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Subject. The article focuses on institutional transformations in bodies of the Federal Treasury in adopting the treasury payment mechanism and treasury service. Objectives. I analyze the performance of the public finance system in Russia during the COVID-19 crisis, and identify steps of the institutional environment emergence so as to adopt the treasury payment system and treasury service. I also evaluate whether it is possible to ensure the balance of the federal budget and regional budgets in Russia by placing temporarily available funds when introducing the treasury payment mechanism and implementing the technology of the single treasury account. Methods. The study is based on the systems and institutional approaches. I also resorted to general methods, such as the analysis, synthesis, comparison. Results. The revenue of the federal budget was found to drop considerably due to the lower business activity in Russia and worldwide and a decrease in the demand and prices for strategic power resources. The article outlines steps of the institutional environment emergence to adopt the treasury payment system and treasury service. Additional income from the placement of temporarily available federal and regional funds was found to be insufficient to ensure the balance of the above budgets. Conclusions and Relevance. Going beyond the structural conservatism of the national economy will lower the public finance sustainability risks amid rapidly changing development challenges, including the COVID-19 that outbroke worldwide in 2020. The findings can be used for practical purposes by the Federal Treasury and other parties to the treasury payment systems so as to improve its performance.

The subject of the study is public–private partnership (hereinafter – PPP) mechanisms in the modern economy of the Russian Federation. The article considers economic factors affecting the demand for PPP mechanisms in Russia from the state and business. The paper carries out a comparative assessment of the supply and demand of PPP and alternative mechanisms of interaction between the state and business. The study reveals that PPP does not have significant advantages in comparison with traditional public procurement or leasing. According to the results of the study, the author makes a forecast that in the near future we should expect a significant reduction in the volume of private and public co-financing of projects implemented on the basis of PPP. Assumption by state additional obligations to finance projects in the absence of budget restrictions can increase the demand for PPP on the part of business.

Crowdsourcing has become a popular paradigm to leverage the collective intelligence of massive crowd workers to perform certain tasks in a cost-effective way. Task assignment is an essential issue in crowdsourcing platforms owing to heterogeneous tasks and work skills. In this article, we focus on assigning workers with diversified skill levels to crowdsourcing tasks with different quality requirements and budget constraints. Task assignment is fundamentally a many-to-one matching problem, where one task is allocated to multiple users who can meet the minimum quality requirement of the task within the limited budget. While most existing works try to maximize the utility of the crowdsourcing platform, we take into account the individual preferences of crowdsourcers and workers toward each other to ensure the stability of task assignment results. In this article, we propose task assignment mechanisms that can guarantee stable outcomes for the many-to-one matching problem with lower and upper bounds (i.e., quality requirement and budget constraint) in regard to heterogeneous worker skill levels. Extensive simulation results show that the proposed algorithms can greatly improve the success ratio of task accomplishment and worker happiness compared with existing algorithms.

This paper proposes a multiple discrete continuous (MDC) choice modelling framework with the following features for analyzing individuals’ time use decisions: (a) accommodates non-additively separable (NAS) utility functions with respect to time allocation across different non-work activities, (b) considers both time and money constraints by integrating the two constraints into a single economic constraint, and (c) allows corner solutions for (i.e., non-participation in) discretionary, non-work activities. The proposed framework is applied to analyze weekly time use of employed individuals in Netherlands using a data set derived from the Time use and Consumption survey of the Longitudinal Internet Studies for the Social Sciences (LISS) panel. The empirical results of the proposed model are compared to those from two simpler models – (a) a model that ignores the monetary budget constraint but employs the NAS utility function and (b) a model that considers both time and money budget constraints but uses a simpler, additively separable utility function. The results suggest that considering both time and money constraints, when combined with the NAS utility function, facilitates in capturing rich substitution patterns in time allocation to non-work activities, in addition to revealing demographic heterogeneity in satiation effects. Further, the proposed model offers a superior goodness of fit when compared to that from the simpler models that ignore either the monetary constraint or the NAS utility function. Finally, the model results provide important policy insights for reducing demographic inequities in time allocation among employed individuals.

