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We developed a compact, diode-end-pumped, eye-safe laser rangefinder transmitter, which is based on the passively Q-switched Er–Yb:glass laser with a Co:Spinel plate as a saturable absorber (SA). The linear cavity laser considers a concave and a plane mirror with the cavity length is only 20 mm. The repetition rate can be tuned from 1 Hz to 8 Hz at the wavelength of 1535 nm. Our laser system operates stably at peak power > 250 kW and pulse width of 4.5 ns.

This work presents the design, thermal simulation and performance analysis of a diode- end-pumped, eye-safe laser. The finite element method is applied to compute the temperature distribution of Yb:Er glass laser rods end pumped by laser diodes.

In the past few years, the application of Machine Learning Techniques (MLT) has become a popular way to enhance the accuracy of predicting concrete properties. This study aims to compare and contrast the performance of Artificial neural network (ANN) and Decision Tree (DT) methods in predicting the compressive strength and slump values of concrete samples. Experimental data used for model building and comparison were obtained from a previous research project. R-squared value (RSQ) and Mean Squared Error (MSE) metrics were used to determine which regression method was the most efficient in predicting concrete compressive strength and slump values. The results from the comparison between ANN and DT methods would be able to identify which of the two regression models is the better choice for forecasting concrete properties.

Random Forest (RF) has been successfully applied to a variety of engineering problems due to its simplicity, versatility, and suitability for both classification and regression tasks. Concrete, as a material composed of multiple complex elements, is influenced by numerous factors, posing challenges in accurately predicting its properties. In this article, an RF model is developed in predicting the slump and strength of concrete using mixed mineral admixtures from blast furnace slag and silicafume. The criterions to evaluate the accuracy of the models are the R squared (R2 ) and the root mean square error (RMSE). Comparing the predicted data with the tested data, the result indicates that RF model should be used in predicting both the slump and strength of concrete.