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Resource reservation is an essential step to enable wireless data networks to support a wide range of user demands. In this paper, we consider the problem of joint resource reservation in the backhaul and Radio Access Network (RAN) based on the statistics of user demands and channel states, and also network availability. The goal is to maximize the sum of expected traffic flow rates, subject to link and access point budget constraints, while minimizing the expected outage of downlinks. The formulated problem turns out to be non-convex and difficult to solve to global optimality. We propose an efficient Block Coordinate Descent (BCD) algorithm to approximately solve the problem. The proposed BCD algorithm optimizes the link capacity reservation in the backhaul using a novel multipath routing algorithm that decomposes the problem down to link-level and parallelizes the computation across backhaul links, while the reservation of transmission resources in RAN is carried out via a novel scalable and distributed algorithm based on Block Successive Upper-bound Minimization (BSUM). We prove that the proposed BCD algorithm converges to a Karush-Kuhn-Tucker solution. Simulation results verify the efficiency and the efficacy of our BCD approach against two heuristic algorithms.

Vehicular crowdsourcing is a promising paradigm that takes advantage of powerful onboard capabilities of vehicles to perform various tasks in smart cities. To fulfill this vision, a well-designed incentive mechanism is essential to stimulate the participation of vehicles. In this paper, we propose a timeliness-aware incentive mechanism for vehicular crowdsourcing by taking vehicle's uncertain travel time into account. In view of the stochastic nature of traffic conditions, we derive a tractable expression for the probability distribution of task delay based on a discrete-time traffic model. By leveraging reverse auction framework, we model the utility of a service requester as a function in terms of uncertain task delay and incurred payment. To maximize the requester's utility under a budget constraint, we cast the mechanism design as a non-monotone submodular maximization problem over a knapsack constraint. Based on this formulation, we develop a truthful budgeted utility maximization auction (TBUMA), which is truthful, budget feasible, profitable, individually rational and computationally efficient. Through extensive trace-based simulations, we demonstrate the effectiveness of our proposed incentive mechanism.

Humanitarian aid organizations generally need to decide how to allocate their limited budget among different materials for disaster preparedness and disaster response. The budget can either be used before the disaster for stocking decisions under uncertainty or it can be used after the disaster to satisfy the required demand at a higher cost when the uncertainty is resolved. In this study, we aim to decide how many of each type of material to procure before and after the disaster considering the budget limitations. For this purpose, we first develop single product models with and without budget constraints using a newsvendor approach. Then, we build a two-stage stochastic model with budget constraint for the two-product case and determine its optimal solution by first solving the second stage problem to allocate the remaining budget among the products after the disaster. Then, we solve the first stage problem using KKT conditions for the resulting constrained nonlinear optimization problem. Finally, we extend our analysis for multiple product cases and propose approximation algorithms utilizing the optimal solutions derived for the single and two-product cases. We provide managerial insights using our results through detailed numerical experiments, and also present a case study with real life data. It is observed that, contrary to expectations, higher quantities need to be purchased before the disaster when there is a limited budget, as compared to the unlimited budget case. When multiple products are considered, it is important to consider the interactions between the products, especially when the budget is moderate.

Try-Before-You-Buy (TBYB) is an emerging retail strategy of online retailers that allows consumers to have multiple items delivered to them and then determine which items they wish to keep or return for free. This paper addresses the TBYB retailer’s problem in its operations: the decision regarding how many items to send to a consumer to maximize its expected profit when the products are chosen by a retailer recommendation system. To this end, we develop a stylized model of the retailer’s quantity decision by taking into account the consumer’s purchase decision, which is influenced by the consumer’s budget constraint and the retailer’s recommendation accuracy. We derive the retailer’s optimal quantity decision and discuss how it is determined. We also examine how the retailer’s optimal quantity decision and corresponding profit are influenced by two factors reflecting the TBYB industry: recommendation accuracy and new consumer influx. Regarding recommendation accuracy, our results show that a minimum level of accuracy exists, which acts as an entry barrier to TBYB retail. Once the retailer’s accuracy exceeds the threshold, we show that the relationship between the accuracy and the optimal quantity is characterized as an inverted-U relationship. Regarding the latter, interestingly, we find that an influx of new consumers may hurt the retailer’s profit even when the accuracy applied to them is above the entry barrier.

Wireless virtualization has become a key concept in future cellular networks which can provide virtualized wireless networks for different mobile virtual network operators (MVNOs) over the same physical infrastructure. Resource allocation is a main challenging issue in wireless virtualization for which auction approaches have been widely used. However, for most existing auction-based allocation schemes, the objective is to maximize the social welfare due to its simplicity. While in reality, MVNOs are more interested in maximizing their own revenues. However, the revenue-maximization auction problem is much more complex since the price is unknown before calculation. In this paper, we give a first attempt for designing a revenue-optimal auction mechanism for resource allocation in wireless virtualization. Considering the complexity in revenue maximization, deep learning techniques are applied. Specifically, we construct a multi-layer feed-forward neural network based on the analysis of optimal auction design. The neural network adopts users' bids as the input and the allocation rule and conditional payment rule as the output. The training set of this neural network is the users' valuation profiles. The proposed auction mechanism possesses several satisfactory properties, e.g., individual rationality, incentive compatibility and budget constraint. Finally, simulation results demonstrate the effectiveness of the proposed scheme.