This research analyses the re-organization of a blood supply chain organization. Blood supply chain network design is a hard problem. Uncertainties of the blood supply and demand, perishability of blood over time and compatibility of blood types are some factors that make the problem difficult. This paper presents a novel multi-objective mixed-integer location-allocation model for a blood supply chain design problem. Unlike many studies on blood supply chain design in the literature, supply chain network consisting of mobile and permanent units is planned together effectively with our mixed-integer programming model. Multi-objective structure of the model minimizes distances between the blood supply chain elements and the length of the mobile unit routes. The objectives are prioritized by experts using the Analytical Hierarchical Process. Finally, the model is implemented on a real life case study using real data from the Eastern Anatolia region of Turkey for various supply demand scenarios. The solutions offered by the model are compared with the current situation in the region. It is shown that the proposed model gives at least %25 more effective solutions. Moreover, sensitivity analysis on the budget constraint is conducted, and robustness of the model is empirically illustrated.

A stochastic flow network composed of multistate arcs can be utilized to describe several practical systems such as computer networks, where transmission time taken for sending data to a sink is an important index. Determining a path with minimum transmission time is known as the quickest path problem (QPP). All algorithms addressing the QPP assume that the determined minimal paths (MPs) are disjoint. Further, for the general case of intersectional MPs, if a congestion phenomenon occurs during the transmission process, these algorithms will lead to an incorrect outcome. Moreover, in practical scenarios, as a budget limit is considered, spare routing is applied to consolidate the system. The objective is to develop an algorithm based on Monte Carlo simulations (MCSs) for evaluating the system reliability while considering the congestion phenomenon. The system reliability is the probability that a specific amount of data can be transmitted successfully through multiple MPs under both time and budget constraints. Furthermore, spare routing to increase the system reliability is established in advance to specify the main and spare MPs. Experiments validate the evaluation of system reliability based on MCSs. The credibility and efficiency of the proposed algorithm are also discussed.

Without proper flow channelization, congestion and overcrowding in pedestrian traffic may lead to significant inefficiency and safety hazards. Thus, the design of guideway networks that provide a fine balance between traffic congestion and infrastructure construction investment is vital. This paper presents a mathematical formulation and topology optimization framework for paved pedestrian guideway design under physics-based traffic equilibrium in a continuous space. Pedestrians are homogeneous, and their destination and path choices under the Nash equilibrium condition are described by a set of nonlinear partial differential equations. The design framework optimizes the deployment of pavement, which alters the road capacity and directly affects pedestrians’ free flow travel speed. A maximum crowd density constraint is included in the design model to address public safety concerns (e.g., over stampede risks). A series of numerical experiments are conducted to illustrate the effectiveness of the proposed model as well as solution techniques. The proposed framework, which builds on the traffic equilibrium theory, produces optimized guideway designs with controllable maximum pedestrian density, accounts for budget constraints (through an adjustable multiplier that balances pavement construction and travel costs), and allows for control of the spatial configuration of road branches. Comparison with lamellar structures and more conventional guideway designs demonstrates better performance of the outcomes from the proposed modeling and optimization framework.

A highway in poor conditions can raise transportation costs. Due to budgetary constraints, pavement maintenance programming is considered a difficult decision-making problem. In this article we pro...

Selecting a set of requirements to implement in the next software release is an NP-Hard problem known as NRP. We propose multi-objective versions of grey wolf optimizer and whale optimization algorithm for solving bi-objective NRP. We used these two algorithms and three other evolutionary algorithms to solve NRP problem instances from four datasets. The cost-to-score ratio and the roulette wheel are used to satisfy constraints of the NRP problem. We compare obtained Pareto fronts based on eight quality indicators. In addition to four general multi-objective optimization quality indicators, the three aspects of fairness among clients and also uncertainty are reconfigured as quality indicators. These quality indicators are computed for a Pareto front. Results show that MOWOA performs better than others and makes requirement selection fairer. MOGWO works better than the rest when budget constraints are reduced.

