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We propose a theoretical study to investigate the spread of the SARS-CoV-2 virus, reported in Wuhan, China. We develop a mathematical model based on the characteristic of the disease and then use fractional calculus to fractionalize it. We use the Caputo-Fabrizio operator for this purpose. We prove that the considered model has positive and bounded solutions. We calculate the threshold quantity of the proposed model and discuss its sensitivity analysis to find the role of every epidemic parameter and the relative impact on disease transmission. The threshold quantity (reproductive number) is used to discuss the steady states of the proposed model and to find that the proposed epidemic model is stable asymptotically under some constraints. Both the global and local properties of the proposed model will be performed with the help of the mean value theorem, Barbalat’s lemma, and linearization. To support our analytical findings, we draw some numerical simulations to verify with graphical representations.
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The major goal of this study is to create an optimal technique for managing COVID-19 spread by transforming the SEIQR model into a dynamic (multistage) programming problem with continuous and discrete time-varying transmission rates as optimizing variables. We have developed an optimal control problem for a discrete-time, deterministic susceptible class (S), exposed class (E), infected class (I), quarantined class (Q), and recovered class (R) epidemic with a finite time horizon. The problem involves finding the minimum objective function of a controlled process subject to the constraints of limited resources. For our model, we present a new technique based on dynamic programming problem solutions that can be used to minimize infection rate and maximize recovery rate. We developed suitable conditions for obtaining monotonic solutions and proposed a dynamic programming model to obtain optimal transmission rate sequences. We explored the positivity and unique solvability nature of these implicit and explicit time-discrete models. According to our findings, isolating the affected humans can limit the danger of COVID-19 spreading in the future.
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Much attention is paid to data science and machine learning as an effective means for getting value out of data and as a means for dealing with the large amounts of data we are accumulating at companies and organizations. This has gained importance with the major waves of digitization we have seen, especially with the COVID-19 pandemic accelerating digital everything. However, in reality, most machine learning models, despite achieving good technical solutions to predictive problems wind up not being deployed. The reasons for this are many and have their origin in data scientists and machine learning practitioners not paying enough attention to issues of deployment in production. The issues range all the way from establishing trust by business stakeholders and users, to failure to explain why models work and when they do not, to failing to appreciate the importance of establishing a robust quality data pipeline, to ignoring many constraints that apply to deployed models, and finally to a lack of understanding the true cost of production deployment and the associated ROI. We discuss many of these problems and we provide what we believe is a pragmatic approach to getting data science models successfully deployed in working environments.
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Land-use transition is one of the most profound human-induced alterations of the Earth’s system. It can support better land management and decision-making for increasing the yield of food production to fulfill the food needs in a specific area. However, modeling land-use change involves the complexity of human drivers and natural or environmental constraints. This study develops an agent-based model (ABM) for land use transitions using critical indicators that contribute to food deserts. The model’s performance was evaluated using Guilford County, North Carolina, as a case study. The modeling inputs include land covers, climate variability (rainfall and temperature), soil quality, land-use-related policies, and population growth. Studying the interrelationships between these factors can improve the development of effective land-use policies and help responsible agencies and policymakers plan accordingly to improve food security. The agent-based model illustrates how and when individuals or communities could make specific land-cover transitions to fulfill the community’s food needs. The results indicate that the agent-based model could effectively monitor land use and environmental changes to visualize potential risks over time and help the affected communities plan accordingly.
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Problem, research strategy, and findingsCity governments and planners alike commonly seek to increase pedestrian activity on city streets as part of broader sustainability, community building, and economic development strategies. Though walkability has received ample attention in planning literature, most planners still lack practical methods for predicting how development proposals could affect pedestrian activity on specific streets or public spaces at different times of the day. Cities typically require traffic impact assessments (TIAs) but not pedestrian impact assessments. In this study I present a methodology for estimating pedestrian trip generation and distribution between detailed origins and destinations in both existing and proposed built environments. Using the betweenness index from network analysis, I introduce a number of methodological improvements that allow the index to model pedestrian trips with parameters and constraints to account for pedestrian behavior in different settings. I demonstrate its application in the Kendall Square area of Cambridge (MA), where estimated foot traffic is compared during lunch and evening peak periods with observed pedestrian counts.Takeaway for practiceThe proposed approach can be particularly useful for TIAs, neighborhood plans, and large-scale development projects, where pedestrian flow estimates can be used to guide pedestrian infrastructure and safety improvements and public space investments or for locating pedestrian priority streets during the COVID-19 pandemic.