How firms use workforce size adjustment as a strategic response to external shocks such as exchange-rate fluctuations is an important but not yet fully understood topic. To address the void in the literature, we apply the strategy tripod theoretical framework, which sees the external shocks as an industrial impact, the budget constraints as an institutional force, and the firm leverage as an organisational capability to examine the firms’ strategic response in terms of workforce size adjustment in the Chinese economy context. Based on longitudinal data of Chinese manufacturing firms, our analysis finds that facing exchange rate shocks, firms with hard budget constraint are more responsive in adjusting their workforce than firms with soft budget constraint. Furthermore, highly levered firms, with hard budget constraint, will adjust their workforce more aggressively than firms with less debt in their financial structure.

The urban demographic in most developing countries often depend on fossilized fuel generators to meet the electricity supply gap from the national grid. Due to health and environmental concerns with fossilized fuel, local renewable energy resources offer a tentative replacement but often involve high initial upfront costs for their deployment. This work addresses the initial cost barrier issue with renewable energy deployment by focusing on Nigeria as a case study. More specifically, this work proposes a Solar Nano grid concept as an alternative to fossilized generators. The proposed Solar Nano grid concept presents the flexibility to deploy PV systems based on budgetary constraints and the option of upgrading the system (adding newer units to existing ones) in the future. While adding new units to old ones gives rise to compatibility and aging issues in the PV systems, simulation studies show that it is possible to eliminate such concerns by applying a set of design protocols and principles in the proposed Solar Nano grid. An assembled lab-scale Solar Nano grid system validated the simulation findings. With the validated model, this work puts forth possible strategy for reducing the upfront cost of PV system deployment to about 10% of the initial value.

Problem definition: Operational disruptions can impact a firm’s risk, which manifests in a host of operational issues, including a higher holding cost for inventory, a higher financing cost for capacity expansion, and a higher perception of the firm’s risk among its supply chain partners. Academic/practical relevance: Although disruptions have been studied extensively in the operations management literature, the emphasis has been on mitigating the deleterious impact on the firm’s production potential rather than its risk. We examine whether implementing and credibly attesting to having effective internal control systems will meaningfully influence the impact of operational disruptions on the firm’s risk and market valuation. Methodology: We exploit a 2004 regulatory change that required certain firms to attest to having control systems in place. Our triple-difference regression specification takes advantage of a regulatory anomaly that excluded a well-defined set of firms from complying with the control system requirement. Results: We find that firms that were obligated to comply with this regulatory requirement experienced a materially smaller increase in their risk and a smaller decrease in their market value in the aftermath of an operational disruption. Firms that were not obligated to comply with this requirement did not experience such benefits in the aftermath of a disruption and instead, experienced a larger increase in their risk. Managerial implications: Fostering a better understanding of whether credible control systems can reduce the impact of disruptions on the firm’s risk and value is important as it identifies a broader set of mitigation strategies available to operations managers. This can help managers achieve a better fit between their risk mitigation initiatives and their objectives and budget constraints.

Artificial Intelligence of Things (AIoT) is an emerging trend that integrates artificial intelligence into the Internet of Things, which enables intelligent IoT operations and smart industrial applications. AIoT can generate a large amount of data from the network edge. Due to the concerns of bandwidth and privacy, it is often impractical to move the collected data to the cloud. To address this issue, a collaborative distributed learning has been proposed to let the clients collaboratively train a machine learning model together with their local data in a distributed manner. In this paper, we study the free market incentive mechanism for collaborative distributed learning, where multiple parameter servers (PSs) compete with each other to motivate clients to contribute model training. Specifically, the Stackelberg game with multiple leaders and multiple followers has been designed to analyze the incentive mechanism. Different experiments have been performed to illustrate the efficiency of the proposed approach. In particular, compared with the state-of-the-art under the same budget constraint, the final average utility of the PSs can be increased by at least 94.3%.

Cost optimization of heating, ventilation and air conditioning systems (HVAC) in buildings has been approached by using sophisticated methodologies such as model predictive control (MPC) and real-time optimization. However, such techniques are not usually user-friendly to individuals without a process control background. Namely, it can be very difficult for building managers to estimate the economic impact of set point changes. For this reason, it is proposed an economic nonlinear model predictive control (eMPC) using a budget constraint. Using this concept, building managers will be able to balance the trade-off between operational cost and thermal comfort. To demonstrate the proposed approach, a first-principles model of a HVAC system is extended from a previous authors contribution to consider two new features: (1) thermal lag due to energy accumulation in the building envelope; (2) energy consumption by the fans. Then, the implemented HVAC system is tested in open- and closed-loop conditions. The closed-loop simulations showed the advantage for building managers of using the budget-constrained eMPC over the classical approach and On–Off control when using two different budget strategies. This approach ensures an user-friendly interface between building managers (i.e., non-expert users) and eMPC.

Uncontrolled glycated hemoglobin (HbA1c) levels are associated with adverse events among complex diabetic patients. These adverse events present serious health risks to affected patients and are as...

