Optimal Distribution Of Resources Research Articles

Integrated water resource management in the Segura Hydrographic Basin: An artificial intelligence approach

Managing resources effectively in uncertain demand, variable availability, and complex governance policies is a significant challenge. This paper presents a paradigmatic framework for addressing these issues in water management scenarios by integrating advanced physical modelling, remote sensing techniques, and Artificial Intelligence algorithms. The proposed approach accurately predicts water availability, estimates demand, and optimizes resource allocation on both short- and long-term basis, combining a comprehensive hydrological model, agronomic crop models for precise demand estimation, and Mixed-Integer Linear Programming for efficient resource distribution. In the study case of the Segura Hydrographic Basin, the approach successfully allocated approximately 642 million cubic meters (hm3) of water over six months, minimizing the deficit to 9.7% of the total estimated demand. The methodology demonstrated significant environmental benefits, reducing CO2 emissions while optimizing resource distribution. This robust solution supports informed decision-making processes, ensuring sustainable water management across diverse contexts. The generalizability of this approach allows its adaptation to other basins, contributing to improved governance and policy implementation on a broader scale. Ultimately, the methodology has been validated and integrated into the operational water management practices in the Segura Hydrographic Basin in Spain.

Assessment of COVID-19 severity levels and associated factors among patients admitted to the treatment centers in Southern Ethiopia

BackgroundCOVID-19, a highly impactful infectious disease, has been observed to result in psychological distress, organ impairment, and mortality. The severity and consequences of the illness appear to vary based on geographical location and individual characteristics. Understanding the disease and optimizing resource distribution through early classification depend on having data on the severity of COVID-19 patients. There is a dearth of information in this particular region regarding the severity of COVID-19 patients and related factors. Therefore, this study used an ordinal logistic regression model to determine the severity levels of COVID-19 and its associated components.Materials and methodsA retrospective follow-up study was conducted on COVID-19 patients admitted between May 30, 2020, and October 15, 2021, at care centers in southern Ethiopia. 845 patients were included in this research. The mean (standard deviation) and median (interquartile range) were used to summarize the data. A multivariable ordinal logistic regression model was used to study the association between independent variables and COVID-19 severity levels.ResultsIn terms of the severity of the disease, 12.07% of patients had severe COVID-19, 7.81% had critical disease, and 6.39% had moderate disease. 8.28% of the 845 patients died, while 88.88% of them made a full recovery. Older age (> = 40 years) (AOR = 5.75, 95% CI = 3.99, 8.27), comorbidities (AOR = 4.17, 95% CI = 3.03, 5.88), and low oxygen saturation (AOR = 3.44, 95% CI = 2.23, 5.56) were significantly linked to higher odds of experiencing more severe levels of COVID-19 compared to their counterparts.Conclusion7.81% of patients were critically ill, while more than one-tenth (12.07%) were considered severely ill. Low oxygen saturation, comorbidities, and advanced age were found to be significantly associated with COVID-19 severity. Therefore, it is crucial to manage comorbidities, provide special treatment, and provide COVID-19 patients with underlying medical issues more attention due to the higher risk of poor outcomes. To speed up their recovery, medical professionals should regularly monitor and provide specialized care to older COVID-19 patients. In order to identify patients who are more likely to experience a severe illness and to better manage their treatment, it is imperative that oxygen saturation levels in COVID-19 patients be promptly identified and monitored.

Designing circular supply chains with digital technologies for competitive sustainability: An operation management perspective

The objective of this research is to investigate how digital technologies, specifically blockchain, artificial intelligence (AI), and the Internet of Things (IoT), can be integrated into circular supply chains to enhance sustainability and preserve a competitive advantage. The goals of circular supply chains, which are founded on the ideas of the circular economy, are to reduce waste, promote resource regeneration, and lessen their negative effects on the environment. The study highlights the critical role that digital technologies play in guaranteeing transparency, promoting real-time monitoring, and optimizing resource distribution. Blockchain offers traceability and trust, IoT boosts visibility and operational efficiency within supply chains, and AI improves demand forecasting and resource efficiency. Though the benefits are obvious, there are significant obstacles to overcome, including high implementation costs, complex technological issues, scalability issues, and regulatory barriers. The research demonstrates how these digital technologies can effectively promote sustainability while enhancing operational efficiency through case studies of well-known companies like HP, Walmart, and Philips. The report also highlights important future trends and makes suggestions for companies looking to develop their workforces, invest in digital technologies, and work together on circular economy platforms. In the end, digital tool adoption is essential for creating supply chains that are more sustainable and competitive in the modern global economy.

