Resource Management Strategies Research Articles

Time Series Analysis to Estimate the Volume of Drinking Water Consumption in the City of Meoqui, Chihuahua, Mexico

Water is a vital resource for sustaining life and for numerous processes within the transformation industry. It is a finite resource, albeit one that can be renewed, and thus sustainable management is imperative. To achieve this objective, it is necessary to have the appropriate tools to assist with the planning policies for its management. This paper presents a time series analysis approach to measure and predict the pattern of water consumption by humans throughout subsectors (domestic, commercial, public sector, education, industry, and raw water) and total water consumption in Meoqui, Chihuahua, Mexico with data from 2011 to 2023, applying calibration model techniques to measure uncertainty in the forecasting. The municipality of Meoqui encompasses an area of 342 km2. The climate is semi-arid, with an average annual rainfall of 272 mm and average temperatures of 26.4 °C in summer and 9.7 °C in winter. The municipal seat, which has a population of 23,140, is supplied with water from ten wells, with an average consumption of 20 ± 579 m3 per user. The consumption of the general population indicates the existence of a seasonal autoregressive integrated moving average (SARIMA) (0,1,2)(0,0,2)12 model. (Sen’s Slope = 682.7, p < 0.001). The domestic sector exhibited the highest overall consumption, with a total volume of 17,169,009 m3 (13 ± 93). A SARIMA (2,1,0)(2,0,0)12 model was estimated, with a Sen’s slope of 221.65 and a p-value of less than 0.001. The second-largest consumer of total water was the “raw water” sector, which consumed 5,124,795 (30,146 ± 35,841) m3 and exhibited an SARIMA (0,1,1)(2,0,0)12 model with no statistically significant trend. The resulting models will facilitate the company’s ability to define water resource management strategies in a sustainable manner, in alignment with projected consumption trends.

Pengaruh Beban Kerja dan Stres Kerja terhadap Turnover Intention di Instansi Badan Penyelenggara Jaminan Produk Halal Kota Jakarta Timur

The purpose of this study is to look at the impact of workload and work stress on employee turnover intentions at the Halal Product Assurance Agency (BPJPH). The method used is quantitative, and data is collected via questionnaires. A total of 50 BPJPH employees across multiple work units participated. Data were gathered through a questionnaire designed to quantify turnover intention, workload, and job stress. The data was analyzed with multiple linear regression to determine the association between work stress, workload, and employees' intention to quit the job. The results indicate that both work stress and workload significantly affect turnover intention at BPJPH, both partially and simultaneously. The combined impact of work stress and workload on turnover intention is 89.5%, while the rest is 10.5% is influenced by other variables not covered in this research. These findings highlight the importance of managing workload and work stress to reduce employees' intention to leave the organization, and provide insights for human resource management strategies at BPJPH.

Spatiotemporal change of lake shrinkage and its response to the unprecedented heat waves in the middle and lower Yangtze River Basin

ABSTRACT Due to the joint influence of human activities and climate change, lakes experience significant shrinkage or expansion, posing enormous challenges for water resource management and the ecological balance of lake ecosystems. As an important region abundant in water resources in China, the balance of water resources in the middle and lower Yangtze River Basin (MLY) is crucial for region sustainable development. However, to date, few studies have quantitatively analysed the lake area during heat waves in the MLY. To fill this gap, this study selected the summer of 2022, which encountered unprecedented heat waves, to analyse the changes of lake area in the MLY. Specifically, Sentinel-1 images were used as data sources, and the Random Forest (RF) model was applied to extract the monthly area of 62 lakes (area >10 km2) in the MLY from 2019 to 2022. The results showed that: (1) Compared to the Support Vector Machine (SVM) model and the Otsu method, the RF model had the highest accuracy, with its accuracy and Kappa coefficient in the testing samples of 97.14% and 0.94, respectively. During the period from June to September in 2022, the total area of 62 lakes decreased by 3475.50 km2. (2) Compared to the same period in the summer of 2019–2021, 23 lakes (37.10%) were observed to experience shrinkage. Among them, the area of Poyang Lake and Dongting Lake decreased to the level of the previous winter dry seasons in August 2022. (3) Temperature was the most important meteorological factor that caused changes in the lake area in the MLY, compared to precipitation, evaporation, and runoff. This research can help understand the impact of climate change on lakes and provide references for decision-making in the MLY to formulate the strategies of water resource management under the changing climate.