In the United States (U.S.), most reports regarding racial disparities in incidence and death from COVID-19 understate the importance of majority-Black local jurisdictions’ fiscal capacity in shapi...

The article deals with the tasks of an applied nature. It was found out what preparatory tasks are advisable to consider when studying a course in mathematics. The article also indicates that the proposed tasks contribute to the development of applied mathematical culture, the development of the necessary skills for applying mathematical knowledge and methods of action when solving practical problems.

We provide the first firm-level evidence of the relation between state ownership and debt structure. Using an international sample of newly privatized firms (NPFs) from 76 countries over the 1998– 2017 period, we find that state ownership is associated with a more diversified debt structure. This evidence is more pronounced for higher levels of state control, and is robust to accounting for endogeneity, using alternative samples, and controlling for other owner types. Additional analysis shows that our main evidence is consistent with the soft budget constraint, political, and social views of state ownership. Our results have several policy implications for financial system stability and the efficient allocation of financial resources in the economy.

The increasing need for improving mobility and road safety has led developing countries to make significant changes in their infrastructure, especially when it comes to the modernization of the transport infrastructure. The purpose of this paper is to present the experience and challenges of the implementation of intelligent transportation systems (ITS) in Qatar, a developing country in the Middle East. ITS has been developed in the country and currently in the implementation stage. A detailed review of existing and proposed ITS technologies is provided. Many challenges were identified to achieve a fully functional, practical, and integratable ITS network. Some of these challenges include coordination with different stakeholders, adopting different countries’ ITS systems, keeping up with the technology, integration with existing systems, and budget constraints. The paper provides lessons learned that can benefit other developing countries going through the same transition.

Mobile CrowdSensing (MCS) has emerged as a novel paradigm for performing large-scale sensing tasks. Many incentive mechanisms have been proposed to encourage user participation in MCS. However, most of them ignore the inevitable cold start stage of MCS, where the MCS system has just begun releasing tasks. Also, they all adopt the single-round incentive without considerations of the continuous cumulative effect. Given the severe shortage of participants in the cold start stage of MCS, this paper proposes a Multi-Round Incentive Mechanism (MRIM). MRIM is based on monetary incentives by adopting multi-round cooperation and alternating between task information diffusion and task allocation operations, both of which are NP-hard problems even without inter-round coupling imposed by system budget constraints. We explore a method to predict the probability of users participating in tasks accurately. Furthermore, we present an efficient task information diffusion algorithm to maximize the number of users participating in tasks by submitting bids. We propose a fast task allocation algorithm based on truthful auction, comprising an approximation algorithm for solving the one-round winner selection and payment calculation. With budget constraints, MRIM maximizes the number of completed tasks by iteratively performing task information diffusion and task allocation. We also prove that MRIM also possesses desired properties such as computational efficiency, user rationality, platform profitability, and price truthfulness, which can further guarantee the robustness of MRIM. The extensive simulations conducted on real-world datasets have proved the efficiency of MRIM.

Public sector organizations have the responsibility to provide public services, while having to deal with a dynamic and turbulent environment and budget constraints. The pressure to innovate and improve performance has led these organizations to look for more effective management methods and tools. Management Control Systems (MCS) have been used to improve organizations’ performance and previous studies show their relevance in private and non-profit sectors, but there is a gap in the literature concerning the adoption of MCS in the public sector. This work aims to study the adoption and impact of MCS in a sample of public organizations. Based on fsQCA, our study shows how MCS are associated with the accomplishment of organizational objectives. These results contribute to the ongoing academic debate on MCS in the public sector, while being useful for top managers in public sector organizations aiming to improve the performance through the implementation of MCS packages.

