as hydrogen and oxygen isotopes. The primary objectives are to identify the potential sources of the hydrothermal waters and provide a detailed assessment of heavy metal pollution, with a particular focus on fluoride. The results of δD and δ18O values indicate that the hydrothermal waters primarily originate from atmospheric precipitation, with some samples (Dagunguo and Huangguajing) showing evidence of high-temperature water-rock interactions. The study reveals significantly high concentrations of F, Hg, and As in the geothermal waters, accompanied by heightened concentrations of other trace elements, including V, Cr, Cu, Zn, Cd, Sb, and Pb. Among these, F and Hg stand out as posing severe contamination risks, particularly at the Rehai and Xiangda sites where the highest water quality index (WQI) values were documented. The fluoride levels, in particular, far surpass safe drinking water thresholds, raising serious public health concerns. The study identifies key mechanisms for fluoride enrichment, including water-rock interactions, cation exchange, and high temperatures. Specifically, the dissolution of fluoride-bearing minerals like fluorite (CaF2), enhanced by elevated temperatures, significantly contributes to fluoride release. Additionally, the exchange of calcium (Ca2+) with sodium (Na+) promotes fluoride enrichment in the geothermal waters. The study highlights the need for effective monitoring and management of geothermal resources to mitigate the risks associated with trace element contamination.

The Influence of Company Quality in Product Development

This abstract discusses the impact of a company's management quality on product development, which is a key factor in business success in today's competitive era. This study analyzes the relationship between management quality and the effectiveness of new product development, as well as how it affects the overall performance of the company. High management qualities, which include visionary leadership, good strategic planning, and effective resource management, are proven to accelerate the innovation process and improve product quality. In addition, good management also plays a role in overcoming obstacles that arise during product development, such as technical constraints and dynamic market changes. This study uses a case study method on several companies that have successfully developed innovative products, and finds that companies with quality management tend to have more efficient, more adaptive product development cycles to market changes, and able to produce products that are more in line with consumer needs. The results of this study emphasize the importance of the role of quality management in supporting the success of product development, which in turn increases the company's competitiveness and profitability. These findings can be a reference for companies in strengthening their management to drive product innovation and business growth.

Technology & Digitization for Sustainable Tourism Growth: A Review of Indonesia & New Zealand

Integrating information and communication technologies (ICT) significantly enhances sustainable tourism practices' efficiency by optimising operations, improving visitor experiences, and promoting eco-friendly initiatives. This technological transformation, often called "e-tourism," facilitates better resource management and supports aligning tourism activities with sustainability goals. Technology is essential to the advancement of environmentally friendly tourism activities. More sustainable operations result from effective resource management, waste reduction, and carbon footprint monitoring, which are made possible by these technologies. Technology also impacts sustainable tourism by increasing operational effectiveness, cutting expenses, and drawing eco-aware tourists. It makes better tourist management possible by guaranteeing a satisfying experience while reducing the adverse effects on nearby populations and ecosystems. This study investigates how technology balances sustainability, economic growth, and enhanced visitor experiences in the post-pandemic tourism landscape. It compares and contrasts New Zealand's regenerative tourism model with Indonesia's conservation-focused approach, highlighting the impact of technology on these models. The study employs a comparative case study approach, examining the post-pandemic tourism strategies of New Zealand and Indonesia. It analyses government initiatives, private sector innovations, and community-based programs related to technology integration in tourism management. Specific examples include the Borobudur Temple in Indonesia and various initiatives in New Zealand. The study reveals that technology plays a crucial role in enhancing tourism sustainability. New Zealand utilises technology to support its regenerative tourism model, focusing on environmental restoration and cultural preservation. Indonesia leverages technology for conservation efforts, including visitor management and protection of cultural sites. Both countries demonstrate the potential of technology to improve accessibility and operational efficiency.