With the goal of informing federal and state policy makers in a time of budget constraints, we used a systematic review methodology to identify and summarize findings from studies that examined the effects of losing grant aid due to policy changes and students’ failure to meet renewal requirements. Studies reviewed in this policy brief show negative effects on student outcomes when grant aid is reduced or eliminated. While results vary, this general conclusion applies when grant aid is reduced or eliminated from programs that differ in scope (federal and state), eligibility requirements (merit and need), and award amounts. This brief illuminates the importance of maintaining grant aid funding for college student enrollment, persistence, achievement, and completion. Especially in context of other pandemic-related stressors, reducing need-based grant aid will likely exacerbate declines in college enrollment, progression through college, and degree completion for vulnerable students.

Background. The article investigates the principles of budget financing, the principles of enterprise and corporate financing, and the principles of social entrepreneurship. The scientific approaches on the interpretation of the given principles of financing are generalized and systematized. Expediency of the separate principle’s application in the context of the municipal enterprise functioning is substantiated. The principles of the municipal utility enterprises financing are proposed with consideration to social significance of the municipal enterprises in the system of economic activity of the territorial community. Materials and methods. The historical method, analysis and synthesis, the method of deduction were used in theoretical systematization of the budget financing and enterprise financing principles. Derivation of the principles of financing utilities on the basis of scientist’s opinions systematization is performed using the inductive method. The conclusions of the study are formed by the abstract-logical method. Results. The principles of municipal utilities financing should be based on the principles of budget financing, as these enterprises are recipients of funds from local budgets.However, the utility enterprise operates on the commercial basis, the principles of its financing should take into account the principles of private sector enterprises financing. We have developed the principles of a municipal utility company financing, which are grouped into two categories – general and specific principles. Conclusion. We have proposed the principles of utilities financing with their division into universal and specific, taking into account the commercial nature of such enterprises and their social significance. And furthermore, these principles allow to justify the functions, methods and approaches to utility enterprise financing, which will be a topic for future research. Keywords: municipal utility enterprise, financing principles, budget financing, corporate financing.

Timely diagnostic testing for active SARS‐CoV‐2 viral infections is key to controlling the spread of the virus and preventing severe disease. A central public health challenge is defining test allocation strategies with limited resources. In this paper, we provide a mathematical framework for defining an optimal strategy for allocating viral diagnostic tests. The framework accounts for imperfect test results, selective testing in certain high‐risk patient populations, practical constraints in terms of budget and/or total number of available tests, and the purpose of testing. Our method is not only useful for detecting infections, but can also be used for long‐time surveillance to detect new outbreaks. In our proposed approach, tests can be allocated across population strata defined by symptom severity and other patient characteristics, allowing the test allocation plan to prioritize higher risk patient populations. We illustrate our framework using historical data from the initial wave of the COVID‐19 outbreak in New York City. We extend our proposed method to address the challenge of allocating two different types of diagnostic tests with different costs and accuracy, for example, the RT‐PCR and the rapid antigen test (RAT), under budget constraints. We show how this latter framework can be useful to reopening of college campuses where university administrators are challenged with finite resources for community surveillance. We provide a R Shiny web application allowing users to explore test allocation strategies across a variety of pandemic scenarios. This work can serve as a useful tool for guiding public health decision‐making at a community level and adapting testing plans to different stages of an epidemic. The conceptual framework has broader relevance beyond the current COVID‐19 pandemic.

We introduce a multiple linear regression model methodology for selecting best subsets of independent variables subject to active budget constraints. Computational studies on multiple data sets to ...

The combination of machine learning (ML) and electronic health records (EHR) data may be able to improve outcomes of hospitalized COVID-19 patients through improved risk stratification and patient outcome prediction. However, in resource constrained environments the clinical utility of such data-driven predictive tools may be limited by the cost or unavailability of certain laboratory tests. We leveraged EHR data to develop an ML-based tool for predicting adverse outcomes that optimizes clinical utility under a given cost structure. We further gained insights into the decision-making process of the ML models through an explainable AI tool. This cohort study was performed using deidentified EHR data from COVID-19 patients from ProMedica Health System in northwest Ohio and southeastern Michigan. We tested the performance of various ML approaches for predicting either increasing ventilatory support or mortality. We performed post hoc analysis to obtain optimal feature sets under various budget constraints. We demonstrate that it is possible to achieve a significant reduction in cost at the expense of a small reduction in predictive performance. For example, when predicting ventilation, it is possible to achieve a 43% reduction in cost with only a 3% reduction in performance. Similarly, when predicting mortality, it is possible to achieve a 50% reduction in cost with only a 1% reduction in performance. This study presents a quick, accurate, and cost-effective method to evaluate risk of deterioration for patients with SARS-CoV-2 infection at the time of clinical evaluation.

