Mental Health Awareness in Arroyo Seco Academy

Arroyo Seco Academy is an elementary school located in Greenfield (CA) serving students from k through 6th grade. The capstone project and its implementation took place at Arroyo Seco’s counseling department. The community that is served by the counseling department is children who experience social and behavioral issues. There School has seen an increase of mental health illness in Monterey County that affects children directly and indirectly. The project; named Mental Health Awareness in Arroyo Seco Academy, consisted of an educational intervention that focused on 6 th grade students. The intervention focused on the importance of mental health and touched on the subjects of anxiety, depression, ADD, ADHD, and other mental health illnesses in weekly sessions over a span of six weeks. It is recommended that the agency continues to address students\u27 mental health by incorporating lesson plans into the curriculum or by having the next intern continue to implement the project

COLOMBIA COFFEE SECTOR STUDY.

El presente documento analiza la evolución del sector cafetero colombiano en la última década, periodo en el cual ha perdido valor. Se hace un diagnóstico de las causas que han generado el retroceso del sector y propone unas estrategias de política para que vuelva a ser competitivo en los mercados internacionales. De las conclusiones se destaca la necesidad de mejorar la eficiencia en la producción para poder competir a precios cada día mas bajos en el mercado mundial, se propone una estrategia para desarrollar negocios en los nichos de los llamados cafés especiales a los que se les reconocen primas superiores por parte de los compradores, y en el plano regulatorio se recomienda que la parafiscalidad que afecta al sector sea reformulada para que el impuesto que tributan los cafeteros sea bajo, estable y fijo en el tiempo. Con los recursos que se generen por esta contribución se deberán financiar los programas prioritarios para beneficio de los caficultores. Programas que de manera individual no pueden ser acometidos (Investigación o promoción). El rol de la institucionalidad cafetera deberá ser reformulado. Los recursos del café no deben seguir suplantando los recursos del Estado en obras públicas en las regiones cafeteras, pero la organización cafetera regional puede convertirse en un ejecutor importante de proyectos de inversión con recursos del presupuesto general de la Nación.Economía Agrícola, Comercio Internacional, Economía Institucional

Enseñanza de la ingenieria

The presence of Engineering in the life of humanity dates from the most remote ages: The manufacturers of the Egyptian, Aztec, Mayan, Inca pyramids, and their already well-known means of communication; the makers of the Chinese wall; the builders of the great cathedrals throughout the world; all of them had extensive knowledge of Engineering and Astronomy. These two sciences have been developed hand in hand in the long history of human beings.La presencia de la Ingeniería en la vida de la humanidad data de las más remotas edades: los fabricantes de las pirámides egipcias, aztecas, mayas, incas, y sus ya conocidas vías de comunicación; los fabricantes de la muralla china; los constructores de las grandes catedrales en todo el mundo; todos ellos tenían amplios conocimientos de Ingeniería y Astronomía. Estas dos ciencias se vienen desarrollando de la mano en la larga historia de los seres humanos

Using Deep Learning for Children Brain Image Analysis

Analyzing the correlation between brain volumetric/morphometry features and cognition/behavior in children is important in the field of pediatrics as identifying such relationships can help identify children who may be at risk for illnesses. Understanding these relationships can not only help identify children who may be at risk of illnesses, but it can also help evaluate strategies that promote brain development in children. Currently, one way to do this is to use traditional statistical methods such as a correlation analysis, but such an approach does not make it easy to generalize and predict how brain volumetric/morphometry will impact cognition/behavior. One of the cognition behaviors that can be predicted is the IQ score. In the age of artificial intelligence and machine learning, it has become fundamental to be able to exploit techniques such as deep learning to automate and improve tasks. One of the types of data that is used to make such assessments is mean diffusivity data (MD). In this paper, I propose using a machine learning approach and use the MD data to predict the IQ score of healthy 8-year old children. These predictions will provide insight into how well MD represents the IQ score of healthy 8-year old children and they will allow experts better understand how a child’s neuropsychological score is affected by volumetric/morphometry data. In this paper I examine five different neural network models for predicting the IQ score of healthy 8-year old children. Each model is different in either the architecture and how the data is processed before it is fed to into the neural network. After analyzing these models, I found that the best performing neural network for the data set we are working with consists of using the Principal Component Analysis (PCA) for feature reduction and standardization of the data. On average, this model’s IQ score predictions deviate from the true IQ score by 8.09%. Given the small data set and the high dimensionality of the data, it is concluded that this model’s IQ score predictions are reasonable and well modeled IQ scores for healthy 8-year old children

