Measuring the Effects of Workloss on Productivity With Team Production

Using data from a survey of 800 managers in 12 industries, we find empirical support for the hypothesis that the cost associated with missed work varies across jobs according to the ease with which a manager can find a perfect replacement for the absent worker, the extent to which the worker functions as part of a team, and the time sensitivity of the worker's output. We then estimate wage multipliers' for 35 different jobs, where the multiplier is defined as the cost to the firm of an absence as a proportion (often greater than one) of the absent worker's daily wage. The median multiplier is 1.28, which supports the view that the cost to the firm of missed work is often greater than the wage.

Watershed development for rainfed areas: Concept, principles, and approaches

Land, water, and vegetation are the natural resources, which provide food, feed, fiber, and fuel needs for the survival of human beings. However, the growing biotic pressure and overexploitation of the natural resources are leading to their accelerated degradation, resulting in reduced productivity. The sustainable management of natural resources is the key for the sustenance and well-being of human beings. Water is a finite resource and an elixir of life; however, water is becoming scarce due its overexploitation to meet the demands of the ever increasing demographic pressure. Agriculture is a major consumer (75–80%) of water for food production globally......................

Spatially Resolved Chandra Spectroscopy of the Large Magellanic Cloud Supernova Remnant N132D

We perform detailed spectroscopy of the X-ray-brightest supernova remnant in the Large Magellanic Cloud (LMC), N132D, using Chandra archival observations. By analyzing the spectra of the entire well-defined rim, we determine the mean abundances for O, Ne, Mg, Si, S, and Fe for the local LMC environment. We find evidence of enhanced O on the northwestern and S on the northeastern blast wave. By analyzing spectra interior to the remnant, we confirm the presence of a Si-rich, relatively hot plasma (≳1.5 keV) that is also responsible for the Fe K emission. Chandra images show that the Fe K emission is distributed throughout the interior of the southern half of the remnant but does not extend out to the blast wave. We estimate the progenitor mass to be 15 ± 5 M⊙ using abundance ratios in different regions that collectively cover a large fraction of the remnant, as well as from the radius of the forward shock compared with models of an explosion in a cavity created by stellar winds. We fit ionizing and recombining plasma models to the Fe K emission and find that the current data cannot distinguish between the two, so the origin of the high-temperature plasma remains uncertain. Our analysis is consistent with N132D being the result of a core-collapse supernova in a cavity created by its intermediate-mass progenitor.We are grateful to Keith Arnaud and Craig Gordon for providing support with the X-ray fitting package Xspec. P.S. acknowledges the Birla Institute of Technology and Science Pilani Alumni Association (BITSAA) undergraduate summer research scholarship and the Australian Government Research Training Program Scholarship (AGRTP). T.J.G., V.L.K., and P.P.P. acknowledge support under NASA contract NAS8-03060 with the Chandra X-ray Center. The scientific results reported in this article are based on data obtained from the Chandra Data Archive and observations made by the Chandra X-ray Observatory. This research has also made use of software provided by the Chandra X-ray Center (CXC) in the application package CIAO, and the NASA Astrophysics Data System (ADS)

Physicochemical characterization and improved in vitro dissolution performance of diacerein solid dispersions with PVP K30

Solid dispersions (SDs) of poorly water soluble diacerein were prepared with polyvinylpyrrolidone K30 at drug to polymer ratios of 1:1, 1:3 and 1:5 w/w utilizing kneading technique. Physical mixture (PM) was prepared at drug to polymer ratio of 1:5 w/w for comparison. All formulations were further characterized by TLC, DSC, XRPD, SEM and dissolution studies. TLC indicated an absence of chemical interaction between drug and polymer. A prominent decrease in the crystallinity was accounted for diacerein in binary systems from XRPD data. DSC thermograms revealed a uniform molecular dispersion and generation of amorphous entities of drug accompanied by loss of crystalline and irregular shape with distinct changes in surface morphological features of diacerein detected in SEM photomicrographs. The drug dissolution properties of SDs were significantly improved (DP2: 95.87-100%) in comparison to crystalline diacerein and PM suggesting suitability of kneading method for improving the release rate properties of diacerein.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Spatio-temporal evaluation of plant height in corn via unmanned aerial systems

Detailed spatial and temporal data on plant growth are critical to guide crop management. Conventional methods to determine field plant traits are intensive, time-consuming, expensive, and limited to small areas. The objective of this study was to examine the integration of data collected via unmanned aerial systems (UAS) at critical corn (Zea mays L.) developmental stages for plant height and its relation to plant biomass. The main steps followed in this research were (1) workflow development for an ultrahigh resolution crop surface model (CSM) with the goal of determining plant height (CSM-estimated plant height) using data gathered from the UAS missions; (2) validation of CSM-estimated plant height with ground-truthing plant height (measured plant height); and (3) final estimation of plant biomass via integration of CSM-estimated plant height with ground-truthing stem diameter data. Results indicated a correlation between CSM-estimated plant height and ground-truthing plant height data at two weeks prior to flowering and at flowering stage, but high predictability at the later growth stage. Log–log analysis on the temporal data confirmed that these relationships are stable, presenting equal slopes for both crop stages evaluated. Concluding, data collected from low-altitude and with a low-cost sensor could be useful in estimating plant height.Sociedad Argentina de Informática e Investigación Operativ

Physicochemical characterization and improved in vitro dissolution performance of diacerein solid dispersions with PVP K30

Solid dispersions (SDs) of poorly water soluble diacerein were prepared with polyvinylpyrrolidone K30 at drug to polymer ratios of 1:1, 1:3 and 1:5 w/w utilizing kneading technique. Physical mixture (PM) was prepared at drug to polymer ratio of 1:5 w/w for comparison. All formulations were further characterized by TLC, DSC, XRPD, SEM and dissolution studies. TLC indicated an absence of chemical interaction between drug and polymer. A prominent decrease in the crystallinity was accounted for diacerein in binary systems from XRPD data. DSC thermograms revealed a uniform molecular dispersion and generation of amorphous entities of drug accompanied by loss of crystalline and irregular shape with distinct changes in surface morphological features of diacerein detected in SEM photomicrographs. The drug dissolution properties of SDs were significantly improved (DP2: 95.87-100%) in comparison to crystalline diacerein and PM suggesting suitability of kneading method for improving the release rate properties of diacerein.Colegio de Farmacéuticos de la Provincia de Buenos Aire

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