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This paper addresses the effective management of water resources during the hydraulic fracturing process used for natural gas extraction. With increasing demand and scrutiny on shale gas production due to economic, environmental, and social factors, effective water management strategies are needed to address the challenges of flow-back water usage during hydraulic fracturing. This research contributes to the existing literature from a multi-period reverse logistics network design perspective with realistic modeling approaches. A mathematical formulation is developed for optimizing the design of a multi-period reverse logistics network for effective water resource management that incorporates production scheduling of a hydraulic fracturing process. This mixed integer programming model determines the optimal locations and capacities of impoundments, treatment facilities, and disposal sites while minimizing fixed and operational costs under constraints such as freshwater withdrawal limits, production schedules, inventory balance, and treatment and disposal options. A two-stage stochastic programming formulation is further developed as a means to address the uncertainty associated with water flows during the fracturing operation. The strategic location and capacity decisions are given in the first stage of this formulation, whereas operational decisions such as water flows are handled in the second stage under independently sampled scenarios. A sample average approximation algorithm is proposed to solve the stochastic formulation. The model is tested on a case study from the United States that explores potential impacts on water resource management due to cost variations, changes in operational parameters, and uncertainty in demands and supplies. The base case setup delivers a 12.8% reduction in freshwater usage. The benefit of multi-period reverse logistics network design is further demonstrated through variations in water demand and equipment degradation. The managerial insights derived through sensitivity analysis highlight the values of the proposed multi-period formulation, capacity expansions, and parameter estimations. • Developed models for multi-period reverse logistics network design. • The model optimizes water resource management during hydraulic fracturing. • Implemented the models on a case study from the United States. • Analyzed the effects of problem parameters on optimal water management strategies. • Derived managerial insights from extensive computational experiments. [ABSTRACT FROM AUTHOR]

• Vaccine-derived herd immunity against COVID-19 can be achieved if at least 60% of the population is fully-vaccinated. • COVID-19 elimination is feasible by June 2022 if the current baseline full vaccination coverage is increased by about 20%. • Elimination prospects are improved significantly if vaccination is combined with the use of moderate to high-quality masks. • Having more people wear surgical masks is more beneficial than having fewer people wear the highly-effective N95 masks. • Treatment of symptomatic individuals has a significant impact on reducing daily hospitalizations. The effectiveness of control interventions against COVID-19 is threatened by the emergence of SARS-CoV-2 variants of concern. We present a mathematical model for studying the transmission dynamics of two of these variants (Delta and Omicron) in the United States, in the presence of vaccination, treatment of individuals with clinical symptoms of the disease and the use of face masks. The model is parameterized and cross-validated using observed daily case data for COVID-19 in the United States for the period from November 2021 (when Omicron first emerged) to March 2022. Rigorous qualitative analysis of the model shows that the disease-free equilibrium of the model is locally-asymptotically stable when the control reproduction number of the model (denoted by R c) is less than one. This equilibrium is shown to be globally-asymptotically stable for a special case of the model, where disease-induced mortality is negligible and both vaccine-derived immunity in fully-vaccinated individuals and natural immunity do not wane, when the associated reproduction number is less than one. The epidemiological implication of the latter result is that the combined vaccination-boosting strategy can lead to the elimination of the pandemic if its implementation can bring (and maintain) the associated reproduction number to a value less than one. An analytical expression for the vaccine-derived herd immunity threshold is derived. Using this expression, together with the baseline values of the parameters of the parameterized model, we showed that the vaccine-derived herd immunity can be achieved in the United States (so that the pandemic will be eliminated) if at least 68 % of the population is fully-vaccinated with two of the three vaccines approved for use in the United States (Pfizer or Moderna vaccine). Furthermore, this study showed (as of the time of writing in March 2022) that the control reproduction number of the Omicron variant was approximately 3.5 times that of the Delta variant (the reproduction of the latter is computed to be ≈ 0.2782), indicating that Delta had practically died out and that Omicron has competitively-excluded Delta (to become the predominant variant in the United States). Based on our analysis and parameterization at the time of writing of this paper (March 2022), our study suggests that SARS-CoV-2 elimination is feasible by June 2022 if the current baseline level of the coverage of fully-vaccinated individuals is increased by about 20 %. The prospect of pandemic elimination is significantly improved if vaccination is combined with a face mask strategy that prioritizes moderately effective and high-quality masks. Having a high percentage of the populace wearing the moderately-effective surgical mask is more beneficial to the community than having low percentage of the populace wearing the highly-effective N95 masks. We showed that waning natural and vaccine-derived immunity (if considered individually) offer marginal impact on disease burden, except for the case when they wane at a much faster rate (e.g., within three months), in comparison to the baseline (estimated to be within 9 months to a year). Treatment of symptomatic individuals has marginal effect in reducing daily cases of SARS-CoV-2, in comparison to the baseline, but it has significant impact in reducing daily hospitalizations. Furthermore, while treatment significantly reduces daily hospitalizations (and, consequently, deaths), the prospects of COVID-19 elimination in the United States are significantly enhanced if investments in control resources are focused on mask usage and vaccination rather than on treatment. [ABSTRACT FROM AUTHOR]

• This paper proposed a novel wind speed forecasting algorithm. • A new dataset division mechanism based on the moving window is used in this paper. • In order to improve the accuracy and stability simultaneously, the multi-objective particle swarm optimization is applied. • We compared the proposed MOPSO-Combined method with the combined model that published before in the paper. Accurate wind speed forecasting is important in power grid security, power system management, operation and market economics. However, most research has focused only on improving either accuracy or stability, with few studies addressing the two issues, simultaneously. Therefore, we proposed a novel combined model based on multi-objective particle swarm optimization, which is applied to optimize the key parameters of the echo state network. Most combined wind speed forecasting methods just use the combination theory to combine individual methods, this paper uses echo state network to combine the intermediate wind speed forecasting results of three artificial neural networks. Moreover, a new dataset division mechanism based on the moving window is applied in this paper. Firstly, the length of the input data is changed from 5 to 15 for 1-step, 2-step and 3-step wind speed forecasting, after that, the optimal length of the input vector can be got. And then we apply this optimal length of the input vector to another dataset for further verifying the proposed method. In order to verify the forecasting effectiveness of the proposed forecasting model, the 80/min wind speed data of M2 tower of the National Wind Power Technology Center of the United States were taken as an example. The experimental results indicate that the proposed algorithm is superior to the other ten comparative models in prediction accuracy and stability, and it also performs better than the combined model that we have proposed before. [ABSTRACT FROM AUTHOR]

• We propose an epidemic model of imperfect vaccination with a latent period a general nonmonotone incidence rate function. • We study the threshold dynamics with respect to the effective reproduction number. • We discuss the critical vaccination coverage rate that is required to eliminate the disease. • We apply the model to data on measles disease transmission and provide sensitivity analysis. • The latent period shows a noticeable effect on system dynamics but not on the effective reproduction number. In this paper, we investigate the effects of the latent period on the dynamics of infectious disease with an imperfect vaccine. We assume a general incidence rate function with a non-monotonicity property to interpret the psychological effect in the susceptible population when the number of infectious individuals increases. After we propose the model, we provide the well-posedness property by verifying the non-negativity and boundedness of the models solutions. Then, we calculate the effective reproduction number R E. The threshold dynamics of the system is obtained with respect to R E. We discuss the global stability of the disease-free equilibrium when R E < 1 and explore the system persistence when R E > 1. Moreover, we prove the coexistence of an endemic equilibrium when the system persists. Then, we discuss the critical vaccination coverage rate that is required to eliminate the disease. Numerical simulations are provided to: (i) implement a case study regarding the measles disease transmission in the United States from 1963 to 2016; (ii) study the local and global sensitivity of R E with respect to the model parameters; (iii) discuss the stability of endemic equilibrium; and (iv) explore the sensitivity of the proposed model solutions with respect to the main parameters. [ABSTRACT FROM AUTHOR]

Due to a variety of risks related to aging, construction, material degradation, harsh environment, increasing traffic, and insufficient capacity, a large percentage of bridges in the U.S. highway system are deteriorating beyond acceptable standards. Although significant investments are needed to bring bridges back to acceptable condition, most highway agencies lack the appropriate funding and therefore need effective methodologies for allocating limited resources efficiently and cost-effectively. This paper presents a life-cycle optimization model using a semi-Markov process and demonstrates how the proposed method can assist highway agencies to make more quantitative and explicit decisions for bridge maintenance. The 2012 National Bridge Inventory (NBI) dataset for the State of Texas was analyzed in this study to illustrate bridge structural responses and behaviors under uncertainty and risks. The proposed method is accurate when compared to real data and customized to help highway agencies to optimize their decisions on structuring bridge maintenance, and consequently, leading to cost savings and more efficient sustainability of their bridge systems. The major contribution of this research is the low-error model and process algorithm for selecting the most appropriate maintenance strategy. If employed properly, it may allow agencies to more effectively maintain an aging infrastructure system. [ABSTRACT FROM AUTHOR]