2,4-Dinitrophenylhydrazones of 2,4-dihydroxybenzaldehyde, 2,4-dihydroxyacetophenone and 2,4-dihydroxybenzophenone

In 2,4-dihydroxybenzaldehyde 2,4-dinitrophenylhydrazone N,N-dimethylformamide solvate (or 4-[(2,4-dinitrophenyl)-hydrazonomethyl]benzene-1,3-diol N,N-dimethylformamide solvat

High-Resolution Image Reconstruction from a Sequence of Rotated and Translated Frames and its Application to an Infrared Imaging System

Some imaging systems employ detector arrays that are not sufficiently dense to meet the Nyquist criterion during image acquisition. This is particularly true for many staring infrared imagers. Thus, the full resolution afforded by the optics is not being realized in such a system. This paper presents a technique for estimating a high-resolution image, with reduced aliasing, from a sequence of undersampled rotated and translationally shifted frames. Such an image sequence can be obtained if an imager is mounted on a moving platform, such as an aircraft. Several approaches to this type of problem have been proposed in the literature. Here we extend some of this previous work. In particular, we define an observation model that incorporates knowledge of the optical system and detector array. The high-resolution image estimate is formed by minimizing a regularized cost function based on the observation model. We show that with the proper choice of a tuning parameter, our algorithm exhibits robustness in the presence of noise. We consider both gradient descent and conjugate-gradient optimization procedures to minimize the cost function. Detailed experimental results are provided to illustrate the performance of the proposed algorithm using digital video from an infrared imager

Production of a videotape series to promote forage-based livestock production in the Upper Midwest

Although properly managed grazing can increase farm income and enhance environmental quality, it has not been widely used in the Upper Midwest. Instead, grazing has been viewed as an adjunct to row crop production, and state-of-the-art management techniques have been adopted slowly. However, recent research has developed forage grazing systems that can compete economically with row crops, especially on more erodible land. In order to implement such systems successfully, producers need practical information on the technical aspects of grazing and pasture management. Most grazing videos produced prior to this project were tailored to other geographic regions or weren\u27t sufficiently detailed. The five videotapes produced in this project cover controlled grazing, principles for managing pasture plants, animal management, fencing and water systems, and year-round systems for the Upper Midwest

Is There a Role for Benefit-Cost Analysis in Environmental, Health, and Safety Regulation?

Benefit-cost analysis has a potentially important role to play in helping inform regulatory decision-making, although it should not be the sole basis for such decision-making. This paper offers eight principles on the appropriate use of benefit-cost analysis.Environment, Health and Safety, Regulatory Reform

Benefit-Cost Analysis in Environmental, Health, and Safety Regulation: A Statement of Principles

Benefit-cost analysis can play a very important role in legislative and regulatory policy debates on improving the environment, health, and safety. It can help illustrate the tradeoffs that are inherent in public policymaking as well as make those tradeoffs more transparent. It can also help agencies set regulatory priorities. Benefit-cost analysis should be used to help decisionmakers reach a decision. Contrary to the views of some, benefit-cost analysis is neither necessary nor sufficient for designing sensible public policy. If properly done, it can be very helpful to agencies in the decisionmaking process. Decisionmakers should not be precluded from considering the economic benefits and costs of different policies in the development of regulations. Laws that prohibit costs or other factors from being considered in administrative decisionmaking are inimical to good public policy. Currently, several of the most important regulatory statutes have been interpreted to imply such prohibitions. Benefit-cost analysis should be required for all major regulatory decisions, but agency heads should not be bound by a strict benefit-cost test. Instead, they should be required to consider available benefit-cost analyses and to justify the reasons for their decision in the event that the expected costs of a regulation far exceed the expected benefits. Agencies should be encouraged to use economic analysis to help set regulatory priorities. Economic analyses prepared in support of particularly important decisions should be subjected to peer review both inside and outside government. Benefits and costs of proposed major regulations should be quantified wherever possible. Best estimates should be presented along with a description of the uncertainties. Not all benefits or costs can be easily quantified, much less translated into dollar terms. Nevertheless, even qualitative descriptions of the pros and cons associated with a contemplated action can be helpful. Care should be taken to ensure that quantitative factors do not dominate important qualitative factors in decisionmaking. The Office of Management and Budget, or some other coordinating agency, should establish guidelines that agencies should follow in conducting benefit-cost analyses. Those guidelines should specify default values for the discount rate and certain types of benefits and costs, such as the value of a small reduction in mortality risk. In addition, agencies should present their results using a standard format, which summarizes the key results and highlights major uncertainties.

Benchmarking impact of nitrogen inputs on grain yield and environmental performance of producer fields in the western US Corn Belt

