Job Satisfaction and Employee Turnover Determinants in Fortune 50 Companies: Insights from Employee Reviews from Indeed.com

We explored 682176 employee reviews of Fortune 50 companies from Indeed.com using topic discovery techniques like Latent Dirichlet Allocation (LDA) and Structural Topic Modeling (STM) to identify salient aspects in employee reviews and automatically infer latent topics that tend to drive employee satisfaction. We also studied how various satisfaction factors could be related to employee turnover. We discovered important topics in the reviews, including Management and Leadership, Advancement Opportunity, Pay and Benefits, Work-Life Balance, and Culture, which we compare to the five Job Descriptive Index (JDI) facets. Both LDA and STM discovered well-separated and distinguishable topics. We also incorporated a “Job Status” covariate in STM, which helped distinguish between what topics were talked about most by “Former” vs “Current” employees, and consequently helped us analyze the factors that could have caused employee turnover. We found that Leadership and Management and Overwork and Stressful Environment were the dominant factors contrasting between former and current employees, suggesting that they might be a leading cause of employee turnover. Furthermore, we post-processed the topic probability result from the STM model and analyzed it to determine sector-wise topic contribution for each topic, and also analyzed the company-wise topic contribution in each sector. We found that Retail sectors talked the most about Pay and Benefits and Length of Breaks, whereas the Technology sector’s employees were more concerned about the Work-Life Balance issue. Our results are directly usable to support company behavioral management decision makers to conceive and evaluate initiatives intended to enhance employee satisfaction. Furthermore, our techniques, including a novel visualization of topic composition and quality, are generalizable to any setting that uses topic discovery from unstructured text, and especially those comparing topics across entities

Agricultural Management Impact on Greenhouse Gas Emissions

Management practices used on croplands to enhance crop yields and quality can contribute about 10–20% of global greenhouse gases (GHGs: carbon dioxide [CO2], nitrous oxide [N2O], and methane [CH4]). Some of these practices are tillage, cropping systems, N fertilization, organic fertilizer application, cover cropping, fallowing, liming, etc. The impact of these practices on GHGs in radiative forcing in the earth’s atmosphere is quantitatively estimated by calculating net global warming potential (GWP) which accounts for all sources and sinks of CO2 equivalents from farm operations, chemical inputs, soil carbon sequestration, and N2O and CH4 emissions. Net GWP for a crop production system is expressed as kg CO2 eq. ha−1 year.−1 Net GWP can also be expressed in terms of crop yield (kg CO2 eq. kg−1 grain or biomass yield) which is referred to as net greenhouse gas intensity (GHGI) or yield-scaled GWP and is calculated by dividing net GWP by crop yield. This article discusses the literature review of the effects of various management practices on GWP and GHGI from croplands as well as different methods used to calculate net GWP and GHGI. The paper also discusses novel management techniques to mitigate net CO2 emissions from croplands to the atmosphere. This information will be used to address the state of global carbon cycle

REGION-COLOR BASED AUTOMATED BLEEDING DETECTION IN CAPSULE ENDOSCOPY VIDEOS

Capsule Endoscopy (CE) is a unique technique for facilitating non-invasive and practical visualization of the entire small intestine. It has attracted a critical mass of studies for improvements. Among numerous studies being performed in capsule endoscopy, tremendous efforts are being made in the development of software algorithms to identify clinically important frames in CE videos. This thesis presents a computer-assisted method which performs automated detection of CE video-frames that contain bleeding. Specifically, a methodology is proposed to classify the frames of CE videos into bleeding and non-bleeding frames. It is a Support Vector Machine (SVM) based supervised method which classifies the frames on the basis of color features derived from image-regions. Image-regions are characterized on the basis of statistical features. With 15 available candidate features, an exhaustive feature-selection is followed to obtain the best feature subset. The best feature-subset is the combination of features that has the highest bleeding discrimination ability as determined by the three performance-metrics: accuracy, sensitivity and specificity. Also, a ground truth label annotation method is proposed in order to partially automate delineation of bleeding regions for training of the classifier. The method produced promising results with sensitivity and specificity values up to 94%. All the experiments were performed separately for RGB and HSV color spaces. Experimental results show the combination of the mean planes in red and green planes to be the best feature-subset in RGB (Red-Green-Blue) color space and the combination of the mean values of all three planes of the color space to be the best feature-subset in HSV (Hue-Saturation-Value)

