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Brief examples of susceptibility of value chain operations and of their vulnerability to COVID-19 lockdown measures are given. A focus on resistance and resilience encourages investigation of local-level responses by communities and NGOs, which with appropriate monitoring and learning could be scaled up.Zoonotic pathogens and parasites that are transmitted from vertebrates to humans are a major public health risk with high associated global economic costs. The spread of these pathogens and risk of transmission accelerate with recent anthropogenic land-use changes (LUC) such as deforestation, urbanisation, and agricultural intensification, factors that are expected to increase in the future due to human population expansion and increasing demand for resources.We systematically review the literature on anthropogenic LUC and zoonotic diseases, highlighting the most prominent mammalian reservoirs and pathogens, and identifying avenues for future research.The majority of studies were global reviews that did not focus on specific taxa. South America and Asia were the most-studied regions, while the most-studied LUC was urbanisation. Livestock were studied more within the context of agricultural intensification, carnivores with urbanisation and helminths, bats with deforestation and viruses, and primates with habitg in mammals.We consider a retail firm selling a durable product in a volatile market where the demand is price-sensitive and random but its distribution is unknown. The firm dynamically replenishes inventory and adjusts prices over time and learns about the demand distribution. Assuming that the demand model is of the multiplicative form and unmet demand is partially backlogged, we take the empirical Bayesian approach to formulate the problem as a stochastic dynamic program. We first identify a set of regularity conditions on demand models and show that the state-dependent base-stock list-price policy is optimal. We next employ the dimensionality reduction approach to separate the scale factor that captures observed demand information from the optimal profit function, which yields a normalized dynamic program that is more tractable. We also analyze the effect of demand learning on the optimal policy using the system without Bayesian update as a benchmark. We further extend our analysis to the case with unobserved lost sales and the case with additive demand.There has been an increased interest in optimizing pricing and sourcing decisions under supplier competition with supply disruptions. Bcl-2 cancer In this paper, we conduct an analytical game-theoretical study to examine the effects of supply capacity disruption timing on pricing decisions for substitute products in a two-supplier one-retailer supply chain setting. We investigate whether the timing of a disruption may significantly impact the optimal pricing strategy of the retailer. We derive the optimal pricing strategy and ordering levels with both disruption timing and product substitution. By exploring both the Nash and Stackelberg games, we find that the order quantity with the disrupted supplier depends on price leadership and it tends to increase when the non-disrupted supplier is the leader. Moreover, the equilibrium market retail prices are higher under higher levels of disruption for the Nash game, compared to the Stackelberg game. We also uncover that the non-disrupted supplier can always charge the highest wholesale price if a disruption occurs before orders are received. This highlights the critical role of order timing. The insights can help operations managers to proper design risk mitigation ordering strategies and re-design the supply contracts in the presence of product substitution under supply disruptions.Concepts of sharing and commons are normatively and historically ambivalent. Some forms of sharing, such as sharecropping or alms-giving, proceed from and sustain asymmetrical relations to the means of life. Access to commons in other social contexts merely serves to make unequal forms of life more bearable. In other words, some expressions of sharing and commons are “functional” within hierarchical societies. Departing from these observations, this contribution traces contests over the logic of sharing, and apportioned shares of common land, from Brazil’s slave period through contemporary land rights movements in the northeastern state of Bahia. For former slaves and their descendants, “freedom” often meant sharecropping on the same plantations from which they had been released. However, rural Brazilians have also succeeded in transforming shared land into more equal and equitable distributions, from “peasant breaches” that emerged in slave gardens from the early colonial period through the abolition of slavery, to land occupations that occurred in the late twentieth century. By sharing land and other material resources-especially tree seeds, seedlings, and cuttings-rural laborers have established unexpected reconfigurations in distributions of property and social recognition that exceed institutionalized norms of sharing common land. With such outcomes in view, this contribution distinguishes socially replicative and transformative sharing.In this paper, we model the trajectory of the cumulative confirmed cases and deaths of COVID-19 (in log scale) via a piecewise linear trend model. The model naturally captures the phase transitions of the epidemic growth rate via change-points and further enjoys great interpretability due to its semiparametric nature. On the methodological front, we advance the nascent self-normalization (SN) technique (Shao, 2010) to testing and estimation of a single change-point in the linear trend of a nonstationary time series. We further combine the SN-based change-point test with the NOT algorithm (Baranowski et al., 2019) to achieve multiple change-point estimation. Using the proposed method, we analyze the trajectory of the cumulative COVID-19 cases and deaths for 30 major countries and discover interesting patterns with potentially relevant implications for effectiveness of the pandemic responses by different countries. Furthermore, based on the change-point detection algorithm and a flexible extrapolation function, we design a simple two-stage forecasting scheme for COVID-19 and demonstrate its promising performance in predicting cumulative deaths in the U.