Bipartite Graph Learning for Autonomous Task-to-Sensor Optimization

NPS NRP Technical ReportThis study addresses the question of how machine learning/artificial intelligence can be applied to identify the most appropriate 'sensor' for a task, to prioritize tasks, and to identify gaps/unmet requirements. The concept of a bipartite graph provides a mathematical framework for task-to-sensor mapping by establishing connectivity between various high-level tasks and the specific sensors and processes that must be invoked to fulfil those tasks and other mission requirements. The connectivity map embedded in the bipartite graph can change depending on the availability/unavailability of resources, the presence of constraints (physics, operational, sequencing), and the satisfaction of individual tasks. Changes can also occur according to the valuation, re-assignment and re-valuation of the perceived task benefit and how the completion of a specific task (or group of tasks) can contribute to the state of knowledge. We plan to study how machine learning can be used to perform bipartite learning for task-to-sensor planning.Naval Special Warfare Command (NAVSPECWARCOM)N9 - Warfare SystemsThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Alimentación saludable con frutas y verdura: un tema más complejo que su propio consumo

El consumo de frutas y verduras en estudiantes de medicina, los autores estimaron una prevalencia de bajo consumo de frutas y verduras de 60,1% en una muestra de 371 estudiantes. Las razones más frecuentes encontradas fueron la dificultad de conseguirlas en los cafetines de la universidad, el tiempo insuficiente para su selección, compra y preparación y el hecho de vivir con su padre o madre que se encargue de su alimentación; siendo la segunda, el único factor asociado, y la última, un factor protector. El estudio presenta muchas fortalezas, entre las que resalta la importancia del eje temático que desarrolla: los estilos de vida saludables son uno de los grandes determinantes de salud, resaltando que una de cada 5 muertes en el mundo se asocian a la alimentación no saludable(2). Sin embargo, su análisis permite encontrar ciertas limitaciones y sesgos que los autores no mencionaron en la discusión; en primer lugar, la variable de “consumo de frutas y verduras” podría estar mejor definida, ya que, a diferencia de las frutas, las verduras son difícilmente contadas en unidades, aquí se podrían definir ciertas porciones y tal vez independizarlas. En cuanto al diseño metodológico, al ser de tipo transversal y no tener una medida basal, es difícil de encontrar una causalidad con los factores mencionados ya que los que se incluyen en el estudio están ligados a la condición de ser estudiante universitario, sin embargo, se sabe que la alimentación esta relacionada otros muchos factores, no necesariamente ligados al escenario universitario. Ya dentro del consumo específico de frutas y verduras se pueden encontrar muchos otros factores importantes, el ejercicio físico, la educación, consumo de alcohol, exposición a comida no saludable dentro y fuera de la universidad, entre otros, los cuales no fueron incluidos en el estudio(3). Además, en estudios como el de Adrogué(3), se encontró que estos factores pueden ser diferentes dependiendo del género. Por todo esto, al hacer el estudio multivariado se deberían tener en cuenta también los factores sociodemográficos para el análisis multivariado. Finalmente, si bien en la discusión se menciona que es posible que la realidad de los estudiantes sea similar en otras casas de estudio, debemos recordar que la universidad en la que se hace el estudio pertenece a la serranía peruana y es pública, en realidad es difícil extrapolarla al resto de universidades a nivel nacional, ya que dentro del sistema educativo tenemos universidad privadas y públicas, que obedecen de forma territorial a las regiones naturales, donde con la altura varía el perfil alimentario(4), por lo que hay muchos factores internos y externos que podrían influir en esta conducta. En conclusión, el estudio desarrolla un tema con gran relevancia actual, con ciertos aspectos a mejorar, pero consideramos que es una gran base para realizar el estudio multicéntrico en torno a este eje temático, el cual puede ser útil para la toma de decisiones en las casas de estudios de los futuros médicos de nuestro país

Bipartite Graph Learning for Autonomous Task-to-Sensor Optimization

NPS NRP Executive SummaryThis study addresses the question of how machine learning/artificial intelligence can be applied to identify the most appropriate 'sensor' for a task, to prioritize tasks, and to identify gaps/unmet requirements. The concept of a bipartite graph provides a mathematical framework for task-to-sensor mapping by establishing connectivity between various high-level tasks and the specific sensors and processes that must be invoked to fulfil those tasks and other mission requirements. The connectivity map embedded in the bipartite graph can change depending on the availability/unavailability of resources, the presence of constraints (physics, operational, sequencing), and the satisfaction of individual tasks. Changes can also occur according to the valuation, re-assignment and re-valuation of the perceived task benefit and how the completion of a specific task (or group of tasks) can contribute to the state of knowledge. We plan to study how machine learning can be used to perform bipartite learning for task-to-sensor planning.Naval Special Warfare Command (NAVSPECWARCOM)N9 - Warfare SystemsThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Bipartite Graph Learning for Autonomous Task-to-Sensor Optimization

