Telehealth Services for Transgender Individuals

This study looked into the positive and negative effects of telehealth services on the transgender community during the Covid- 19 pandemic. Via collaboration with the gender health. Center in Sacramento, five participants were interviewed utilizing a semi-structured questionnaire. Some of the major points that have been found in this study show that there are about an equal number of positive and negative effects telehealth and Covid- 19 has had on the transgender community. Interviewers noted that they were generally affected by the Covid- 19 pandemic which led to isolation and deterioration of their mental health. Some participants found the telehealth challenging due to isolation and lack of privacy because of the need of having sessions in their homes. Internet challenges were also found to be an issue that compromised quality services through telehealth. One of the positive effects the pandemic has had on the research participants was that telehealth provided more ease for some and did not have to worry about transportation for their sessions. Some also found they enjoyed having telehealth sessions because they preferred their sessions in the comfort of their own home

Distributed large scale systems: a multi-agent RL-MPC architecture

Universidat Politécnica de Cataluya. Programa de Doctorat: Automàtica, Robòtica I Visiò.[EN]: This thesis describes a methodology to deal with the interaction between MPC controllers in a distributed MPC architecture. This approach combines ideas from Distributed Artificial Intelligence (DAI) and Reinforcement Learning (RL) in order to provide a controller interaction based on cooperative agents and learning techniques. The aim of this methodology is to provide a general structure to perform optimal control in networked distributed environments, where multiple dependencies between subsystems are found. Those dependencies or connections often correspond to control variables. In that case, the distributed control has to be consistent in both subsystems. One of the main new concepts of this architecture is the negotiator agent. Negotiator agents interact with MPC agents to determine the optimal value of the shared control variables in a cooperative way using learning techniques (RL). The optimal value of those shared control variables has to accomplish a common goal, probably different from the specific goal of each agent sharing the variable. Two cases of study, in which the proposed architecture is applied and tested are considered, a small water distribution network and the Barcelona water network. The results suggest this approach is a promising strategy when centralized control is not a reasonable choice.[ES]: Esta tesis describe una metodología para hacer frente a la interacción entre controladores MPC en una arquitectura MPC distribuida. Este enfoque combina las ideas de Inteligencia Artificial Distribuida (DIA) y aprendizaje por refuerzo (RL) con el fin de proporcionar una interacción entre controladores basado en agentes de cooperativos y técnicas de aprendizaje. El objetivo de esta metodología es proporcionar una estructura general para llevar a cabo un control óptimo en entornos de redes distribuidas, donde se encuentran varias dependencias entre subsistemas. Esas dependencias o conexiones corresponden a menudo a variables de control. En ese caso, el control distribuido tiene que ser coherente en ambos subsistemas. Uno de los principales conceptos novedosos de esta arquitectura es el agente negociador. Los agentes negociadores actúan junto con agentes MPC para determinar el valor óptimo de las variables de control compartidas de forma cooperativa utilizando técnicas de aprendizaje (RL). El valor óptimo de esas variables compartidas debe lograr un objetivo común, probablemente diferente de los objetivos específicos de cada agente que está compartiendo la variable. Se consideran dos casos de estudio, en el que la arquitectura propuesta se ha aplicado y probado, una pequeña red de distribución de agua y la red de agua de Barcelona. Los resultados sugieren que este enfoque es una estrategia prometedora cuando el control centralizado no es una opción razonable.Peer Reviewe

Telehealth Services for Transgender Individuals

This study looked into the positive and negative effects of telehealth services on the transgender community during the Covid- 19 pandemic. Via collaboration with the gender health. Center in Sacramento, five participants were interviewed utilizing a semi-structured questionnaire. Some of the major points that have been found in this study show that there are about an equal number of positive and negative effects telehealth and Covid- 19 has had on the transgender community. Interviewers noted that they were generally affected by the Covid- 19 pandemic which led to isolation and deterioration of their mental health. Some participants found the telehealth challenging due to isolation and lack of privacy because of the need of having sessions in their homes. Internet challenges were also found to be an issue that compromised quality services through telehealth. One of the positive effects the pandemic has had on the research participants was that telehealth provided more ease for some and did not have to worry about transportation for their sessions. Some also found they enjoyed having telehealth sessions because they preferred their sessions in the comfort of their own home

Effect of Meteorological Factors on Photovoltaic Power Forecast Based on the Neural Network

In this paper, the effects of meteorological factors (including air temperature, wind speed, and relative humidity) on photovoltaic (PV) power forecast using neural network models have been studied. The research is based on PV power data collected at Nanchang, China. Our results showed that prediction results of three neural network models were overall close to the experimental data. It indicated the accuracy of the neural network approach. The time–power curves showed that the prediction errors were relatively large for some time frames, especially at dusk. The SSE/MSE and the coefficients of determination analysis showed that the model including air temperature had the strongest correlation with experimental data than another 2 models including wind speed and relative humidity, which proves that air temperature is an important factor for predicting the output power of PV cells

Effect of Meteorological Factors on Photovoltaic Power Forecast Based on the Neural Network

In this paper, the effects of meteorological factors (including air temperature, wind speed, and relative humidity) on photovoltaic (PV) power forecast using neural network models have been studied. The research is based on PV power data collected at Nanchang, China. Our results showed that prediction results of three neural network models were overall close to the experimental data. It indicated the accuracy of the neural network approach. The time–power curves showed that the prediction errors were relatively large for some time frames, especially at dusk. The SSE/MSE and the coefficients of determination analysis showed that the model including air temperature had the strongest correlation with experimental data than another 2 models including wind speed and relative humidity, which proves that air temperature is an important factor for predicting the output power of PV cells

A neural network based computational model to predict the output power of different types of photovoltaic cells - Fig 3

<p>Mono-crystalline (a), multi-crystalline (b) and amorphous crystalline (c) cells compared to experimental data using different numbers of hidden neurons (n = 3, 6, and 9).</p

The average correlation coefficients at the different number of hidden layer units in the neuron network algorithm.

<p>The average correlation coefficients at the different number of hidden layer units in the neuron network algorithm.</p

The average correlation coefficients at the different number of hidden layer units in the neuron network algorithm.

<p>The average correlation coefficients at the different number of hidden layer units in the neuron network algorithm.</p

The schematic diagram of network structure.

<p>The schematic diagram of network structure.</p