Workflow scheduling is crucial to the efficient operation of cloud platforms, and has attracted a lot of attention. Up to now, many algorithms have been reported to schedule workflows with budget constraints, so as to optimize workflows' makespan on cloud resources. Nevertheless, the hourly-based billing model in cloud computing is an ongoing challenge for workflow scheduling that easily results in higher makespan or even infeasible solutions. Besides, due to data constraints among workflow tasks, there must be a lot of idle slots in cloud resources. Few works adequately exploit these idle slots to duplicate tasks' predecessors to shorten their completion time, thereby minimizing workflow's makespan while ensuring its budget constraint. Motivated by these, we propose a task duplication based scheduling algorithm, namely TDSA, to optimize makespan for budget-constrained workflows in cloud platforms. In TDSA, two novel mechanisms are devised: 1) a dynamic sub-budget allocation mechanism, it is responsible for recovering unused budget of scheduled workflow tasks and redistributing remaining budget, which is conducive to using more expensive/powerful cloud resources to accelerate completion time of unscheduled tasks; and 2) a duplication-based task scheduling mechanism, which strives to exploit idle slots on resources to selectively duplicate tasks' predecessors, such advancing these tasks' completion time while trying to ensuring their sub-budget constraints. At last, we carry out four groups of experiments, three groups on randomly generated workflows and another one on actual workflows, to compare the proposed TDSA with four baseline algorithms. Experimental results confirm that the TDSA has an overwhelming superiority in advancing the workflows' makespan (up to 17.4%) and improving the utilization of cloud computing resources (up to 31.6%).

We propose a method, whose purpose is to combine a set of GPS traces collected by bicyclists with a set of notifications of problematic situations to determine an optimal action plan for solving safety related problems in a t raffic network. In particular, we use optimization to determine which problem locations to resolve under a given budget constraint in order to maximize the number of impediment free trips. The method aims to suggest a priority of impediments to resolve, which would be manually infeasible. %A set of GPS traces for bicyclists and a set of notifications by bicyclists of problematic situations (spots identified by GPS records) had been collected independently. The data collection periods did not coincide but overlapped and none was contained in the other one. The aim of our work is to use both datasets to determine an optimal action plan for problem solving given a limited budget. The proposed method consists of two steps. First, problematic locations are clustered, where each cluster corresponds to a so-called impediment. Each impediment is associated with trips nearby using a distance function. The trip set is partitioned by matching each trip with the largest set of its affecting impediments. Solving all impediments associated with such a part induces a cost and makes the associated part of trips impediment free. The second step aims to find the set of impediments that can be solved with a given budget and that makes the maximum number of trips impediment free. A branch-and-bound optimizer for the second step is presented and evaluated. The clustering parameters affect the set of identified impediments and the extent of each of them. In order to evaluate the sensitivity of the result to the clustering parameters a technique is proposed to consistently estimate the impediment resolution cost. Our study aims to support the interactive urban designer to improve the urban bicycle road infrastructure. By providing a method to prioritize between impediments to resolve, it also aims to contribute to a safer and more attractive traffic situation for bicyclists. The research leading to this paper was partially supported by the Smarta Ofentliga Miljöer II (SOM II) project of the Lund (Sweden) municipality by supplying data.

Dedicated infrastructures are commonly used for urgent computations. However, using dedicated resources is not always affordable due to budget constraints. As a result, utilizing shared infrastructures becomes an alternative solution for urgent computations. Since the infrastructures are meant to serve many users, the urgent jobs may arrive when regular jobs are using the necessary resources. In such a case, it is necessary to preempt the regular jobs so that urgent jobs can be executed immediately. Most conventional methods for job scheduling have focused on reducing the response times and waiting times of all jobs. However, these methods can delay urgent jobs and hinder them from being completed within a stipulated deadline. Furthermore, in heterogeneous systems with coprocessors, preemption becomes more difficult because coprocessors rely on several system software functionalities provided by the host processor. In this paper, we propose a parallel job scheduling method to effectively use shared heterogeneous systems for urgent computations. Our method employs an in-memory process swapping mechanism to preempt jobs running on the coprocessor devices. The results of our simulations show that our method can achieve a significant reduction in the response time and slowdown of regular jobs without substantial delays of urgent jobs.

