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Recycling centers sort and process collected waste in the interest of the environment, but also lead to damaging climate effects via released emissions and pollutants in their operation. Consequently, governments are closing such centers to fulfill climate and carbon neutrality goals. However, such closures risk populations being forced to travel further to facilities that collect waste, and can cause an unfair burden on the remaining open centers, thereby reducing participation in recycling. Using a facility location optimization model and mobility survey data within the state of Bavaria in Germany, we show how selective closures of these centers can still lead to high levels of recycling access. Our analysis ensures that even when 20% of facilities are closed smartly, the median travel distance by residents to their assigned recycling center increases by only 450 m. Additionally, we find Bavaria suffers from disparity in recycling patterns in rural and urban regions, both in terms of motivation to recycle and the locations of the facilities. We promote a policy that favors retention of recycling centers in rural regions by reserving 75% of open facilities to be in rural areas, while selectively closing facilities in urban regions, to remove these regional differences. Success of recycling campaigns depends on public perception of closures of such facilities and also on their ease of access. As policymakers gradually implement further closures, such data‐driven strategies can assist in being more transparent to the public thereby increasing the willingness to participate in such recycling programs. [ABSTRACT FROM AUTHOR]

Turkey's 2009 series of banknotes feature six eminent scholarly figures, with at least two of interest to a mathematical audience. We lay these mathematicians within a historical context, and provide a short summary of how they have contributed to the country's promotion and revival of scientific knowledge. Such campaigns lead to an increased awareness for both mathematics and mathematicians in the general audience, and can also be an effective strategy in boosting public opinion of the science, technology, engineering, and mathematics (STEM) disciplines. [ABSTRACT FROM AUTHOR]

Influenza pandemics occur when novel strains of the influenza virus emerge in human populations and spread worldwide (1). There were 3 influenza pandemics in the 20th century (1918, 1957, and [...]

Tuberculosis was ranked No. 3 among 29 types of infectious diseases in January 2022 of Jiangsu Province, in the southeast of China, and it has strong seasonality whose incidence rate has been high in springs and low in winters. This comprehensive study extends the spreading dynamics to the control strategies of TB in Jiangsu Province with time‐varying transmission rate, which is the first one in this type. The periodic transmission rate is incorporated to the classic SEIR model with susceptible, latent, infectious, and recovered compartments. The analysis shows that when the basic reproduction number R0$$ {R}_0 $$ is less than one, the epidemic will eventually disappear but, if R0$$ {R}_0 $$ is larger than one, the disease may persist with a periodic solution. The R0$$ {R}_0 $$ value of Jiangsu TB is calculated as 1.297. In addition, the model predicts that the epidemic will not disappear over time and there will be a major outbreak after certain years. To eradicate TB, this work introduces three control strategies on susceptible, latent, and infectious populations. It turns out that the optimal control solutions can reduce the incidence rate in 2035 by 90% from 2015, the goal of World Health Organization. Simultaneously, we find that the optimal controls have the same seasonality as the transmission rate. This indicates that Jiangsu Province needs to invest more control efforts in springs than in winters. In addition, the cost of control strategies is analyzed in terms of the unit cost, total cost, and incremental cost‐effectiveness. Summarizing all results, we recommend targeting the latent patients, or both latent and infectious individuals, with the transmission rate‐synchronized control strength as the most effective control strategy, to mitigate or even eradicate TB disease in Jiangsu Province. [ABSTRACT FROM AUTHOR]

Widespread, convenient access to COVID-19 testing has been challenging in the United States. We make a case for provisioning COVID-19 tests through the United States Postal Service (USPS) facilities and demonstrate a simple method for selecting locations to improve access. We provide quantitative evidence that even a subset of USPS facilities could provide broad access, particularly in remote and at-risk communities with limited access to health care. Based on daily travel surveys, census data, locations of USPS facilities, and an established care-seeking model, we estimate that more than 94% of the US population would be willing to travel to an existing USPS facility if warranted. For half of the US population, this would require traveling less than 2.5 miles from home; for 90%, the distance would be less than 7 miles. In Georgia, Illinois, and Minnesota, we estimate that testing at USPS facilities would provide access to an additional 4.1, 3.1, and 1.3 million people and reduce the median travel distance by 3.0, 0.8, and 1.2 miles, respectively, compared with existing testing sites per 28 July 2020. We also discuss the option of distributing test-at-home kits via USPS instead of private carriers. Finally, our proposal provides USPS an opportunity to increase revenues and expand its mission, thus improving its future prospects and relevance. [ABSTRACT FROM AUTHOR]

We introduce the two‐stage stochastic minimum s − t cut problem. Based on a classical linear 0‐1 programming model for the deterministic minimum s − t cut problem, we provide a mathematical programming formulation for the proposed stochastic extension. We show that its constraint matrix loses the total unimodularity property, however, preserves it if the considered graph is a tree. This fact turns out to be not surprising as we prove that the considered problem is NP‐hard in general, but admits a linear time solution algorithm when the graph is a tree. We exploit the special structure of the problem and propose a tailored Benders decomposition algorithm. We evaluate the computational efficiency of this algorithm by solving the Benders dual subproblems as max‐flow problems. For many tested instances, we outperform a standard Benders decomposition by two orders of magnitude with the Benders decomposition exploiting the max‐flow structure of the subproblems. [ABSTRACT FROM AUTHOR]