The use of electric buses is increasing all over the world; this is due to the aim of limiting pollution in heavily urbanized areas. Using electric buses is one element of the desire to drop local pollution to zero emissions. The necessary electricity can be generated through centralized production, and in the case of electric buses, the pollution level is directly proportional to the amount of electricity produced. Their limited onboard power needs optimization, both in terms of traction and in auxiliary energy consumption. Heating in electric buses consumes the most energy from the auxiliaries, which can reduce the range of the vehicle up to a half, or more in the coldest days of the winter months. In this context, a precise estimation of heat loss and of the energy necessary for heating electric buses is crucial. Using the heat transfer theory, the heat balance method, and the U-value estimation, this article estimates the heat loss for a typical 12 m electric bus for a harsh winter day. Thermal simulations were made in order to estimate the heat flux through the structure of the bus (windows, walls, roof, and floor). Heat loss components were calculated in order to determine the most affected zones of the bus. The calculated data for the energy necessary to heat the bus were compared with the heating system data from an electric bus. By optimizing the necessary auxiliary energy consumption, the emissions at the source of electricity production will be significantly reduced.

This paper presents the influence of the environmental factors over the electrical equipment thermal stresses when it is monitored with infrared monitoring device. A good definition of the correction parameters for this monitoring device and the choice of the optimal conditions for monitor performing can lead to good results. In particular, the paper presents the influence of the dust layer from electrical equipment over the captured thermal image by the infrared monitoring device and further more to estimate its thermal stress. To achieve this, it was used an experimental stand where a copper bar was split in different areas and they were covered with different dust types and in the end the thermal stress of each area was estimated with the infrared monitoring device.

The electrical contacts are important elements which can be found on all of the electrical installations from the power source to the consumer. Also, the electrical contacts have an important role in the construction and operation of low voltage electrical equipment. The presence of an electrical contact on a current path always introduce an additional electrical resistance, named contact resistance. In this paper is studied the influence of the voltage drops to contact resistance, respectively to contact temperature of the low voltage electrical equipment.

This paper describes an innovative integrated solution for monitoring and protection of the power supply system of electric traction. The development of electronics devices, new possibilities to communicate (wireless), and new sensors makes it possible to design, develop and implement new hardware–software structures in various fields such as energy systems, transportation infrastructure, etc. This contributes to increasing developments in the monitoring and protection of railway infrastructure. A monitoring and protection system that uses sensors and devices to acquire electrical parameters from railway infrastructure has been developed and applied for fault detection and protection of power supply systems from electric traction. The solution of monitoring and protection presented are composed of a hardware–software structure with Global System for Mobile Communications (GSM) communication for monitoring of power supply installations from the electric traction and a central remote system composed of a device with GSM communication and a server that will allow, among others things, accurate detection of the block section (SC), in which an electrical fault (short circuit) has occurred, determination of the circuit breakers electro-erosion from the railway installations and an indication of the opportune moment for maintenance activity, respectively, as well as knowledge of the technical condition of some equipment from the return circuit. The proposed and developed method for monitoring devices has been validated in the railway laboratory to confirm its capability to detect defects and was tested in the field. Experimental results in the field and appropriate data analysis are included in this article.

Nowadays, sensors and condition monitoring systems are expanding rapidly and becoming cheaper. This contributes to increasing developments in condition monitoring in railway transport infrastructure. A condition monitoring system that uses an online device and sensors to acquire electrical parameters from railway infrastructure has been developed and applied for fault detection and diagnosis of impedance bonds. The impedance bond condition is monitored in real-time using current and temperature sensors, providing early warning if predefined thresholds are exceeded in terms of currents, imbalance currents, and temperatures. The proposed method and the developed monitoring device have been validated in the railway laboratory to confirm its capability to detect defects. The acquired parameters from impedance bonds are used to extract thermal stresses and technical conditions of this equipment. Experimental results and appropriate data analysis are included in the article.

An initial public offering (IPO) refers to a process by which private corporations offer their shares in a public stock market for investment by public investors. This listing of private corporations in the stock market leads to the easy generation and exchange of capital between private corporations and public investors. Investing in a company’s shares is accompanied by careful consideration and study of the company’s public image, financial policies, and position in the financial market. The stock market is highly volatile and susceptible to changes in the political and socioeconomic environment. Therefore, the prediction of a company’s IPO performance in the stock market is an important study area for researchers. However, there are several challenges in this path, such as the fragile nature of the stock market, the irregularity of data, and the influence of external factors on the IPO performance. Researchers over the years have proposed various artificial intelligence (AI)-based solutions for predicting IPO performance. However, they have some lacunae in terms of the inadequate data size, data irregularity, and lower prediction accuracy. Motivated by the aforementioned issues, we proposed an analytical model for predicting IPO gains or losses by incorporating regression-based AI models. We also performed a detailed exploratory data analysis (EDA) on a standard IPO dataset to identify useful inferences and trends. The XGBoost Regressor showed the maximum prediction accuracy for the current IPO gains, i.e., 91.95%.