HUMAN RESOURCE MANAGEMENT STRATEGIES: ANALYSIS AND EVALUATION

In this article, based on the latest literature sources, such an important issue of management theory and practice as the analysis and evaluation of human resource management strategies is investigated. The main stages of the human resource management strategy analysis process that a company should go through to determine the compliance of the strategy with the expected results are considered: analysis, diagnosis, generalisation and recommendations, drawing up an action plan.,resource planning, costs and benefits. Two main human resource management strategies are presented: a general strategy, such as improving the quality of job performance, and a specific strategy dealing with various aspects of managing employees' skills and abilities: training, rewards, increasing motivation. Human resource management strategies such as: commitment-based, collaborative, paternalistic and traditional are analysed and evaluated.

Global trends and future research resources-based view theory in management science through a scoping review and bibliometric analysis

The objective of this study was to analyze recent research conditions and future research themes related to the resource-based view (RBV). The scoping review and bibliometric analysis (ScoRBA) method was used in this study. The data sources of this study were 171 scientific articles collected from Scopus. This study was conducted to visualize the results of the bibliometric analysis via co-occurrence analysis and thematic maps via VOSviewer software. The keyword used to find the scientific articles was a resource-based view, which was sought in the titles, abstracts, and keywords. The selected articles were scientific articles published from 1997--2024. The data analysis used in this research was through a co-occurrence analysis applied to the citations of the scientific articles, authors, and countries. The results of this study revealed six clusters of research variables related to the RBV, e.g., cluster 1 (capability, competitiveness, company performance, and strategy), cluster 2 (competitive advantage, personal management skills, investment, project management, and resource management strategies), cluster 3 (entrepreneurship, human resources, innovation, and social capital), cluster 4 (industrial management, information management, information technology, resource allocation, and planning strategies), and clusters 5 and 6 (intangible resources, profitability, corporate sustainability, and institutions). In addition, the future research themes associated with the RBV include dynamic capabilities, financial resources, human capital, profitability, and competitive ability.

Efficient resource management in direct and indirect transmission in V2X communications

Autonomous driving systems consist of vehicles that are able to communicate not only with other vehicles but also with entities in their environment, forming vehicle-to-everything (V2X) communication. However, the V2X applications have intense requirements posing a significant challenge to the telecommunication infrastructure. In this work, we consider two types of transmission, i.e. direct and indirect, and we utilize analytical traffic-engineering models with a view to conduct a performance analysis of a vehicular network that enables V2X communication. We additionally propose two resource management strategies in order to decrease the request rejection probability and consequently ensure enhanced communication conditions. The results reveal that the proposed resource management strategies constitute a strong asset for improving the system’s service provisioning capability.

Assessing terrestrial water storage dynamics and multiple factors driving forces in China from 2005 to 2020

In the context of global warming, comprehending the dynamics of terrestrial water storage (TWS) and its responses to natural and anthropogenic factors is paramount for hydrological research and the management of water resources in China. This study utilized GRACE (Gravity Recovery and Climate Experiment)/GRACE-Follow On (GRACE-FO) satellite data to analyze terrestrial water storage across nine basins in China from 2005 to 2020 at multiple temporal and spatial scales. Subsequently, employing a Geographic detector model, potential influencing factors were identified, and an enhanced Geographically Weighted Regression (GWR) method was proposed for attributing changes in TWS in China. The findings reveal a consistent declining trend in TWS based on GRACE/GRACE-FO data across different temporal scales, with the most pronounced decreases observed in August and September. Geographic Detector analysis unveils significant interactions among various environmental factors, with climate variables playing a pivotal role in modulating hydrological characteristics of major river basins, where rising temperatures can exacerbate the severity of precipitation events, thus increasing the risk of floods and droughts. Moreover, analysis of the primary influencing factors indicates significant impacts of population density and topography on water resources in the southeastern and southwestern regions, particularly amidst increasing human activities and urbanization expansion. The results of this study are crucial for comprehending the dynamic changes and mechanisms of TWS in China, as well as for formulating water resource management strategies.

