Tool For Resource Management Research Articles

Model Permintaan Pekerjaan - Sumber Daya Untuk Kesejahteraan Dan Produktivitas Karyawan

Employee well-being is an important factor for individuals and to ensure company sustainability. The research objective is to develop a Job Demand-Resources (JD-R) model that can predict employee well-being and productivity at PT Fengtay Indonesia Enterprise. Explanatory survey of 250 randomly selected non-manager employees of PT Fengtay Indonesia Enterprise. Inferential analysis using SEM with SPSS25 tools. The results of the study show that job demands, such as high task demands and tight time pressure, have a negative and significant relationship with employee welfare, while job resources, such as social support from colleagues and superiors, have a positive and significant effect on employee well-being. ultimately increase productivity. The proposed JD-R model can be a useful tool for human resource management in developing effective strategies to improve employee welfare and individual productivity. The JD-R model helps managers to improve employee well-being and productivity. Management can ensure support to employees thereby increasing their well-being and productivity.
 Keywords: Job demand, Job Resources, Productivity, well-being, textile manufacturing

Applying Crew Resource Management tools in Emergency Response Driving and patient transport—Finding consensus through a modified Delphi study

BackgroundEmergency Response Driving (ERD) comprises a significant risk to safety in Emergency Medical Services (EMS). Crew Resource Management (CRM) tools play a major role in securing actions in high-risk procedures. The aim of this study was to find consensus on the important factors to consider when applying CRM tools in ERD and patient transport. MethodsERD experts (n = 50) were recruited for a modified three-round Delphi study. Round 1 was based on previous research. The experts evaluated the items as important, neutral, or not important. The predetermined level of consensus was set at ≥ 80%. Answers given to the open-ended questions were analyzed using inductive content analysis. ResultsPredetermined consensus was reached on 64 of 86 presented items (74.4 %). The mean values of items reaching consensus varied between 3.81 and 4.86 on a five-point Likert scale. The items where consensus was reached were rated as “important” on a trichotomized scale. ConclusionMultiple important factors to consider when applying CRM tools to ERD and patient transport were highlighted. This study provides valuable information to consider regarding EMS safety improvements. Further scientific research is needed to develop comprehensive recommendations.

Proposed Project Management Development for AMA Group

The research aims to improve AMA Group’s project management to address project delays and identify the root causes. It uses PMBOK as a problem-solving approach, utilizing expert judgment and historical data to identify root causes and assess project management aspects. The root cause of project delays is related to project scope, schedule, and resource management. Work Breakdown Structure (WBS) and Critical Path Method (CPM) are recommended tools for project scope management, scheduling, and resource management. These techniques enable effective change control, comprehensive scope planning, realistic schedule planning, streamlining workflow processes, addressing labour shortages, and regularly reviewing and improving project structures and processes. By integrating these tools, AMA Group can mitigate potential delays and enhance project efficiency.

The prevalence of horizontal violence with different cultural dimensions

Purpose The study aims to look at the link between horizontal violence (HV) and organizational culture aspects. Bullying and hostility including intergroup conflict are referred to as HV. HV is a significant issue that is encountered in various professions. The different cultural typologies of group, developmental, hierarchical and rational culture have been addressed in this study. Additionally, it is identified that the prevalence of HV in organizations with different cultural dimensions.Design/methodology/approach Using a non-probability multistage sampling strategy, a quantitative method was used and questionnaires were circulated to collect data from the information technology sector. The data were analyzed using multiple regression analysis.Findings The findings demonstrated that HV has a positive and substantial association with the group and developmental culture, whereas HV has a negative link with hierarchical and rational culture.Research limitations/implications These results provide a valuable tool for human resource managers and policymakers in promoting a healthy work environment and employee interpersonal collaboration, which will improve the organization’s overall performance.Originality/value This study is a novel work exploring the HV among employees in technological firms, and also combining the concepts of HV and organizational culture and also assists future researchers in many folds.

