Effective Resource Management Research Articles

Estimating the Evapotranspiration using Hybrid Artificial Intelligence Techniques in Arid and Semi-Arid Regions of India

Evapotranspiration (ET) is a crucial component of the hydrological cycle, particularly in arid and semi-arid regions where water resources are limited. Accurate estimation of ET is essential for effective water resource management and agricultural planning. This review explores the application of hybrid artificial intelligence (AI) techniques for estimating ET in the arid and semi-arid regions of India. By combining traditional AI methods, such as artificial neural networks (ANNs) and support vector machines (SVMs), with advanced optimization algorithms like genetic algorithms (GA), particle swarm optimization (PSO), and evolutionary strategies, researchers have enhanced the accuracy and reliability of ET predictions. This paper summarizes the state-of-the-art hybrid AI models, their methodologies, and the challenges and opportunities they present for managing water resources in India’s arid and semi arid zones.

An Assessment of Business Management Practices’ Effect on the Performance of Small and Medium Enterprises in Lusaka Central Business District

This study purposed to assess business management practices’ effect on the performance of small and medium enterprises (SMEs) in Lusaka's Central Business District (CBD). The study was underpinned by three research objectives: to examine current business management practices adopted by SMEs, to determine the effect of these practices on SMEs’ performance, and to explore strategies for enhancing business practices. A mixed-method research approach guided the study. A mixed-method research approach guided the study, which targeted a population of 37,000 SMEs in the CBD, with a sample size of 100 participants, which comprised 83 business owners/managers and 17 officials from the Ministry of Small and Medium Enterprises Development. Data was collected through questionnaires and interviews and analyzed using the Statistical Package for Social Sciences (SPSS). 100 questionnaires were distributed to SME owners/managers and officials from MSMED while interviews were conducted with 10 selected SMEs participating in the questionnaire administration. Out of the 100 questionnaires circulated to SMEs, 80 were returned representing an 80% response rate. The study employed purposive and random sampling to strengthen the findings' validity, reliability, and generalizability. The findings revealed that financial management, human resource management, marketing strategies, and strategic planning are key management practices employed by SMEs in CBD. The study found that business management practices significantly and positively affect business performance, leading to improved productivity, profitability, competitiveness, and overall success. However, challenges such as lack of management expertise, high employee turnover, and ineffective performance measurement hinder the adoption and effectiveness of these practices. The study concludes that SMEs can enhance their business management practices and improve performance by adopting effective human resource management, financial management, and strategic marketing practices. Key recommendations include SME owners/managers identifying and implementing missing management practices, embracing modern technologies, and employing SME support mechanisms to help managers and owners acquire the necessary skills. Additionally, the study recommends that SMEs should regularly assess their performance against management practices adopted to ascertain their effect on performance.

Talent Management Implementation Strategy to Increase Retention in Managerial Positions: Case Study in Banjarbaru City

Talent management plays an important role in improving managerial job retention, especially in the public sector. This study aims to explore talent management implementation strategies that can improve employee loyalty in Banjarbaru City. Using a descriptive qualitative approach, data were collected through in-depth interviews and document analysis. The results of the study indicate that competency development, performance-based rewards, and work-life balance policies are the main factors in reducing turnover. As well as providing practical recommendations for policy makers. This study also highlights the need for transparency in promotion and employee participation in policy making. The theoretical and practical implications of this study provide insights for more effective human resource management in the government sector. Further studies are recommended to expand the scope and use quantitative methods to increase the validity of the findings.

