Study of effective interaction from single particle transfer reactions on f-p shell nuclei

The present study concentrates on the average effective two-body interaction matrix elements being extracted, using sum-rule techniques, from transfer reactions on target states having single orbital as well as two orbitaloccupancy. This investigation deals with transfer reactions on f-p shell nuclei involving (i) $1f_{7/2}$ and $2p_{3/2}$ transfer on target states using $^{40}$Ca as inert core, and (ii) $2p_{3/2}$ and$1f_{5/2}$ transfer on states using $^{56}$Ni as core.Comment: 12 pages, ptptex Subj-Classes: Nuclear Shell Structure e-mail:[email protected]

Effective two-body interactions in the s-d shell nuclei from sum rules equations in tranfer reactions

Average effective two-body interaction matrix elements in the s-d shell have been extracted, from data on experimentally measured isospin centroids, by combining the recently derived new sum rules equations for pick-up reactions with similar known equations for stripping reactions performed on general multishell target states. Using this combination of stripping and pick-up equations, the average effective matrix elements for the shells, 1d^2_5/2, 2s^2_1/2 and 1d^2_3/2 respectively have been obtained. A new feature of the present work is that the restriction imposed in earlier works on target states, that it be populated only by active neutrons has now been abandoned.Comment: 12 pages, RevTeX, e-mail: [email protected]

Development and evaluation of an automated spray patternator using digital liquid level sensors

Citation: Luck, J. D., Schaardt, W. A., Forney, S. H., & Sharda, A. (2016). Development and evaluation of an automated spray patternator using digital liquid level sensors. Applied Engineering in Agriculture, 32(1), 47-52. doi:10.13031/aea.32.11381The purpose of this study was to develop and evaluate an automated spray pattern measurement system which utilized digital liquid level sensors to quantify the coefficient of variation (CV) for different nozzle configurations. The overall system was designed to measure nozzle effluent in 25 mm divisions from 38.1 to 76.2 cm in width for multiple nozzle configurations with a total patternator surface width of 3.05 m. The patternator surface and data collection system were designed and developed to achieve three primary goals: patternator surface division accuracy, data collection system accuracy, and data collection system repeatability. Patternator surface measurements indicated an average standard deviation of approximately 0.1 mm (0.4%) which would not contribute significantly to spray pattern CV estimates. To quantify the measurement accuracy, the automated system was compared to manual data collection using weights collected from graduated cylinders. Statistical analysis revealed no difference (p > 0.05) between CV estimates from the manual and automated data collection methods. The average difference in CV between the two methods was 0.15% which considered 12 tests per method. Repeatability was also a primary concern, the standard deviation among CV values for tests conducted with the automated system was only 0.35%. The evaluation of the system provided confidence that suitable results would be acquired for different nozzle configurations consisting of acceptable or relatively poor spray patterns. © 2016 American Society of Agricultural and Biological Engineers

Ecological Validity of Don\u27t Remember and Don\u27t Know For Distinguishing Accessibility-Versus Availability-Based Retrieval Failures In Older and Younger Adults: Knowledge For News Events

With pursuit of incremental progress and generalizability of findings in mind, we examined a possible boundary for older and younger adults’ metacognitive distinction between what is not stored in memory versus merely inaccessible with materials that are not process pure to knowledge or events: information regarding news events. Participants were asked questions about public events such as celebrity news, tragedies, and political events that were widely experienced in the previous 10–12 years, responding “I don’t know” (DK) or “I don’t remember” (DR) when retrieval failed. Memories of these events are relatively recently acquired in rich, naturalistic contexts and are likely not fully separated from episodic details. When retrieval failed, DR items were recognized with higher accuracy than DK items, both immediately and 2 years later, confirming that self-reported not remembering reflects failures of accessibility, whereas not knowing better captures a lack of availability. In fact, older adults distinguished between the causes of retrieval failures more precisely than younger adults. Together, these findings advance the reliability, validity, and generalizability of using DR and DK as a metacognitive tool to address the phenomenological experience and behavioral consequences of retrieval failures of information that contains both semantic and episodic features. Implications for metacognition in aging and related constructs like familiarity, remembering, and knowing are discussed

Simulating the Impacts of Irrigation Levels on Soybean Production in Texas High Plains to Manage Diminishing Groundwater Levels

There is an increasing need to strategize and plan irrigation systems under varied climatic conditions to support efficient irrigation practices while maintaining and improving the sustainability of groundwater systems. This study was undertaken to simulate the growth and production of soybean [Glycine max (L.)] under different irrigation scenarios. The objectives of this study were to calibrate and validate the CROPGRO-Soybean model under Texas High Plains’ (THP) climatic conditions and to apply the calibrated model to simulate the impacts of different irrigation levels and triggers on soybean production. The methodology involved combining short-term experimental data with long-term historical weather data (1951–2012), and use of mechanistic crop growth simulation algorithms to determine optimum irrigation management strategies. Irrigation was scheduled based on five different plant extractable water levels (irrigation threshold [ITHR]) set at 20%, 35%, 50%, 65%, and 80%. The calibrated model was able to satisfactorily reproduce measured leaf area index, biomass, and evapotranspiration for soybean, indicating it can be used for investigating different strategies for irrigating soybean in the THP. Calculations of crop water productivity for biomass and yield along with irrigation water use efficiency indicated soybean can be irrigated at ITHR set at 50% or 65% with minimal yield loss as compared to 80% ITHR, thus conserving water and contributing toward lower groundwater withdrawals

Sum rules for isospin centroids in pick-up reactions on general multishell target states

