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Lift and drag forces on moving intruders in flowing granular materials are of fundamental interest but have not yet been fully characterized. Drag on an intruder in granular shear flow has been studied almost exclusively for the intruder moving across flow streamlines, and the few studies of the lift explore a relatively limited range of parameters. Here, we use discrete element method simulations to measure the lift force,$$F_{{L}}$$, and the drag force on a spherical intruder in a uniformly sheared bed of smaller spheres for a range of streamwise intruder slip velocities,$$u_{{s}}$$. The streamwise drag matches the previously characterized Stokes-like cross-flow drag. However,$$F_{{L}}$$in granular shear flow acts in the opposite direction to the Saffman lift in a sheared fluid at low$$u_{{s}}$$, reaches a maximum value and then decreases with increasing$$u_{{s}}$$, eventually reversing direction. This non-monotonic response holds over a range of flow conditions, and the$$F_{{L}}$$versus$$u_{{s}}$$data collapse when both quantities are scaled using the particle size, shear rate and overburden pressure. Analogous fluid simulations demonstrate that the flow around the intruder particle is similar in the granular and fluid cases. However, the shear stress on the granular intruder is notably less than that in a fluid shear flow. This difference, combined with a void behind the intruder in granular flow in which the stresses are zero, significantly changes the lift-force-inducing stresses acting on the intruder between the granular and fluid cases. 

Abstract Contrary to common intuition, a group of people recalling information together remembers less than the same number of individuals recalling alone (i.e., the collaborative inhibition effect). To understand this effect in a free recall task, we build a computational model of collaborative recall in groups, extended from the Context Maintenance and Retrieval (CMR) model, which captures how individuals recall information alone. We propose that in collaborative recall, one not only uses their previous recall as an internal retrieval cue, but one also listens to someone else’s recall and uses it as an external retrieval cue. Attending to this cue updates the listener’s context to be more similar to the context of someone else’s recall. Over an existing dataset of individual and collaborative recall in small and large groups, we show that our model successfully captures the difference in memory performance between individual recall and collaborative recall across different group sizes from 2 to 16, as well as additional recall patterns such as recency effects and semantic clustering effects. Our model further shows that collaborating individuals reach similar areas in the context space, whereby their contexts converge more than the contexts of individuals recalling alone. This convergence constrains their ability to search memories effectively and is negatively associated with recall performance. We discuss the contributions of our modeling results in relation to previous accounts of the collaborative inhibition effect. 

Household air pollution is a pervasive environmental health problem wherever access to cleaner fuels is poor. Despite numerous attempts to transition households away from polluting fuels, interventions are rarely sustainable. This intractability indicates that structural (i.e., systemic) dynamics act to maintain the status quo. In this case study of Ghana's Rural Liquefied Petroleum Gas (LPG) Promotion Program, our objectives were to 1) identify system structures affecting sustained fuel use, and 2) test strategies for improving intervention outcomes. To address these objectives, we applied a system dynamics approach, informed by a systematic literature review. A virtual simulation model was constructed to represent the implementation of the Rural LPG Program and its outcomes. By analyzing the model's structure and behavior, we proposed strategies that would improve the intervention's outcomes and tested the effectiveness of the strategies within the simulation model. Our results show that distributing two LPG cylinders to households (instead of one) contributed toward primary use of the fuel, whereas free weekly delivery of LPG (for up to four years) had limited long-term benefits and diminishing returns. Furthermore, reducing the time for users to perceive the benefits of cleaner fuels enhanced willingness-to-pay, and thereby helped to sustain higher rates of LPG use. This suggests that intervention planners should identify new users' expectations of benefits and proactively design ways to realize those benefits quickly (in a few weeks or less), while policy makers should support this as a design requirement in approval processes. 

Improved sanitation provides many benefits to human health and well-being and is integral to achieving Sustainable Development Goal Six. However, many nations, including most of sub-Saharan Africa, are not on track to meeting sanitation targets. Recognizing the inherent complexity of environmental health, we used systems thinking to study sanitation sustainability in Uganda. Our study participants, 37 sanitation actors in three rural districts, were engaged in interviews, group model building workshops, and a survey. The resulting model was parametrized and calibrated using publicly available data and data collected through the Uganda Sanitation for Health Activity. Our simulations revealed slippage from improved sanitation in all study districts, a behavior reflected in real interventions. This implies that systemic changes-changes to the rules and relationships in the system-may be required to improve sanitation outcomes in this context. Adding reinforcing feedback targeting households’ perceived value of sanitation yielded promising simulation results. We conclude with the following general recommendations for those designing sanitation policies or interventions: (1) conceptualize sanitation systems in terms of reinforcing and balancing feedback, (2) consider using participatory and simulation modeling to build confidence in these conceptual models, and (3) design many experiments (e.g., simulation scenarios) to test and improve understanding. 