Nitrogen Fertilization I: Impact on Crop, Soil, and Environment

Nitrogen (N) is a major limiting nutrient to sustain crop yields and quality. As a result, N fertilizer is usually applied in large quantity to increase crop production throughout the world. Application of N fertilizers has increased crop yields and resulted in achievement of self-sufficiency in food production in many developing countries. Excessive application of N fertilizers beyond crops’ demand, however, has resulted in undesirable consequences of degradation in soil, water, and air quality. These include soil acidification, N leaching in groundwater, and emissions of nitrous oxide (N2O), a potent greenhouse gas that contributes to global warming. Long-term application of ammonia-based N fertilizers, such as urea, has increased soil acidity which rendered to soil infertility where crops fail to respond with further application of N fertilizers. Another problem is the groundwater contamination of nitrate-N (NO3-N) which can be a health hazard to human and livestock if its concentration goes above 10 mg L−1 in drinking water. The third problem is emissions of N2O gas which is 300 times more powerful than carbon dioxide in terms of global warming potential. This chapter examines the effect of N fertilization on soil and environmental quality and crop yields

Nitrogen Fertilization II: Management Practices to Sustain Crop Production and Soil and Environmental Quality

Improved management practices can be used to sustain crop yields, improve soil quality, and reduce N contaminations in groundwater and the atmosphere due to N fertilization. These practices include crop rotation, cover cropping, application of manures and compost, liming, and integrated crop-livestock system. The objectives of these practices are to reduce the rate of N fertilization, enhance N-use efficiency, increase crop N uptake, promote N cycling and soil N storage, and decrease soil residual N. This chapter discusses improved management practices to reduce N fertilization rate, sustain crop yields, and improve soil and environmental quality. The adaptation of these practices by farmers, producers, and ranchers, however, depends on social, economic, soil, and environmental conditions

Particulate and active soil nitrogen fractions are reduced by sheep grazing in dryland cropping systems

Sheep (Ovis aries L.) grazing, a cost-effective method of weed control compared to herbicide application and tillage, may influence N cycling by consuming crop residue and weeds and returning N through feces and urine to the soil. The objective of this experiment was to evaluate the effect of sheep grazing compared to tillage and herbicide application for weed control on soil particulate and active soil N fractions in dryland cropping systems. Our hypothesis was that sheep grazing used for weed control would increase particulate and active soil N fractions compared to tillage and herbicide application. Soil samples collected at the 0–30 cm depth from a Blackmore silt loam were analyzed for particulate organic N (PON), microbial biomass N (MBN), and potential N mineralization (PNM) under dryland cropping systems from 2009 to 2011 in southwestern Montana, USA. Treatments were three weed management practices [sheep grazing (grazing), herbicide application (chemical), and tillage (mechanical)] as the main plot and two cropping sequences [continuous spring wheat (Triticum aestivum L.; CSW) and spring wheat–pea (Pisum sativum L.)/barley (Hordeum vulgare L.) mixture hay–fallow; W–P/B–F] as the split-plot factor arranged in randomized complete block with three replications. The PON and MBN at 0–30 cm were greater in the chemical or mechanical than the grazing treatment with CSW. The PNM at 15–30 cm was greater in the chemical or mechanical than the grazing treatment in 2009 and 2011 and at 5–15 cm was greater with W–P/B–F than CSW in 2010. From 2009 to 2011, PON at 0–30 cm and PNM at 15–30 cm reduced from 2 to 580 kg N ha−1 year−1 in the grazing and chemical treatments, but the rate varied from −400 to 2 kg N ha−1 year−1 in the mechanical treatment. Lower amount of labile than nonlabile organic matter returned to the soil through feces and urine probably reduced soil active and coarse organic matter N fractions with sheep grazing compared to herbicide application and tillage for weed control. Reduction in the rate of decline in N fractions from 2009 to 2011 compared to the herbicide application treatment, however, suggests that sheep grazing may stabilize N fractions in the long-term if the intensity of grazing is reduced. Animal grazing may reduce soil N fractions in annual cropping systems in contrast to known increased fractions in perennial cropping systems