Development and Comparison of Backpropagation and Generalized Regression Neural Network Models to Predict Diurnal and Seasonal Gas and PM10 Concentrations and Emissions from Swine Buildings

The quantification of diurnal and seasonal gas (NH3, H2S, and CO2) and PM10 concentrations and emission rates (GPCER) from livestock production facilities is indispensable for the development of science-based setback determination methods and evaluation of improved downwind community air quality resulting from the implementation of gas pollution control. The purpose of this study was to employ backpropagation neural network (BPNN) and generalized regression neural network (GRNN) techniques to model GPCER generated and emitted from swine deep-pit finishing buildings as affected by time of day, season, ventilation rates, animal growth cycles, in-house manure storage levels, and weather conditions. The statistical results revealed that the BPNN and GRNN models were successfully developed to forecast hourly GPCER with very high coefficients of determination (R2) from 81.15% to 99.46% and very low values of systemic performance indexes. These good results indicated that the artificial neural network (ANN) technologies were capable of accurately modeling source air quality within and from the animal operations. It was also found that the process of constructing, training, and simulating the BPNN models was very complex. Some trial-and-error methods combined with a thorough understanding of theoretical backpropagation were required in order to obtain satisfying predictive results. The GRNN, based on nonlinear regression theory, can approximate any arbitrary function between input and output vectors and has a fast training time, great stability, and relatively easy network parameter settings during the training stage in comparison to the BPNN method. Thus, the GRNN was characterized as a preferred solution for its use in air quality modeling

Expressions 1981

Expressions contains selected work from the 1981 Creative Writing Contest entrants, Campus Chronicle Photography Contest entrants, and Commercial Art students at Des Moines Area Community College. Design , typography and the layout was done by Journalism students .https://openspace.dmacc.edu/expressions/1003/thumbnail.jp

Real-Time Ventilation Measurements from Mechanically Ventilated Livestock Buildings for Emission Rate Estimations

A six-state USDA-IFAFS funded research project (Aerial Pollutant Emissions from Confined Animal Buildings, APECAB) was conducted with the purpose of determining hydrogen sulfide, ammonia, PM10, and odor emission rates from selected swine and poultry housing systems. An important aspect of emission studies is to be able to measure the mass flow rate of air through the housing system. For this research project, the decision was made to study only fan ventilated buildings due to the difficulty in estimating mass flow rates through naturally ventilated buildings. This paper highlights the various techniques used throughout the study to determine mass flow rate through fan ventilated swine and poultry housing systems

Determinants of Depressive Symptoms at 1 Year Following ICU Discharge in Survivors of $ 7 Days of Mechanical Ventilation : Results From the RECOVER Program, a Secondary Analysis of a Prospective Multicenter Cohort Study

Abstract : Background: Moderate to severe depressive symptoms occur in up to one-third of patients at 1 year following ICU discharge, negatively affecting patient outcomes. This study evaluated patient and caregiver factors associated with the development of these symptoms. Methods: This study used the Rehabilitation and Recovery in Patients after Critical Illness and Their Family Caregivers (RECOVER) Program (Phase 1) cohort of 391 patients from 10 medical/surgical university-affiliated ICUs across Canada. We determined the association between patient depressive symptoms (captured by using the Beck Depression Inventory II [BDI-II]), patient characteristics (age, sex, socioeconomic status, Charlson score, and ICU length of stay [LOS]), functional independence measure (FIM) motor subscale score, and caregiver characteristics (Caregiver Assistance Scale and Center for Epidemiologic Studies-Depression Scale) by using linear mixed models at time points 3, 6, and 12 months. Results: BDI-II data were available for 246 patients. Median age at ICU admission was 56 years (interquartile range, 45-65 years), 143 (58%) were male, and median ICU LOS was 19 days (interquartile range, 13-32 days). During the 12-month follow-up, 67 of 246 (27.2%) patients had a BDI-II score ≥ 20, indicating moderate to severe depressive symptoms. Mixed models showed worse depressive symptoms in patients with lower FIM motor subscale scores (1.1 BDI-II points per 10 FIM points), lower income status (by 3.7 BDI-II points; P = .007), and incomplete secondary education (by 3.8 BDI-II points; P = .009); a curvilinear relation with age (P = .001) was also reported, with highest BDI-II at ages 45 to 50 years. No associations were found between patient BDI-II and comorbidities (P = .92), sex (P = .25), ICU LOS (P = .51), or caregiver variables (Caregiver Assistance Scale [P = .28] and Center for Epidemiologic Studies Depression Scale [P = .74]). Conclusions: Increased functional dependence, lower income, and lower education are associated with increased severity of post-ICU depressive symptoms, whereas age has a curvilinear relation with symptom severity. Knowledge of risk factors may inform surveillance and targeted mental health follow-up. Early mobilization and rehabilitation aiming to improve function may serve to modify mood disorders

Seasonal Ammonia Emissions from a Free-Stall Dairy in Central Texas

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Development and Comparison of Backpropagation and Generalized Regression Neural Network Models to Predict Diurnal and Seasonal Gas and PM 10 Concentrations and Emissions from Swine Buildings

The quantification of diurnal and seasonal gas (NH3, H2S, and CO2) and PM10 concentrations and emission rates (GPCER) from livestock production facilities is indispensable for the development of science-based setback determination methods and evaluation of improved downwind community air quality resulting from the implementation of gas pollution control. The purpose of this study was to employ backpropagation neural network (BPNN) and generalized regression neural network (GRNN) techniques to model GPCER generated and emitted from swine deep-pit finishing buildings as affected by time of day, season, ventilation rates, animal growth cycles, in-house manure storage levels, and weather conditions. The statistical results revealed that the BPNN and GRNN models were successfully developed to forecast hourly GPCER with very high coefficients of determination (R2) from 81.15% to 99.46% and very low values of systemic performance indexes. These good results indicated that the artificial neural network (ANN) technologies were capable of accurately modeling source air quality within and from the animal operations. It was also found that the process of constructing, training, and simulating the BPNN models was very complex. Some trial-and-error methods combined with a thorough understanding of theoretical backpropagation were required in order to obtain satisfying predictive results. The GRNN, based on nonlinear regression theory, can approximate any arbitrary function between input and output vectors and has a fast training time, great stability, and relatively easy network parameter settings during the training stage in comparison to the BPNN method. Thus, the GRNN was characterized as a preferred solution for its use in air quality modeling.This article is from Transactions of the ASABE 51, no. 2 (2008): 685–694.</p