Impact of hospital volume and facility characteristics on postoperative outcomes after hepatectomy: A mediation analysis

BackgroundThe impact of hospital procedural volume on outcomes after hepatectomy relative to other facility-related factors remains unclear. We sought to define the comparative impact of hospital volume compared with other facility-related factors on postoperative outcomes among Medicare beneficiaries undergoing hepatectomy. MethodsData on patients who underwent hepatectomy between 2013 and 2021 were collected from the Medicare Standard Analytic Files and linked with facility-level data from the American Hospital Association Survey databases. Hospital volume was stratified into high- (top 10%) and low-volume centers. Propensity score matching was used to account for variable imbalances in patient characteristics among high-compared with low-volume centers. Mediation analysis was employed to delineate facility-related factors responsible for the impact of hospital volume on outcomes with a specific focus on incidence of complications, in-hospital mortality, and failure to rescue. ResultsThe analytic cohort included 22,969 patients from 340 institutions. After propensity score matching, receipt of surgery at a high-volume center was associated with a lower likelihood of postoperative complications (39.9% vs 41.7%, P = .01), in-hospital mortality (2.2% vs 2.8%, P = .02), and failure to rescue (5.4% vs 6.5%, P = .04) versus low-volume centers. Mediation analysis revealed that hospital capacity (bed capacity and nurse-to-bed ratio) contributed the most to the variations in risk of complications and in-hospital mortality, whereas liver transplant program status had the largest impact on failure to rescue. ConclusionsHospital volume is a significant determinant of postoperative outcomes after hepatectomy, with hospital capacity and liver transplant program status being important mediators of this effect. Centralization and optimal resource distribution are important to achieve favorable outcomes following liver resection.

Global supply chain flow planning for Chinese manufacturing under the BRI: An SFG-DRO method

The Belt and Road Initiative (BRI) has fostered the free flow of commodities and optimal distribution of resources for the global manufacturing supply chain (GMSC), meanwhile confronted with risks and uncertainties. This paper proposes the SFG-DRO method, which can well solve the flow distribution problem of GMSC under the BRI with uncertainties. The proposed method combines trend prediction from the stochastic frontier gravity (SFG) model with 1-Wasserstein fuzzy set-based distributionally robust optimization (DRO). Additionally, this paper integrates the principles of the column and constraint generation (C&CG) algorithm with the particle swarm optimization (PSO) algorithm to handle the above two-stage nonlinear problems efficiently. The results show that the SFG-DRO distribution method significantly reduces transportation costs by 25% and enhances security. By examining flow distribution across three key BRI channels, this paper identifies underexploited markets along the southward channel and substantial market potential with security risks along the northeast sea-land channel, proposing relevant improvement suggestions.

Improved Cell Allocation Strategies Using K-Means Clustering in Congested 6TiSCH Environments.

The 6TiSCH protocol (IEEE 802.15.4e) is crucial for the Industrial Internet of Things (IIoT), utilizing a time-slotted channel hopping (TSCH) mode based on node distribution. In this study, we propose an innovative cell allocation strategy based on node position clustering using the K-means algorithm, specifically designed to address congestion and optimize resource distribution in the 6TiSCH network. Our mechanism effectively groups nodes into clusters, allowing for dynamic adjustment of cell capacities in congested areas by analyzing traffic patterns and the spatial distribution of nodes. This clustering approach enhances the efficiency of slot frame utilization and minimizes communication delays by reducing interference and improving routing stability. The proposed strategy leverages the clustering results to improve cell usage efficiency and reduce communication latency between nodes. By tailoring cell allocation to the specific traffic needs of each cluster, we significantly reduce packet loss, manage congestion more effectively, and enhance data transmission reliability. We evaluated the clustering method using the K-means algorithm through experiments with the 6TiSCH simulator. Additionally, we considered using objective functions in Routing Protocol for Low-Power and Lossy Networks (RPL), such as OF0 and MRHOF, to assess clustering results and their impact on throughput and packet delivery. Our method resulted in significantly improved average performance metrics. Under the OF0 routing protocol, we achieved a 30.01% latency reduction, a 15.95% faster joining time, an 8% higher packet delivery ratio, and a 13.82% throughput increase. Similarly, we observed a 12.34% improvement in packet delivery ratio, 21.06% latency reduction, 12.68% faster joining time, and 25.97% higher throughput speed with the MRHOF routing protocol. These findings highlight the effectiveness of the improved cell allocation strategy in congested 6TiSCH environments, offering a better solution for enhancing network performance in IIoT applications.