The value of travel time savings (VTTS) representing the willingness to pay to reduce travel time, consists of two components: the value of liberating time [equal to the value of leisure (VoL)] and the value of time assigned to travel (VTAT), representing the travel conditions of a trip. Their relative values indicate which dimension to emphasize when investing in transport: speed or comfort. In this paper, we formulate and estimate a framework aimed at the improvement in the estimation of the VoL. By introducing a novel treatment of time assigned to domestic work, we consider that unpaid labor should be assigned a wage rate as a measure of the expenses avoided when assigning time to those chores. We use state-of-the-art data on time use and expenses as well as online data on gig workers collected in Austria, and apply the time-use and expenditure model of Jara-Diaz et al. (Transp Res Part B 42(10):946–957, 2008). The wage rates for paid and unpaid work were combined to re-formulate the budget constraint, which affected women more than men due to the higher involvement of the former in domestic activities. Compared against the original estimation, the VoL changed from €10/h for men and €6/h for women to €9/h for both genders, which in turn yields a larger average VTAT, which becomes positive for public transport. As a conclusion, the novel treatment of domestic labor contributes to closing the gap in the VoL between genders and highlights the power of unveiling the components behind the VTTS. The empirical findings imply that investments in travel time reductions rather than in comfort should be prioritized, given the very good conditions of public transport in Austria., Transportation, 49, ISSN:0049-4488, ISSN:1572-9435

A consumer is considered as a person or a group of people who uses purchased goods, products, or services only for personal use, and not for manufacturing or resale. Consumers usually purchase valuable and useful commodities or goods by spending all or partial of their income. The property of a commodity that enables it to satisfy human wants is called utility. Producers must be conscious to increase the utility among the consumers. This study has considered the maximization of utility problem of consumers of Bangladesh subject to two constraints; namely, budget constraint and coupon constraint. Consequently, in the study two Lagrange multipliers are used and interpreted these with mathematical analysis. Prediction of consumer behavior will help both producers and consumers to take decision of their future economic productions and consumptions, respectively. This article is ornamented with sufficient theorems and economic analyses. So that all the readers find interest when go through the economic analysis of utility maximization.

Joint activities for global water partnerships in regional development programs are usually facilitated by implementing transboundary water projects. Most projects are however hampered by the absence of a clear economic base for making investment decisions. In this paper, we propose a zero-one integer programming model to determine the optimal decisions for selection of project portfolios on transboundary waters; where project selection is based on several time periods in the future.The objective is to determine whether to undertake a project or not; so that the net present value of investment returns is maximized to support needy communities. A numerical example is presented for illustration; demonstrating the optimal choice of investment projects under budget constraints. The zero-one integer programming model provides a feasible solution for choice of transboundary project investment decisions; given the competing nature of capital budgets prior project implementation. The proposed model can be efficient; where limited funds among competing projects serve as a basis for project selection criteria; a decision for facilitation enhancement towards water partnership for regional development.

Characterizing the demand curve of products is important for pricing them optimally. However, in deriving empirical demand curves, econometricians have to contend with identification issues. Furthermore, theoretical demand curves derived using standard economic theory are divorced from empirical realities: firms rarely have information on customers’ budget constraints; theoretical utility functions are seldom derived empirically. Recognizing these issues, we propose an experimental approach for determining a product’s demand curve and, in turn, its profit-maximizing price in online environments. The proposed approach yields precise estimates and is quick and inexpensive to implement.

We study whether two groups of mental health care providers—each paid according to a different payment scheme—adjusted the duration of their patients' treatments after they faced an exogenous 20% drop in the number of patients. For the first group of providers, self-employed providers, we find that they did not increase treatment duration to recoup their income loss. Treatment duration thresholds in the stepwise fee-for-service payment function seem to have prevented these providers to treat patients longer. For the second group of providers, large mental health care institutions who were subject to a budget constraint, we find an average increase in treatment duration of 8%. Prior rationing combined with professional uncertainty can explain this increase. We find suggestive evidence for overtreatment of patients as the longer treatments did not result in better patient outcomes, i.e. better General Assessment of Functioning scores.

Types and complexity of information security related vulnerabilities are growing rapidly and present numerous challenges to the enterprises. One of the key challenges is to identify the optimal set of precautions with limited budget. Despite the fact that majority of enterprises have a budget constraint for installing and maintaining the protection systems, the majority of the previous work only focus on prioritization of security targets and do not consider the preventative actions and budget constraints. This article presents a decision support system (DSS) based on analytical hierarchical process and mixed integer programming techniques for optimal selection of enterprise information security preventative actions. The proposed approach enables maximizing the amount of risk prevented for a fixed amount of budget by identifying the optimal set of precautions. The new DSS also assists enterprise decision-makers in determining the minimum enterprise information security budget for a given level of risk. The main contribution of the paper is that it provides a risk management method to identify a multi-level threat model and the corresponding optimal combination of preventative actions for an enterprise while considering the budget constraints. The treemap information visualization technique is also integrated into the proposed method to improve information security related management decisions.

Based on the perspective of new structural economics, this paper reviews each stage of China's state-owned enterprise reform, summarizes their main features, and argues that the root of state-owned enterprise's low efficiency and soft budget constraints is their strategic and social policy burdens. According to the state-owned enterprise's existing situation, the paper provides suggestions for deepening its reform.

