There is growing interest in producing more beef from cattle raised in pasture-based systems, rather than grain-finishing feedlot systems in the USA. Given the availability of high-quality forage, pastureland, and markets in the northeastern USA, an expansion of beef production in the region contributes to a gradual shift toward grass-based finishing systems. However, the existing capacity of slaughter and processing facilities in the region is not sufficient to meet the service demand as grass-finished beef cattle production expands. This article examines slaughter and processing bottleneck problems under three scenarios of grass-finished beef production expansion. Through modeling the optimal utilization and expansion of currently existing plant capacity in the region, this study identifies capacity expansion solutions to overcome the emerging bottleneck problems. The plant utilization and expansion problem is formulated as an optimization model with the objective of minimizing total costs associated with cattle assembly, slaughter, processing, and distribution. Our results suggest that slaughter bottlenecks in New York State coincide with underutilized slaughter capacity in New England. Reducing plant numbers while increasing plant utilization rates or expanding the capacity of the remaining plants, would likely lead to greater cost savings.

Aiming at the problems of many path inflection points, unsmooth paths, and poor local obstacle avoidance in path planning of inspection robots in static-dynamic scenes under complex geological conditions in coal mine roadways, a hybrid path planning method based on the improved A* algorithm and dynamic window approach (DWA) algorithm is proposed. First, the inspection robot platform and system model are constructed. An improved heuristic function that incorporates target weight information is proposed based on the A* global path planning algorithm. Additionally, redundant nodes are eliminated, and the path is smoothed using the Floyd algorithm and B-spline curves. Second, the global path planning A* algorithm and the local path planning DWA algorithm are fused. The dynamic path planning is carried out by setting the key node information of the global path extracted from the improved A* algorithm as the local target point of the DWA algorithm. On this basis, a grid map is established to simulate and analyze the proposed path planning algorithm. Finally, the autonomous path planning and walking experiment of inspection robot in simulated roadway environment are carried out. The results show that the hybrid path planning method based on improved A* algorithm and DWA algorithm proposed in this paper is more efficient and safer, which can meet the motion requirements of inspection robot in coal mine roadway.

In this paper, we present a standardized approach for using cancer incidence and survival data to account for the timing between a reduction in carcinogen exposure and the subsequent reduction in cancer risk and fatality. While the estimates for this timing between a reduction in carcinogen exposure and reduced cancer risk would ideally come from high-quality studies specifically examining this question, very few such studies are available. Thus, we designed an approach to account for this timing when sufficient data are not available elsewhere. Our approach can be used in estimating monetized values for achieving small reductions in the risks for many common specific types of cancer in benefit–cost analyses of regulatory and non-regulatory policies in the United States that achieve cancer risk reductions by reducing carcinogen exposures. We provide estimated values for 108 different cancer sites and for all cancer sites combined. We accompany this paper with a spreadsheet-based tool that presents our results separately for non-fatal and fatal risks so that results can easily be calculated using different combinations of discount rates, latency between carcinogen exposure and cancer diagnosis, values for the willingness-to-pay to avoid fatal and non-fatal cancer risks, and potentially affected populations.

Quantitative easing (QE) has been a favourite tool of central banks in their post-financial crisis monetary policy apparatus. Social science literature has interpreted QE as a shift away from performative governance characterising pre-crisis monetary policy. With reference to the Bank of England’s experience, I offer a reinterpretation of QE as a performative intervention in the conditions of financial markets, as an attempt to alter the state of financial markets away from dysfunctionality and towards efficiency. I claim that, following the financial crisis, the model of complete and efficient markets – a mainstay in central banking prior to the crisis – was transformed from a real-world approximation to a ‘performative object’ to be achieved. In deploying the balance sheet, central banks attempt to performatively enact complete and efficient markets. The article rejects the claim of discontinuity between pre-crisis and post-crisis monetary policy, arguing that QE is a continuation of inflation targeting though with important innovations. While pre-crisis performativity relied on central bankers’ communicative framing of market expectations, QE is performative via the ontological shaping of financial markets, driven by epistemic models. The article relies on a set of 51 interviews with central bankers and financial market participants and a corpus of documents.