Search

Soybean production is greatly affected by different types of weeds such as horseweed, kochia, ragweed, redroot pigweed, and waterhemp in the midwestern region of the United States. Identification of the soybean plants and the weeds is crucial to controlling the weeds in precision agriculture. The objective of this study was to classify soybean plants and five weed species where a hyperspectral imaging camera with a spectral range of 400 to 1000 nm was used to acquire the images. To acquire the HSI images, a customized robotic hyperspectral data collection scanning platform was developed and used in the greenhouse. A total of 983 hyperspectral data cubes were captured from the greenhouse environment (n = 252, soybean; n = 731, weeds). Spectral information was extracted from the collected images and then a classification model was developed by applying partial least squares discriminant analysis (PLS-DA). To construct the calibration and validation data set, the images were each divided into 70% and 30% ratios for model training and testing, respectively. Seven types of data preprocessing techniques, including mean normalization, maximum normalization, range normalization, multiplicative scatter correction (MSC), standard normal variate (SNV), Savitzky-Golay first derivatives, and Savitzky-Golay second derivatives, were explored individually and accepted as the best preprocessing method based on the highest performance in calibration and validation results. The results showed maximum validation model performance was found at 86.2% by applying the Savitzky-Golay second derivatives preprocessing method for multiclass and 89.4% for binary class. The most important wavelength information was evaluated from the beta coefficient developed using the same preprocessing method. Finally, chemical images were generated using a best-performer model to identify the soybean plants from weeds. The generated images showed a significant difference in chemical composition between soybean and weed plants at 443 nm, 633 nm, 743 nm, and 968 nm. The correlation between these peaks and the chemical components of the plants involves a-carotenoid, chlorophyll, and moisture, respectively. This study shows promising results for the application of HSI in weed control systems for soybeans and relevant weed identification in precision agriculture applications. [ABSTRACT FROM AUTHOR]

Soybean production is greatly affected by different types of weeds such as horseweed, kochia, ragweed, redroot pigweed, and waterhemp in the midwestern region of the United States. Identification of the soybean plants and the weeds is crucial to controlling the weeds in precision agriculture. The objective of this study was to classify soybean plants and five weed species where a hyperspectral imaging camera with a spectral range of400 to 1000 nm was used to acquire the images. To acquire the HSI images, a customized robotic hyperspectral data collection scanning platform was developed and used in the greenhouse. A total of 983 hyperspectral data cubes were captured from the greenhouse environment (n = 252, soybean; n = 731, weeds). Spectral information was extracted from the collected images and then a classification model was developed by applying partial least squares discriminant analysis (PLS-DA). To construct the calibration and validation data set, the images were each divided into 70% and 30% ratios for model training and testing, respectively. Seven types of data preprocessing techniques, including mean normalization, maximum normalization, range normalization, multiplicative scatter correction (MSC), standard normal variate (SNV), Savitzky-Golay first derivatives, and Savitzky-Golay second derivatives, were explored individually and accepted as the best preprocessing method based on the highest performance in calibration and validation results. The results showed maximum validation model performance was found at 86.2% by applying the Savitzky-Golay second derivatives preprocessing method for multiclass and 89.4% for binary class. The most important wavelength information was evaluated from the beta coefficient developed using the same preprocessing method. Finally, chemical images were generated using a best-performer model to identify the soybean plants from weeds. The generated images showed a significant difference in chemical composition between soybean and weed plants at 443 nm, 633 nm, 743 nm, and 968 nm. The correlation between these peaks and the chemical components of the plants involves α-carotenoid, chlorophyll, and moisture, respectively. This study shows promising results for the application of HSI in weed control systems for soybeans and relevant weed identification in precision agriculture applications. [ABSTRACT FROM AUTHOR]

Objective: To convert the Agency for Healthcare Research and Quality's (AHRQ) Quality Indicators (QIs) from International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) specifications to ICD, 10th Revision, Clinical Modification and Procedure Classification System (ICD-10-CM/PCS) specifications.Data Sources: ICD-9-CM and ICD-10-CM/PCS classifications, General Equivalence Maps (GEMs).Study Design: We convened 77 clinicians and coders to evaluate ICD-10-CM/PCS codes mapped from ICD-9-CM using automated GEMs. We reviewed codes to develop "legacy" specifications resembling those in ICD-9-CM and "enhanced" specifications addressing enhanced capabilities of ICD-10-CM/PCS.Data Collection/extraction Methods: We tabulated the numbers of mapped codes, added nonmapped codes, and deleted mapped codes to achieve the specifications.Principal Findings: Of 212 clinical concepts (sets of codes) that comprise the QI specifications, we either added nonmapped codes to or deleted mapped codes from 115 (54 percent). The legacy and enhanced specifications differed for 46 sets (22 percent), affecting 67 of the 101 QIs (66 percent). Occasionally, concepts that defied conversion required reformulation of indicators.Conclusions: Converting the AHRQ QIs to ICD-10-CM/PCS required a detailed, thorough process beyond automated mapping of codes. Differences between the legacy and enhanced versions of the QIs are frequently minor but sometimes substantive. [ABSTRACT FROM AUTHOR]

While there is increased interest among engineers and scientists to collaborate with environmental justice organizations (EJOs), and while there are well-known programs that facilitate collaborations between engineers, scientists, and underserved communities, little is known systematically about the engineering and scientific challenges faced by EJOs. We lay the foundation for such a systematic understanding through exploratory research in which we conducted 47 semi-structured interviews with EJO staff across the United States to ask what their engineering and scientific challenges to combating environmental injustice are. Using deductive-inductive coding we discover that EJOs are looking for support to engineering and scientific challenges that fit specific topical areas and methods. Using the words of our informants, we then unpack the qualitative themes of select topical areas (air quality, public and environmental health, fossil fuel infrastructure, clean and just energy transitions, environmental restoration, and indigenous groups) and methods (data collection, data dissemination, data analysis, spatial analysis, online platform building, networking of experts). We explore the patterns of how these themes overlap with five different strategies (policy change, utilizing the media, educating the public, providing legal advice, and developing collaborative projects) used by the EJOs in our study. We show that EJOs want to collaborate with engineers and scientists to solve technical problems, mitigate harm to their communities, and create positive futures. Our research informs the conception of strategic efforts to expand the impact of engineering and scientific work done to address EJ challenges, particularly given the limited resources that currently exist to support collaborative efforts in this space. [ABSTRACT FROM AUTHOR]

The article discusses advantages of using interstate passport for transfer of students across the U.S. education system, as of September 2016. Topics include statistics regarding rate of student transfer like transfer in two- and four-year colleges and universities; affirmation from passport institutions regarding harmony between learning outcomes and Passport Learning Outcomes; and use of a minimum grade requirement and a data collection and tracking system for analyzing quality.

The paper provides an update on the Global Terrorism Database (GTD)*, an open source event database that now includes information on over 82,000 domestic and international terrorist attacks since 1970. [1] GTD was launched by computerizing data originally collected by the Pinkerton Global Intelligence Service (PGIS).[2] Following computerization, the research team has continued working to update and validate the data. This paper describes original data collection efforts and the strategies employed to improve the quality and comprehensiveness of the data, addressing also the strengths and weaknesses of Open Source data in general and the GTD in particular. The paper also provides descriptive statistics on the contents of the most recently available version of the GTD and offer observations about the future of event databases. [ABSTRACT FROM AUTHOR]

Weight discrimination is a well-established risk factor for disordered eating cognitions and behaviors. However, little is known about what may account for this association. Recent research suggests that anticipated weight stigma may explain the relationship between weight discrimination and non-eating disorder related health outcomes; the present study seeks to replicate this premise and extend it to the disordered eating realm. In a non-clinical sample of adults in the United States (N = 297) we test the hypothesis that weight discrimination has an indirect association with eating disorder symptomatology through anticipated stigma. At a single timepoint, participants recruited from the online data collection platform SocialSci completed self-report, online surveys of weight discrimination in day-to-day life, anticipated weight stigma, eating disorder symptoms, and demographic information. As hypothesized, weight discrimination was indirectly associated with greater disordered eating symptoms via its association with anticipated weight stigma. This pattern of results held when controlling for gender, body mass index, and self-perceived weight status. These findings suggest that anticipated stigma is relevant in the association between weight discrimination and greater disordered eating. This premise deserves additional attention using methodological approaches that can facilitate stronger causal claims. We discuss the potential for this line of research to inform clinical interventions. [ABSTRACT FROM AUTHOR]

The article examines the Collection of Licensed Practical Nurse (LPN)/Licensed Vocational Nurse (LVN) Educational Statistics of the National League for Nursing (NLN). According to the U.S. Health Resources and Services Administration (HRSA) 2004 National Sample Survey of Registered Nurses, over three quarters had been actively employed as LPNs/LVNs prior to beginning their initial nursing education. It was in 2004 when HRSA determined wide disparities in estimates of the size of the LPN/LVN workforce produced by key agencies and organizations. Also discussed are the challenges affecting data quality about LPNs/LVNs, such as the entry points into practical-vocational nursing practice and lack of electronic mail access and ambiguity over who should be reported as LPN students.