A tractable analytical model for large-scale congested protein synthesis networks

This paper presents an analytical model, based on finite capacity queueing network theory, to evaluate congestion in protein synthesis networks. These networks are modeled as a set of single server bufferless queues in a tandem topology. This model proposes a detailed state space formulation, which provides a fine description of congestion and contributes to a better understanding of how the protein synthesis rate is deteriorated. The model approximates the marginal stationary distributions of each queue. It consists of a system of linear and quadratic equations that can be decoupled. The numerical performance of this method is evaluated for networks with up to 100,000 queues, considering scenarios with various levels of congestion. It is a computationally efficient and scalable method that is suitable to evaluate congestion for large-scale networks. Additionally, this paper generalizes the concept of blocking: blocking events can be triggered by an arbitrary set of queues. This generalization allows for a variety of blocking phenomena to be modeled.Swiss National Science Foundation (Grant 205320-117581

A Simulation-Based Optimization Framework for Urban Transportation Problems

This paper proposes a simulation-based optimization (SO) method that enables the efficient use of complex stochastic urban traffic simulators to address various transportation problems. It presents a metamodel that integrates information from a simulator with an analytical queueing network model. The proposed metamodel combines a general-purpose component (a quadratic polynomial), which provides a detailed local approximation, with a physical component (the analytical queueing network model), which provides tractable analytical and global information. This combination leads to an SO framework that is computationally efficient and suitable for complex problems with very tight computational budgets. We integrate this metamodel within a derivative-free trust region algorithm. We evaluate the performance of this method considering a traffic signal control problem for the Swiss city of Lausanne, different demand scenarios, and tight computational budgets. The method leads to well-performing signal plans. It leads to reduced, as well as more reliable, average travel times

An Analytical Approximation of the Joint Distribution of Aggregate Queue-Lengths in an Urban Network

Traditional queueing network models assume infinite queue capacities due to the complexity of capturing interactions between finite capacity queues. Accounting for this correlation can help explain how congestion propagates through a network. Joint queue-length distribution can be accurately estimated through simulation. Nonetheless, simulation is a computationally intensive technique, and its use for optimization purposes is challenging. By modeling the system analytically, we lose accuracy but gain efficiency and adaptability and can contribute novel information to a variety of congestion related problems, such as traffic signal optimization. We formulate an analytical technique that combines queueing theory with aggregation-disaggregation techniques in order to approximate the joint network distribution, considering an aggregate description of the network. We propose a stationary formulation. We consider a tandem network with three queues. The model is validated by comparing the aggregate joint distribution of the three queue system with the exact results determined by a simulation over several scenarios. It derives a good approximation of aggregate joint distributions

Dynamic network loading: a stochastic differentiable model that derives link state distributions

We present a dynamic network loading model that yields queue length distributions, accounts for spillbacks, and maintains a differentiable mapping from the dynamic demand on the dynamic queue lengths. The model also captures the spatial correlation of all queues adjacent to a node, and derives their joint distribution. The approach builds upon an existing stationary queueing network model that is based on finite capacity queueing theory. The original model is specified in terms of a set of differentiable equations, which in the new model are carried over to a set of equally smooth difference equations. The physical correctness of the new model is experimentally confirmed in several congestion regimes. A comparison with results predicted by the kinematic wave model (KWM) shows that the new model correctly represents the dynamic build-up, spillback, and dissipation of queues. It goes beyond the KWM in that it captures queue lengths and spillbacks probabilistically, which allows for a richer analysis than the deterministic predictions of the KWM. The new model also generates a plausible fundamental diagram, which demonstrates that it captures well the stationary flow/density relationships in both congested and uncongested conditions