In this work, solid random gain media were fabricated from laser dye solutions containing nanoparticles as scattering centers. Two different rhodamine dyes (123 and 6G) were used to host the highly-pure titanium dioxide nanoparticles to form the random gain media. The spectroscopic characteristics (mainly fluorescence) of these media were determined and studied. These random gain media showed laser emission in the visible region of electromagnetic spectrum. Fluorescence characteristics can be controlled to few nanometers by adjusting the characteristics of the host and nanoparticles as well as the preparation conditions of the samples. Emission of narrow linewidth (3nm) and high intensity in the visible region (533-537nm) was obtained.

In this work, results from an optical technique (laser speckle technique) for measuring surface roughness was done by using statistical properties of speckle pattern from the point of view of computer image texture analysis. Four calibration relationships were used to cover wide range of measurement with the same laser speckle technique. The first one is based on intensity contrast of the speckle, the second is based on analysis of speckle binary image,  the third is on size of speckle pattern spot, and the latest one is based on characterization of the energy feature of the gray level co-occurrence matrices for the speckle pattern. By these calibration relationships surface roughness of an object surface can be evaluated within the

The cathodic deposition of zinc from simulated chloride wastewater was used to characterize the mass transport properties of a flow-by fixed bed electrochemical reactor composed of vertical stack  of stainless steel nets, operated in batch-recycle mode. The electrochemical reactor employed potential value in such a way that the zinc reduction occurred under mass transport control. This potential was determined by hydrodynamic voltammetry using a borate/chloride solution as supporting electrolyte on stainless steel rotating disc electrode. The results indicate that mass transfer coefficient (K_m) increases with increasing of flow rate (Q) where .The electrochemical reactor proved to be efficient in removing zinc and was abl

In the present study, activated carbon supported metal oxides was prepared for thiophene removal from model fuel (Thiophene in n-hexane) using adsorptive desulfurization technique. Commercial activated carbon was loaded individually with copper oxide in the form of Cu₂O/AC. A comparison of the kinetic and isotherm models of the sorption of thiophene from model fuel was made at different operating conditions including adsorbent dose, initial thiophene concentration and contact time. Various adsorption rate constants and isotherm parameters were calculated. Results indicated that the desulfurization was enhanced when copper was loaded onto activated carbon surface. The highest desulfurization percent for Cu₂O/AC and o

The present study is to investigate the possibility of using wastes in the form of scrap iron (ZVI) and/ or aluminum ZVAI for the detention and immobilization of the chromium ions in simulated wastewater. Different batch equilibrium parameters such as contact time (0-250) min, sorbent dose (2-8 g ZVI/100 mL and 0.2-1 g ZVAI/100 mL), initial pH (3-6), initial pollutant concentration of 50 mg/L, and speed of agitation (0-250) rpm were investigated. Maximum contaminant removal efficiency corresponding to (96 %) at 250 min contact time, 1g ZVAI/ 6g ZVI sorbent mass ratio, pH 5.5, pollutant concentration of 50 mg/L initially, and 250 rpm agitation speed were obtained.

The best isotherm model for the batch single Cr(III) uptake by ZVI

Electromyogram (EMG)-based Pattern Recognition (PR) systems for upper-limb prosthesis control provide promising ways to enable an intuitive control of the prostheses with multiple degrees of freedom and fast reaction times. However, the lack of robustness of the PR systems may limit their usability. In this paper, a novel adaptive time windowing framework is proposed to enhance the performance of the PR systems by focusing on their windowing and classification steps. The proposed framework estimates the output probabilities of each class and outputs a movement only if a decision with a probability above a certain threshold is achieved. Otherwise (i.e., all probability values are below the threshold), the window size of the EMG signa

General Background: Deep image matting is a fundamental task in computer vision, enabling precise foreground extraction from complex backgrounds, with applications in augmented reality, computer graphics, and video processing. Specific Background: Despite advancements in deep learning-based methods, preserving fine details such as hair and transparency remains a challenge. Knowledge Gap: Existing approaches struggle with accuracy and efficiency, necessitating novel techniques to enhance matting precision. Aims: This study integrates deep learning with fusion techniques to improve alpha matte estimation, proposing a lightweight U-Net model incorporating color-space fusion and preprocessing. Results: Experiments using the AdobeComposition-1k