Sustainable Development Strategy Based on Value-Addedin the Fisheries Sector: Study on the Kangean Islands, Sumenep Regency, Indonesia

Objective: Kangean Islands is a group of small islands in Sumenep Regency, East Java Province, Indonesia, which is administratively divided into three sub-districts, namely Arjasa District, Kangayan District, and Sapeken District. This archipelago has very large fisheries potential as seen from its contribution to the Gross Regional Domestic Product (GRDP) of Sumenep Regency, but the utilization of the fisheries sector in this archipelago is still carried out traditionally and tends to be exploitative. This study aims to see the level of sustainability of fisheries sector management and recommend fisheries resource management strategies, in order to increase the economic added value of the fisheries sector and maintain the sustainability of fish resources and the environment in the archipelago. Method: The analytical method used to analyze the degree of sustainability in fisheries resource management in the Kangean Islands is Rapid Appraissal for Fisheries (RAPFISH) with the Multi-Dimensional Scaling (MDS) ordination technique. While the analytical tool used to compile a hierarchy of fisheries sector development strategies to increase economic added value is the Analytical Hierarchy Process (AHP). Results and Discussion: The results of the sustainability analysis in the management of the fisheries sector found that, of the five aspects identified, the results showed that the financial aspect and infrastructure aspect had the category of "Sustainable Sustainability", with values of each aspect of 63.113 and 53.885. While the other three aspects, namely: Human Resources (HR) Aspect, Natural Resources Aspect, and Social Aspect are included in the category of "Less Sustainable". Meanwhile, based on the results of the analysis of the hierarchy of fisheries sector development policies, it shows that the most priority policy to be carried out in the development of a sustainable fisheries sector is the "identification of investment potential and opportunities" program with a priority value of 46%. Furthermore, the strategy that must be chosen is to map the potential of the fisheries sector with a limiting value of 21%. With a Rater Agreement value strength of 0.472727 or 47%, it means that the informants agree in providing an assessment on the paired comparison questionnaire, in determining problems and strategies in the development of the fisheries sector in the archipelago.

Development and implementation of an optimization model for the assignment of medical specialties in clinics: application of the Knapsack algorithm with a graphical interface in Python

This project presents the development and application of an optimization model designed to allocate medical specialties to clinics based on a predefined schedule. By employing the Knapsack algorithm in Python, the model aims to optimize resource utilization within a medical environment, ensuring that the distribution of services is both efficient and effective. The model incorporates critical constraints such as clinic capacity and patient service demand, which are essential for balancing resources across various specialties. Furthermore, the model addresses potential scheduling conflicts, ensuring that no overlaps occur among different specialty professionals assigned to the same clinic. The integration of these constraints ensures that the allocation process is not only optimal but also practical for real-world implementation. The results demonstrate the potential of this approach to enhance operational efficiency, minimize resource wastage, and improve patient care by strategically managing the availability of specialized medical services across clinics. This model serves as a robust framework for healthcare organizations aiming to improve their resource management strategies.

Evolution and Analysis of Water Yield under the Change of Land Use and Climate Change Based on the PLUS-InVEST Model: A Case Study of the Yellow River Basin in Henan Province

Understanding the interrelationships between land use, climate change, and regional water yield is critical for effective water resource management and ecosystem protection. However, comprehensive insights into how water yield evolves under different land use scenarios and climate change remain elusive. This study employs the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models, Patch-generating Land Use Simulation (PLUS) model, and Geodetector within a unified framework to evaluate the dynamics of land use, water yield, and their relationships with various factors (meteorological, social, economic, etc.). To forecast the land use/cover change (LUCC) pattern of the Yellow River Basin by 2030, three scenarios were considered: economic development priority (Scenario 1), ecological development priority (Scenario 2), and cropland development priority (Scenario 3). Climate change scenarios were constructed using CMIP6 data, representing low-stress (SSP119), medium-stress (SSP245), and high-stress (SSP585) conditions. The results show the following: (1) from 2000 to 2020, cropland was predominant in the Yellow River Basin, Henan Province, with significant land conversion to impervious land (construction land) and forest land; (2) water yield changes during this period were primarily influenced by meteorological factors, with land use changes having negligible impact; (3) by 2030, the water yield of Scenario 1 is highest among different land use scenarios, marginally surpassing Scenario 2 by 1.60 × 108 m3; (4) climate scenarios reveal significant disparities, with SSP126 yielding 54.95 × 108 m3 higher water yield than SSP245, driven predominantly by precipitation; (5) Geodetector analysis identifies precipitation as the most influential single factor, with significant interactions among meteorological and socio-economic factors. These findings offer valuable insights for policymakers and researchers in formulating land use and water resource management strategies.

