A Python tool and database of Amtrak departure and arrival times with weather information

This article proposes a Python-based Amtrak and Weather Underground (PAWU) tool to collect data on Amtrak (the main passenger train operator in the United States) departure and arrival times with weather information. In addition, this article offers a database, developed with PAWU, of the operating characteristics of 16 Amtrak routes from 2008 to 2019. More specifically, PAWU enables users to retrieve Amtrak departure and arrival times of any train number throughout the United States. It then automatically retrieves weather information from Weather Underground for each rail station and stores the data collected in a local MySQL database. Users can easily select any desired train number(s) and date range(s) without dealing with the code and the raw data from those sources that are in different formats. The database itself can be used, in part, to develop, apply, and benchmark models that assess the performance of rail services such as the one offered by Amtrak.

Reweigh Temperature as Estimator for Evaluation and Prediction

This study aimed to develop estimator for evaluation of reweigh temperature for prediction research extent. Research conducted in Jabalpur district of Madhya Pradesh, India, which comes under the humid subtropical climate region. Temperature recorded at one hour, two hour or three hour either side of maximum temperature may be averaged to get a plateaued value for that much time period. Hourly data on temperature recorded at Weather Underground site are regrouped into different temperature forms namely average of maximum and minimum temperature (Tav), weighted temperature (Twt), maximum temperature (Tmax), Temperature plateaued one hour, two hour and three hour either side of maximum temperature (Tp2, Tp4 and Tp6 respectively). These temperature forms are plotted for all twelve months. Integration of Tav and Tmax was done for estimation of weighted temperature. Values of coefficient of determination raised from fitting of linear regression between each of temperature form; Tmax, Tav, Twt, Tp2 Tp4 and Tp6 with actual pan evaporation. Data set comprises of daily records separately for all twelve months. Daily records are also regrouped into four more categories i.e. for whole year (365 days), hot months (April-May), cold months (December- January) and wet months (July-August). Though the r-squared values are found very low and explains that temperature alone cannot be taken as predictor of evaporation, which is a well comparative fact, but the purpose of presenting these values here to show the comparative effect of different temperature forms on evaporation. In hot months, the Twt with r-squared values of 0.49 seems to be more correlated than other temperature forms. But, in cold months Tmax, Tp2, Tp4 and Tp6 have more influence on evaporation than the Tav or Twt. The research outcome of the present study will be helpful to estimation of reweigh temperature rather average of maximum and minimum temperature for use in prediction research work.

Rain–Induced Flood Prediction for Niger Delta Sub-Region of Nigeria Using Neural Networks

Climate change generates so many direct and indirect effects on the environment. Some of those effects have serious consequences. Rain-induced flooding is one of the direct effects of climate change and its impact on the environment is usually devastating and worrisome. Floods are one of the most commonly occurring disasters and have caused significant damage to life, including agriculture and economy. They are usually caused in areas where there is excessive downpour and poor drainage systems. The study uses Feedforward Multilayer Neural Network to perform short-term prediction of the amount of rainfall flood for the Niger Delta sub region of Nigeria given previous rainfall data for a specified period of time. The data for training and testing of the Neural Network was sourced from Weather Underground official web site https://www.wunderground.com. An iterative Methodology was used and implemented in MATLAB. We adopted multi-layer Feedforward Neural Networks. The study accurately predicts the rain-induced flood for the Niger Delta sub region of Nigeria.

Avaliação dos efeitos microclimáticos de diferentes zonas climáticas locais em cidade de clima temperado