Logistics network is one of the most important parts of supply chains with significant share in achieving sustainability across them. In this paper, we investigate a new multi-objective mixed integer linear programming model for the design of multimodal logistics network. A bi-objective mathematical model is introduced and two conflicting objectives including the minimization of total cost and the total environmental impact are taken into account. Effective environmental life cycle assessment-based method is incorporated in the model to estimate the relevant environmental impacts. Due to budget constraints, financing decisions for facility construction are considered in the proposed model. To cope with the model objective functions, the augmented ε-constraint method is applied. Computational analysis is also provided by using a cement multimodal rail-road logistics network case study to present the significance of the proposed model. Results show that utilizing the proposed multi-period optimization model influences the location of multimodal terminals and their construction time. Also, the results show that the use of the proposed model enhances the efficiency of terminals. On the other hand, computational results indicate that preferences of decision-makers and the importance of environmental objective have significant impacts on the topology of transportation network.

Tourism transport is becoming a crucial part of the tourism industry. Determining the reliability of tourism transport networks is required by travel agencies and other practitioners. However, much of recent reliability assessments cannot utilize directly for tourism transport networks. Moreover, there exists a lack of passengers-oriented reliability evaluation that involves the impacts of latency. Therefore, this study assesses reliability regarding carrying the required number of passengers to their destination under budget and time constraints when considering late arrivals. For reliability assessment, an algorithm, which combines the concept of the minimal path adding a searching procedure with the Recursive Sum of Disjoint Products method, is proposed. An illustrative example is adopted to provide travel agencies with an intuitive visualization of the proposed algorithm. Reliability analysis suggests improving the performance of tourism transport networks.

In today’s multisector configurations, there is little clarity about whether and how public and private subsidies influence social enterprises’ pursuit of financial stability. We address the strategic role of donors in the social-business life cycle whereby social enterprise start-ups rely on subsidies, while mature social enterprises strive for independence from donors. To address the “missing middle,” we develop a typology of subsidy instruments and an intermediary signaling model to clarify how subsidies shape the evolution of outcomes for social enterprises. We argue that source variation matters for certain instruments like corporate intangibles and governmentally subsidized credit guarantees, which trigger crowding-in effects and attract commercial partners, while preventing perverse crowding-out effects, such as soft budget constraints. To illustrate this commercialization story, we draw upon a microfinance case study, demonstrating how public and private donors can induce crowding-in and crowding-out effects. In short, our subsidy typology helps unpack the signals that public and private subsidies send to commercial funders of social enterprises and how they shape the path to future financial independence.

Using the Heckman procedure with the data of the European Investment Bank on investment projects implemented on the principles of PPP, the paper identifies factors that are significant for the development of PPP. In particular, the use of PPP turned out to be most sensitive to the maturity of economic development, as well as to the state’s budgetary constraints, which do not allow building all the necessary infrastructure for providing public goods at the expense of the budget. At the same time, there has been found no statistically stable relationship between the institutional environment and the implementation of PPP projects, which may be so due to the quality of the sample — for developing countries, the importance of institutional environment factors, as well as macroeconomic stability, would most likely be more obvious.

Purpose The purpose of this paper is to study how budgetary constraints can have ethical implications on patient treatment options. Design/methodology/approach By applying a qualitative methodological approach (interviews) and participant observation, this paper studies the behaviour of surgeons in scenarios of financial restriction. Findings The empirical findings show that despite the conflict between the economy and the leges artis, surgeons maintain the ethical and deontological principles of their profession with fair rules of orientation. Practical implications The importance of this study can be realised by its continuity. One of the authors is already implicated on a wider research to investigate the influence of the economic scarcity of resources on general surgeons’ ethical behaviours. Social implications This paper is a contribution to understanding the rules that restrain the activities of surgeons. Politicians sometimes do not have a full understanding of the pressures that the medical profession faces in their day-to-day activities. Currently, with the addition of problems relate to COVID-19, politicians and populations seem to better understand the importance of the Serviço Nacional de Saúde (SNS), This paper hopes that this understanding will be not only a conjectural moment. Originality/value In conjunction with the economic recession that began in the first decade of this century, health institutions have long faced budgetary constraints that condition their material and human resources and correspondingly shape the scope of health professional activities. Until now, it has not been studied the impact of economic crises on the ethical behaviour of Portuguese surgeons. Therefore, this research is a first step and a useful contribution to understanding the rules that can restrain (or not) the ethical conduct of these health professionals.

This paper analyzes how aging parents' health conditions affect household labor supply. I propose a time-use model with wage heterogeneity and assume that care responsibilities involve both time and budget constraints. When households can purchase care services, differences in secondary earners' wages and care requirements lead to heterogeneity in household responses to a parental health shock. Primary earners and high-wage secondary earners work more. I confirm the model's predictions by using panel data from the Health and Retirement Study to construct an exogenous shock from the change in parents' health conditions and estimate its impact on labor supply.

Cloud Infrastructure as a Service (IaaS) has been known as a suitable platform for the execution of workflow applications. Quality of service (QoS) in such platforms is considered a challenging problem from both customers’ and service providers’ perspectives to perform workflow schedules. This paper proposes Budget Deadline Delicate Cloud (BDDC) and Budget Deadline Cloud (BDC) algorithms to consider both budget and deadline constraints for scheduling scientific workflows on cloud IaaS platforms. Methods for distribution of budget and deadlines under task leveling are proposed. Four metrics (success rate, time ratio, cost ratio, and utilization rate) are utilized to evaluate the proposed algorithms’ performance. Results of our proposed algorithms are compared with the BDHEFT, DBCS, and BDSD algorithms under various scenarios. Simulation results demonstrate that BDDC outperforms other algorithms in achieving cheaper costs while earning a higher success rate and utilization rate, and BDC accomplishes higher success rates and faster makespan. The performance of the proposed methods is confirmed using a real cloud environment.