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The shift from mass tourism to more personalized travel denotes great importance in the construction of tourist itineraries. Given the negative impacts of transport and tourism on the environment, sustainability criteria play an important role. The Tourist Trip Design Problem is related to the design of itineraries for tourists. Planning is complex in tourist regions of developing countries where the information associated with tourist activities is difficult to access, vague, and incomplete. With this information, tourists must plan their trip, and the conditions and limitations they establish for it are flexible and imprecise. Fuzzy optimization can address problems with this type of information and constraints. Therefore, in this paper, an analysis of the tourism supply chain is carried out, taking as a case study the Department of Sucre on Colombia's Caribbean coast. A Multiconstraint Multimodal Team Orienteering Problem with Time Windows and fuzzy constraints is developed to model the tourism trip design problem that maximizes profit and minimizes CO2 emissions. The model is tested using datasets from the literature and the real world. The results demonstrate consistency with the fuzzy approach and generate a set of low-emission solutions.
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Today, upon the higher internet usage and the Covid-19 pandemic, the use of omni-channel distribution has experienced significant growth. The shopping experience in omni-channel distributions is influenced by the physical environment of the buyer, delivery time, and the cost of production to distribution of the goods which have a significant impact on customer loyalty and customer satisfaction. The lack of comprehensive studies in this field, and the number of constant variables in most of the available studies in the literature, especially uncertainty-laden demand, illustrate the significance of this study. After a related literature review and experts interviews, based on omni-channel Approach, all important factors influencing time, cost, and customer satisfaction have been included within a multi-objective mathematical model. Thus, defining constraints and decision variables, the objective functions have been solved within two new meta-heuristic algorithms, namely MOGWO and NSGA-II. Besides, these algorithms have been validated using NPS, DM, MID, and SNS indices. Upon comparing the outputs of these two algorithms and inserting 30 numerical instances, it has been shown that the MOGWO method has a stronger Pareto frontier and organized scattering for Pareto solutions. However, averagely, the NSGA-II algorithm produces fewer and more values compared with the first and second objectives, respectively.
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Road transport is in most cases the only available transport option in rural regions with undeveloped railway infrastructure. The problem of choosing the structure of the logistics chain is one of the most important ones that forwarding companies must solve when planning freight transportation. Due to political peculiarities, transportation of goods by road through the territory of Kazakhstan must be carried out by national forwarders, which results in centralizing the decision-making process and shifting the tasks of designing the structure of supply chains to the Kazakh forwarding companies. In this paper, we develop a mathematical model to solve the problem of choosing the right structure for a logistics chain. The proposed model considers the existing legal constraints in the region. Based on a simulated demand for cargo deliveries from China to Russia, we use a numerical example to show how to justify the structure of the logistics chain characterized by minimal total costs of the companies involved in the delivery process.
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In this work, we study the optimal investment and premium control problem with the short-selling constraint under the mean-variance criterion. The claim process is assumed to follow the non-homogeneous compound Poisson process. The insurer invests the surplus in one risk-free asset and one risky asset described by the Heston model. Under these, we consider an optimization objective that maximizes the return (the expectation of terminal wealth) and minimizes the risk (the variance of terminal wealth). By constructing the extended Hamilton–Jacobi–Bellman (HJB) system with the dynamic programming method, the time-consistent strategies and the corresponding value function are obtained. Furthermore, we provide numerical examples to illustrate the effects of the model parameters on the optimal policies.
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This research investigates the potential of inducing willingness to travel less by car with a Car-Free Day campaign and reveals under which circumstances it could be more effective. An online survey was conducted after the event, wherein questions about attitudes toward the campaign, participation and intention of traveling less by car, as well as sociodemographic attributes and travel features were asked. First, the impacts of situational constraints (travel distance, trip chaining and perceived insecurity) on participation were investigated. Secondly, it was examined whether engaging with the campaign increases the intention of traveling less by car after controlling for sociodemographic attributes, attitudes toward the campaign and situational constraints. Logistic regression models have shown that increased travel distance and trip chaining curb participation in the campaign and that the odds of being positively influenced by the campaign is almost four times higher for individuals who engaged with the campaign compared with those who did not participate. This study provides important empirical evidence of a Car-Free Day campaign’s potential of fostering a more sustainable travel behavior, which so far has not been systematically investigated. Finally, relevant policy implications and guidelines on the planning and conduction of a Car-Free Day event that could enhance the likelihood of its success were discussed.
