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Bridges serve as critical links in road networks, requiring continuous maintenance to ensure proper functionality throughout their lifespan. Given their pivotal role in the urban landscape, connecting various parts of a city, this research presents a multi-objective optimization model for the maintenance and repair of bridges in Babolsar, Iran. The model takes into account budget constraints and aims to minimize the total life cycle and user costs, encompassing traffic delays and vehicle expenses, while maximizing the reliability of the bridge network. Recognizing the inherent complexity of this problem, a multi-objective particle swarm optimization algorithm has been developed for an accurate solution. The study further conducts sensitivity analysis on the objective function concerning the available budget, evaluating key parameters such as hourly costs and the time value of vehicles. The results show that with an increase in the budget level, the number of repairs related to the most costly maintenance has significantly risen. In other words, as the budget expands, the model tends to favor repairs with higher costs because their impact on the bridge's performance is more substantial., (© 2024. The Author(s).)

Healthcare decision-makers face difficult decisions regarding COVID-19 booster selection given limited budgets and the need to maximize healthcare gain. A constrained optimization (CO) model was developed to identify booster allocation strategies that minimize bed-days by varying the proportion of the eligible population receiving different boosters, stratified by age, and given limited healthcare expenditure. Three booster options were included: B 1 , costing US $1 per dose, B 2 , costing US $2, and no booster (NB), costing US $0. B 1 and B 2 were assumed to be 55%/75% effective against mild/moderate COVID-19, respectively, and 90% effective against severe/critical COVID-19. Healthcare expenditure was limited to US$2.10 per person; the minimum expected expense using B 1, B 2, or NB for all. Brazil was the base-case country. The model demonstrated that B 1 for those aged <70 years and B 2 for those ≥70 years were optimal for minimizing bed-days. Compared with NB, bed-days were reduced by 75%, hospital admissions by 68%, and intensive care unit admissions by 90%. Total costs were reduced by 60% with medical resource use reduced by 81%. This illustrates that the CO model can be used by healthcare decision-makers to implement vaccine booster allocation strategies that provide the best healthcare outcomes in a broad range of contexts.

The spread of the COVID-19 pandemic has caused severe damage to the Chinese cruise market since 2020. It is crucial for the local government to reformulate the subsidy policy to respond to the changing environment. We propose a cruise supply chain system to investigate the choice of subsidy recipients and the setting of optimal subsidy levels with a budget-constrained government during the access restriction period and post-epidemic period. We find that in both periods, as long as the subsidy achieves the optimal level, either the cruise lines, the travel agency, or the passengers as recipients of the subsidy policy can maximize the market demand and recover the cruise market after the COVID-19 outbreak. However, as the budget increases, subsidizing passengers can improve the "low price dilemma" of the Chinese cruise market. Compared with the access restriction period, the local government should adjust the subsidy level in the post-epidemic period. Interestingly, the subsidy policy does not always positively impact the international cruise line's profit in the post-epidemic period., (© 2022 Elsevier Ltd. All rights reserved.)

Budget-constraint admissible output consensus tracking design and analysis for the intermittent-interaction singular multiagent networks with given cost budgets are addressed in this paper. Two critical characteristics of many practical multiagent networks are focused. The first one is that the dynamics of each agent contains dynamical modes, as well as impulsive modes and static modes, and interactions among agents are intermittent. The second one is that the network is constrained by the cost budget, and the weighted design between tracking performances and energy consumptions should be guaranteed under budget constraints. Firstly, in virtue of the decomposition of observability, a new singular consensus tracking protocol with the dynamic output feedback is proposed by intermittent protocol states. Meanwhile, a cost index is constructed by considering both the tracking performance and the energy consumption. Then, criteria of budget-constraint admissible output consensus tracking analysis and design are given. By designing the gain matrices of consensus tracking protocols, not only multiagent networks with dynamical modes and static modes reach output consensus tracking while eliminating impulsive modes, but also the upper bound of the cost index can be kept within the cost budget; that is, the weighted design with the budget constraint is achieved. Finally, theoretical results are demonstrated by numerical simulations., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 ISA. Published by Elsevier Ltd. All rights reserved.)