Strategic Enhancement of Healthcare Services During the Hajj Season in Makkah: A Comprehensive Geographic Information System (GIS) Analysis.

Annually, over two million international pilgrims embark on the Hajj pilgrimage to Makkah, presenting a significant challenge for healthcare services. This study analyzes the spatial distribution of healthcare facilities in the Al Mashaer area using Geographic Information System (GIS) technology to enhance healthcare during this religious gathering. It evaluates the accessibility and efficacy of healthcare facilities, including primary care centers, clinics, and hospitals, each addressing distinct medical needs to ensure a holistic approach for pilgrims. The study maps the distribution, service radius, and services offered by each facility, along with an analysis of travel distances and times, to evaluate the viability of healthcare services. Identifying coverage gaps and accessibility issues is critical for making strategic recommendations to enhance resource allocation and distribution. The research addresses challenges such as data precision, population density, infrastructural constraints, and resource limitations. The study offers recommendations to optimize resource distribution, improve transportation strategies, expand healthcare capacity, and enhance cultural competency, resulting in improved healthcare accessibility, reduced congestion, quicker medical responses, and a safer pilgrimage experience, promoting a world-class pilgrimage management system.

Impacts of the Middle Route of the South-to-North Water Diversion Project on Land Surface Temperature and Fractional Vegetation Coverage in the Danjiang River Basin

The Middle Route of the South-to-North Water Diversion Project is a critical infrastructure that ensures optimal water resource distribution across river basins and safeguards the livelihood of people in China. This study investigated its effects on the land surface temperature (LST) and fractional vegetation coverage (FVC) in the Danjiang River Basin. Moreover, it examined the spatial and temporal patterns of this project, providing a scientific basis for the safe supply of water and ecological preservation. We used the improved interpolation of mean anomaly (IMA) method based on the digital elevation model (DEM) to reconstruct LST while FVC was estimated using the image element dichotomous model. Our findings indicated a general increase in the average LST in the Danjiang River Basin post-project implementation. During both wet and dry seasons, the cooling effect was primarily observed in the south-central region during the daytime, with extreme values of 6.1 °C and 5.9 °C. Conversely, during the nighttime, the cooling effect was more prevalent in the northern region, with extreme values of 3.0 °C and 2.3 °C. In contrast, the warming effect during both seasons was predominantly located in the northern region during the daytime, with extreme values of 5.3 °C and 5.5 °C. At night, the warming effect was chiefly observed in the south-central region, with extreme values of 5.8 °C and 5.9 °C. FVC displayed a seasonal trend, with higher values in the wet season and overall improvement over time. Statistical analysis revealed a negative correlation between vegetation change and daytime temperature variations in both periods (r = −0.184, r = −0.195). Furthermore, a significant positive correlation existed between vegetation change and nighttime temperature changes (r = 0.315, r = 0.328). Overall, the project contributed to regulating LST, fostering FVC development, and enhancing ecological stability in the Danjiang River Basin.

