Modeling of influential predictors of gastric cancer incidence rates in Golestan Province, North Iran

Golestan province has a reputation for relatively high incidence rates of gastric cancer in Iran. Along with dietary, lifestyle and environmental influential factors, soil selenium and high levels of pesticide used may exert influence in this region. The present study was designed for modeling the influential predictors on incidence of gastric cancer in Golestan. All registered cases of gastric cancer from March 2009 to March 2010 (49 females and 107 males) were investigated. Data were gathered by both check list and researcher made questionnaire (demographic, clinical and lifestyle characteristics) and analysed using logistic regression. Mean (±SD) age at diagnosis was 62.9±13.8 years. CIR and ASR of gastric cancer showed 9.16 and 13.9 per 100,000 people, respectively. Based on univariate logistic regression, a history of smoking (OR= 2.076), unwashed hands after defecation (OR= 2.612), history of cancer in relatives (OR= 2.473), history of gastric cancer in first-degree relatives (OR= 2.278), numbers of gastric cancers in first-degree relatives (OR= 2.078), history of X-ray and dye exposure (OR= 2.395), history of CT scan encounter (OR= 2.915), improper food habits (OR= 3.320), specific eating behavior (OR= 0.740), consumption of probable high risk foods (OR= 2.942), charred flesh (OR= 1.945), and animal fat (OR= 2.716) were confirmed as a risk factors. Changes in lifestyle may be expected to increase gastric cancer incidence dramatically in the near future. Therefore, appropriate educational interventions should be designed and implemented by competent authorities

A Machine Learning Method for Modeling Wind Farm Fatigue Load

Wake steering control can significantly improve the overall power production of wind farms. However, it also increases fatigue damage on downstream wind turbines. Therefore, optimizing fatigue loads in wake steering control has become a hot research topic. Accurately predicting farm fatigue loads has always been challenging. The current interpolation method for farm-level fatigue loads estimation is also known as the look-up table (LUT) method. However, the LUT method is less accurate because it is challenging to map the highly nonlinear characteristics of fatigue load. This paper proposes a machine-learning algorithm based on the Gaussian process (GP) to predict the farm-level fatigue load under yaw misalignment. Firstly, a series of simulations with yaw misalignment were designed to obtain the original load data, which considered the wake interaction between turbines. Secondly, the rainflow counting and Palmgren miner rules were introduced to transfer the original load to damage equivalent load. Finally, the GP model trained by inputs and outputs predicts the fatigue load. GP has more accurate predictions because it is suitable for mapping the nonlinear between fatigue load and yaw misalignment. The case study shows that compared to LUT, the accuracy of GP improves by 17% (RMSE) and 0.6% (MAE) at the blade root edgewise moment and 51.87% (RMSE) and 1.78% (MAE) at the blade root flapwise moment

Barriers to Summer Food Service Program (SFSP) Implementation Before and After COVID-19: A Qualitative, Collective Case Study

Objectives: SFSP, as a federal food assistance program, could ensure that children have sufficient nutritious food during summer. According to a 2016 USDA report, only 0.4–0.6% of eligible children participated in this program in Nebraska, which makes for one of the lowest SFSP participation rates in the U.S. This study aims to better understand how the COVID-19 pandemic has impacted SFSP implementation in urban and rural settings across the state. Conclusions: Insights from this study may inform the development of policies that expand food access to vulnerable families. On the organizational level, this includes providing participating kids and their parents with both meal distribution options - meal sites and delivery options - during summer. On the policy level, this calls for revising the eligibility of SFSP by decreasing the threshold level from 50% to 30% to allow more schools in low-income neighborhoods to participate

Discrete-Time Domain Modeling of a High-Power Medium-Voltage Resonant Converter

LLC resonant converters are widely used indiverse industrial applications, due in part to their highefficiency and high power density. In spite of their numerousadvantages, these converters are still considered themost challenging converters from a modeling and controlperspective. Several factors contribute to this complexity,including the nonlinear behavior and different operatingmodes. Therefore, a high-power medium-voltage resonantconverter is modeled in this manuscript, and its dynamicbehavior is investigated. The nonlinear model simulationperformed by MATLAB/Simulink and the electrical circuitsimulation performed by PLECS are then compared toverify the accuracy of the obtained model

Socioeconomic Inequalities in Child Malnutrition in Bangladesh: Do They Differ by Region?

Socioeconomic inequality in child malnutrition is well-evident in Bangladesh. However, little is known about whether this inequality differs by regional contexts. We used pooled data from the 2011 and 2014 Bangladesh Demographic and Health Survey to examine regional differences in socioeconomic inequalities in stunting and underweight among children under five. The analysis included 14,602 children aged 0-59 months. We used logistic regression models and the Concentration index to assess and quantify wealth- and education-related inequalities in child malnutrition. We found stunting and underweight to be more concentrated among children from poorer households and born to less-educated mothers. Although the poverty level was low in the eastern regions, socioeconomic inequalities were greater in these regions compared to the western regions. The extent of socioeconomic inequality was the highest in Sylhet and Chittagong for stunting and underweight, respectively, while it was the lowest in Khulna. Regression results demonstrated the protective effects of socioeconomic status (SES) on child malnutrition. The regional differences in the effects of SES tend to diverge at the lower levels of SES, while they converge or attenuate at the highest levels. Our findings have policy implications for developing programs and interventions targeted to reduce socioeconomic inequalities in child malnutrition in subnational regions of Bangladesh