There are many ways to maintain the safety of workers on a working site, such as using a human supervisor, computer supervisor, and smoke–flame detecting system. In order to create a safety warning system for the working site, the machine-learning algorithm—Haar-cascade classifier—was used to build four different classes for safety equipment recognition. Then a proposed algorithm was applied to calculate a score to determine the dangerousness of the current working environment based on the safety equipment and working environment. With this data, the system decides whether it is necessary to give a warning signal. For checking the efficiency of this project, three different situations were installed with this system. Generally, with the promising outcome, this application can be used in maintaining, supervising, and controlling the safety of a worker.

ZnO nanorods (Nrods) with ~20–50 nm lengths were synthesized using an aqueous solution of zinc acetate and glacial acetic acid. Bulk heterojunction solar cells were fabricated with the structure of indium tin oxide (ITO)/polyethylenedioxythiophene doped with polystyrene-sulfonic acid (PEDOT:PSS)/ZnO-Nrods + polymer/electron transport layer (ETL)/Al. Current density-voltage characterization of the resulting cells showed that, by adding an ETL and using polymers with a low band gap energy, the photoactive layer surface morphology and the device performance can be dramatically improved.

Czochralski crystal growth has become a popular technique to produce pure single crystals. Many methods have also been developed to optimize this process. In this study, a charge-coupled device camera was used to record the crystal growth progress from beginning to end. The device outputs images which were then used to create a classifier using the Haar-cascade and AdaBoost algorithms. After the classifier was generated, artificial intelligence (AI) was used to recognize the images obtained from good dipping and calculate the duration of this operating. This optimization approach improved a Czochralski which can detect a good dipping step automatically and measure the duration with high accuracy. Using this development, the labor cost of the Czochralski system can be reduced by changing the contribution of human specialists’ mission.