Optimal distribution of emergency resources to accidents for pre-rescue in chemical industrial parks

Medical and fire resources are scheduled to chemical industrial parks which have caused mass casualties due to accidents. However, most of models of emergency logistics do not account for pre-rescue before large-scale rescue which may increase the casualty rate because of the long emergency response time and distance. This paper presents a mathematical model proposed for optimal distribution of emergency resources for rescuing the trapped victims who are highly likely to be seriously injured or even killed before the arrival of ambulances and fire engines. Due to the different types and risks of accidents at various locations have different impacts on people, we consider the detailed characteristics of accident scenarios and maximum tolerance time for the trapped victims to determine the priority of rescue sites. In addition, this paper proposes two indices of pavement resistance coefficient and obstacle to reflect the environment after accidents, which are not involved in other models of emergency resources distribution. Based on these, a penalty cost objective function is established to maximize the rescue rate. The optimal delivery route which reflects the distribution process is planned for the robot that will save more casualties. Two simulations and a real accident case study were conducted by minimizing the objective function, and the numerical results showed the reliability and effectiveness of the presented model for making optimal decisions for emergency distribution to accidents in chemical industrial parks.

ОБОБЩЕННЫЙ АЛГОРИТМ РАСПРЕДЕЛЕНИЯ РЕСУРСОВ КОМПЬЮТЕРНОГО ГЕНЕРАТОРА ИЗОБРАЖЕНИЯ ПО 3D-МОДЕЛИ ТЕРРИТОРИИ МЕСТНОСТИ

The development of modern software systems that use computer graphics algorithms in real time to create 3D-models of the terrain expands the range of training tasks available to the Customer for training pilots on aviation simulators. Particular attention is paid to training pilots in the skills of solving navigation problems while flying over certain 3D terrain, using visual references in the form of three-dimensional models of reference objects. To achieve this goal, it is necessary to solve several important aspects: the selection of 3D-objects on the terrain as reference points, the development of an information resource algorithm and the optimal distribution of computer image generator resources throughout the 3D-model of the terrain to ensure the possibility of synthesizing a sufficient number of 3D-models reference objects in real time. Solutions are presented for modeling a terrain area of sufficient size for distributing information resources and training the pilot to solve navigation problems.

Evaluating the Spatial-Temporal Dynamics of Urbanization in Prefecture Cities of China Using SNPP-VIIRS Nighttime Light Remote Sensing Data

Ensuring the well-being of urban communities hinges on sustainable urban planning strategies informed by current data, particularly in China since urbanization has been one of the most significant demographic shifts in recent decades. Therefore, our research aimed to evaluate the spatio-temporal dynamics of urbanization and sub urbanization across prefecture and provincial levels in China by utilizing consistent SNPP-VIIRS-like and NPP-VIIRS nighttime data spanning the years 2000 to 2020. The k-means method was applied to derive urban and sub urban features from above datasets. The findings uncovered a significant expansion of urban entities at the prefecture level, escalating from 16,209 km2 to 89,631 km2 over the specified period showing a 5% growth. Among five main urban agglomerations, the Yangtze River Delta stands out with the highest urbanization rate, witnessing a remarkable expansion of urban entities from 2,684 km2 to 41,465 km2. This growth reflects an average growth rate of 72.2% per annum. The analysis revealed that the overall area of suburbs expanded from 59,151 km2 to 120,339 km2 between 2012 and 2020 indicating a proportional growth rate ranging from 0.4% to 1.9%. The peak growth rate of suburbs was recorded between 2012 and 2014, reaching 18%. Guizhou, Hunan, and Hubei provinces have exhibited growth rates of 334%, 258%, and 246% respectively while Beijing, Guangdong, Tianjin, and Shanghai have experienced relatively low growth rates of 50%, 56%, 46%, and 17%. The analysis of urban growth with GDP, population, and electricity consumption revealed an inverse relationship during the specified period. Therefore, the findings of this research can provide immense support to sustainable urban planning initiatives at both the provincial and prefecture-level cities in China. The findings can assist city planning authorities in making informed decisions regarding optimizing resource distribution, all while prioritizing the preservation of ecological footprint within urban environments. Also, the limitations addressed in our study must be taken into account in future research works aimed at deriving reliable urban extraction results using nighttime light remote sensing data.

Researcher, research thyself? Mapping the landscape of canine health and welfare research funding provided by UK not-for-profit organisations from 2012-2022.

