The kinetics of the thermal decomposition of Moroccan oil shale have been studied by isothermal and nonisothermal thermogravimetry (TG). The nonisothermal weight loss data have been analyzed by using three models, (1) Chen and Nuttall model, (2) Coats and Redfern model, and (3) Anthony and Howard model, while the isothermal TG data have been correlated by using the integral method of kinetic analysis. The combined use of nonisothermal and isothermal TG measurements has shown that the thermal decomposition of Moroccan oil shale involves two consecutive reactions with bitumen as an intermediate. The pyrolysis rates of Moroccan shale are compared with those of Colorado and Jordan shale.
Fatty alcohols, derived from natural sources, are commercially produced by hydrogenation of fatty acids or methyl esters in slurry-phase or fixed-bed reactors. One slurryphase hydrogenation of methyl ester process flows methyl esters and powdered copper chromite catalyst into tubular reactors under high hydrogen pressure and elevated temperature. In the present investigation, slurry-phase hydrogenations of C 12 methyl ester were carried out in semi-batch reactions at nonoptimal conditions (i.e., low hydrogen pressure and elevated temperature). These conditions were used to accentuate the host of side reactions that occur during the hydrogenation. Some 14 side reaction routes are outlined. As an extension of this study, copper chromite catalyst was produced under a number of varying calcination temperatures. Differences in catalytic activity and selectivity were determined by closely following side reaction products. Both activity and selectivity correlate well with the crystallinity of the copper chromite surface; they increase with decreasing crystallinity. The ability to follow the wide variety of side reactions may well provide an additional tool for the optimized design of hydrogenation catalysts. JAOCS 74, 333-339 (1997). FIG. 2. Potential reaction routes in the hydrogenation of alkyl methyl ester.
The Android operating system has gained popularity and evolved rapidly since the previous decade. Traditional approaches such as static and dynamic malware identification techniques require a lot of human intervention and resources to design the malware classification model. The real challenge lies with the fact that inspecting all files of the application structure leads to high processing time, more storage, and manual effort. To solve these problems, optimization algorithms and deep learning has been recently tested for mitigating malware attacks. This manuscript proposes Summing of neurAl aRchitecture and VisualizatiOn Technology for Android Malware identification (SARVOTAM). The system converts the malware non-intuitive features into fingerprint images to extract the quality information. A fine-tuned Convolutional Neural Network (CNN) is used to automatically extract rich features from visualized malware thus eliminating the feature engineering and domain expert cost. The experiments were done using the DREBIN dataset. A total of fifteen different combinations of the Android malware image sections were used to identify and classify Android malware. The softmax layer of CNN was substituted with machine learning algorithms like K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF) to analyze the grayscale malware images. It observed that CNN-SVM model outperformed original CNN as well as CNN-KNN, and CNN-RF. The classification results showed that our method is able to achieve an accuracy of 92.59% using Android certificates and manifest malware images. This paper reveals the lightweight solution and much precise option for malware identification.
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