The Influence of Performance Appraisal Justice on Employee Job Performance: A Dual Path Model

Performance appraisal, as a powerful tool for human resource management, has become an important driving force for organizations to achieve sustainability. However, only when employees believe that a performance appraisal is fair can an organization’s performance appraisal truly work and have a positive impact on employees. Hence, based on social exchange theory and social identity theory, we theorized and validated a dual path model to explore the influence of performance appraisal justice on employee job performance through work engagement and organizational identification. Survey data from 404 Chinese employees and their direct supervisors in high-tech enterprises were analyzed using multiple regression and structural equation modeling. Results indicate that performance appraisal justice of employees has a positive impact on their work engagement, organizational identification, and job performance. In addition, both work engagement and organizational identification of employees partly mediated the relationship between performance appraisal justice and their job performance. Our conclusions mean that when employees perceive justice in organizational performance appraisals, they will actively engage in their daily work and truly identify with their organizations, thus improving job performance, which is conducive to the sustainable development of organizations. The limitations and implications for future research and practice are also discussed.

Adaptation of corporate management and human resource management in non–profit organizations

The socio–economic changes that societies have been experiencing in recent decades, caused by structural factors or specific circumstances, cause the emergence of problems, the solution of which the public sector, due to various circumstances, cannot fully provide. In these conditions, the activities of non–profit organizations — organizations of the third sector — require increasing efficiency and searching for the most effective mechanisms to ensure sustainable development and the achievement of social goals outlined in the mission of the organizations.Practice shows that there are serious differences in approaches to management in the corporate sector and the non–profit sphere. However, the methods of strategic management and human resource management characteristic of corporations can be adapted and successfully applied in the management of non–profit organizations. The paper concludes that in general, in order for these public organizations to be more effective in efforts to improve their management processes, they should focus on strategic management and human resource management tools.

ACCOUNTING INFORMATION SYSTEMS IN THE MANAGERIAL PRACTICE OF ENTERPRISE MANAGEMENT

Introduction. Modern business activity of any enterprise requires the use of financial resources management tools. Financial activity of enterprises is a key element that ensures the efficiency and success of modern business. However, in order to ensure effective management of finances and organization of their high-quality distribution, placement and use, it is necessary to form a rational procedure of the accounting process, within the framework of which the company's accounting is carried out. Methods. General scientific and special methods were used in the work in order to achieve the set goal: dialectical method of scientific knowledge – to consider the essence of accounting; synthesis – to clarify the relationships between business entities in the process of accounting and its impact on management processes; graphic – for visual display of the obtained results; abstract and logical method – for forming research conclusions and proposals. Results. The role and significance of accounting in the enterprise management information system as a specific management function is substantiated. It has been established that in modern business conditions, the role of accounting as an information system is not only to record information in reporting on events that took place in past reporting periods, but primarily to help and facilitate the management system of the enterprise to determine the current and strategic goals of the activity and to model future ones management decisions. It is proven that the use of modern information systems allows solving the problem of integration of management and financial accounting with the management system. Discussion. Further scientific research of the problem should be directed to an in-depth study of the application of digital computer and telecommunication technology, which is based on the use of artificial intelligence for enterprise management. Keywords: accounting, information support, management, information base, information system, digitalization, information management technologies.

Human Resources Development Performance Appraisal in Nigerian Universities

The study investigated human resources development performance appraisal in Nigerian Universities. Adopting a descriptive research design, the target population was made up of both teaching and non-teaching staff of Rivers State University, Port Harcourt, and Ignatius Ajuru University of Education, Port Harcourt totaling 4,200 (four thousand two hundred). This study adopted the Taro Yamane technique to arrive at a sample size of 382. A self-structured questionnaire tagged, Performance Appraisal as a Tool for Human Resources Management (PATHRD) with a modified 4-point Likert scale ranging from1-4 Very High Extent = (VHE), High Extent = (HE), Low Extent = (LE), Very Low Extent = (VLE) was used to collect data. 382 copies of the questionnaire were administered to respondents in the stated institutions out of which 350 representing 96% were duly completed and returned which was used for the analyses. The research questions were answered using mean and standard deviation at the 2.5 mean criterion while the hypotheses were tested using regression statistical tool at .05 significance level. The study found that the consciousness of a time for appraisal was major bait for staff to be committed to duty and concluded that a reward system, as tailored from information obtained from staff performance appraisal be used to reward the staff according to performance. The study among others recommended that feedback obtained from appraisal exercises be applied without bias. Those who require training or development should be applied irrespective of the relationship within the system.

3D Radio Map Reconstruction Based on Generative Adversarial Networks Under Constrained Aircraft Trajectories

Three-dimensional (3D) radio map, which characterizes the spatial distribution of the received signal strength (RSS) across a 3D space, can be a significant tool for wireless resource management and spectrum surveillance. However, incomplete 3D radio map is usually obtained in practice due to the infinite number of positions where aircraft perform RSS measurement suffering from constrained trajectories. In order to tackle this problem, we propose a 3D radio map reconstruction scheme based on generative adversarial networks (GANs), where a novel GAN variant with unsupervised learning is proposed. Specifically, the cost function of the variant integrates a reconstruction loss and an adversarial loss, while the network structure exploits ResNet and dilated convolution under the idea of patchGAN. Numerical results reveal that the network structure and the cost function can benefit the reconstruction scheme, yielding low-volatility and lower average mean squared error (MSE). Moreover, it is shown that the proposed scheme can outperform baselines in terms of the average MSE, even if the number of measured samples for inference significantly decreases.

Toward a national eDNA strategy for the United States

AbstractEnvironmental DNA (eDNA) data make it possible to measure and monitor biodiversity at unprecedented resolution and scale. As use‐cases multiply and scientific consensus grows regarding the value of eDNA analysis, public agencies have an opportunity to decide how and where eDNA data fit into their mandates. Within the United States, many federal and state agencies are individually using eDNA data in various applications and developing relevant scientific expertise. A national strategy for eDNA implementation would capitalize on recent scientific developments, providing a common set of next‐generation tools for natural resource management and public health protection. Such a strategy would avoid patchwork and possibly inconsistent guidelines in different agencies, smoothing the way for efficient uptake of eDNA data in management. Because eDNA analysis is already in widespread use in both ocean and freshwater settings, we focus here on applications in these environments. However, we foresee the broad adoption of eDNA analysis to meet many resource management issues across the nation because the same tools have immediate terrestrial and aerial applications.

Monitoring the storage volume of impounding reservoirs in inaccessible regions using multi-sensor satellite data: the case study of Tuyen Quang Reservoir (Vietnam)

Water stored by reservoirs is critical for irrigation, electricity generation, drinking water supply, recreation, fisheries, and flood control. Therefore, the reservoir's water storage volume (SW) must be measured and monitored frequently for better watershed management. Since SW data is often not publicly available, finding a method to quantify SW objectively and accurately but to facilitate local water management is necessary. This study proposes a method for monitoring water surface area and storage volume using multi-sensor satellite remote sensing data through the Tuyen Quang Reservoir case study in Northern Vietnam. Accordingly, the water surface area was first delineated from multi-temporal optical satellite images, such as Landsat series and Sentinel-2 images, using the Modified Normalized Difference Water Index and resampled into 30-m pixel resolution data. Using the Shuttle Radar Topography Mission Digital Elevation Model data, the water depth at each pixel was then calculated by the difference between its elevation and the reservoir shoreline's mean elevation. The results showed that the reservoir's water surface area increased rapidly during 2003-2007 (from 579 ha to 5,516 ha), fluctuated insignificantly in 2008-2020, and reached 7,196 ha in 2021. Consequently, SW was raised from 11.8 million m3 in 2003 to 1.68 billion m3 in 2021. Our estimations agree with the depth and SW of Tuyen Quang Reservoir published in 2019. Our proposed method could be an effective water resource management tool in developing countries where the number of impounding reservoirs increases dramatically yearly without the financial afford to build gauging stations.

Advancing water quality monitoring through artificial neural networks: present insights and future opportunities in scientific exploration

The increasing demand for clean water resources and the challenges posed by rapid urbanization, industrialization, and climate change have intensified the need for effective water quality monitoring and management. This paper delves into the application of Artificial Neural Networks (ANNs) as an innovative tool for assessing water quality, highlighting current issues and prospects for the development of scientific research. ANNs, with their inherent ability to learn complex patterns, offer a promising solution to address non-linear relationships between water quality parameters, allowing for accurate predictions and modeling. We provide an overview of the existing literature on ANN applications in water quality assessment and discuss the benefits and limitations of these models. Key factors contributing to the success of ANNs in this field include appropriate selection of input features, model architecture, and training methodologies. We also examine recent advancements in hybrid and deep learning models that can further improve the accuracy and efficiency of water quality predictions. The paper concludes by identifying potential areas for future research, such as the integration of remote sensing data, the implementation of real-time monitoring systems, and the development of decision support tools for water resource management. By fostering collaboration between researchers, engineers, and policymakers, we can harness the power of ANNs to safeguard our water resources and ensure sustainable development.

Deterministic and probabilistic modeling of microbiological quality using the QUAL-UFMG: a water resource management tool applied on the slope waters of the Grande River, Brazil.

The present work presents a methodology for modeling the quality of surface water, aiming at the management of hydrographic basins and the best allocation of resources in the treatment of sanitary sewage, considering the predominant uses of water and microbiological quality. The QUAL-UFMG model was used, composed of Excel spreadsheets, where visual basic for applications (VBA) routines were implemented, enabling deterministic and probabilistic modeling through Monte Carlo simulations. The proposed methodology was applied to a Brazilian hydrographic basin, called the GD2 Planning Unit (Grande River Slopes), considering the discharges of sanitary sewage from 30 municipal seats and approximately 740,000 inhabitants. Four scenarios were studied: the current situation (C-01), the trend for the year 2033 (C-02), compliance with environmental legislation (C-03) and compliance with the main uses of the basin (C-04). The results showed that for C-01 and C-02, the water quality, in terms of thermotolerant coliforms, in most stretches does not meet the defined uses. Even complying with the provisions of environmental legislation (C-03), which do not provide for disinfection, only the largest watercourses would have adequate quality for use. Complete service would only be achieved in C-04, which provides for universal sewage treatment with disinfection for the vast majority of municipalities.

Shifting linguistic patterns in oyster restoration news articles surrounding the Deepwater Horizon disaster

Populations of the eastern oyster Crassostrea virginica are declining globally. With the loss of oysters, sustainable provision of natural resources and ecosystem services are also threatened. In 2010, the Deepwater Horizon (DWH) oil spill became the largest marine oil spill in history, imperiling coastal and marine habitats in the Gulf of Mexico. Whereas ecological restoration serves as an important tool in natural resource management, its success depends on achieving ecological objectives and meeting public expectations. However, little is known about how the public perceives ecological restoration—even less in the context of disasters. It has long been understood that mass media messaging helps shape public understanding. Documenting patterned representation of oyster restoration in mass media texts can help set goals, improve stakeholder communication, and ensure required support for restoration activities. To address this goal, this study asks, does newspaper language on the topic of oyster reef restoration change in relation to an environmental disaster? If so, how? A 1.1 million-word Deepwater Horizon Oyster Restoration (DHORN) Corpus—a comprehensive body of newspaper articles about oyster restoration from 3 national and 18 gulf-state newspapers—was developed for the period April 2008–April 2014. The distribution and deployment of collocates of oyster* across three DHORN subcorpora delimited by time (pre-, during, and post-Deepwater Horizon oil spill) were compared through iterative quantitative and qualitative analysis. Examination of salient collocates in use over time indicated an increase in the representation of oysters as quantifiable entities during/post-DWH; at the same time, there was a decrease in the representation of the roles of oyster* in the Gulf ecosystem. Furthermore, multiple propositions associating oysters and oyster restoration activity with DWH and oil spills were introduced into language use by the disaster and persisted for years afterwards. This association was not present pre-DWH. Understanding shifts in linguistic patterns of oyster restoration in news articles before, during, and after Deepwater Horizon can be used to deliberately refine communication between the conservation community and both journalists and policymakers to promote conservation initiatives.

Risk Prediction of Digital Human Resource Management Based on Artificial Intelligence

The latest information technologies have greatly accelerated the digitalization progress of Human Resource Management (HRM) and many useful techniques and tools have been developed for that purpose. However, in terms of risk management, effective enough tools and methods are still insufficient. Existing studies generally fail to give a turnkey solution to the operational risks in digital HRM system, and the macro measurement models are not suitable for dealing with the risks in the digital HRM system of each single enterprise. In view of these defects, this paper studied the prediction of risks in digital HRM systems based on Artificial Intelligence (AI). Firstly, the paper outlined the functions of a digital HRM system, defined the risk management mechanism of a HRM system, and built a conceptual model for it. Then, this paper proposed a novel method for predicting the risks in the digital HRM system, which innovatively integrates the digital HRM risk event chains with the risk event graph. After that, the paper elaborated on the structures and building principles of the risk event representation layer, risk event chain module, risk event graph module, and attention fusion module. At last, experimental results verified that the proposed model has obvious advantages in digital HRM risk prediction in terms of both stability and accuracy.

Connecting soil health and water quality in agricultural landscapes.

As soil plays an integral role in the water cycle, the dynamic and inherent attributes of soil are important drivers of the amount and quality of water in streams, lakes, and groundwater. Studies have demonstrated links between agricultural soil management and water movement in soil, to the edge of fields, and across entire watersheds that feed water bodies of interest. Still, not enough is known about linkages between soil management, soil health, and watershed water quality to adequately predict impacts of land use changes and to effectively use soil management as a tool for water resource management. We describe here the mechanisms connecting soil health and water quality, the state of the science at the nexus of the two fields of study, and an overview of recent studies on the topic. A fundamental challenge is measuring and elucidating connections between processes working at different temporal and spatial scales. Research needs include expansion of field-scale data and analysis of combined datasets, greater understanding of the mechanisms that explain observed associations between management practices and water quality, ensuring that hydrologic models and decision-support tools more effectively reflect these soil-water mechanisms, and greater use of systems-based research designs.

DiveRpine: Diversification of pine plantations in Mediterranean mountains. An interactive R tool to help decision makers

Forest plantations are an example of widespread land-use change shaping terrestrial ecosystems. They usually have high stand density, low tree diversity, and homogeneous structure. Their conversion into more natural forests, i.e. naturalization, to foment active regeneration, heterogeneous structure, high biodiversity levels, and high resilience to disturbances such as pests and fires, is urgently needed. More diverse and heterogeneous forest stands display greater resilience to global change, in addition to protecting the ecosystem services that mountainous pine-plantations provide.We present diveRpine (diveRsification of pine plantation), an interactive application designed to show how the species richness (and therefore resilience) varies in pine plantations based on the mountain landscape configuration, the internal structure of the stand, and the composition of the dispersal vectors. The aim of the application is double. On the one hand, it would provide a guidance tool for natural resource managers that aid in the naturalization of forest plantations to recover the multifunctionality of these ecosystems. On the other hand, this tool could be a valuable teaching resource in ecology and conservation classes, since it has great value to explore virtual scenarios and demonstrate the process of prioritization of the management actions. The user can simulate different combinations and analyze how they would affect the tree-species richness in a specific pine plantation stand. It also allows the user to visualize some of the complex ecological processes that underlie the diversification of pine plantations in Mediterranean mountain areas. This tool provides a valuable aid for decision making, for example helping managers to decide whether or not to intervene in a certain pine stand, by projecting the most probable ecological succession under a specific scenario. Our diveRpine concept combining scientific rigor with simplicity of presentation and interpretation is applicable in any restoration context.

3D electrical structure definition of aquifer systems in the Kalahari basin in Southern Angola based on legacy data reprocessing

The Kalahari-Ohangwena transboundary aquifer system, recently identified in Northern Namibia, comprises 3 major aquifers with very different characteristics. The shallowest is discontinuous and with limited reserves, but it has local importance in water supply for the population, since it is easy to reach, and often presents good hydrochemical quality. An intermediate deeper aquifer is characterized by high salinity while the deepest aquifer, also mostly saline, can present zones with fresh water. However, the latter is located at considerable depths and is shaped by the bottom of the basin basement. There hasn't been a systematic hydrogeological data acquisition for decades in this area of Angola, but legacy electrical resistivity data reprocessing from geophysical surveys conducted >50 years ago in the Cunene Province allowed the construction of a quasi-3D geoelectrical model for the Angolan side of KOH aquifer system in the Cuvelai-Etosha basin. This model is based on 482 vertical electrical soundings carried out in 1966–67, using the Schlumberger array, that contribute to confirming the presence of the Kalahari-Ohangwena aquifer system in Angola. The obtained quasi-3D model highlights the geoelectrical features of hard bedrock and is validated with other hydrogeological and geophysical information. The quasi-3D electrical resistivity data is interpreted using selected boreholes and two time-domain electromagnetics transects carried out in Namibia, in the 2000s. Although both geophysical data acquisitions were >40 years apart, the results show a very good correlation between the deeper aquifer and the aquitard separating the intermediate aquifer from the deeper aquifer either with the results from Namibia or the borehole data. This is a direct result of the lack of alteration in the hydraulic conditions over these decades, without significant anthropogenic activity and negligible extraction from deep wells. Based on this analysis, the original dataset was considered a reliable source and this quasi-3D model was validated. Furthermore, the model can be considered in the future as an important tool for groundwater resources management, as well as a good starting point for further hydrogeological research in the province of Cunene.

Spatio-Temporal Variations in the Potential Habitat Distribution of Pacific Sardine (Sardinops sagax) in the Northwest Pacific Ocean

Pacific sardine (Sardinops sagax) is a commercially important species and supports important fisheries in the Northwest Pacific Ocean (NPO). Understanding the habitat distribution patterns of Pacific sardine is of great significance for fishing ground prediction and stock management. In this study, both single-algorithm and ensemble distribution models were established through the Biomod2 package for Pacific sardine by combining the species occurrence data, sea surface temperature (SST), sea surface height (SSH), sea surface salinity (SSS) and chlorophyll-a concentration (Chla) in the NPO during the main fishing season (June–November) from 2015 to 2020. The results indicated that the key environmental variables affecting the habitat distribution of Pacific sardine were the SSH and SST. The suitable habitat area for Pacific sardine showed significant monthly changes: the suitable habitat range in June was larger than that in July and August, while the suitable habitat range gradually increased from September to November. Furthermore, the monthly geometric centers of habitat suitability index (HSI) for Pacific sardine presented a counterclockwise pattern, gradually moving to the northeast from June, and then turning back to the southwest from August. Compared with single-algorithm models, the ensemble model had higher evaluation metric values and better spatial correspondence between habitat prediction and occurrence records data, which indicated that the ensemble model can provide more accurate prediction and is a promising tool for potential habitat forecasting and resource management.

Soil moisture monitoring by downscaling of remote sensing products using LST/VI space derived from MODIS products

Abstract Soil moisture (SM) has an important role in the earth's water cycle and is a key variable in water resources management. Considering the critical state of water resources in the Urmia Lake basin, northwest Iran, this study examined the potential for utilizing a variety of remote sensing data and products, in conjunction with a promising downscaling method, to monitor soil moisture with a reasonable spatial and temporal resolution, as a novel and effective tool for agricultural and water resource management. Accordingly, remote sensing products of surface soil moisture were scaled to MODIS's image scale (∼1 km) using the UCLA downscaling method and Temperature, Vegetation, Drought Index (TVDI) values obtained from the scattering space method. Results showed that the LPRM, ESA-CCI, and GLDAS downscaled images had the highest inverse correlation with the TVDI (best results) accordingly equal to −0.600, −0.787, and −0.630. Also, based on the evaluation of the obtained results with the ground stations data, the LPRM and the ESA-CCI downscaled images had the best statistical indices values accordingly in 2010 and 2014 that confirm the promising application of remote sensing soil moisture data (rLPRM (2010) = 0.92, MAELPRM (2010) = 4.14%, RMSELPRM (2010) = 6.39% and rESA-CCI (2014) = 0.7, MAEESA-CCI (2014) = 2.23%, RMSEESA-CCI (2014) = 2.59%).