Benchmarking crop yields against nitrogen (N) input levels can help provide opportunities to improve N ferti-lizer efficiency and reduce N losses on maize in the US Corn Belt by identifying fields most likely to benefit from improved N management practices. Here, we evaluated a large producer database that includes field-level data on yield and applied N inputs from 9280 irrigated and rainfed fields over a 7-year period (2009–2015) in Nebraska (USA). A spatial framework, based on technology extrapolation domains, was used to cluster each field into spatial units with similar climate and soil type that represent 1.3 million ha of US farm land sown annually with maize. Three metrics were employed to evaluate agronomic and environmental performance: partial factor productivity for N inputs (PFPN, ratio between yield and N inputs), N balance (difference between N inputs and grain N removal), and yield-scaled N balance (ratio between N balance and yield). Nitrogen inputs included N from fertilizer and N contained in applied irrigation water. Average yield and N inputs were 40 and 44% higher in irrigated versus rainfed fields. The N balance was ca. 2-fold greater in irrigated versus rainfed fields (81 versus 41 kg N ha−1). Of the total number of field-years, 58% (irrigated) and 15% (rainfed) had N balance ≥ 75 kg N ha−1, which was considered a threshold to identify fields with potentially large N losses. Very large (\u3e 150 kg N ha−1) and negative N balance estimates were not apparent when analysis was based on field averages using a minimum of three years\u27 data instead of individual field-years. Nitrogen balance was smaller for maize crops following soybean compared to continuous maize. Despite the larger N balance (on an area basis), irrigated fields exhibited smaller yield-scaled N balance relative to rainfed fields. The approach proposed here can readily be adopted to benchmark current use of N fertilizer for other cereal-based crop systems, inform policy, and identify opportunities for improvement in N management

Benchmarking impact of nitrogen inputs on grain yield and environmental performance of producer fields in the western US Corn Belt

Benchmarking crop yields against nitrogen (N) input levels can help provide opportunities to improve N ferti-lizer efficiency and reduce N losses on maize in the US Corn Belt by identifying fields most likely to benefit from improved N management practices. Here, we evaluated a large producer database that includes field-level data on yield and applied N inputs from 9280 irrigated and rainfed fields over a 7-year period (2009–2015) in Nebraska (USA). A spatial framework, based on technology extrapolation domains, was used to cluster each field into spatial units with similar climate and soil type that represent 1.3 million ha of US farm land sown annually with maize. Three metrics were employed to evaluate agronomic and environmental performance: partial factor productivity for N inputs (PFPN, ratio between yield and N inputs), N balance (difference between N inputs and grain N removal), and yield-scaled N balance (ratio between N balance and yield). Nitrogen inputs included N from fertilizer and N contained in applied irrigation water. Average yield and N inputs were 40 and 44% higher in irrigated versus rainfed fields. The N balance was ca. 2-fold greater in irrigated versus rainfed fields (81 versus 41 kg N ha−1). Of the total number of field-years, 58% (irrigated) and 15% (rainfed) had N balance ≥ 75 kg N ha−1, which was considered a threshold to identify fields with potentially large N losses. Very large (\u3e 150 kg N ha−1) and negative N balance estimates were not apparent when analysis was based on field averages using a minimum of three years\u27 data instead of individual field-years. Nitrogen balance was smaller for maize crops following soybean compared to continuous maize. Despite the larger N balance (on an area basis), irrigated fields exhibited smaller yield-scaled N balance relative to rainfed fields. The approach proposed here can readily be adopted to benchmark current use of N fertilizer for other cereal-based crop systems, inform policy, and identify opportunities for improvement in N management

Deployment and Analysis of Instance Segmentation Algorithm for In-field Grade Estimation of Sweetpotatoes

Shape estimation of sweetpotato (SP) storage roots is inherently challenging due to their varied size and shape characteristics. Even measuring "simple" metrics, such as length and width, requires significant time investments either directly in-field or afterward using automated graders. In this paper, we present the results of a model that can perform grading and provide yield estimates directly in the field quicker than manual measurements. Detectron2, a library consisting of deep-learning object detection algorithms, was used to implement Mask R-CNN, an instance segmentation model. This model was deployed for in-field grade estimation of SPs and evaluated against an optical sorter. Storage roots from various clones imaged with a cellphone during trials between 2019 and 2020, were used in the model's training and validation to fine-tune a model to detect SPs. Our results showed that the model could distinguish individual SPs in various environmental conditions including variations in lighting and soil characteristics. RMSE for length, width, and weight, from the model compared to a commercial optical sorter, were 0.66 cm, 1.22 cm, and 74.73 g, respectively, while the RMSE of root counts per plot was 5.27 roots, with r^2 = 0.8. This phenotyping strategy has the potential enable rapid yield estimates in the field without the need for sophisticated and costly optical sorters and may be more readily deployed in environments with limited access to these kinds of resources or facilities.Comment: 21 pages, 11 figure

Determination of early summer pasture conditions to optimize forage and calf productivity and profitability

Forages can be valuable elements in a sustainable production system. The date when animals are first allowed to graze on pastures can impact the quality and amount of forages available. This study explored various factors that influence successful grazing

Toward transduodenal diffuse optical tomography of proximal pancreas

We demonstrate the feasibility of diffuse optical tomography (DOT) of the proximal pancreas by using optical applicator channels deployed longitudinally along the exterior surface of a duodenoscope. As the duodenum that nearly encircles the proximal pancreas forms a natural "C-loop" that is approximately three-quarters of a circle of 5-6 cm in diameter, a multichannel optical applicator attached to a duodenoscope has the potential to perform transduodenal DOT sampling of the bulk proximal pancreas wherein most cancers and many cystic lesions occur. The feasibility of transduodenal DOT is demonstrated on normal porcine pancreas tissues containing an introduced gelatinous inclusion of approximately 3 cm in diameter, by using nine source channels and six detector channels attached to a duodenoscope. Concurrent ultrasonography of the gelatinous inclusion in the porcine pancreas parenchyma provided a coarse, albeit indispensable, anatomic prior to transduodenal DOT in reconstructing a contrast of optical properties in the pancreas.Electrical and Computer EngineeringVeterinary Clinical Science