Since 1991, China has implemented two significant tax reforms. The first reform, in 1994, was a large-scale adjustment of the tax distribution system between the central and local governments, and the second reform, in 2012, replaced business tax with value-added tax. Also, the size of China’s underground economy decreased from 13.55% in 1995 to 12.30% in 2016. The paper presents an evaluation of the effect of the two tax reforms and the existing underground economy on GDP growth in China. GDP is defined as explained variable, the explanatory variables include: the ratio of declared income to actual income, the change of concealed income, and the influence of tax rate change on declared income and concealed income. According to the tax reform in 1994 and 2012, two dummy variables are set respectively. In methodology, this paper uses Simultaneous equations model, SUR-OLSs and Slutsky identity. Our estimation is based on the official statistics of China National Bureau of Statistics in the period from 1991 to 2019. In empirical analysis, we decomposed tax changes into tax rate effect (change of budget constraint slope) and income effect (change of tax liability), then analyzed the impact of tax elasticity on GDP growth. The empirical results demonstrate that both the 1994 tax reform and 2012 tax reform have had a positive impact on GDP, with high statistical significance respectively. The results also confirm that the increase of tax rate leads to the increase of hidden income, which eventually leads to the decrease of GDP. The offered methodology can also be applied to most countries for time series analyses. For citation Wang Y.K., Zhang L. Underground economy and GDP growth: Evidence from China’s tax reforms. Journal of Tax Reform. 2021;7(1):87–107. DOI: 10.15826/jtr.2021.7.1.092 Article info Received December 15, 2020; Revised February 26, 2021; Accepted March 15, 2021

The focus of this work is on designing influencing strategies to shape the collective opinion of a network of individuals. We consider a variant of the voter model where opinions evolve in one of two ways. In the absence of external influence, opinions evolve via interactions between individuals in the network, while, in the presence of external influence, opinions shift in the direction preferred by the influencer. We focus on a finite time-horizon and an influencing strategy is characterized by when it exerts influence in this time-horizon given its budget constraints. Prior work on this opinion dynamics model assumes that individuals take into account the opinion of all individuals in the network. We generalize this and consider the setting where the opinion evolution of an individual depends on a limited collection of opinions from the network. We characterize the nature of optimal influencing strategies as a function of the way in which this collection of opinions is formed., 6 pages

The construction industry features a major impact on all country economies. It is one among the components that contributes to the expansion of any economy. The achievement of the project's goal and goals within the time and budget constraints is critical to any project's success. In this article, an attempt has been made to figure out how much time is delayed and how much cost is overrun on the Kanaka Durga Flyover in Vijayawada. Planning of the Kanaka Durga flyover alignment plan and crash barrier & expansion joints progress map using AutoCAD software were done. It is found that due to river Krishna Pushkaralu, unexpected water overflow at barrage, lack of machinery and its maintenance, lack of skilled labor pool and improper scheduling and high change of project scopes are the explanations liable for delays. As a result, it is often concluded that the careful planning and follow-up will help to stop cost overruns and time delays.