Research into canine health and welfare is supported by Government, charitable and private UK funding organisations. However, there is no current overall visibility or coordination of these funding activities, potentially compromising optimal distribution of limited resources. This study aimed to survey UK canine health and welfare funding by not-for-profit funders between 2012 and 2022, providing a novel baseline analysis to inform future sector stakeholder priorities. Funding data were collected from 10 wide-scope funders (UK Government funding councils and medical charities), 18 animal-directed funders (organisations specifically concerned with animal health and welfare) and 81 breed community groups. These 109 UK funders together provided traceable canine-relevant funding of £57.8 million during the surveyed period, comprising 684 individual grant awards supporting over 500 separate research projects. Wide-scope funders contributed £41.2 million (71.2% of total funding); animal-directed organisations, £16.3 million (28.1% of total funding); and breed-specific groups, £370K (0.6% of total funding). Individual grants ranged from £2.3 million to £300. Funding patterns varied between sectors. Animal-directed funders provided £14.7 million of canine-relevant research funding that foregrounded the dog, 73% of all such funding; wide-scope funders provided £17.5 million of canine-relevant One Health research funding, 97% of all such funding. Customised metrics developed for this study assessed the 'benefit to the dog' and 'pathway to impact' of individual research projects. Overall, studies supported by animal-directed funders achieved significantly higher 'benefit to the dog' scores (Mann-Whitney U = 45235, p<0.001) and 'pathway to impact' scores (Mann-Whitney U = 43506.5, p<0.001) than those supported by wide-scope funders. The landscape of UK not-for-profit funding of canine health and welfare research is complex, with considerable variation between providers. Although wide-scope funders provide the majority of overall canine-relevant research funding, animal-directed funders provide the majority of canine-focused funding and support research with greater direct impact on canine welfare. Visibility of past funding patterns will enable stakeholders in this sector to make more informed decisions about future research. To increase clarity, certain words and phrases are used in specific ways within the context of this paper. Animal-directed funders-Charities and other funding organisations whose remit primarily concerns animals or veterinary work Canine-focused research-Investigations where the primary purpose is to advance understandings of canine health and/or welfare Canine-relevant research-All research that is framed as advancing understandings of canine health and/or welfare as a primary or subsidiary purpose Institution-Refers to universities and other centres where research is carried out Organisation-Refers to funding bodies, including research councils, charities and other groups Research grant-A single funding event originating from one or more funders Research project-A cohesive piece of research concerning a particular topic; may involve multiple researchers and/or multiple research grants, in series or in parallel Wide-scope funders-Large organisations whose remit does not primarily concern animals, i.e. (in this dataset) UKRI councils and the Wellcome Trust.

Evaluating the spatial accessibility and spatial layout optimization of HIV/AIDS healthcare services in Shandong Province, China

Improving access to HIV/AIDS healthcare services is of great concern to government and policymakers striving to strengthen overall public health. How to reasonably allocate HIV/AIDS healthcare resources and maximize the equality of access to healthcare services across subdistrict areas has become an urgent problem to be solved. However, there is limited research on this topic in China. It is necessary to evaluate spatial accessibility to improve the accessibility and equity of HIV/AIDS healthcare services. In this study, the improved multi-modal two-step floating catchment area (2SFCA) and inverted 2SFCA (i2SFCA) methods are used to measure the spatial accessibility of HIV/AIDS healthcare services and the crowdedness of the healthcare sites in Shandong Province, China. Then, the theoretical supply and the optimal spatial distribution of resources are calculated and visualized by minimizing the accessibility gaps between demand locations. This study showed that the spatial accessibility of HIV/AIDS service resources in Shandong Province was concentrated and unevenly distributed, and the accessibility scores in the marginal areas of prefecture-level cities were significantly lower than those in other areas. Regions with a large number of doctors had significantly higher levels of spatial accessibility. The ART accessibility scores in the southwest of Shandong Province were higher than those in other regions. As the travel friction coefficient increased, the accessibility scores formed an approximately circular cluster distribution centered on the healthcare sites in geographical distribution. More ART drugs needed to be supplied in marginal areas and more doctors were needed to work on HIV/AIDS in urban areas to address the spatial distribution imbalance of HIV/AIDS healthcare services. This study profoundly analyzed the spatial accessibility of HIV/AIDS healthcare services and provided essential references for decision-makers. In addition, it gives a significant exploration for achieving the goal of equal access to HIV/AIDS healthcare services in the future.