Network reliability evaluation and management science applications in resource allocation for police organizations

The effective allocation of resources within police patrol departments is crucial for maintaining public safety and operational efficiency. Traditional methods often fail to account for uncertainties and variabilities in police operations, such as fluctuating crime rates and dynamic response requirements. This study introduces a fuzzy multi-state network (FMSN) model to evaluate the reliability of resource allocation in police patrol departments. The model captures the complexities and uncertainties of patrol operations using fuzzy logic, providing a nuanced assessment of system reliability. Virtual data were generated to simulate various patrol scenarios. The model’s performance was analyzed under different configurations and parameter settings. Results show that resource sharing and redundancy significantly enhance system reliability. Sensitivity analysis highlights critical factors affecting reliability, offering valuable insights for optimizing resource management strategies in police organizations. This research provides a robust framework for improving the effectiveness and efficiency of police patrol operations under conditions of uncertainty.

System dynamic model of water, energy and food nexus for policy implementation

In this study, a spatiotemporal disaggregated simulation model was developed based on water–food–energy (WFE) nexus approach to assess water and food supply security considering ecosystem provisioning services. The main components of the developed model in this study (SD-WFE model) are population, water, agriculture, and energy modules. The model, which was developed using system dynamics (SD) approach, was utilized to simulate effectiveness of sectoral municipal, industrial, and agricultural water and energy consumption management and environmental protection policies in improving ecosystem provisioning services during a 20-year period. Through sensitivity analysis utilizing the Monte Carlo model, the study addresses the formulation of sustainable water resource policies across four main categories: water demand management, water supply management, food resource management, and energy resource demand management. Additionally, it explores the integration of policies within an optimal framework. The simulation of proposed solutions revealed that a combination of water demand management and food resource management yielded the most promising outcome. Specifically, the recommended solution entails enhancing water irrigation efficiency by 18% through the expansion of pressurized irrigation network coverage, adjusting cropping patterns by 14%, reducing agricultural product losses by 8% via improved food supply management, minimizing food demand by 9% due to reduced food consumption losses, and achieving an annual 10% increase in crop performance. These selected policies form the foundation for sustainable water resource management strategies.

Trend Analysis and Change Point Detection of Climatic Parameters in Ambedkar Nagar District of Uttar Pradesh, India

Climate change is one of the most serious challenges confronting humanity today, and its effects can be seen all over the world. The assessment of changes in the long-term weather trend is critical for developing water resource management strategies under climate change scenarios. Long-term trends for annual, monsoon, non-monsoon rainfall, rainy days, maximum temperature, and minimum temperature of Akbarpur, Allapur, Bhiti, Jalalpur, and Tanda stations in Uttar Pradesh's Ambedkar Nagar district were examined in this study. The Mann-Kendall test revealed a non-significant decreasing trend for annual and monsoon rainfall at Akbarpur, Bhiti, and Jalalpur stations with an average rate of 1.64 mm/year whereas, it shows a non-significant increasing trend for annual and monsoon rainfall at the Allapur and Tanda stations with an average rate of 3.168 mm/year. Non-monsoon rainfall showed an in-significant decreasing trend at Allapur and Jalalpur stations with an average rate of 0.415 mm/year, whereas a non-significant increasing trend was shown at Akbarpur, Bhiti, and Tanda stations with an average rate of 0.197 mm/year. Annual rainy days shows non-significant decreasing trend for all stations of Akbarpur district with an average rate of 0.117 day/year (one day decrease with in nine year). It indicates seasonal shift and increased possibility of high intensity rainfall. The impact of extreme events of rainfall on the trend was evaluated on the whole and partial series before and after the break point and it was observed that there was no significant climate change in Ambedkar Nagar district and non-significant changing year of precipitation data series was found in 1995. A significant increasing trend in maximum temperature was observed for all stations in the months of August and December, with an average rate of 0.02 0C per year, while Allapur station also shows a significant increasing trend in maximum temperature in September, with a rate of 0.012 0C per year. However, a significant decreasing trend was observed in minimum temperature in the month of May at Akbarpur, Allapur, Jalalpur, Tanda station with an average rate of 0.027 0C per year. Rainfall and temperature are a principal climatic parameter which regulates the environmental condition of a particular region by directly influencing the agricultural productivity and water resources.