NPS NRP Project PosterThis study addresses the question of how machine learning/artificial intelligence can be applied to identify the most appropriate 'sensor' for a task, to prioritize tasks, and to identify gaps/unmet requirements. The concept of a bipartite graph provides a mathematical framework for task-to-sensor mapping by establishing connectivity between various high-level tasks and the specific sensors and processes that must be invoked to fulfil those tasks and other mission requirements. The connectivity map embedded in the bipartite graph can change depending on the availability/unavailability of resources, the presence of constraints (physics, operational, sequencing), and the satisfaction of individual tasks. Changes can also occur according to the valuation, re-assignment and re-valuation of the perceived task benefit and how the completion of a specific task (or group of tasks) can contribute to the state of knowledge. We plan to study how machine learning can be used to perform bipartite learning for task-to-sensor planning.Naval Special Warfare Command (NAVSPECWARCOM)N9 - Warfare SystemsThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Bipartite Graph Learning for Autonomous Task-to-Sensor Optimization

NPS NRP Project PosterThis study addresses the question of how machine learning/artificial intelligence can be applied to identify the most appropriate 'sensor' for a task, to prioritize tasks, and to identify gaps/unmet requirements. The concept of a bipartite graph provides a mathematical framework for task-to-sensor mapping by establishing connectivity between various high-level tasks and the specific sensors and processes that must be invoked to fulfil those tasks and other mission requirements. The connectivity map embedded in the bipartite graph can change depending on the availability/unavailability of resources, the presence of constraints (physics, operational, sequencing), and the satisfaction of individual tasks. Changes can also occur according to the valuation, re-assignment and re-valuation of the perceived task benefit and how the completion of a specific task (or group of tasks) can contribute to the state of knowledge. We plan to study how machine learning can be used to perform bipartite learning for task-to-sensor planning.Naval Special Warfare Command (NAVSPECWARCOM)N9 - Warfare SystemsThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Are We Really Getting Into the Green Scene? Investigating the Effects of Kyoto Protocol Mechanisms on the Environment and Economy

The world has changed drastically in favor of the human race as the processes of production improve, leading to the stimulation of economic growth. However, we failed to consider how these changes affect the environment, causing pollution levels to rise due to it being an unavoidable by-product of production. Eventually, nations were driven to develop a strategy to combat climate change and increase greenhouse gas emissions in the form of the Kyoto Protocol. This research compares two of the Kyoto Protocol Mechanisms, specifically the Clean Development Mechanism (CDM) and the Emissions Trading Scheme (ETS), to investigate the effect of such mechanisms on the economic growth and the greenhouse gas emissions of developed and developing countries that have chosen to adopt their specific mechanism. The research utilizes a panel data regression model and the difference-in-difference (DID) model in quantifying the contribution rate of the country-specific economic indicators and the effect of the Kyoto protocol mechanisms on developed and developing countries. Findings from the study depict the ineffectiveness of the aforementioned mechanisms for most of the developed and developing countries, given that the drivers in pursuit of viable development vary in different areas. In assessing the findings, we have provided aspects to consider for the extension of the study on investigating emission reduction approaches. Policy insights were also devised for leaders and institutions that aim to mitigate emissions further

Investigating the Effects of Kyoto Protocol Mechanisms on the Environment and Economy

As production processes improve, the stimulation of economic growth has overlooked its externalities over time. In pursuit of sustainable development, nations were driven to combat climate change and increasing greenhouse gas emissions through the Kyoto Protocol. The policy brief is based on the investigation of the Kyoto Protocol Mechanisms (Clean Development and Emissions Trading Scheme) through difference-in-difference (DID) estimation, together with panel regression to evaluate the path towards viability using the metrics for externalities (total greenhouse gas emissions) and economic growth (GDP). For this reason, the DID takes into account the quantitative aspect in evaluating the effectiveness of the said mechanisms by investigating the pre- and post-treatment periods. The panel regression results then quantify the influence of the drivers of development towards the specified metrics. Results show that relying on the mechanisms aforementioned in promoting and reinforcing cleaner economic growth throughout the globe is inefficient. Such evidence will be fundamental in enhancing the said mechanisms for its continuity

Structured Resistance Training Improves Self-Esteem and Strength in Previously Untrained College Females

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