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Purpose>COVID-19 has pushed many supply chains to re-think and strengthen their resilience and how it can help organisations survive in difficult times. Considering the availability of data and the huge number of supply chains that had their weak links exposed during COVID-19, the objective of the study is to employ artificial intelligence to develop supply chain resilience to withstand extreme disruptions such as COVID-19.Design/methodology/approach>We adopted a qualitative approach for interviewing respondents using a semi-structured interview schedule through the lens of organisational information processing theory. A total of 31 respondents from the supply chain and information systems field shared their views on employing artificial intelligence (AI) for supply chain resilience during COVID-19. We used a process of open, axial and selective coding to extract interrelated themes and proposals that resulted in the establishment of our framework.Findings>An AI-facilitated supply chain helps systematically develop resilience in its structure and network. Resilient supply chains in dynamic settings and during extreme disruption scenarios are capable of recognising (sensing risks, degree of localisation, failure modes and data trends), analysing (what-if scenarios, realistic customer demand, stress test simulation and constraints), reconfiguring (automation, re-alignment of a network, tracking effort, physical security threats and control) and activating (establishing operating rules, contingency management, managing demand volatility and mitigating supply chain shock) operations quickly.Research limitations/implications>As the present research was conducted through semi-structured qualitative interviews to understand the role of AI in supply chain resilience during COVID-19, the respondents may have an inclination towards a specific role of AI due to their limited exposure.Practical implications>Supply chain managers can utilise data to embed the required degree of resilience in their supply chains by considering the proposed framework elements and phases.Originality/value>The present research contributes a framework that presents a four-phased, structured and systematic platform considering the required information processing capabilities to recognise, analyse, reconfigure and activate phases to ensure supply chain resilience.
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The COVID-19 pandemic has significantly affected the energy sector. The new behavior of industrial and non-commercial consumers changes the energy consumption model. In addition, the constraints associated with the coronavirus crisis have led to environmental effects from declining economic activity. The research is based on evidence from around the world showing significant reductions in emissions and improved air quality. This situation requires rethinking the energy development strategy, particularly the construction of smart grids as a leading direction of energy development. Evaluating the efficiency of smart grids is a vital tool for disseminating successful experience in improving their management. This paper proposes an approach to a comprehensive assessment of smart grids based on a comparative analysis of existing methods, taking into account the changes that need to be considered after the experience gained from the COVID-19 pandemic. The approach provides an accurate set of efficiency indicators for assessing smart grids to account for the direct and indirect effects of smart grids’ implementation. This evaluation approach can be helpful to policymakers in developing energy efficiency programs and implementing energy policy.
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Featured ApplicationManagement model for air mobility and Service-oriented On-Demand Air Mobility. Modeling method for aircraft units between different vertiports within a given region considering mobility needs, capacity constraints, maintenance and charging needs. Exemplary application in a simulation model for a regional area of fifteen vertiports and their interconnection by means of electric aircraft units.Vertical mobility, as a commercial service, has been considered for scheduled volume and long-distance mobility services. To overcome its limits and increase its potential coverage, flexibility, and adaptability, centralized mobility hubs, similar to airports, will need to be constructed. Within this context, a customized and on-demand air mobility concept providing high flexibility in location combinations and time schedules could provide a solution for regional mobility needs. The aim of this research was to provide a generic framework for various mobility schemes as well as to design a holistic air mobility management concept for electric vertical mobility. A system dynamics simulation case study applied the conceptual model for an on-demand air mobility network of electric aircraft in a regional area with capacity constraints including vertiports, aircraft, charging, and parking stations. Therefore, bottlenecks and delays were quantified using a digital twin tool for customized scenarios. Simulation results showed that optimized maintenance management and the redistribution of aircraft units improved service indicators such as the number of customers served, and customer wait times as well as a reduction in the amount of time an aircraft spent on the ground. As a result, a digital twin air mobility network model with simulation capabilities may be a key factor for future implementation.
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Achieving sustainable, healthy diets remains a global challenge to meet the sustainable development agenda by 2030. The purpose of this study is to derive optimal dietary recommendations for children that consider nutritional, environmental, and economic parameters of sustainability, using Lebanon as a case study. Data from the latest national food consumption survey conducted among Lebanese children were used. Optimized diets were derived using Optimeal, a software that produces similar patterns to the usual diet while considering nutrition constraints (energy, and macro/micronutrient needs), environmental footprints ((EFPs): water use, energy use, and greenhouse emissions), and cost. Three optimized diets were derived that meet the nutritional needs of children aged 4–8, 9–13, and 14–18 years, while considering EFPs and cost. Compared to the usual intake, optimized diets included higher intake of vegetables, legumes and dairy, and a decrease in saturated oils, processed meats, sugar, salty snacks, sweets, and sugar-sweetened beverages. Overall, the optimized diets decreased cost by 20% and reduced water use, energy use, and GHG emissions, by 20%, 11%, and 22%, respectively. The proposed models consider various constraints and provide sustainable solutions for decision makers within a country undergoing crises.