Oluseye O Oludoye,1,2 Mark G Robson,3,4 Wattasit Siriwong5 1International Postgraduate Program in Hazardous Substance and Environmental Management, Graduate School, Chulalongkorn University, Bangkok, 10330, Thailand; 2Center of Excellence on Hazardous Substance Management (HSM), Chulalongkorn University, Bangkok, 10330, Thailand; 3Office of Continuing Professional Education, New Jersey Agricultural Experiment Station, Rutgers University, New Brunswick, NJ, USA; 4Department of Plant Biology,School of Environmental and Biological Sciences, Rutgers University, New Brunswick, NJ, USA; 5College of Public Health Sciences, Chulalongkorn University, Bangkok, 10330, ThailandCorrespondence: Wattasit Siriwong Email wattasit.s@chula.ac.thPurpose: The aim of this study was to explore the roles of stakeholders on cocoa farmers’ safe pesticide use in Nigeria using the socio-ecological model as the theoretical framework.Methods: This was a cross-sectional qualitative study where participants were purposefully selected based on some criteria. Then, they were interviewed in-depth using a semi-structured interview approach. The interview guide was designed based on other literature and the study objectives. The interview transcripts were transcribed verbatim and analyzed using the content analysis technique.Results: In all, 57 participants were involved in the interview process across all the study locations. Five layers of influence (cocoa farmers, pesticide retailers, extension officers, pesticide importers and government agencies) were considered. At individual level, cocoa farmers complained about lack of information from other stakeholders regarding pesticide use except retailers due to the farmers’ proximity to their stores. However, pesticide safety information from the retailers were found to be inadequate due to their interest in financial gains rather than a focus on human health and the environment. Additionally, most retailers had no understanding about the characteristics of products they sold as they could not read pesticide labels. Furthermore, lack of motivations, human resources and facilities hindered effective extension education in the rural communities. Also, pesticide importers took efforts to train the farmers within their network, but majority of these trainings focused on lead farmers who were expected to disseminate the information to others. Besides, the agencies did organize training or workshop sometimes, but it was inadequate due to budget constraint. Lastly, environmental or agri-policies relating to Nigerian cocoa farmers concerning safe pesticide use and disposal were also inadequate.Conclusion: Generally, there was a large information gap with respect to pesticide safety between cocoa farmers and other stakeholders which undermined pesticide use safety. This study highlighted the need for government agencies in Nigeria to develop mechanisms to monitor information exchange among other stakeholders and cocoa farmers with respect to safe pesticide use and disposal.Keywords: pesticide retailers, extension officers, pesticide importers, agri-policies, in-depth interview

Objectives The retail food industry, a major essential business, is among the very few thriving sectors during the COVID-19 pandemic. However, such prosperity on the store side does not guarantee a sufficient food supply for all populations. This study aims to understand if people's risk perception and food security status shaped their food procurement behaviors during the early outbreak of the pandemic. Study design Extended from the theory of risk perception, food consumers may behave differently during a disastrous event in terms of store patronization. The study evaluates how food procurement behaviors are affected by perceived risk aversion, resource scarcity, and consumers’ food security status. Methods The study examines how people with different food security statuses made grocery shopping decisions at the risk of epidemic exposure based on a nationwide survey of 2590 participants in the U.S. during the early break of the pandemic in April 2020. The study uses a moderated mediation analysis on in-store shopping frequency and food expenditure. Results People having a food-secure status before the pandemic spent significantly more as a result of the reduced shopping frequency (i.e., the secure-insecure subgroup β = −0.18, P .05; the insecure-secure subgroup, β = −0.11, P > .05). Conclusions The study reports that in general people reduced the frequency of grocery shopping trips to avoid epidemic exposure while increasing the food expenditure per trip. The increase in food expenditure was not statistically significant among the food-insecure populations likely due to their budget constraints.

Objectives Most Medicaid beneficiaries with hepatitis C virus (HCV) are not treated with direct-acting agents because of budget constraints, but they experience costly complications after becoming Medicare eligible. Maryland's "total coverage" proposal could receive a credit from Medicare to offset Medicaid investments in treatments that could lead to Medicare savings. This study analyzes the cost-effectiveness and budget impact of total coverage for HCV treatments sponsored by state Medicare and Medicaid. Study design A Markov model simulated patients going through the care continuum of HCV. The model simulated 3 pathways: standard coverage with a 50% probability of screening for HCV and 20% probability of treatment; risk-stratified total coverage with assumed 80% probability of screening and 60% treatment rate; and total coverage with assumed 80% probability of screening and 100% treatment rate. Methods The model calculated US$ and quality-adjusted life-years (QALYs) to produce an incremental cost-effectiveness ratio evaluated at a willingness-to-pay threshold of $100,000/QALY. The budget impact for the state of Maryland was calculated in terms of per member per year. Results Total coverage and risk-stratified coverage saved $158 per patient and $178 per patient, respectively, compared with standard care at an increased effectiveness of 0.05 and 0.02 QALYs over 25 years. Total coverage and risk-stratified total coverage would save $1.0 billion and $1.1 billion, respectively, after 25 years. Conclusions Medicare-Medicaid partnerships to pay for all HCV treatments today represent good value and a low budget impact. States with trouble covering HCV treatments should consider using this model to plan coverage decisions.