Budget constraints become an important consideration in modern predictive modeling due to the high cost of collecting certain predictors. This motivates us to develop cost-constrained predictive modeling methods. In this paper, we study a new high-dimensional cost-constrained linear regression problem, that is, we aim to find the cost-constrained regression model with the smallest expected prediction error among all models satisfying a budget constraint. The non-convex budget constraint makes this problem NP-hard. In order to estimate the regression coefficient vector of the cost-constrained regression model, we propose a new discrete first-order continuous optimization method. In particular, our method delivers a series of estimates of the regression coefficient vector by solving a sequence of 0-1 knapsack problems. Theoretically, we prove that the series of the estimates generated by our iterative algorithm converge to a first-order stationary point, which can be a globally optimal solution under some conditions. Furthermore, we study some extensions of our method that can be used for general statistical learning problems and problems with groups of variables. Numerical studies using simulated datasets and a real dataset from a diabetes study indicate that our proposed method can solve problems of fairly high dimensions with promising performance. Supplementary materials for this article are available online.

Remanufacturing plays an important role in a circular economy, by shifting supply chains from linear to closed loop. However, the development of the remanufacturing industry faces many challenges. Consumers' uncertainty about the quality of remanufactured products can hamper their decision to make a purchase (i.e., uncertainty behavior). Such uncertainty can be reduced when they learn that more consumers are purchasing remanufactured products (i.e., network externality behavior). Considering the aforementioned behaviors, this paper investigates how a government could set the optimal subsidy level to maximize the sales quantity of remanufactured products with a limited budget. We modeled a Stackelberg game between the government and an original equipment manufacturer, under two settings, over two periods. Setting 1 only considers an original equipment manufacturer that produces remanufactured products, and Setting 2 considers an original equipment manufacturer that produces both new and remanufactured products. We show that the original equipment manufacturer should adjust its pricing strategy (i.e., markup vs. markdown) according to the subsidy levels. Our analysis on the government budget constraint shows that an original equipment manufacturer earns more profits in Setting 1 than Setting 2, only when the budget constraint is high, and less profits when budget constraint is low., Competing Interests: The authors declare no conflict of interest.

Background: With modern methods in biotechnology, the search for biomarkers has advanced to a challenging statistical task exploring high dimensional data sets. Feature selection is a widely researched preprocessing step to handle huge numbers of biomarker candidates and has special importance for the analysis of biomedical data. Such data sets often include many input features not related to the diagnostic or therapeutic target variable. A less researched, but also relevant aspect for medical applications are costs of different biomarker candidates. These costs are often financial costs, but can also refer to other aspects, for example the decision between a painful biopsy marker and a simple urine test. In this paper, we propose extensions to two feature selection methods to control the total amount of such costs: greedy forward selection and genetic algorithms. In comprehensive simulation studies of binary classification tasks, we compare the predictive performance, the run-time and the detection rate of relevant features for the new proposed methods and five baseline alternatives to handle budget constraints., Results: In simulations with a predefined budget constraint, our proposed methods outperform the baseline alternatives, with just minor differences between them. Only in the scenario without an actual budget constraint, our adapted greedy forward selection approach showed a clear drop in performance compared to the other methods. However, introducing a hyperparameter to adapt the benefit-cost trade-off in this method could overcome this weakness., Conclusions: In feature cost scenarios, where a total budget has to be met, common feature selection algorithms are often not suitable to identify well performing subsets for a modelling task. Adaptations of these algorithms such as the ones proposed in this paper can help to tackle this problem.

There is a growing scientific interest in the use and development of just-in-time adaptive interventions in mobile health. These mobile interventions typically involve treatments, such as reminders, activity suggestions and motivational messages, delivered via notifications on a smartphone or a wearable to help users make healthy decisions in the moment. To be effective in influencing health, the combination of the right treatment and right delivery time is likely critical. A variety of prediction/detection algorithms have been developed with the goal of pinpointing the best delivery times. The best delivery times might be times of greatest risk and/or times at which the user might be most receptive to the treatment notifications. In addition, to avoid over burdening users, there is often a constraint on the number of treatments that should be provided per time interval (e.g., day or week). Yet there may be many more times at which the user is predicted or detected to be at risk and/or receptive. The goal then is to spread treatment uniformly across all of these times. In this paper, we introduce a method that spreads the treatment uniformly across the delivery times. This method can also be used to provide data for learning whether the treatments are effective at the delivery times. This work is motivated by our work on two mobile health studies, a smoking cessation study and a physical activity study.