Future Projections of Clouds and Precipitation Patterns in South Asia: Insights from CMIP6 Multi-Model Ensemble Under SSP5 Scenarios

Projecting future changes in monsoon rainfall is crucial for effective water resource management, food security, and livestock sustainability in South Asia. This study assesses precipitation, total cloud cover (categorized by cloud top pressure), and outgoing longwave radiation (OLR) across the region using Coupled Model Intercomparison Project Phase 6 (CMIP6) data. A multi-model ensemble (MME) approach is employed to analyze future projections under the Shared Socio-Economic Pathway (SSP5-8.5) scenario, which assumes radiative forcing will reach 8.5 W/m2 by 2100. The MME projects a ~1.5 mm/day increase in total rainfall during 2081–2100. Convective and stratiform precipitation are expected to expand spatially, with convective rainfall increasing from 3 mm/day in historical simulations to 3.302 mm/day in the far future. Stratiform precipitation also shows an increase from 0.822 mm/day to 0.962 mm/day over the same period. A notable decrease in OLR (~60 W/m2 along the Western Ghats) and an increase in high cloud cover suggest intensified monsoon rainfall. The pattern correlation coefficient (PCC) reveals reduced OLR in future scenarios (PCC ~0.77 vs. ~0.81 historically), likely due to cloud feedback mechanisms. These results highlight enhanced monsoonal activity under warming scenarios, with implications for regional climate adaptation.

Monitoring Eco-Efficiency and Its Convergence Towards Sustainability in the European Rubber and Plastics Industry Through Circular Economy Transition

Eco-efficiency is crucial for the European rubber and plastics industry to minimize production costs through effective resource management (e.g., energy management) and reduce environmental impacts like greenhouse gases (GHGs) emissions. Circular economy (CE) solutions can support the industry’s competitiveness while aligning with sustainability goals and regulatory requirements. In the present research, we employ a hybrid window data envelopment analysis (WDEA) methodology to measure panel data eco-efficiency via the application of the moving average principle. The examination of 27 European countries as decision-making units (DMUs), in the period 2014–2022, led to the conclusion that the average eco-efficiency is 70.33%, showing that most of the DMUs can ameliorate their performance regarding pollution control. The highest eco-efficiency in 2014 can be monitored in Ireland, Switzerland, Norway, and Poland, but in 2022, only Ireland and Switzerland kept their positions, whereas Norway dropped to the 16th position and Poland plummeted to the 24th hierarchical position. Geographical disparities can be spotted, as Northern and Western Europe have greater eco-efficiency than Eastern and Southern Europe. At a second level of analysis, the convergence between the 27 European countries in the period under consideration is examined using the log t regression test and club clustering. The analysis leads to three final clubs where conditional convergence dominates.

ASSESSING RAINFALL PATTERNS AND TREND DISTRIBUTION IN THE KELANTAN RIVER BASIN, MALAYSIA AND KLANG RIVER BASIN, MALAYSIA

This study examined rainfall patterns and trends in the Kelantan and Klang River Basin in Malaysia over a ten-year period from 2010 to 2020. The analysis revealed that the Kelantan River Basin experienced an upward trend in rainfall during the Southwest Monsoon season, indicating a gradual increase in precipitation levels over the years. On the other hand, the Klang River Basin showed a relatively stable rainfall pattern without a significant trend observed. The study employed an independent t-test to compare the mean yearly rainfall between the two basins, with the findings showing no significant difference in overall precipitation levels (t(22) = -0.8, p = 0.07). However, the timing and duration of rainy seasons varied between the two basins. The Kelantan River Basin experienced a rainfall pattern that is aligned with the regular monsoon season. In contrast, the Klang River Basin displayed a notable gap in rainfall during a later month in the monsoon season. Moreover, the analysis considered the spatial variability of precipitation patterns within each basin. Specific stations in the Kelantan River Basin showed a positive trend in rainfall during the monsoon seasons (March, p = 0.013), whereas stations along the Klang River exhibited a declining trend (August, p = 0.119). These findings emphasize the influence of geographical locations, local climate conditions, and topographical features on rainfall distribution within the same country. In conclusion, this research highlights the need for further investigation into the factors driving these patterns, emphasizing the importance of effective water resource management and climate adaptation strategies in these basins to mitigate the impact of climate change.

Analyzing Teacher Performance in the Digital Era: The Interplay of Cyberloafing Behavior, Digital Competence, and Work Motivation (Case Study: 3 Muhammadiyah Vocational Schools in Cianjur)

This study explores the influence of cyberloafing behavior, digital competence, and work motivation on teacher performance at SMK Muhammadiyah Cianjur. Using a quantitative approach, this study involved 100 teachers as samples and analyzed the data using the Partial Least Squares (PLS) method. The results showed that digital competency has a significant positive effect on work motivation and teacher performance, while cyberloafing has a negative impact on performance. Work motivation was found to mediate the relationship between digital competencies and teacher performance. This research highlights the importance of digital competency training to support technology-based learning, while reducing cyberloafing behavior that can reduce productivity. Institutional support and clear policies are important factors in motivating teachers to achieve optimal performance. The findings offer practical and theoretical insights to improve education quality through more effective human resource management in the digital era.

Mapping irrigation requirements of major crops in the coastal agro-climatic zone of Odisha using CROPWAT 8.0 and geospatial techniques

The demand for water in agriculture sector is increasing due to the need for higher crop production to feed the growing population. The east and south-eastern coastal plain agro-climatic zone (ACZ) of Odisha is one of the fertile zones that demands sustainable water resource management in agriculture. A study was carried out during 2023 at College of Agricultural Engineering and Technology (Odisha University of Agriculture and Technology), Bhubaneswar, Odisha to determine the crop water requirement (CWR), irrigation requirement (IR) and irrigation scheduling of major crops grown in different districts of east and south-eastern coastal plain ACZ of Odisha using CROPWAT model and GIS software. Based on 10-years of the meteorological data from 7 districts (Kendrapara, Khordha, Jagatsinghpur, Puri, Nayagarh, Cuttack and part of Ganjam) and the soil condition of the coastal region, daily evapotranspiration was found to range from 3.73–4.86 mm/day and the effective rainfall ranged from 778.6–986 mm. The crop water requirement for kharif rice was the greatest at 850 mm, followed by maize at approximately 400 mm. During kharif, no irrigation was required for pearl millet or finger millet in this region. A map of crop water requirements and irrigation needs in the zone would be very useful for sustainable irrigation planning for different crops in seven different districts of the east and south-eastern coastal plain ACZ of Odisha. These findings offer valuable insights for optimizing water allocation in the agricultural sector which will contribute to more effective water resource management and agricultural planning in the region.

Dynamic reconfiguration of application and communication in Industry 4.0 with Digital Twin for enhancing flexibility

Abstract Industry 4.0 emphasizes the flexibility of production systems, requiring dynamic adaptation to varied product requirements. This paper presents a framework for managing production systems by integrating application and communication systems. Central to it is the use of Asset Administration Shell (AAS) to create Digital Twins of assets, enabling continuous adaptation and optimization of production processes through effective resource management. The paper discusses a use case involving negotiation between communication and application systems, proposing a model-based solution leveraging AAS. It details the implementation of the proposed model with AAS components, providing an analysis of the use case and an automation solution. Validation results show how the AAS can improve the performance and dependability of wireless communication systems.

PENGARUH FAKTOR PENGELOLAAN SUMBER DAYA MANUSIA (SDM) TERHADAP KUALITAS LAYANAN REHABILITASI DI KLINIK BNNP JAWA TIMUR

Rehabilitation service quality is an important consideration for drug addicts in accessing rehabilitation services. Effective human resources management is necessary to improve the personal performance of staff, which impacts healthcare service quality. The objective of this research is to understand the influence of human resources management on the quality of rehabilitation services at the East Java BNNP Clinic. This study used a cross-sectional method with total sampling of 108 clients. Data collection was conducted using Google Forms completed by respondents and analyzed using Path Analysis with a significance level (α) of 0.05. Competence has an influence on officer performance by 0.395. Discipline has an influence on officer performance by 0.249. Cooperation has an influence on officer performance by 0.230. Competence has an influence on rehabilitation service quality by 0.219. Discipline has an influence on rehabilitation service quality by 0.217. Cooperation has an influence on rehabilitation service quality by 0.165. Officer performance has an influence on rehabilitation service quality by 0.331. There is a significant influence of competence on rehabilitation service quality through officer performance by 0.130. There is no significant influence of discipline on rehabilitation service quality through officer performance. There is a significant influence of cooperation on rehabilitation service quality through officer performance by 0.076. To improve the quality of rehabilitation services, it is recommended to enhance competence, discipline, cooperation, and officer performance.

Management and Improvement of Non-Oil Revenues in South Sudan

Purpose: The study looked at the management and improvement of non-oil revenue collection band mobilization on public financial management, as well as the obstacles that non-oil revenue collection faces in South Sudan. Non-oil revenues face several challenges, ranging from ineffective service delivery due to non-oil revenue collection. The key goal was to determine the management of non-oil revenue collection, develop public financial control and examine mechanisms for dealing with non-oil revenue collection issues, and ensure effective and consistent management of non-oil resources under South Sudan government policies and objectives. The study aimed at eliminating or minimizing to a bare minimum, the issues associated with non-oil revenue generation, non-oil revenue administration, tax base penetration, and difficulties in recognizing non-oil revenue mobilization. Methodology: The study used random sampling technique to determine a sample size of 150 respondents. Qualitative data was obtained from interviews using interview guides which were coded into themes and interpreted. Further data was analyzed to give it a broader and more meaningful picture of the sample. The study looked at the difficulties that come with collecting non-oil revenue. Findings: It was revealed that the National revenue authority does not keep a complete or accurate record of all taxable events or levies in its jurisdiction. There was no mechanism in place for keeping track of invoices and payments. Data on utilities, equipment, levies, and other items were done manually, making it vulnerable to manipulation and hence a major non-oil revenue loss. Unique Contribution to Theory, Policy and Practice: The study recommends digitalization and computerized systems of all forms of non – oil revenues to improve non-oil revenue leakage along with property rate administration. The study further recommended for a non-oil revenue database method for capturing non-oil revenue, as well as a computerized non-oil revenue system to improve revenue capture and mitigate leakages. In addition, policymakers should enact policies that support non-oil revenue management to maintain improved government financial efficiency in South Sudan.

Optimal rainwater harvesting locations for arid and semi-arid regions by using MCDM-based GIS techniques.

Optimal rainwater harvesting locations for arid and semi-arid regions by using MCDM-based GIS techniques.

Assessment of pollution in Alibeykoy Dam Lake (Istanbul, Türkiye) and its influent streams: Phytoplankton composition and heavy metal accumulation.

In this study, the pollution levels of Alibeykoy Dam Lake were assessed by examining phytoplankton distribution, physicochemical parameters, nutrient content, and heavy metal concentrations. Alibeykoy Dam is a critical drinking water source for the Istanbul metropolitan area. Water samples were collected from the lake and six influent streams (Cebeci, Pirincci, Sidan, Bolluca, Gulgen, and Kocaman) in January, February, May, and September of 2021. A total of 36 taxa from seven divisions were identified, including Bacillariophyta (14), Charophyta (2), Chlorophyta (9), Cryptophyta (1), Cyanobacteria (3), Euglenozoa (4), and Miozoa (3). Diatoms were found to be the dominant group in terms of species richness and abundance. The dominant species recorded in each site were as follows: Microcystis aeruginosa in Alibeykoy Dam and Sidan Creek, Cyclotella ocellata in Cebeci and Gulgen Creeks, Scenedesmus sp. in Pirincci Creek, Navicula cryptocephala in Bolluca Creek, and Sphaerocystis planctonica in Kocaman Creek. Mesotrophic and eutrophic phytoplankton species suggested that the lake is nearing eutrophic conditions. This conclusion was further supported by high concentrations of heavy metals and nutrients detected in the water samples. This research is significant because it provides a comprehensive understanding of the ecological status of Alibeykoy Dam Lake, a critical resource for drinking water in Istanbul. The study highlights potential risks associated with eutrophication and heavy metal accumulation by identifying pollution levels and dominant species. These findings are vital for implementing effective water resource management strategies, ensuring the lake's sustainability, and protecting public health. PRACTITIONER POINTS: The pollution rate of Alibeykoy Dam Lake was determined. The presence of mesotrophic and eutrophic species of phytoplankton indicated the lake's trophic structure. The measurements were done to estimate the lake's heavy metal and nutrient contents.

MANAJEMEN SDM DALAM PENDIDIKAN : UPAYA PENGELOLAAN SUMBER DAYA MANUSIA DI SEKOLAH

Human resources (HR) are the most valuable assets in an organization, including in the education sector. Effective Human Resource Management can support the achievement of educational goals, both short and long term goals. Management within the school environment must involve important elements, namely educators and educational staff so that it can be ensured that there are competent, motivated and in line with the vision and mission of the institution. By using a literature review method, this journal aims to explore the concepts and practices of Human Resource Management in the school environment, as well as identifying Human Resource management efforts that can help educational institutions achieve their goals. The discussion will cover key aspects of Human Resources Management such as developing the competence of teaching staff, establishing a conducive work culture, implementing technology in managing human resources, and improving the welfare of Human Resources. It is hoped that this journal can provide insight and recommendations for educational practitioners in managing Human Resources in the school environment strategically.

Evaluation of tourism ecological security and its obstacles in semi-arid river valley urban: a case study of Lanzhou, China

Assessing tourism ecological security is essential for balancing development with environmental sustainability, thereby guiding effective resource management and policy formulation within the tourism industry. This study evaluates the ecological security of Lanzhou’s tourism sector, a city rich in historical and natural resources yet facing unique ecological challenges. Our objectives were to assess the current status of tourism ecological security in Lanzhou and provide actionable policy recommendations. We employed a comprehensive evaluation framework that integrates both qualitative and quantitative methods, including the DPSIR model, mean square deviation, and entropy methods for weight calculation, as well as TOPSIS for overall assessment and coupled coordination degree and obstacle degree models for subsystem analysis. The results indicate an upward trend in Lanzhou’s tourism ecological security from 2009 to 2021, peaking in 2019 before experiencing a pandemic-induced decline in 2020–2021. While subsystem coupling coordination generally improved, the pressure and state subsystems exhibited instability. These findings offer valuable insights for similar regions and underscore the necessity for holistic management strategies. We recommend enhancing ecological monitoring, promoting sustainable tourism practices, and enhancing local community involvement in tourism management. These measures are crucial to improving Lanzhou’s tourism ecological security and could serve as a model for other cities with similar development trajectories. Future research should focus on refining ecological security evaluation models and exploring the broader socio-economic impacts of ecological sustainability in tourism.

An Integrated Paradigm for Advanced Irrigation Systems Leveraging Internet of Things (IoT)

As the demand for precision in agriculture and effective sustainable resource management increases, there has been a lot of pressure to have more accurate and efficient soil moisture data-transmission systems. This paper includes the discussion on how machine learning (ML) and deep learning (DL) techniques can be improvise to further accuracy and performance in IoT-based smart irrigation systems. The system is based on the perception of soil moisture using IoT sensors collecting real-time environmental data in fields, such as soil moisture, temperature, humidity, and sunlight, to be transmitted to farmers or end-users using ThingSpeak and Thinger.io platforms for analysis, storage, and visualization. It enables real-time decisions and remote agricultural systems control using web page or mobile applications. The WSNs(Wireless sensor network) helps to automate irrigation and water management in agricultural sites. The developed SIS(Smart Irrigation Systems) is based on the sensor network that carries real-time information relating to moisture content in the soil, temperature, and humidity levels as very critical determinants of the proper irrigation schedule. These are analyzed every 15 minutes at the edge server. The system uses deep learning models to predict when the soil moisture falls below a threshold and automatically activates water pumps or sprinklers, which reduces human intervention and optimizes water usage in agriculture. An important part of research forms machine learning algorithms to improvise the performance. The recent advancements relies on several models, among them KNN (K-Nearest neighbors) and TimeGPT models [TimeGPT is a time-series forecasting model that utilizes the power of GPT (Generative Pre-trained Transformer) architecture to predict future values in a sequence.], in the prediction of soil moisture while achieving optimal irrigation schedules from previous available data and weather forecasting. The comparative analyses included an accuracy rate of 97 Parsant to 98 Parsant in KNN thus describing the high level of accuracy that the system can attain regarding the soil conditions and water requirements. This research provides the possibility of embedding IoT into machine learning that may form a new age smart agriculture system that, apart from irrigation automation, enhances decision-making in farmer practices. Adoption of such systems could contribute immensely to the sustainability of such agricultural practices mainly due to reduced water wastage, decrease operation costs, and improved crop yield. Integrating real-time environmental data and predictive analytics farmlands can be optimized and maintained even the land has water scarcity or difficult to receive rainfall or other climatic conditions.

The Role of Cultural Festivals in the Sustainability of Micro and Small Enterprises

Business continuity refers to a company's ability to sustain growth and development in a competitive market while ensuring long-term sustainability and profitability. Micro and Small Enterprises (MSMEs) play a crucial role in the Indonesian economy, contributing 97% to the workforce and 60.4% to economic activity. This study analyzes the influence of Human Resources (HR), materials, and capital on MSME business sustainability, with festivals as a moderating variable, in Serang Beach, Blitar. Despite their significance, MSMEs face challenges in maintaining business continuity. This quantitative study used primary data from 52 MSME actors, analyzed using SmartPLS. The results showed that materials and capital positively affected business sustainability, while HR had no significant impact. Festivals strengthened the relationship between materials and capital and business sustainability but did not moderate the effect of HR. These findings provide new insights into the role of cultural festivals in enhancing MSME sustainability and highlight the importance of effective resource management in achieving a sustainable competitive advantage.

Enhanced Electricity Forecasting for Smart Buildings Using a TCN‐Bi‐LSTM Deep Learning Model

ABSTRACTIntegration of sensor technology and advanced software empowers consumers to manage energy usage proactively. This proactive approach yields positive impacts at both micro and macro levels, benefiting individuals and contributing to broader environmental conservation efforts. By leveraging predictive models, consumers can make informed decisions that serve their interests and promote a greener and more sustainable future for all. Thus, energy consumption (EC) prediction is crucial for effective resource management. In this study, we propose an innovative deep‐learning approach to predict EC, focusing specifically on smart buildings. Our model utilises a hybrid deep learning architecture to effectively capture low and high information patterns present in multivariate time series data of various sensors deployed in smart buildings and numerous influencing factors. To address the nonlinear and dynamic nature of this data, our model combines a deep neural network (DNN) with a deep learning sequential model (DLS). Specifically, temporal convolutional networks (TCN) within the DNN family are employed to extract various trends from the data, while the DLS model, which consists of Bi‐directional Long Short‐term Memory Networks (Bi‐LSTM), is employed to learn and capture these trends effectively. Consequently, we present a hybrid deep learning framework that leverages for learning multivariate time series data related to EC with shared feature representation. To validate our approach, we extensively evaluate our model using a dataset from an office building in Berkeley, California. Experimental results demonstrate that our model achieves satisfactory accuracy in EC prediction. For the 7‐h horizon and on multivariate TS data, an R2 of 0.97 is realised for the proposed model. This is confirmed by the 1.65% improvement in transiting from univariate to multivariate data, which supports using multiple modalities.

Development of a methodology for digital modeling of community land use and land tenure

Development of a methodology for digital modeling of community land use and land tenure

The Influence of Training, Social Capital, and Commitment on Employee Performance (A Study at the Fire and Rescue Department of Surabaya City)

This study examines the influence of training, social capital, and commitment on employee performance at the Fire and Rescue Department of Surabaya City. Effective human resource management is crucial for developing superior and high-quality human resources, enabling employees to work reliably and professionally. Training is a vital investment that provides long-term benefits to both organizations and employees. Social capital is considered a valuable asset because employee quality and competence directly affect organizational productivity and success. Organizational commitment serves as a key indicator of employee performance. This study aims to analyze the partial and simultaneous effects of training, social capital, and commitment on employee performance. A quantitative explanatory research design was employed, utilizing questionnaires and interviews. Path analysis was used for data analysis. The results indicate that training does not have a significant effect on employee performance, whereas social capital and commitment significantly influence performance. Additionally, training, social capital, and commitment collectively have a significant impact on employee performance. This finding highlights the importance of continuous training and fostering social capital and commitment among employees to enhance their performance.