This research provides a novel technique for using metal organic frameworks (HKUST-1) as a gas storage system for liquefied petroleum gas (LPG) in Iraqi vehicles to avoid the drawbacks of the currently employed method of LPG gas storage. A low-cost adsorbent called HKUST-1 was prepared and characterized in this research to investigate its ability for propane storage at different temperatures (25, 30, 35, and 40 ^oC) and pressures of (1-7) bar. HKUST-1 was made using a hydrothermal method and characterized using powder X-ray diffraction, BET surface area, scanning electron microscopic (SEM), and Fourier Transforms infrared spectroscopy (FTIR). The HKUST-1 was produced using a hydrothermal technique and possesses a high crys

The use of piezocatalysis with photocatalysis in the degradation of organic pollutants has become an attractive research field due to the enhanced synergistic effect of the two processes. Herein, binary composites of AgBr decorated onto Br-doped g-C3N4 in different weight percentages (AgBr content was varied from 10 wt% to 40 wt%) have been built as heterojunction piezo-photo catalysts with a highly efficient performance. The 20 wt%AgBr/Br-g-C3N4 catalysts showed synergistically improved piezo-photo catalytic decomposition toward Methylene blue (MB) under visible-light and sonication exposure in a simple constructed split-plate airlift contactor, completing 99.7 % degradation of MB within 60 min. The heterogeneous junction established among

This study has been undertaken to postulate the mechanism of impact test at low velocities. Thin-walled tubes of 100Cr6 were deformed under axial compression. In the present work there are seven velocities (4.429,4.652,5.240,5.600,5.942,6.264, 6.569) m\sec were applied to show how they effect the load, change in length, also the kinetic energy. However, the comparison between the obtained results and the other studies (Alexandar[3] , Abramowicz[4], Ayad[5]) was made the present work and Ayad data show good agreement. Load, change in length, kinetic energy were determined to understand the impact test.

Patients infected with the COVID-19 virus develop severe pneumonia, which typically results in death. Radiological data show that the disease involves interstitial lung involvement, lung opacities, bilateral ground-glass opacities, and patchy opacities. This study aimed to improve COVID-19 diagnosis via radiological chest X-ray (CXR) image analysis, making a substantial contribution to the development of a mobile application that efficiently identifies COVID-19, saving medical professionals time and resources. It also allows for timely preventative interventions by using more than 18000 CXR lung images and the MobileNetV2 convolutional neural network (CNN) architecture. The MobileNetV2 deep-learning model performances were evaluated

Solar cells has been assembly with electrolytes including I−/I−3 redox duality employ polyacrylonitrile (PAN), ethylene carbonate (EC), propylene carbonate (PC), with double iodide salts of tetrabutylammonium iodide (TBAI) and Lithium iodide (LiI) and iodine (I2) were thoughtful for enhancing the efficiency of the solar cells. The rendering of the solar cells has been examining by alteration the weight ratio of the salts in the electrolyte. The solar cell with electrolyte comprises (60% wt. TBAI/40% wt. LiI (+I2)) display elevated efficiency of 5.189% under 1000 W/m2 light intensity. While the solar cell with electrolyte comprises (60% wt. LiI/40% wt. TBAI (+I2)) display a lower efficiency of 3.189%. The conductivity raises with the

Botnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet