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Solar energy systems are an effective way to meet the needs of zone heating, cooling, electricity, and domestic hot water. However, to reach sustainability, and energy storage unit should be considered for installation. In this study, two combined cooling, heating and power (CCHP) systems are simulated and studied using TRNSYS software; both using natural gas engine generators and photovoltaics as prime movers and a hydrogen fuel cell/electrolyzer storage unit, one with absorption chiller and another with compression chiller cooling. For the study, a residential building is modeled for three major populated climate zones of the United States of America, namely, Hot-humid, mixed-humid and cold using DesignBuilder and EnergyPlus software. The energy demand for its HVAC operation and domestic electricity is obtained and used for system simulation in TRNSYS software. Due to choosing actual equipment for the CCHP arrangement, precise economic and environmental models are designed to further evaluate the possibility of execution of the system. The results show that absorption chiller-equipped CCHP has better performance both environmentally and economically. In addition, the outcome shows that the suggested systems show less favorability to be utilized in hot humid climate zones. • Design a multi objective optimization system with TRNSYS simulator. • Various geographical location is considered for study the proposed system. • Designed system with chiller in colder climates has less Carbon-dioxide emission. • Hot climate leads to a higher energy consumption in the designed unit. [ABSTRACT FROM AUTHOR]

In multi-body dynamics, we model a non-conformal wheel/rail contact at one centre point since this contact is flat and Hertzian. However, the quasi-conformal contact requires more points since the contact is curved and non-Hertzian. The methodology in solving these points during dynamic simulations is the basis of this development. In this online effort, first, we present a general contact joint and the gutter search method of Pascal and Jourdan [The rigid-multi-Hertzian method as applied to conformal contacts. USA: ASME; 2007] in the context of a multi-body approach. Next, by adopting the non-iterative approach, a subset of these points with positive profile interpenetrations is selected to idealise one curved contact by a set of multi-Hertzian patches for which the Hertz normal contact solution is available. Finally, the feasibility of this multibody approach together with its implementation in two different codes is evaluated by simulating the motion of an isolated wheelset with realistic inertia. On tangent tracks, the wheelset with non-conformal pairing displays self-excited unstable oscillations while it displays a stable behaviour below a critical speed with conformal profiles. A further study reveals the net friction losses at multiple patches within the curved contact being the reason for the stable behavior. [ABSTRACT FROM AUTHOR]

Cutler, E.M.; Albert, M.R., and White, K.D., 2019. A low-cost shoreline dynamic simulation model for proposed beach nourishment and dune construction: Introducing a new feasibility analysis tool. Journal of Coastal Research, 35(4), 907–919. Coconut Creek (Florida), ISSN 0749-0208. Beach nourishment and dune construction are common coastal risk-management strategies for shoreline erosion in the United States. Federal involvement with such projects relies on cost–benefit analyses, which in turn rely on life-cycle models of shoreline processes, such as the Army Corps of Engineers' Beach-fx event-driven physical and economic coastal model. However, use of these models can be computationally and data intensive. Costs associated with conducting a feasibility analysis for beach nourishment and dune construction may leave a community with few resources to explore other risk-management options. This paper, therefore, presents a first-order, screening-level, low-cost dynamic model that delivers results approximately comparable with those from life-cycle models. Applying the model to three locations in Florida shows that simulated nourishment intervals are within 5 years of those predicted by Beach-fx, leading to a similar number of nourishments over a 50-year project life span. A discussion on how one could apply the model to other areas is also included. It is intended that this model could serve as a first-pass screening tool for communities considering beach nourishment and dune construction before they decide to invest in the more thorough, but costly and data-intensive, life-cycle model simulations. [ABSTRACT FROM AUTHOR]

Abstract: Compressed air systems account for a significant proportion of industrial energy usage in the European Union and the United States. Improving the energy efficiency of final air consumers is an essential aspect of overall system optimisation. This paper investigates and compares the minimisation of air consumption in linear pneumatic actuator systems using two approaches: dual pressures and the utilisation of expansion energy in compressed air. In order to simplify the implementation of energy saving circuits for industrial end-users, a software program has been developed based on a combination of genetic algorithms and dynamic simulations. The developed program, described in this paper, automates the selection process of optimal design parameters and control input variables for pneumatic cylinders. The robustness of the optimisation software has been experimentally validated. The results from a vertical lifting application case study indicate that the proposed optimisation approaches can reduce compressed air usage by up to 29% per cycle, whilst simultaneously satisfying other common automation design objectives or constraints. The paper concludes with a discussion on methods to increase the application of such energy efficiency measures in pneumatic production machinery. [Copyright &y& Elsevier]

Abstract: A dynamic simulation framework is used to compare benefit-cost ratios of riparian restoration investment strategies to pursue ecosystem service benefits. The model is meant to be adaptable to generic restoration planning applications, with the Middle Rio Grande riparian corridor in Albuquerque, New Mexico, U.S.A. presented here as the illustrating case. Model inputs include ecosystem service values from an original choice experiment, values from regional benefit transfer studies, and information from land managers. The model includes three control variable modules: forest management, river restoration, and recreation infrastructure. Investment influences these modules, which in turn affect ecosystem service flows for the region. The model is exercised to compare a “No-Action” alternative with “Optimal Benefit-Cost Ratio” restoration funding. An extended sensitivity analysis explores a range of both physical and economic assumptions. The analysis has two major outcomes. The first is that directed restoration funding yields significant gains as compared with No-Action for all scenarios tested. The second major finding is that although optimized benefit-cost ratios are above unity for all “states of the world” tested, the ratio itself and funding patterns varied widely. These sensitivities underscore the need for a transparent adaptive management decision process supported by tools aimed not at deterministic prediction, but rather at structuring dialogue and inquiry into issues that defy simple intuition. [Copyright &y& Elsevier]