Advances in Computational Methods for Modeling Photocatalytic Reactions: A Review of Recent Developments.

Photocatalysis is a fascinating process in which a photocatalyst plays a pivotal role in driving a chemical reaction when exposed to light. Its capacity to harness light energy triggers a cascade of reactions that lead to the formation of intermediate compounds, culminating in the desired final product(s). The essence of this process is the interaction between the photocatalyst's excited state and its specific interactions with reactants, resulting in the creation of intermediates. The process's appeal is further enhanced by its cyclic nature-the photocatalyst is rejuvenated after each cycle, ensuring ongoing and sustainable catalytic action. Nevertheless, comprehending the photocatalytic process through the modeling of photoactive materials and molecular devices demands advanced computational techniques founded on effective quantum chemistry methods, multiscale modeling, and machine learning. This review analyzes contemporary theoretical methods, spanning a range of lengths and accuracy scales, and assesses the strengths and limitations of these methods. It also explores the future challenges in modeling complex nano-photocatalysts, underscoring the necessity of integrating various methods hierarchically to optimize resource distribution across different scales. Additionally, the discussion includes the role of excited state chemistry, a crucial element in understanding photocatalysis.

Optimizing Network Service Continuity with Quality-Driven Resource Migration

Despite advances in security technology, it is impractical to entirely prevent intrusion threats. Consequently, developing effective service migration strategies is crucial to maintaining the continuity of network services. Current service migration strategies initiate the migration process only upon detecting a loss of service functionality in the nodes, which increases the risk of service interruptions. Moreover, the migration decision-making process has not adequately accounted for the alignment between tasks and node resources, thereby amplifying the risk of system overload. To address these shortcomings, we introduce a Quality-Driven Resource Migration Strategy (QD-RMS). Specifically, QD-RMS initiates the migration process at an opportune moment, determined through an analysis of service quality. Subsequently, it employs a method combining Pareto optimality and the simulated annealing algorithm to identify the node most suitable for migration. This approach not only guarantees seamless service continuity but also ensures optimal resource distribution and load balancing. The experiments demonstrate that, in comparison with conventional migration strategies, QD-RMS achieves superior service quality and an approximate 20% increase in maximum task capacity. This substantiates the strategic superiority and technological advancement of the proposed strategy.

Long-Term Optimal Generation Expansion Planing considering CO2 Reduction Policies and Mechanisms

In a deregulated power system, a well-designed market mechanism could promote the optimal distribution of resources within the system. As one of the major sources of CO2 emissions, power industry can be seriously impacted by carbon emission related policies and regulations. This paper considers three widely promoted emission policies, using a long-term power system planning model to obtain the optimal generation expansion plan for twenty years. By analysing and comparing system investment schemes under different emission-related mechanisms, the processes of how these policies influence generation investment are revealed. The effectiveness of these policies is compared to determine the optimal carbon emission mechanism.

6G Traffic Prediction with a Novel Parallel Convolutional Neural Networks Architecture and Matrix Format Method Integration

In the evolving world of wireless communication, sixth generation (6G) networks represent a significant leap forward. Beyond its high-speed and reliable communication, 6G integrates Artificial Intelligence (AI), making networks intelligent entities. This elevates the infrastructure of smart cities and other ecosystems. A critical factor in 6G's success is real-time traffic analysis. As 6G aims to interconnect billions of devices, it faces unprecedented traffic patterns. Practical traffic analysis ensures optimal performance, resource distribution, and energy efficiency. It also supports the network in handling vital sectors like healthcare and transportation by anticipating congestion and prioritizing crucial data. However, traditional traffic analysis techniques designed for earlier generations cannot accommodate 6G's demands. With 6G's integration of diverse technologies, understanding traffic becomes more challenging. Recent advancements have incorporated deep learning architectures, notably Convolutional Neural Networks (CNNs), for traffic analysis. While these models show potential, adapting them to 6G's specifics remains challenging. This research presents a unique parallel CNN architecture for 6G traffic prediction. It converts network data into an image using the Matrix Format Method (MFM), making it suitable for CNN processing. This innovation addresses the limitations of traditional methods and meets 6G's requirements. Compared to other models, our parallel CNN architecture highlights enhanced performance, promising increased traffic prediction accuracy. It also paves the way for improved resource allocation, energy management, and quality of service in 6G environments.