Beslenme ve sağlık ekonomisi sonuçları arası ilişki birey ve toplum düzeyinde önem taşımaktadır. Günümüzde sağlık bakım hizmetleri yüksek mali sınırlılıklar yaşamakta ve eldeki olanaklarla maksimum olası sağlık yararlarının sağlanabilmesi için politika yapıcılar ile sağlık bakım hizmeti sunanlar büyüyen baskı altındadır. Çalışmalar hastalıkların tedavisi için diyetisyen tarafından verilen yüz yüze tıbbi beslenme tedavisinin klinik etkinlikte, maliyet ve yararında önemli iyileşme sağladığını göstermektedir. Beslenme müdahalelerinin ve tıbbi beslenme tedavisinin maliyet ve yararını değerlendirmek üzere ekonomik çerçevenin oluşturulması gelecekte yürütülecek olan araştırmalarda önemli bir alanı oluşturacaktır. Bu derleme yazıda diyetisyen tarafından verilen tıbbi beslenme tedavisinin maliyet ve yararı, klinik etkinliği özetlenecektir. The relationship between nutrition and health economic outcomes is important at both in the individual and the populationlevel. Nowadays, health-care systems are currently facing tremendous budget constraints resulting in growing pressureon decision makers and health-care providers to obtain the maximum possible health benefits of the resources available.Studies show that, face-to-face medical nutrition therapy by a dietitian for the treatment of disease led to significantimprovements in clinical effectiveness and cost benefit. Development of an economic framework to evaluate costs andbenefits from nutrition interventions and medical nutrition treatments are a potential area for future research. This reviewsummarizes some studies of medical nutrition therapy in which health-related economic and clinical nutrition implicationsof the intervention by dietitians have been addressed.

Crises like the COVID-19 pandemic affect firms’ innovation management and decision making. On the downside, crises lead to detriments like budget constraints, to which firms often respond by reducing their innovation activities. On the upside, crises are opportunities, where some firms exploiting changing market requirements and necessities excel. No matter in which direction, decision makers must react quickly but often rely on ad-hoc decisions or even gut feeling when drafting their crisis response strategies. Through a series of distinct cases, we demonstrate that innovation management may fill this void through patent analytics. Drawing on biochemical expertise, we particularly describe the functions and effects of COVID-19. To counter downside detriments, firms may circumvent budget constraints by discerning patents that can be (1) monetized, for example via sales or licensing deals, or (2) abandoned to achieve cost-savings, allowing firms to maintain their innovation activities. To realize upside opportunities, firms and governments may use patent analytics to detect key biotechnology firms that are likely to successfully develop treatments and vaccinations against pandemics like COVID-19. Promulgated U.S. interest in relocating foreign firms to the United States is not without technological and commercial reasoning. Herein, the insights of this study contribute to a better understanding of the use of patent information, such as smart patent indicators, harmonized patent data, novel annuity fee measures, and hand-collected datasets of COVID-19 and related antibodies’ patents to the management of innovation in times of crisis.

Kornai (2014) described the problems of municipal indebtedness in Hungary and analysed the process of bailout carried out between 2011 and 2014. In the same period, the central government also reformed the local government system, which included serious limitations of their financial independence. This study re-examines the state of the soft budget constraint (SBC) of Hungarian local governments. To start, the general theoretical framework of SBC is introduced. Then, the budget constraint on the Hungarian local governments before the bailout is described briefly, followed by an assessment of the corresponding measures which were expected to offset the negative messages of the completed bailout and to harden the budget constraint. The study concludes that the central government decided to harden the budget constraint through the introduction of new hierarchical mechanisms, while the development of fiscal discipline stopped. On the one hand, this resulted in the consolidation of municipal budgets, but on the other, it was accompanied by a serious limitation of local autonomy, projects and borrowing in general, while the central government employs specific administrative tools to show favour to some settlements according to its (political) interests.

Given a linear dynamical system affected by noise, we study the problem of optimally placing sensors (at design-time) subject to a sensor placement budget constraint in order to minimize the trace of the steady-state error covariance of the corresponding Kalman filter. While this problem is NP-hard in general, we consider the underlying graph associated with the system dynamics matrix, and focus on the case when there is a single input at one of the nodes in the graph. We provide an optimal strategy (computed in polynomial-time) to place the sensors over the network. Next, we consider the problem of attacking (i.e., removing) the placed sensors under a sensor attack budget constraint in order to maximize the trace of the steady-state error covariance of the resulting Kalman filter. Using the insights obtained for the sensor placement problem, we provide an optimal strategy (computed in polynomial-time) to attack the placed sensors. Finally, we consider the scenario where a system designer places the sensors under a sensor placement budget constraint, and an adversary then attacks the placed sensors subject to a sensor attack budget constraint. The resilient sensor placement problem is to find a sensor placement strategy to minimize the trace of the steady-state error covariance of the Kalman filter corresponding to the sensors that survive the attack. We show that this problem is NP-hard, and provide a pseudo-polynomial-time algorithm to solve it.