Evaluating the Impact of Mini Dams on Agricultural Production and Socio-economic Conditions: A Case Study of Latamber’s Dam District Karak (Pakistan)

Water scarcity is an escalating global concern, and Pakistan is no exception, facing increasing water crises and growing demand. Effective water supply is crucial for the development of any area. Mini dams not only provide water for irrigation but also support domestic uses. This study evaluates the impact of mini dams on agricultural productivity in Latamber village, focusing on crop yield, land use and land cover (LULC) changes, and socio-economic conditions. A multifaceted methodology, including field surveys, departmental data collection, questionnaires, and remote sensing, was employed. Data collected through questionnaires and farmer interviews highlighted the economic and environmental benefits of mini dams. Statistical analysis and image processing revealed significant expansions in cultivation areas for crops like maize and rice, alongside increases in wheat yield and the adoption of new vegetable crops during the Rabi season. Additionally, socioeconomic benefits, such as enhanced agricultural incomes and employment opportunities, were observed due to improved water availability and irrigation technologies. The study emphasizes the importance of evaluating the impact of mini dams on agricultural production and socio-economic conditions to inform sustainable water resource management strategies, which are vital for the long-term development of similar regions.

Drying and wetting trend in Xinjiang and related circulations background over the past 60 years

The study of climate variability and trend provides important evidence for water resource management strategies in Xinjiang. The scientific community has systematically studied the ‘warming and wetting’ process in the Xinjiang region. However, significant controversy persists regarding whether this trend has stagnated, and there remains a gap in understanding the decadal climate variability across different seasons within the subregions of Xinjiang. Here, we analyzed the spatiotemporal characteristics of seasonal-to-annual climate variations in Southern and Northern Xinjiang over the past 60 years by the Standardized Precipitation Evapotranspiration Index (SPEI-3) and identified the change points of mean and trend changes in the SPEI series and further explored the atmospheric circulation patterns before and after the transition from dry/wet conditions. Specifically, although the mean change point test indicated a wetter climate after 1986 at both the annual and seasonal scales, the trend analysis showed a shift from wet to dry from 1996, which has persisted until the present. There were significant interdecadal features in the spatial distribution of SPEI. Xinjiang exhibited a predominantly dry pattern from 1961 to 1980, which changed to a wetter pattern from 1981 to 2000, Xinjiang experienced its wettest decade from 1991 to 2000, and the whole region returned to a drier pattern from 2001 to 2020. From the annual and seasonal variation, the rate of change is greater in the south, with stronger change amplitude in summer and autumn and a higher frequency of anomalous SPEI. The results of the atmospheric circulation analysis demonstrated that the net water vapor flux in the Xinjiang region increased by about 8.30 g·kg−1·m·s−1 in the period 1987–2020 compared to 1961–1986 due to the combined interaction of high-, mid-, and low-latitude systems with the Central Asian low-pressure system. The results indicated a sharp increase in aridification in Xinjiang, providing valuable insights for disaster prevention, mitigation, and risk management related to regional drought hazards.