ПЕРИОДИЗАЦИЯ ЭКОНОМИЧЕСКОГО ПЛАНИРОВАНИЯ В РЕГИОНАХ РОССИИ НА ОСНОВЕ НОРМАТИВНЫХ ИЗМЕНЕНИЙ

The purpose of the article is to identify the periodization of the development of regional economic planning, the criterion of which was the degree of regulation of planning work in the regions by the federal center and the needs of the regions themselves for strategic documents that allow them to resolve key problems of territorial economic development. The article is devoted to the issues of periodization of regional socio-economic planning. It is indicated that planning is the most important foundation, thanks to which there is an optimal distribution of resources and the achievement of national and regional development goals. As a result of the study, six periods of development of regional planning in Russia were identified: – the period of directive regional planning within the framework of the Soviet economy (1917–1991) – a time of absence of widespread economic and planning self-sufficiency of the regions;– the period of “planning failure” (1991–1995) – a time of widespread independence of regions and rejection of planning mechanisms; – the period of program documents and regional initiatives in planning (1995–2002) – the time of development of strategic plans from below – by decision of regional authorities, initiative regional organizations; – a period of growing attention from the federal center to regional planning issues (2002–2014) – development of standard layouts and recommendations for long-term strategic plans of the regions on the part of the federal center; – the period of formation of the regional strategic planning system (2014–2021) – the time when mandatory requirements and guidelines for regional planning documents appeared; – development of the trend of increasing controllability of regional strategizing and growth of technocraticism (2020–2023) – time to specify the requirements of the federal center for the indicators of regional planning documents. In conclusion, the article presents the idea that over the past decades in Russia there have been processes of growth in the controllability of regional planning through the introduction of specific requirements and guidelines, as well as mechanisms and tools that should contribute to the harmonization of planned indicators of the federal center and regions

The Role of Fog Device Density in IoT-Fog-Cloud Systems

Fog computing has emerged as an essential technology for enabling real-time, low-latency responses in cloud-based applications within Internet of Things (IoT) systems. This study explored the impact of the number of fog devices (NFDs) on the performance of IoT-fog-cloud systems. Through comparative analysis, two scheduling algorithms—First Come First Serve (FCFS) and Priority—were evaluated across different scales of system deployment. The results indicated that the FCFS algorithm was optimal for systems with fewer NFDs, whereas the Priority algorithm proved advantageous in larger settings. These findings not only establish guidelines for selecting appropriate scheduling strategies based on the scale of fog device deployment but also provide strategic insights for businesses on the optimal distribution of computational resources. This research aids companies in deciding whether to centralize resources in fewer branches with more employees or to decentralize into more branches with fewer employees, thereby optimizing operational efficiency and responsiveness.

Designing and Solving two Mathematical Models for the Optimal Distribution of Urban Water Resources

The lack of optimal distribution of water resources in cities with a lack of quality water resources can be a threat to the sustainability of these cities.&#x0D; The present study seeks to design a method for urban water distribution among urban districts using two optimization mathematical models applied in Qom Responding to the water needs of the consumption districts was to be carried out with the least distribution costs, towards which end the first mathematical model was designed. Thus, by solving the first model, it will be clear how much water is needed to be transferred from each surface and ground resources that, while considering resources’ limitation of allocation to consumption districts, the needs of the districts are satisfied and the costs of the water company kept at the minimum.&#x0D; The scarcity of high-quality water resources in Qom has rendered water allocation from these resources to all consumption districts in the city impossible. On the other hand, urban consumption districts demand the highest-quality water given their predominant fabric (residential, commercial, or industrial). Thus, distribution management must take measures to allocate high-quality water to each district depending on its fabric, to which end the second mathematical model has been designed. Hence, solving the second model would answer the question of whether, by using the mathematical model of water distribution from various resources with different qualities, it would be possible to satisfy citizens’ needs in terms of water quality (so that the quality of the allocated water is higher than the quality demanded by the district) and ensure that optimized resource allocation would take place at minimum costs for the company.