According to the fiscal theory of the price level, in a non-Ricardian regime, high public debt should lead to a price-level increase so as to fulfill intertemporal government budget constraint. Forward-looking agents should anticipate this rise by pushing up their inflation expectations. This study tests the above hypothesis by analyzing the impact of fiscal sustainability on the formation of inflation expectations by private-sector agents – consumers and professional forecasters – in European countries. In the study, we estimate models of inflation expectations, and we include public debt among the explanatory variables. The novelty of this study lies in our analysis of whether the relationship between public debt and inflation expectations is affected by the degree to which fiscal policy is sustainable in European economies. We find that fiscal stance influences the inflation expectations of consumers and professional forecasters. Contrary to intuition, the reaction of consensus economic forecasts to public debt in countries with sustainable fiscal policies seems stronger than in economies with less disciplined fiscal policies. This may suggest that fiscal authorities are constrained by the way in which private-sector agents form their expectations, conducting more responsible fiscal policy in countries where the reaction of economic agents to fiscal variables is stronger.

In recent decades, the number of schools in Norwegian rural municipalities has been reduced substantially, often accompanied by much contestation in the local community. In Norway's decentralized governance system, the municipal-level authorities have autonomy over decisions on school structure. However, municipal school administrations may have considerable indirect influence, inter alia in preparing the cases prior to meetings of the local council. This article examines experiences with, perceptions of and main concerns with school-closure processes among those handling school issues in the municipalities. It builds on an online survey of the local school administrations in Norwegian municipalities with fewer than 15,000 inhabitants, as well as case studies in three municipalities where school closure was or had recently been high on the agenda. Although the municipal school administrations recognize the importance of local schools for the communities, their primary concern is to secure a good learning and social environment for pupils, within the defined budgetary constraints. In contested cases, this usually leads them to side with politicians favoring closure. However, acknowledging the importance of the school for the community and the risks of local tensions, municipal school administrations also stress the importance of transparent and open discussions among those involved, before a decision is made.

Roadside unit (RSU) planning is vital for the operation of an intelligent transportation system (ITS). RSUs provide ground coverage limited by obstacles. Unmanned aerial vehicles (UAVs) can complement RSU coverage by providing flexible connectivity capable of adapting coverage for traffic fluctuations, energy consumption, and budgetary constraints that all have effects on ITS operations. This article proposes a general RSU/UAV joint planning solution, where complex dynamic parameters are investigated. The objective is to maximize the effective coverage of placed RSUs and UAV docks given: a budget comprised of periodic operating expenses and capital expenditures, limitations of the ground transceivers and UAVs, and use of renewable energy to offset the on-grid electricity cost. We formulate a mixed-integer quadratically constrained problem that can determine the optimal placement of RSUs and UAV stations, RSU activation schedules, if solar panels are attached, and their coverage during each time period. Due to NP-hard complexity of such a planning problem, we design a heuristic algorithm that produces suboptimal solutions in less time. Afterward, we perform a sensitivity analysis and show that changes to the parameters lead to logical shifts in infrastructure coverage. Additionally, we visualize the algorithm's performance on a large setting—Manhattan Island.