Background: The current economic constraints cause hospital management to use the available public resources as rationally as possible. At the same time, there is the necessity to improve current scientific knowledge. This is even more relevant in the case of patients with malignant pleural mesothelioma (MPM), given the severity of the disease, its dismal prognosis, and the cost of chemotherapy drugs. This work aims to evaluate the standard cost of patients with MPM, supporting physicians in their decision-making process in relation to budget constraints, as well as policy-makers with respect research policy., Methods: The authors conducted a retrospective cost analysis on all the patients with MPM who were first admitted to a reference hospital specialized in MPM care between 2014 and 2015, collecting data on their diagnostic pathways and active treatments, as well as on the related official fees for each procedure. Then, using a multiple regression model, we estimated the overall expected cost of a patient with MPM treated in our hospital, to be born by the Regional Healthcare System based on the chosen clinical pathway., Results: According to results, the economic impact of caring for a patient with MPM is mostly related to the selected active treatments, with drug and hospitalization costs as main drivers. Our analysis suggests that the expected reimbursed fee to care for a patient with MPM is equal to € 18 214.99, with chemotherapy and monitoring costs equal to € 12 861.43 and hospitalization cost equal to € 5353.55. This cost decreases to € 320.18 in the case of enrollment in an experimental trial of first-line treatment. In the other cases (second-line or third-line trials), the expected cost borne by the healthcare system for treating patients grows exponentially (€ 40,124.18 and € 59 839.94, respectively)., Conclusion: Experimental trials might be a solution to decrease the economic burden for the public healthcare system only in the case of first-line treatments, where the cost of chemotherapy is relevant. Nevertheless, policy-makers have to accept the sharing of this economic burden between society and the pharmaceutical industry to broaden the current scientific knowledge., (© 2018 The Author(s); Published by Kerman University of Medical Sciences. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.)

The World Health Organization's (WHO's) World Health Report 2010, "Health systems financing, the path to universal coverage," promoted universal health coverage (UHC) as an aspirational objective for country health systems. Yet, in addition to the dimensions of services and coverage, distribution of coverage in the population, and financial risk protection highlighted by the report, the consideration of the budget constraint should be further strengthened in the ensuing debate on resource allocation toward UHC. Beyond the substantial financial constraints faced by low- and middle-income countries, additional considerations, such as the geographical context, the underlying country infrastructure, and the architecture of health systems, determine the feasibility, effectiveness, quality and cost of healthcare delivery. Therefore, increased production and use of local evidence tied to the criteria of health benefits, equity, financial risk protection, and costs accompanying health delivery are needed so that to highlight pathways and acceptable trade-offs toward UHC., (© 2016 by Kerman University of Medical Sciences.)

Feature selection is an important problem in machine learning and data mining. We consider the problem of selecting features under the budget constraint on the feature subset size. Traditional feature selection methods suffer from the "monotonic" property. That is, if a feature is selected when the number of specified features is set, it will always be chosen when the number of specified feature is larger than the previous setting. This sacrifices the effectiveness of the non-monotonic feature selection methods. Hence, in this paper, we develop an algorithm for non-monotonic feature selection that approximates the related combinatorial optimization problem by a Multiple Kernel Learning (MKL) problem. We justify the performance guarantee for the derived solution when compared to the global optimal solution for the related combinatorial optimization problem. Finally, we conduct a series of empirical evaluation on both synthetic and real-world benchmark datasets for the classification and regression tasks to demonstrate the promising performance of the proposed framework compared with the baseline feature selection approaches., (Copyright © 2015 Elsevier Ltd. All rights reserved.)