Large-scale rollout of extension training in Bangladesh: Challenges and opportunities for gender-inclusive participation

Despite the recognized importance of women’s participation in agricultural extension services, research continues to show inequalities in women’s participation. Emerging capacities for conducting large-scale extension training using information and communication technologies (ICTs) now afford opportunities for generating the rich datasets needed to analyze situational factors that affect women’s participation. Data was recorded from 1,070 video-based agricultural extension training events (131,073 farmers) in four Administrative Divisions of Bangladesh (Rangpur, Dhaka, Khulna, and Rajshahi). The study analyzed the effect of gender of the trainer, time of the day, day of the week, month of the year, Bangladesh Administrative Division, and venue type on (1) the expected number of extension event attendees and (2) the odds of females attending the event conditioned on the total number of attendees. The study revealed strong gender specific training preferences. Several factors that increased total participation, decreased female attendance (e.g., male-led training event held after 3:30 pm in Rangpur). These findings highlight the dilemma faced by extension trainers seeking to maximize attendance at training events while avoiding exacerbating gender inequalities. The study concludes with a discussion of ways to mitigate gender exclusion in extension training by extending data collection processes, incorporating machine learning to understand gender preferences, and applying optimization theory to increase total participation while concurrently improving gender inclusivity

Construcción de Wavelets Ortonormales y su implementación computacional

TesisEl Análisis Wavelet se ha desarrollado vertiginosamente en los últimos 20 años y sus aplicaciones han alcanzado campos de la ciencia de los mas diversos que van desde la Teoría De Aproximación en Matemática Pura hasta el Procesamiento de Señales en Ingeniería de Telecomunicaciones. La aparente extralimitada extensión de este trabajo, creo que se compensa con la carencia de un material (En nuestro idioma) de introducción al Análisis Wavelet en la Universidad Nacional de Piura. Espero que este intento sirva de apoyo a matemáticos e ingenieros interesados en iniciar un estudio serio en este campo. En el Capítulo I, analizamos las Bases Locales de Senos y Cosenos debido a su importancia en la construcción de las Wavelets de Lemarié-Meyer, la primera clase de Wavelets Ortonormales introducidas, tal que, estas y sus Transformadas de Fourier son suaves. En el Capitulo II, desarrollaremos un método general que fue introducido por Mallat y Meyer para la construcción de Wavelets Ortonormales: El Análisis de Multirresolución (MRA), gracias a este método podremos estudiar las Wavelets de Soporte Compacto. El Capítulo III, esta dedicado a las Wavelets de Banda Limitada (Aquellas cuyas Transformada de Fourier tiene Soporte Compacto), mostraremos algunas propiedades interesantes de estas, como que sus Transformadas de Fourier se anulan en una Vecindad del Origen, además las series involucradas tienen un número finito de términos no nulos, lo que evita que nos preocupemos por su convergencia. En el Capitulo IV, desarrollaremos las Transformada de Fourier Discreta y Rápida, además describiremos los Algoritmos de Descomposición y Reconstrucción de Wavelets, finalmente presentaremos los programas computacionales descritos por los algoritmos de este capitulo

Construcción de Wavelets Ortonormales y su implementación computacional

El Análisis Wavelet se ha desarrollado vertiginosamente en los últimos 20 años y sus aplicaciones han alcanzado campos de la ciencia de los mas diversos que van desde la Teoría De Aproximación en Matemática Pura hasta el Procesamiento de Señales en Ingeniería de Telecomunicaciones. La aparente extralimitada extensión de este trabajo, creo que se compensa con la carencia de un material (En nuestro idioma) de introducción al Análisis Wavelet en la Universidad Nacional de Piura. Espero que este intento sirva de apoyo a matemáticos e ingenieros interesados en iniciar un estudio serio en este campo. En el Capítulo I, analizamos las Bases Locales de Senos y Cosenos debido a su importancia en la construcción de las Wavelets de Lemarié-Meyer, la primera clase de Wavelets Ortonormales introducidas, tal que, estas y sus Transformadas de Fourier son suaves. En el Capitulo II, desarrollaremos un método general que fue introducido por Mallat y Meyer para la construcción de Wavelets Ortonormales: El Análisis de Multirresolución (MRA), gracias a este método podremos estudiar las Wavelets de Soporte Compacto. El Capítulo III, esta dedicado a las Wavelets de Banda Limitada (Aquellas cuyas Transformada de Fourier tiene Soporte Compacto), mostraremos algunas propiedades interesantes de estas, como que sus Transformadas de Fourier se anulan en una Vecindad del Origen, además las series involucradas tienen un número finito de términos no nulos, lo que evita que nos preocupemos por su convergencia. En el Capitulo IV, desarrollaremos las Transformada de Fourier Discreta y Rápida, además describiremos los Algoritmos de Descomposición y Reconstrucción de Wavelets, finalmente presentaremos los programas computacionales descritos por los algoritmos de este capitulo.Tesi

Large-scale rollout of extension training in Bangladesh: Challenges and opportunities for gender-inclusive participation.

Despite the recognized importance of women's participation in agricultural extension services, research continues to show inequalities in women's participation. Emerging capacities for conducting large-scale extension training using information and communication technologies (ICTs) now afford opportunities for generating the rich datasets needed to analyze situational factors that affect women's participation. Data was recorded from 1,070 video-based agricultural extension training events (131,073 farmers) in four Administrative Divisions of Bangladesh (Rangpur, Dhaka, Khulna, and Rajshahi). The study analyzed the effect of gender of the trainer, time of the day, day of the week, month of the year, Bangladesh Administrative Division, and venue type on (1) the expected number of extension event attendees and (2) the odds of females attending the event conditioned on the total number of attendees. The study revealed strong gender specific training preferences. Several factors that increased total participation, decreased female attendance (e.g., male-led training event held after 3:30 pm in Rangpur). These findings highlight the dilemma faced by extension trainers seeking to maximize attendance at training events while avoiding exacerbating gender inequalities. The study concludes with a discussion of ways to mitigate gender exclusion in extension training by extending data collection processes, incorporating machine learning to understand gender preferences, and applying optimization theory to increase total participation while concurrently improving gender inclusivity

Machine-supported decision-making to improve agricultural training participation and gender inclusivity.

Women comprise a significant portion of the agricultural workforce in developing countries but are often less likely to attend government sponsored training events. The objective of this study was to assess the feasibility of using machine-supported decision-making to increase overall training turnout while enhancing gender inclusivity. Using data obtained from 1,067 agricultural extension training events in Bangladesh (130,690 farmers), models were created to assess gender-based training patterns (e.g., preferences and availability for training). Using these models, simulations were performed to predict the top (most attended) training events for increasing total attendance (male and female combined) and female attendance, based on gender of the trainer, and when and where training took place. By selecting a mixture of the top training events for total attendance and female attendance, simulations indicate that total and female attendance can be concurrently increased. However, strongly emphasizing female participation can have negative consequences by reducing overall turnout, thus creating an ethical dilemma for policy makers. In addition to balancing the need for increasing overall training turnout with increased female representation, a balance between model performance and machine learning is needed. Model performance can be enhanced by reducing training variety to a few of the top training events. But given that models are early in development, more training variety is recommended to provide a larger solution space to find more optimal solutions that will lead to better future performance. Simulations show that selecting the top 25 training events for total attendance and the top 25 training events for female attendance can increase female participation by over 82% while at the same time increasing total turnout by 14%. In conclusion, this study supports the use of machine-supported decision-making when developing gender inclusivity policies in agriculture extension services and lays the foundation for future applications of machine learning in this area