Search results for: M. S. Matlala

Authors: M. S. Matlala, I. Ignatious

Abstract:

Environmental sustainability has taken the center stage in human life all over the world. Energy is the most essential component of our life. The conventional sources of energy are non-renewable and have a detrimental environmental impact. Therefore, there is a need to move from conventional to non-conventional renewable energy sources to satisfy the world’s energy demands. The study aimed at screening for microbial cellulolytic enzymes using a proteomic approach. The objectives were to screen for microbial cellulases with high specific activity and separate the cellulolytic enzymes using a combination of zymography and two-dimensional (2-D) gel electrophoresis followed by tryptic digestion, Matrix-assisted Laser Desorption Ionisation-Time of Flight (MALDI-TOF) and bioinformatics analysis. Fungal and bacterial isolates were cultured in M9 minimal and Mandel media for a period of 168 hours at 60°C and 30°C with cellobiose and Avicel as carbon sources. Microbial cells were separated from supernatants through centrifugation, and the crude enzyme from the cultures was used for the determination of cellulase activity, zymography, SDS-PAGE, and two-dimensional gel electrophoresis. Five isolates, with lytic action on carbon sources studied, were a bacterial strain (BARK) and fungal strains (VCFF1, VCFF14, VCFF17, and VCFF18). Peak cellulase production by the selected isolates was found to be 3.8U/ml, 2.09U/ml, 3.38U/ml, 3.18U/ml, and 1.95U/ml, respectively. Two-dimensional gel protein maps resulted in the separation and quantitative expression of different proteins by the microbial isolates. MALDI-TOF analysis and database search showed that the expressed proteins in this study closely relate to different glycoside hydrolases produced by other microbial species with an acceptable confidence level of 100%.

Keywords: cellulases, energy, two-dimensional gel electrophoresis, matrix-assisted laser desorption ionisation-time of flight, MALDI-TOF MS

Procedia PDF Downloads 97

Authors: Michael Moeti, Khuliso Sigama, Thapelo Samuel Matlala

Abstract:

One of the principal challenges that the world is confronted with is insecurity. The crime rate is increasing exponentially, and protecting our physical assets especially in the motorist industry, is becoming impossible when applying our own strength. The need to develop technological solutions that detect and report theft without any human interference is inevitable. This is critical, especially for vehicle owners, to ensure theft detection and speedy identification towards recovery efforts in cases where a vehicle is missing or attempted theft is taking place. The vehicle theft detection system uses Convolutional Neural Network (CNN) to recognize the driver's face captured using an installed mobile phone device. The location identification function uses a Global Positioning System (GPS) to determine the real-time location of the vehicle. Upon identification of the location, Global System for Mobile Communications (GSM) technology is used to report or notify the vehicle owner about the whereabouts of the vehicle. The installed mobile app was implemented by making use of python as it is undoubtedly the best choice in machine learning. It allows easy access to machine learning algorithms through its widely developed library ecosystem. The graphical user interface was developed by making use of JAVA as it is better suited for mobile development. Google's online database (Firebase) was used as a means of storage for the application. The system integration test was performed using a simple percentage analysis. Sixty (60) vehicle owners participated in this study as a sample, and questionnaires were used in order to establish the acceptability of the system developed. The result indicates the efficiency of the proposed system, and consequently, the paper proposes the use of the system can effectively monitor the vehicle at any given place, even if it is driven outside its normal jurisdiction. More so, the system can be used as a database to detect, locate and report missing vehicles to different security agencies.

Keywords: CNN, location identification, tracking, GPS, GSM

Procedia PDF Downloads 123