The Strategy of Human Resource Management Based on the K-Means Clustering Algorithm

With the continuous accumulation of enterprise data and the continuous development of technology, data-driven decision-making has become an important trend in enterprise management. Human resource management, as an important component of enterprise management, also requires the use of data analysis tools to optimize the decision-making process. Traditional human resource management strategy algorithms are often based on experience and intuition, lacking scientific data support. These algorithms often struggle to achieve ideal results when dealing with large and complex human resource data. This study aims to provide more scientific and accurate data support for human resource management by introducing the K-means algorithm. The main research purpose of this paper is to detect and identify the data in enterprise human resource management. Data mining can realize the comprehensive detection and analysis of poetry, but the application of data mining algorithms in human resource management data processing is not much. This paper hopes to analyze the anomaly of human resource data statistics by data mining method. According to the characteristics of the K-means algorithm and DBSCAN algorithm, a hybrid K-means algorithm is proposed in this study, which can automatically generate a more appropriate K value and realize parameter self-adaptation with less manual intervention. The optimized clustering K-means algorithm obtains the initial value of DBSCAN through calculation, and then the abnormal data of human resources statistics can be obtained through the second clustering calculation. The optimized K-means algorithm can detect human resource statistics data in real time, and give a new idea for human resource analysis of data anomalies.

The role of big data and AI in enhancing biodiversity conservation and resource management in the USA

The rapid advancement of big data and artificial intelligence (AI) technologies has opened new frontiers in biodiversity conservation and resource management, particularly in the USA. This review paper explores the significant roles these technologies play in enhancing conservation efforts and resource management strategies. The paper begins by contextualizing the importance of biodiversity in the USA and the existing challenges in conservation practices. It then delves into the realm of big data, discussing its sources, collection methods, storage, integration, and real-world applications. Furthermore, it examines AI technologies, highlighting their capabilities in data analysis, predictive modeling, and habitat monitoring. The synergy between big data and AI is also analyzed, showcasing how their integration can lead to more informed decision-making and efficient management practices. Case studies illustrate the practical applications and benefits of these technologies. The paper concludes with a summary of key findings, recommendations for policymakers and practitioners, and suggestions for future research directions. Through this comprehensive review, the paper underscores the transformative potential of big data and AI in safeguarding biodiversity and managing natural resources effectively.

A Cascaded Multi-Agent Reinforcement Learning-Based Resource Allocation for Cellular-V2X Vehicular Platooning Networks.

The platooning of cars and trucks is a pertinent approach for autonomous driving due to the effective utilization of roadways. The decreased gas consumption levels are an added merit owing to sustainability. Conventional platooning depended on Dedicated Short-Range Communication (DSRC)-based vehicle-to-vehicle communications. The computations were executed by the platoon members with their constrained capabilities. The advent of 5G has favored Intelligent Transportation Systems (ITS) to adopt Multi-access Edge Computing (MEC) in platooning paradigms by offloading the computational tasks to the edge server. In this research, vital parameters in vehicular platooning systems, viz. latency-sensitive radio resource management schemes, and Age of Information (AoI) are investigated. In addition, the delivery rates of Cooperative Awareness Messages (CAM) that ensure expeditious reception of safety-critical messages at the roadside units (RSU) are also examined. However, for latency-sensitive applications like vehicular networks, it is essential to address multiple and correlated objectives. To solve such objectives effectively and simultaneously, the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework necessitates a better and more sophisticated model to enhance its ability. In this paper, a novel Cascaded MADDPG framework, CMADDPG, is proposed to train cascaded target critics, which aims at achieving expected rewards through the collaborative conduct of agents. The estimation bias phenomenon, which hinders a system's overall performance, is vividly circumvented in this cascaded algorithm. Eventually, experimental analysis also demonstrates the potential of the proposed algorithm by evaluating the convergence factor, which stabilizes quickly with minimum distortions, and reliable CAM message dissemination with 99% probability. The average AoI quantity is maintained within the 5-10 ms range, guaranteeing better QoS. This technique has proven its robustness in decentralized resource allocation against channel uncertainties caused by higher mobility in the environment. Most importantly, the performance of the proposed algorithm remains unaffected by increasing platoon size and leading channel uncertainties.

Streamflow trends and flood frequency analysis: a regional study of the UK.