Since the introduction of the measure of economic policy uncertainty, businesses, policymakers, and academic scholars closely monitor its momentum due to expected economic implications. The US is the world's top-ranked equity market by size, and prior literature on policy uncertainty and stock prices for the US is conflicting. In this study, we reexamine the policy uncertainty and stock price nexus from the US perspective, using a novel dynamically simulated autoregressive distributed lag setting introduced in 2018, which appears superior to traditional models. The empirical findings document a negative response of stock prices to 10% positive/negative shock in policy uncertainty in the short-run, while in the long-run, an increase in policy uncertainty by 10% reduces the stock prices, which increases in response to a decrease with the same magnitude. Moreover, we empirically identified two significant thresholds: (1) policy score of 4.89 (original score 132.39), which negatively explain stock prices with high magnitude, and (2) policy score 4.48 (original score 87.98), which explains stock prices negatively with a relatively low magnitude, and interestingly, policy changes below the second threshold become irrelevant to explain stock prices in the United States. It is worth noting that all indices are not equally exposed to unfavorable policy changes. The overall findings are robust to the alternative measures of policy uncertainty and stock prices and offer useful policy input. The limitations of the study and future line of research are also highlighted. All in all, the policy uncertainty is an indicator that shall remain ever-important due to its nature and implication on the various sectors of the economy (the equity market in particular). [ABSTRACT FROM AUTHOR]

• The SARS-CoV-2 main protease (Mpro) is an important target against COVID-19. • Peptidomimetic α-ketoamides represent prototypical inhibitors of the SARS-CoV-2 Mpro. • We confirmed the high affinity of the α-ketoamide 13b inhibitor to SARS-CoV-2 Mpro • MD simulations highlight α-ketoamide 13b stability in Mpro compared to amoxicillin • The α-ketoamide compound class could be pursued as potential antivirals against COVID-19 The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes an illness known as COVID-19, which has been declared a global pandemic with over 2 million confirmed cases and 137,000 deaths in 185 countries and regions at the time of writing (16 April 2020), over a quarter of these cases being in the United States. In the absence of a vaccine, or an approved effective therapeutic, there is an intense interest in repositioning available drugs or designing small molecule antivirals. In this context, in silico modelling has proven to be an invaluable tool. An important target is the SARS-CoV-2 main protease (Mpro), involved in processing translated viral proteins. Peptidomimetic α-ketoamides represent prototypical inhibitors of Mpro. A recent attempt at designing a compound with enhanced pharmacokinetic properties has resulted in the synthesis and evaluation of the α-ketoamide 13b analogue. Here, we performed molecular docking and molecular dynamics simulations to further characterize the interaction of α-ketoamide 13b with the active site of the SARS-CoV-2 Mpro. We included the widely used antibiotic, amoxicillin, for comparison. Our findings indicate that α-ketoamide 13b binds more tightly (predicted GlideScore = -8.7 and -9.2 kcal/mol for protomers A and B, respectively), to the protease active site compared to amoxicillin (-5.0 and -4.8 kcal/mol). Further, molecular dynamics simulations highlight the stability of the interaction of the α-ketoamide 13b ligand with the SARS-CoV-2 Mpro (ΔG = -25.2 and -22.3 kcal/mol for protomers A and B). In contrast, amoxicillin interacts unfavourably with the protease (ΔG = +32.8 kcal/mol for protomer A), with unbinding events observed in several independent simulations. Overall, our findings are consistent with those previously observed, and highlight the need to further explore the α-ketoamides as potential antivirals for this ongoing COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

As food demand increases, it is critical to develop effective strategies and evaluate their potential in reducing Greenhouse Gas (GHG) emissions and other environmental footprints of large-scale agricultural systems. This study addresses the challenge by developing a dynamic system modeling framework integrating Life Cycle Assessment (LCA), Agent-Based Modeling (ABM), and Techno-Economic Analysis (TEA). LCA and TEA were coupled with dynamic simulation models of crop yields, costs, and prices, allowing for the estimation of life-cycle environmental impacts and profitability of crop planting activities under changing climate and economic conditions. The framework was demonstrated by a case study for an agricultural system, including 1,000 farms in the United States over a 30-year time frame. The results indicated that information exchange among farmers, farmers' environmental awareness, access to environmental information, and farm size are key factors driving the system's environmental impacts. The results can provide a broad range of stakeholders (e.g., policymakers, nonprofits, agriculture companies) with insightful information to tailor their strategies for effectively managing the environmental footprints of large-scale agricultural systems. The integrated modeling framework has the potential to address sustainability challenges in other systems that are dynamic, involve human behaviors, and have complex interactions among human and nature systems. [ABSTRACT FROM AUTHOR]