Sum Rules equations for pick-up reactions are presented for the first time for the energy centroids of states both for the isospin T_< (\equiv T_0 - 1 \over 2) and T_> (\equiv T_0 + {1 \over 2}) of the final nucleus when a nucleon is picked up from a general multishell target state with isospin T_0. These equations contain two-body correlation terms, , which, at the present moment, are difficult to handle analytically. These terms are managed by combining these equations with the known stripping reactions equations. Sample applications of these equations to experimental data are presented.Comment: 11 pages, LaTe

Early-Season Stand Count Determination in Corn via Integration of Imagery from Unmanned Aerial Systems (UAS) and Supervised Learning Techniques

Corn (Zea mays L.) is one of the most sensitive crops to planting pattern and early-season uniformity. The most common method to determine number of plants is by visual inspection on the ground but this field activity becomes time-consuming, labor-intensive, biased, and may lead to less profitable decisions by farmers. The objective of this study was to develop a reliable, timely, and unbiased method for counting corn plants based on ultra-high-resolution imagery acquired from unmanned aerial systems (UAS) to automatically scout fields and applied to real field conditions. A ground sampling distance of 2.4 mm was targeted to extract information at a plant-level basis. First, an excess greenness (ExG) index was used to individualized green pixels from the background, then rows and inter-row contours were identified and extracted. A scalable training procedure was implemented using geometric descriptors as inputs of the classifier. Second, a decision tree was implemented and tested using two training modes in each site to expose the workflow to different ground conditions at the time of the aerial data acquisition. Differences in performance were due to training modes and spatial resolutions in the two sites. For an object classification task, an overall accuracy of 0.96, based on the proportion of corrected assessment of corn and non-corn objects, was obtained for local (per-site) classification, and an accuracy of 0.93 was obtained for the combined training modes. For successful model implementation, plants should have between two to three leaves when images are collected (avoiding overlapping between plants). Best workflow performance was reached at 2.4 mm resolution corresponding to 10 m of altitude (lower altitude); higher altitudes were gradually penalized. The latter was coincident with the larger number of detected green objects in the images and the effectiveness of geometry as descriptor for corn plant detection.Sociedad Argentina de Informática e Investigación Operativ

Early-Season Stand Count Determination in Corn via Integration of Imagery from Unmanned Aerial Systems (UAS) and Supervised Learning Techniques

Corn (Zea mays L.) is one of the most sensitive crops to planting pattern and early-season uniformity. The most common method to determine number of plants is by visual inspection on the ground but this field activity becomes time-consuming, labor-intensive, biased, and may lead to less profitable decisions by farmers. The objective of this study was to develop a reliable, timely, and unbiased method for counting corn plants based on ultra-high-resolution imagery acquired from unmanned aerial systems (UAS) to automatically scout fields and applied to real field conditions. A ground sampling distance of 2.4 mm was targeted to extract information at a plant-level basis. First, an excess greenness (ExG) index was used to individualized green pixels from the background, then rows and inter-row contours were identified and extracted. A scalable training procedure was implemented using geometric descriptors as inputs of the classifier. Second, a decision tree was implemented and tested using two training modes in each site to expose the workflow to different ground conditions at the time of the aerial data acquisition. Differences in performance were due to training modes and spatial resolutions in the two sites. For an object classification task, an overall accuracy of 0.96, based on the proportion of corrected assessment of corn and non-corn objects, was obtained for local (per-site) classification, and an accuracy of 0.93 was obtained for the combined training modes. For successful model implementation, plants should have between two to three leaves when images are collected (avoiding overlapping between plants). Best workflow performance was reached at 2.4 mm resolution corresponding to 10 m of altitude (lower altitude); higher altitudes were gradually penalized. The latter was coincident with the larger number of detected green objects in the images and the effectiveness of geometry as descriptor for corn plant detection.Sociedad Argentina de Informática e Investigación Operativ

Early-Season Stand Count Determination in Corn via Integration of Imagery from Unmanned Aerial Systems (UAS) and Supervised Learning Techniques

Corn (Zea mays L.) is one of the most sensitive crops to planting pattern and early-season uniformity. The most common method to determine number of plants is by visual inspection on the ground but this field activity becomes time-consuming, labor-intensive, biased, and may lead to less profitable decisions by farmers. The objective of this study was to develop a reliable, timely, and unbiased method for counting corn plants based on ultra-high-resolution imagery acquired from unmanned aerial systems (UAS) to automatically scout fields and applied to real field conditions. A ground sampling distance of 2.4 mm was targeted to extract information at a plant-level basis. First, an excess greenness (ExG) index was used to individualized green pixels from the background, then rows and inter-row contours were identified and extracted. A scalable training procedure was implemented using geometric descriptors as inputs of the classifier. Second, a decision tree was implemented and tested using two training modes in each site to expose the workflow to different ground conditions at the time of the aerial data acquisition. Differences in performance were due to training modes and spatial resolutions in the two sites. For an object classification task, an overall accuracy of 0.96, based on the proportion of corrected assessment of corn and non-corn objects, was obtained for local (per-site) classification, and an accuracy of 0.93 was obtained for the combined training modes. For successful model implementation, plants should have between two to three leaves when images are collected (avoiding overlapping between plants). Best workflow performance was reached at 2.4 mm resolution corresponding to 10 m of altitude (lower altitude); higher altitudes were gradually penalized. The latter was coincident with the larger number of detected green objects in the images and the effectiveness of geometry as descriptor for corn plant detection.Sociedad Argentina de Informática e Investigación Operativ