O clima urbano é influenciado por características morfológicas e de uso e ocupação do solo. Aspectos como grau de verticalização, presença de vegetação e adensamento urbano influenciam diretamente sobre variáveis climáticas, gerando microclimas diferenciados na área urbana. Em relação ao conforto ambiental humano, a possibilidade de se prever fenômenos do clima urbano orienta decisões projetuais arquitetônicas e urbanísticas. Esta pesquisa teve por objetivo analisar diferenças térmicas intraurbanas a partir de dados meteorológicos coletados em estações amadoras participantes da rede Weather Underground, localizadas em diferentes zonas climáticas locais (Local Climate Zones - LCZs) em Londres, Inglaterra. Utilizaram-se dados meteorológicos de dez estações distribuídas nos eixos leste-oeste e norte-sul. Avaliou-se se as variações nos microclimas são significativas o bastante para suprir a demanda de energia para aquecimento e resfriamento de edificações, por meio do somatório de graus-hora de aquecimento e resfriamento para cada ponto. Pela análise do perfil longitudinal das temperaturas, a pesquisa constatou a formação de ilhas de calor nos locais com maior intensidade de uso e ocupação e menor cobrimento vegetal. Com relação à demanda por climatização artificial, notaram-se diferenças consideráveis em função da LCZ de cada ponto.

Co-conspirator for Justice

Alan Berkman (1945–2009) was no campus radical in the mid-1960s; he was a promising Ivy League student, football player, Eagle Scout, and fraternity president. But when he was a medical student and doctor, his politics began to change, and soon he was providing covert care to members of revolutionary groups like the Weather Underground and becoming increasingly radicalized by his experiences at the Wounded Knee takeover, at the Attica Prison uprising, and at health clinics for the poor. When the government went after him, he went underground and participated in bombings of government buildings. He was eventually captured and served eight years in some of America's worst penitentiaries, barely surviving two rounds of cancer. After his release in 1992, he returned to medical practice and became an HIV/AIDS physician, teacher, and global health activist. In the final years of his life, he successfully worked to change U.S. policy, making AIDS treatment more widely available in the global south and saving millions of lives around the world. Using Berkman's unfinished prison memoir, FBI records, letters, and hundreds of interviews, Susan M. Reverby sheds fascinating light on questions of political violence and revolutionary zeal in her account of Berkman's extraordinary transformation from doctor to co-conspirator for justice

HybRecSys: Content-based contextual hybrid venue recommender system

The popularity of location-based social networks has prompted researchers to study recommendation systems for location-based services. When used separately, each existing venue recommendation system algorithm has its own drawbacks (e.g. cold start, data sparsity, scalability). Another issue is that critical information about context is not commonly used in venue recommendation systems. This article proposes a hybrid recommendation model that combines contextual information, user-based and item-based collaborative filtering and content-based filtering. For this purpose, we collected user visit histories, venue-related information (distance, category, popularity and price) and contextual information (weather, season, date and time of visits) related to individual user visits from Twitter, Foursquare and Weather Underground. Experimental evaluation of the proposed hybrid system (HybRecSys) using a real-world dataset shows better results than baseline approaches.

Temperature Forecasting System Using Fuzzy Mathematical Model

Temperature study and model development related to estimation is an essential and important task not only for a human life but also for animal life, agriculture, tourism, water reservation and evaporation, and many other fields. Regression is considered a dominant prediction model which is heavily used in forecasting in spite of the difficulties related to the number of available measurements, the order of the model and the nonlinearity of the data. In this article, the purpose is to use a nonlinear model structure to forecast the temperature at the airport of Mumbai city in India using the fuzzy logic technique. The datasets were collected for twelve months period starting from 1st of January 2009 to 31st of December at a weather underground in India. The datasets were divided into two parts, 288 days (80%) of the data for training and the remaining 72 days (20%) for testing. The results obtained and the error calculated using the fuzzy logic model were satisfactory.

The Criminalization of Late Sixties Militancy in Television Police Procedurals

This chapter analyses episodes from three television police dramas that were inspired by the publicity surrounding radical militant groups including the Weather Underground, the Black Liberation Army, and the Symbionese Liberation Army. Episodes of Law and Order, Life on Mars, and The Chicago Code integrated political rhetoric and journalism coverage of radical militants with the generic conventions of the television police procedural. The chapter argues that these programs conflate radical ideology with violent criminal activity. This conflation cultivates norms of democratic citizenship that call for uncritical assent to law enforcement and suspicion toward dissidents and has troubling implications for contemporary protest movements.