Incident detection using data from social media

This is an accepted manuscript of an article published by IEEE in 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) on 15/03/2018, available online: https://ieeexplore.ieee.org/document/8317967/citations#citations The accepted version of the publication may differ from the final published version.© 2017 IEEE. Due to the rapid growth of population in the last 20 years, an increased number of instances of heavy recurrent traffic congestion has been observed in cities around the world. This rise in traffic has led to greater numbers of traffic incidents and subsequent growth of non-recurrent congestion. Existing incident detection techniques are limited to the use of sensors in the transportation network. In this paper, we analyze the potential of Twitter for supporting real-time incident detection in the United Kingdom (UK). We present a methodology for retrieving, processing, and classifying public tweets by combining Natural Language Processing (NLP) techniques with a Support Vector Machine algorithm (SVM) for text classification. Our approach can detect traffic related tweets with an accuracy of 88.27%.Published versio

Traffic event detection framework using social media

This is an accepted manuscript of an article published by IEEE in 2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC) on 18/09/2017, available online: https://ieeexplore.ieee.org/document/8038595 The accepted version of the publication may differ from the final published version.© 2017 IEEE. Traffic incidents are one of the leading causes of non-recurrent traffic congestions. By detecting these incidents on time, traffic management agencies can activate strategies to ease congestion and travelers can plan their trip by taking into consideration these factors. In recent years, there has been an increasing interest in Twitter because of the real-time nature of its data. Twitter has been used as a way of predicting revenues, accidents, natural disasters, and traffic. This paper proposes a framework for the real-time detection of traffic events using Twitter data. The methodology consists of a text classification algorithm to identify traffic related tweets. These traffic messages are then geolocated and further classified into positive, negative, or neutral class using sentiment analysis. In addition, stress and relaxation strength detection is performed, with the purpose of further analyzing user emotions within the tweet. Future work will be carried out to implement the proposed framework in the West Midlands area, United Kingdom.Published versio

Electron Depletion Due to Bias of a T-Shaped Field-Effect Transistor

A T-shaped field-effect transistor, made out of a pair of two-dimensional electron gases, is modeled and studied. A simple numerical model is developed to study the electron distribution vs. applied gate voltage for different gate lengths. The model is then improved to account for depletion and the width of the two-dimensional electron gases. The results are then compared to the experimental ones and to some approximate analytical calculations and are found to be in good agreement with them.Comment: 16 pages, LaTex (RevTex), 8 fig

Multi-hazard response analysis of a 5MW offshore wind turbine

Wind energy has already dominant role on the scene of the clean energy production. Well-promising markets, like China, India, Korea and Latin America are the fields of expansion for new wind turbines mainly installed in offshore environment, where wind, wave and earthquake loads threat the structural integrity and reliability of these energy infrastructures. Along these lines, a multi-hazard environment was considered herein and the structural performance of a 5 MW offshore wind turbine was assessed through time domain analysis. A fully integrated model of the offshore structure consisting of the blades, the nacelle, the tower and the monopile was developed with the use of an aeroelastic code considering the interaction between the elastic and inertial forces, developed in the structure, as well as the generated aerodynamic and hydrodynamic forces. Based on the analysis results, the dynamic response of the turbine's tower was found to be severely affected by the earthquake excitations. Moreover, fragility analysis based on acceleration capacity thresholds for the nacelle's equipment corroborated that the earthquake excitations may adversely affect the reliability and availability of wind turbines

Investigation of hydrostatic fluid forces in varying clearance turbomachinery seals

Varying clearance, rotor-following seals are a key technology for meeting the demands of increased machine flexibility for conventional power units. These seals follow the rotor through hydrodynamic or hydrostatic mechanisms. Forward-facing step (FFS) and Rayleigh step designs are known to produce positive fluid stiffness. However, there is very limited modeling or experimental data available on the hydrostatic fluid forces generated from either design. A quasi-one-dimensional (1D) method has been developed to describe both designs and validated using test data. Tests have shown that the FFS and the Rayleigh step design are both capable of producing positive film stiffness and there is little difference in hydrostatic force generation between the two designs. This means any additional hydrodynamic features in the Rayleigh step design should have a limited effect on hydrostatic fluid stiffness. The analytical model is capable of modeling both the inertial fluid forces and the viscous fluid losses, and the predictions are in good agreement with the test data

Public Twitter data and transport network status

This is an accepted manuscript of an article published by IEEE in 2020 10th International Conference on Information Science and Technology (ICIST) on 22/09/2022, available online: https://ieeexplore.ieee.org/document/9202204 The accepted version of the publication may differ from the final published version.Twitter data can be collected and analysed to be used for predicting the status of a transport network at a given time and geographic location (e.g. forecasting disruptions, congestions, or road closures). However, this requires geolocating the tweets to define the parts of the transport network which may be related to these tweets. This paper investigates the relationship between the actual transport network status, with that being synthesised using public Twitter data in the Greater Manchester conurbation. Therefore, it answers the following question: are the sentiments of tweets around the incidents and accidents areas (or bounding boxes) different from the sentiments of tweets in the seamless traffic areas?. According to the used research methodology, analysis techniques, and sentiment detection APIs, it has been concluded that there is no significant difference between the sentiments in the tweets regardless the prevailing traffic conditions of the locations the tweets refer to.Published versio

Sensitivity analysis of cost parameters for floating offshore wind farms: An application to Italian waters

Floating offshore wind farms represent the next frontier in wind power industry. However, the development of this technology is strongly dependent on its economic feasibility. There follows that the development of economic analyses is crucial to highlight the possible greater potential of floating offshore wind farms and to support their sustainability and technical value. In this context, the purpose of this paper is to present a sensitivity analysis of the main cost parameters for floating offshore wind farms, namely the distance from the coast, the distance from the closest port and the sea depth. It can give specific information on which parameters are more important, and how much they affect the total cost. To this aim, a comprehensive life cycle cost assessment of floating offshore wind farms has been developed. In this study the cost model has been applied to the Italian waters. The results shown should provide guidance on how to preliminary assess the quality of a given site for floating offshore wind farm installation, and should be helpful for future development of decision-making procedures in the offshore wind sector

Synthesis of glutathione analogues and screening as substrates & inhibitors for human glutathione transferase p1‐1

A major detoxification mechanism of the cell involves the glutathione transferase (GST)‐catalyzed formation of glutathione (GSH) conjugates with various xenobiotics Based on the same mechanism, GST overexpression may lead to multidrug resistant phenotypes Therefore, several compounds with inhibitory potency against GSTs have been developed as potential tools fortackling GST-­‐attributed MDR. Several individual compounds and prodrugs have been proposed as GST‐inhibiting substances. In addition, GSH analogues have been considered as specific GST inhibitors, with particular attention been directed towards the synthesis of GSH analogues stable against γ‐glutamyltranspeptidase (γGT) and peptidases, as GST inhibitors