Interest in craft beers is increasing worldwide due to their flavor and variety. However, craft breweries have high water, energy, and carbon dioxide (CO2) demands and generate large quantities of high-strength waste and greenhouse gases. While many large breweries recover energy using anaerobic digestion (AD) and recapture CO2 from beer fermentation, little is known about the economic feasibility of applying these technologies at the scale of small craft breweries. In addition, compounds in hops (Humulus lupulus), which are commonly added to craft beer to provide a bitter or “hoppy” flavor, have been shown to adversely affect anaerobic microbes in ruminant studies. In this study, biochemical methane potential (BMP) assays and anaerobic sequencing batch reactor (ASBR) studies were used to investigate biomethane production from high-strength craft brewery waste, with and without hop addition. A spreadsheet tool was developed to evaluate the economic feasibility of bioenergy and CO2 recovery depending on the brewery’s location, production volume, waste management, CO2 requirement, energy costs, and hop waste addition. The results showed that co-digestion of yeast waste with 20% hops (based on chemical oxygen demand (COD)) resulted in slightly lower methane yields compared with mono-digestion of yeast; however, it did not significantly impact the economic feasibility of AD in craft breweries. The use of AD and CO2 recovery was found to be economically feasible if the brewery’s annual beer production is >50,000 barrels/year. 

Controlled experiments are widely applied in many areas such as clinical trials or user behavior studies in IT companies. Recently, it is popular to study experimental design problems to facilitate personalized decision making. In this paper, we investigate the problem of optimal design of multiple treatment allocation for personalized decision making in the presence of observational covariates associated with experimental units (often, patients or users). We assume that the response of a subject assigned to a treatment follows a linear model which includes the interaction between covariates and treatments to facilitate precision decision making. We define the optimal objective as the maximum variance of estimated personalized treatment effects over different treatments and different covariates values. The optimal design is obtained by minimizing this objective. Under a semi-definite program reformulation of the original optimization problem, we use a YALMIP and MOSEK based optimization solver to provide the optimal design. Numerical studies are provided to assess the quality of the optimal design. 

Crop diseases are responsible for substantial yield losses worldwide, thereby threatening global food security. In this Research Topic, a collection of high-quality articles reported recent research progress concerning genes, proteins, secondary metabolites involved in the interactions between crop plants and their pathogens as well as utilization of new synthetic chemicals in control of crop diseases. As co-editors of this research topic, we appreciate the contributions from the authors of the papers published under this topic and highlight the three themes drawn from their research findings. 

Abstract Powdery mildew fungi are obligate biotrophic pathogens that only invade plant epidermal cells. There are two epidermal surfaces in every plant leaf: the adaxial (upper) side and the abaxial (lower) side. While both leaf surfaces can be susceptible to adapted powdery mildew fungi in many plant species, there have been observations of leaf abaxial immunity in some plant species including Arabidopsis. The genetic basis of such leaf abaxial immunity remains unknown. In this study, we tested a series of Arabidopsis mutants defective in one or more known defense pathways with the adapted powdery mildew isolate Golovinomyces cichoracearum UCSC1. We found that leaf abaxial immunity was significantly compromised in mutants impaired for both the EDS1/PAD4- and PEN2/PEN3-dependent defenses. Consistently, expression of EDS1–yellow fluorescent protein and PEN2–green fluorescent protein fusions from their respective native promoters in the respective eds1-2 and pen2-1 mutant backgrounds was higher in the abaxial epidermal cells than in the adaxial epidermal cells. Altogether, our results indicate that leaf abaxial immunity against powdery mildew in Arabidopsis is at least partially due to enhanced EDS1/PAD4- and PEN2/PEN3-dependent defenses. Such transcriptionally pre-programmed defense mechanisms may underlie leaf abaxial immunity in other plant species such as hemp and may be exploited for engineering adaxial immunity against powdery mildew fungi in crop plants.