Introduction.The problems of scientific analysis, where the subject of study is the financial independence of undergraduates, mainly focuses on two directories: the study of the academic performance of undergraduate and the factors affecting it; financial condition of undergraduate depending on tuition fees. In modern studies, the issues of financial independence of students, their budget are not given due attention. The employment of students is often seen as one of the factors that negatively affect their academic performance, or in the context of forced work caused by high tuition fees. In today’s pandemic realities, the aspect of the financial independence of students is actualized, while the problem of students’ labor activity during training is of scientific and practical interest as a forced measure to maintain their well-being in order to gain financial independence. Materials and methods.The survey was attended by: 2-4-year students of the Bachelor’s degree program “Applied Economics and Finance” (38.03.01 Economics) of the Institute of Economics and Management of Ural Federal University was carried out. The total number of students in 2-4 courses on the program is 284. Results and discussion.As a result of the study, the key motives for obtaining financial independence have been identified, which boil down either to the forced need to find finances, or to the desire to obtain financial independence and the formation of labor and financial competencies; formulated the basic financial strategies of students in relation to budget planning, budget optimization – passive as an orientation towards transfers from parents and the state, active as a search for grant support or going to work; it was found that the problem of choosing between work and study as an additional criterion included opportunity costs, measured as deterioration in academic performance; it was found that significant financial support from parents (family) and its increase with an increase in student spending forms a “soft budget constraint” for a student, reducing the motivation to gain financial independence. It was founded that more than 20% of 2-4-year students have part-time gob, while 2/3 of the working students do not “sacrifice” their studies for work. This is due to the fact that the motivation for choosing a job is voluntary. The survey data allowed to conclude that students are focused both on the improving of labor and financial competencies. It was revealed that the main source of income for their budget is transfers from parents (more than 90%) but own earns are less than 6% of the student budget. It should be noted that there was a large range in the students’ income: from 2,500 to 36,000 rubles. This may be due to both the income of the family in which the student lives and the model of financial support of the parents: they admit the independence of students according to their budget or the autonomy of their student children is practically absent. Conclusion.The results of this study are aimed at comprehending the educational, scientific, labor activity of a student, taking into account the motives for obtaining financial independence, which can be taken into account in the design of individual educational trajectories of students, the development of grant projects and offers of internship, educational loans, which together ensure the strengthening of the financial independence of students.

The article examines the analysis of current prices in the market of building materials, products, structures, machines, and mechanisms (construction resources), which is performed by construction participants. The aim of the article is to study the possibility of achieving the maximum economic effect of the analysis of current prices in the market of construction resources within a limited budget. A universal mathematical approach is proposed to calculate the economic effect of the analysis of current prices in the market of construction resources, which is based on a selective method of the research of the market value of construction resources. The calculations take into account the difference between the real resources presented on the market and the estimated construction resources (resource position of the estimate), between the market price and the estimated market price of the construction resource. While performing the analysis of current prices on the market of construction resources in the given conditions of the limited budget the option to reduce the general list of resources by a selection of the optimum list with an application of methods of discrete optimization is offered. The condition for the formation of an abbreviated list of resources is the maximum economic effect that can be obtained from the analysis of current prices for selected resources. Method of heuristic rules is proposed, which allow to significantly reducing the initial list of resources by a simple analysis of input data. The application of these rules allows to reduce the variability of input data for discrete optimization or not to carry it out at all, because the amount of resources selected with their help satisfies the condition of the budget constraint. Recommendations on the composition of the source data are given – this is the project documentation and/or the market price of construction resources as a basis for comparison and information on the range of market prices (scope of sample variation) for the resources included in the study. In the article there are given examples of the practical application of the technique in the conditions of the construction industry in Ukraine. In general, the proposed approach can be used at all stages of variant BIMdesign, when it is necessary to determine the most optimal composition of structures and resources that meet the requirements for the life cycle of the object. Including under the condition of gradual detailing of constructs (an increase of LOD of the project), during designing, up to resources at a stage “Working project”. The main advantages of the proposed method are universality – it does not depend on the ultimate goal of the results of the customer and / or contractor and scalability – it can be applied to the structure, its individual design, or several resources.