In recent years, the escalating effects of climate change on surface water bodies have underscored the critical importance of analyzing streamflow trends for effective water resource planning and management. This study conducts a comprehensive regional investigation into the streamflow rate trends of 18 rivers across the United Kingdom (UK). An enhanced Mann-Kendall (MK) test was employed to meticulously analyze both rainfall and streamflow trends on monthly and annual scales. Additionally, the Innovative Trend Analysis (ITA) method was applied to elucidate the variability of streamflow rates, providing a more nuanced understanding of hydrological changes in response to climatic shifts. MK test reveals statistically significant positive trends in streamflow rates, particularly for rivers in south-central Scotland and northern England. Specifically, in January, rivers such as the Tay at Ballathie, Tweed at Peebles, and Teviot at Ormiston showed Z-scores above 2. Annually, similar positive trends were observed, with the Tay at Ballathie (Z = 3.42) and Nith at Friars Carse (Z = 3.35) exhibiting the highest increases in streamflow rates. The ITA method showed no relevant trends for the lowest values of streamflow, except for the Thames at Kingston, while considerable variability was observed for the highest streamflow rates, with several rivers showing positive trends and, however, some England rivers, like Bure at Ingworth, Test at Broadlands, and Trent at Colwick, showing negative trends. From this perspective, a more in-depth analysis of the extreme streamflow trends was carried out. In particular, the flood frequency of the maximum annual streamflow was assessed, based on the fitting of the Generalized Extreme Value (GEV) distribution on the annual maxima. Increasing location parameter (μ) and return period trends were observed for several rivers across the UK. In particular, the Tay at Ballathie (Scotland) showed the most marked increase, with μ that ranged from about 730 m3/s to more than 900 m3/s. At the same time, slight decreasing trends were observed for the Trent River (μ from 378 m3/s to 341 m3/s). The critical comparison of the MK test, ITA, and GEV distribution fitting revealed both agreements and discrepancies among the methods. While the analyses generally aligned in detecting significant trends in streamflow rates, notable discrepancies were observed, particularly in rivers with negligible trends. These inconsistencies highlight the complexity of hydrological responses and the limitations of individual methods. Overall, the study provides a comprehensive view of how streamflow dynamics are evolving in UK rivers, highlighting regional variations in the impact of climate change. This understanding can improve water resource management strategies by integrating diverse analytical approaches.

Искусственный интеллект в управлении персоналом: возможности и угрозы

Introduction. Digital economy has generated a completely new model of human-machine interaction based on the active use of digital technologies in almost all spheres of human activity. Most information systems used by organizations are integrated with big data analytics, which requires higher qualified staff. The job responsibilities of employees change according to industry and corporate needs. The requirements for staff qualification are increasing, urging the company HR service to find, attract and retain specialists of the required qualifications, and organize staff development in the organization. Innovations in decision-making algorithms make artificial intelligence the most useful tool for implementing a human resource management strategy in an organization.Materials and methods. The article uses general scientific research methods, such as qualitative analysis, observation method, synthesis, logical induction method and others. The information basis of the article consists of scientific research works, official documents, and information posted in the media. A secondary analysis of the research on the topic under study was carried out.Results. Despite the fact that the processes related to personnel management have always been a human cognitive ability, artificial intelligence technologies can currently provide technical solutions in the field of recruitment and further staff development. Artificial intelligence plays an important role in collecting candidate data from various sources, and is able to evaluate the required candidates based on a job description more effectively and objectively than a HR officer. Artificial intelligence can more accurately describe a job that corresponds to the business process in skills. Artificial intelligence technologies can play an important role in the process of organizing professional development and retraining of staff. The use of artificial intelligence provides for obtaining more personalized and understandable results excluding the "human factor". In this article, special attention is paid to artificial intelligence technologies, which can be effectively used in staff management. The opportunities to increase the HR employees’ productivity are considered, main risks of artificial intelligence introduction are highlighted and recommendations for its efficient application are given. The research novelty is in identification of the scope of the use of artificial intelligence in the organizational personnel policy, making it possible to realize the opportunities for revealing the creative potential of the organization’s employees and increase competitiveness.Discussion. It is absolutely clear that AI is becoming an integral part of business ecosystems, and necessary to maintain and increase the level of competitiveness of economic agents. Nevertheless, AI is constantly evolving, which allows companies to expand the functionality of its use. However, it is important to define the boundaries of AI use, including how human-machine interaction will be ensured, which functions can be given to AI, and which can be abandoned.