In online advertising, advertisers purchase ad placements by participating in a long sequence of repeated auctions. One of the most important features advertising platforms often provide, and advertisers often use, is budget management, which allows advertisers to control their cumulative expenditures. Advertisers typically declare the maximum daily amount they are willing to pay, and the platform adjusts allocations and payments to guarantee that cumulative expenditures do not exceed budgets. There are multiple ways to achieve this goal, and each one, when applied to all budget-constrained advertisers simultaneously, steers the system toward a different equilibrium. While previous research focused on online stochastic optimization techniques or game-theoretic equilibria of such settings, our goal in this paper is to compare the ``system equilibria'' of a range of budget management strategies in terms of the seller's profit and buyers' utility. In particular, we consider six different budget management strategies including probabilistic throttling, thresholding, bid shading, reserve pricing, and multiplicative boosting. We show these methods admit a system equilibrium in a rather general setting, and prove dominance relations between them in a simplified setting. Our study sheds light on the impact of budget management strategies on the tradeoff between the seller's profit and buyers' utility. Finally, we also empirically compare the system equilibria of these strategies using real ad auction data in sponsored search and randomly generated bids. The empirical study confirms our theoretical findings about the relative performances of budget management strategies.

The era of robotics-based environmental monitoring has given rise to many interesting areas of research. A key challenge is that robotic platforms and their operations are typically constrained in ways that limit their energy, time, or travel distance, which in turn limits the number of measurements that can be collected. Therefore, paths need to be planned to maximize the information gathered about an unknown environment while satisfying the given budget constraint, which is known as the informative planning problem. This review discusses the literature dedicated to information-driven path planning, introducing the key algorithmic building blocks as well as the outstanding challenges. Machine learning approaches have been introduced to solve the information-driven path planning problem, improving both efficiency and robustness. This review started with the fundamental building blocks of informative planning for environment modeling and monitoring, followed by integration with machine learning, emphasizing how machine learning can be used to improve the robustness and efficiency of informative path planning in robotics.

Budget refocusing becomes a problem when program activities cannot be realized according to the target. The effect is to affect the volume of employees to work. Facing this, it is very important to see how ASN can work optimally in the midst of budget constraints. This study looks at the dimensions of qualification, competence, performance and discipline. The purpose of this study is to find out how the performance of ASN in the face of budget refocusing by looking at the four dimensions. The method is descriptive qualitative, the respondents come from elements of the central government and local government which are recorded in the goggle form application. The results of the study were analyzed that the qualification dimension relates between education and class, the competency dimension measures the competence of ASN from their position, the performance dimensions of respondents who already know refocusing information are 66.53% and 62.50% are of the opinion that budget refocusing affects performance, and 81.65 % approve the refocusing policy. The discipline dimension talks about activity planning, independent study and work innovation. The findings of the study are First, ASN continues to carry out activities as usual even though refocusing has been carried out, Second, ASN productivity is shown by budget realization and achievement of fulfilling annual credit scores for researchers, Third General functional officials carry out action plans in dealing with the situation by shifting non-priority activities into activities that are output based. The suggestion is that the first is integrated managerial planning, the second is output-based research. The third is realizing comprehensive work innovation. Thus the achievement of the institution and the target of the institution will be achieved without reducing its usefulness.

PurposeBudget constraints faced by social housing projects (SHPs) developers can lead to poor value delivery to the end-users. Without a structured method to guide the decision-making processes during product development, SHP often fails to meet user needs and aspirations. Function analysis (FA) is an essential step in a value management (VM) study. Still, FA practice in SHP has been treated with secondary importance. This paper aims to propose a method for improving FA practice in the VM process for a Brazilian SHP to achieve optimum benefits.Design/methodology/approachThe research followed Design Science Research (DSR) in a specific single case study context, focusing on solving practical problems while contributing to theory. The method has 11 steps and guides a complete VM exercise. Its development was based on prior VM research. Focus group meetings were held to get feedback about the initial method's version to increase its relevancy and applicability in the studied context.FindingsThe proposed method suggests that scarce financial resources in SHP do not necessarily lead to low-value delivery and low-design quality. When FA is fully implemented, opportunities for cost reallocations to enhance value are identified.Originality/valueVM prior research in construction has not provided a specific method for improving FA practice for social housing developers to enhance value in the context of scarce financial resources.

Infrastructure as a service model of cloud computing provides a tremendous amount of high-performance computing systems for the execution of scientific workflow applications. However, due to explosive growth in energy consumption and high charge cost of using these cloud systems, energy-efficient workflow scheduling under budget constraints becomes the most challenging issue. Very few research works have been done that consider the stated issue. Most of them mainly focus on the minimization of schedule length under user-specified budget constraints or energy consumption constraints. In this article, we propose an energy-efficient workflow scheduling algorithm named reducing energy consumption using fair pre-assignment of available budget (RECFPAB) that reduces energy consumption under client-specified budget constraints. The RECFPAB introduces a flexible mechanism to save energy consumption with the inclusion of energy and cost coefficient factor that enables fair distribution of available budget for unscheduled tasks of the workflow application. In order to compare the performance of the proposed algorithm, an energy-efficient version of the popular existing algorithms such as heterogeneous budget constrained scheduling and minimizing schedule length using budget level are introduced. The experimental evaluation based on Genome, LIGO, and Montage applications shows that RECFPAB gives significant results in comparison with considered algorithms.

The study is projected to analyze a susceptible issue in Gujrat city Pakistan. Green spaces are being replaced by infrastructure in the city, and present green spaces and parks faces the challenges, including infrastructure development, lack of planning and budget. However, current land use planning gave a jerk to the city’s economy in the long run. Therefore, there is strong need exist to analyses this issue to achieve the sustainable goals. It has been analyzed that resort places/parks are situated around the premises of the city and makes it difficult for the middle class to access. The results show that parks enhance social interaction between the residents of the area, the concerning authorities should manage budgetary constraints for green spaces and parks to improve overall picture of the city’s planning. The study suggested that the addition of green spaces is essential to improved urban growth of economy, agriculture and environmental engagement of people within the city. The government should endorse the idea of reserving the urban green places to improve the green ecology in the sight of the best interests of the area.

This paper examines the effects of fiscal decentralization on the default risk of Chinese local government debts based on data from both urban construction investment bonds and local government bonds. The empirical evidence indicates that while fiscal revenue decentralization tends to mitigate the default risk of local government debts, fiscal expenditure decentralization increases it. Four policy measures, namely, fiscal transparency, hard budget constraints, debt quotas, and local officials' ability to manage debt are also introduced to investigate how they affect the influence of fiscal decentralization on Chinese local government debt risk.

This work aimed to study outsourcing strategies in public services and their relations with business continuity and resilience during budgetary constraints. A qualitative analysis was conducted using semi-structured interviews. This methodology was conducted with 51 public managers from the state of Pernambuco, Brazil. Findings showed that, during budget cuts periods, in the short term, due to the urgency of decision-making, it is common for outsourced services to suffer reductions or interruptions first, which directly affects public services continuity. Only a long-term view is able to correctly analyse this situation, but this does not always occur when the main factor observed is the law of fiscal responsibility. Therefore, this work brings new light to the literature concerning the problem situation, and may be useful for the planning of outsourcing strategies in public services.

This article discusses the role of the government with the Law Number 20 of 2008 concerning Small, Micro and Medium Enterprises which aims to realize the role of MSMEs as the backbone and enforcement of the national economic structure, especially in Sidrap Regency. The method used is a qualitative method, with data observation techniques, documentation and interviews. The Regional Government of Sidenreng Rappang Regency has made various efforts to empower MSMEs based on Law Number 20 of 2008 concerning MSMEs. These efforts can be categorized as the growth and development of MSMEs as well as expansion of access and networks of partnerships and investments of MSMEs. Coaching and development of MSMEs is carried out by providing revolving funds to facilitate saving for private banks / companies, assistance for facilities & infrastructure to MSMEs, dissemination of business information, successful business licensing, institutional support even though in reality it has not been running optimally. Meanwhile, the potential for strengthening or business strength is in the form of development in the fields of production and processing, marketing, human resources. and there are also factors hindering umkm in Sidrap district including budget constraints, human resources and lack of government policies as well as less competitive product packaging, so that there are still deficiencies in empowering MSMEs in Sidrap Regency, the government's role in empowering MSMEs in Sidrap Regency has not been maximal.

Celem opracowania jest wskazanie ryzyk charakterystycznych dla sektora samorządowego oraz prezentację oferty ubezpieczeniowej dedykowanej temu sektorowi na przykładzie miasta Łodzi. W pracy postawiono tezę, że kompleksowo skonstruowana oferta ubezpieczeniowa przyczynia się do optymanego transferu ryzyk samorządu terytorialnego. Opracowanie powstało w oparciu o przegląd literatury przedmiotu, akty prawne oraz ofertę ubezpieczeniową skierowaną do jednostek samorządu terytorialnego. Pawidłowo przeprowadzony proces identyfikacji ryzyk oraz procedura wyboru ubezpieczyciela prowadzi nie tylko do wyboru optymalnej oferty ubezpieczeniowej, ale także samego podmiotu. Z ograniczonej analizy przykładu miasta Łodzi wynika, że władze samorządu są zainteresowane kompleksowymi, „szytymi na miarę” polisami ubezpieczeniowymi oferowanymi przez jednego ubezpieczyciela. Przekłada się to na czas przyjęcia, rozpatrzenia szkody i wypłaty odszkodowania. Pozytywnie należy ocenić programy ubezpieczeniowe bezpośrednio adresowane do JST, które pojawiają się na rynku ubezpieczeniowym. Przykładem jest program „Bezpieczna Gmina”. Należy oczekiwać, że w najbliższych latach, z uwagi na szczególne ograniczenia budżetowe wynikające z pandemii Covid-19, zaintersowanie władz samorządowych wzrośnie nowymi, innowacyjnymi rozwiązaniami w zakresie ubezpieczenia ryzyk.

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.

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.

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%).