Thermal power plants pollution assessment based on deep neural networks, remote sensing, and GIS: A real case study in Iran.

To investigate the impact of the Bandar Abbas thermal power plant on the waters of the Persian Gulf coast, a combination of satellite images and ground data was utilized to determine the Sea Surface Temperature (SST) as a thermal index, Total Organic Carbon (TOC) and Chemical Oxygen Demand (COD) as biological indices. Additionally, measurements of SO, O, NO, CO, CO, and CH values in the atmosphere were taken to determine the plant's impact on air pollution. Temperature values of the water for different months were predicted using Long Short-Term Memory (LSTM), Support Vector Regression (SVR), and Cascade neural networks.

Private anomaly detection of student health conditions based on wearable sensors in mobile cloud computing.

With the continuous spread of COVID-19 virus, how to guarantee the healthy living of people especially the students who are of relative weak physique is becoming a key research issue of significant values. Specifically, precise recognition of the anomaly in student health conditions is beneficial to the quick discovery of potential patients. However, there are so many students in each school that the education managers cannot know about the health conditions of students in a real-time manner and accurately recognize the possible anomaly among students quickly.

A multi-criteria GIS-based model for wind farm site selection with the least impact on environmental pollution using the OWA-ANP method.

Wind energy is considered one of the most efficient and cost-effective ways to generate electricity, since it has a low environmental impact. So, it is essential to identify the best places to build wind farms that have the lowest impact on human health and the highest performance. In order to determine the appropriate locations for the construction of wind power plants, in the study first, the interpolation maps of the most important parameters for the construction of wind power plants were created.

Determination of the optimal location for constructing solar photovoltaic farms based on multi-criteria decision system and Dempster-Shafer theory.

Considering environmental concerns regarding air pollution which is induced by burning fossil fuels to generate electrical power, utilizing solar energy as a green and sustainable energy source is of great interest. This study proposes a novel framework to determine the optimal location for constructing solar photovoltaic (PV) farms. To locate the suitable areas for PV farms, firstly, a fuzzy-based method is utilized to homogenize the input parameters, thereafter, the analytical hierarchy process (AHP) and Dempster-Shafer (DS) methods are independently used.

The efficacy of Er,Cr:YSGG laser in reconditioning of metallic orthodontic brackets.

Objective: This study aimed to evaluate the efficiency of erbium, chromium: yttrium-scandium-gallium-garnet (Er,Cr:YSGG) laser in removing adhesive from bracket bases, and its influence on shear bond strength (SBS) of orthodontic brackets, compared to several other recycling methods.

Background Data: Reconditioning the dislodged attachments is frequently required in orthodontic practice to reduce treatment costs.

Materials And Methods: Seventy-five premolar teeth were selected and divided into five groups.