Transactions on Engineering and Computer Science

Self-healing is a concept that has been studied for decades. Self-healing is the ability of cementitious composites to reduce the width of cracks generated due to wear and tear of the material. Of the many self-healing mechanisms, this article reviews and outlines the progress of self-healing in past and recent decades. Primarily, the use of expansive agents for self-healing is discussed in this article. When a cementitious composite fills up the crack with hydration products, expansive or crystalline materials, the composite is said to have undergone autogenous self-healing. Autonomous self-healing refers to the intentional addition of materials to help the composite self-heal. This article delves into these two types of self-healing. At its core, this article studies research on different expansive materials used in self-healing for cementitious composites. The use of magnesium oxide (MgO), quicklime, calcium sulpho-aluminate (Type K), and Sodium Bentonite expansive materials were investigated. Combinations of expansive agents with geomaterials, crystalline additives, swelling minerals in varying proportions were also outlined. Their strength regain, crack sealing and optimum self-healing is subsequently discussed. The use of sand replacing pellets infused with expansive materials for self-healing is studied. Some of the results showed that although self-healing took place, the re-hydration products were unstable proving that a design that had a greater chemical and physical stability is required for effective self-healing. When combinations of expansive agents are used, results show that the composites have a higher crack sealing nature with most cracks being sealed in 14 days. With a new method to improve self-healing, a study was introduced that partially replaced fine aggregates with active granules. The results showed improved crack mouth healing.

In this century engineers, managers, and decision makers are overwhelmed with statistical data for interpretation of road condition for planning purposes. With new development and recording of digital geospatial data acquired from various geometric-GIS have been a useful tool for data manipulation; it simplifies the way data are visualized and interpreted direct. On the other hand, one of the most important issues on smart transportation is to have a passable road. In order to implement smart transport system, the data in the form of Geographical Information Systems (GIS) is essential. This paper discusses the simplified approach used by Tanzania National Roads Agency (TANROADS to convert the non-special data collected many years back into Geodatabase to achieve a cost-effective GIS solution. The result was an integrated GIS system for visualization of Roads network and assist in data interpretation and planning. In this way different data such as condition data sets collected in different times were analyzed in GIS to assist decision makers identify part of the network that need attention. The output was used for Quality Assurance of the data including for instance identification of sections with no survey information. Other information such as sections with poor, fair and good condition could be easily identified and this information may be used for different purposes. It is recommended that road implementers may use this method and available non special data to come up with up with cost effective GIS systems for road asset planning and investment decisions.

The substantive increase in the use of internet-based technologies has subjected organizations to malicious attacks. Cybersecurity is the practice of protecting systems, networks, and programs from digital attacks. A blockchain is a datastructure, which stores transactional records in the form a block chained and stored in several databases, and is fundamentally a digital ledger of transactions (DLT) that upon duplication is distributed on the blockchain throughout the entire computer network. To improve on Cybersecurity, Machine Learning is considered for data mining among the data. This research is purposed to determine a cybsersecurity risk management plan, develop a Blockchain Technology Application Model that manages cyber risks and vulnerabilities, ascertain how the Framework of Blockchain Technology and Cryptocurrency addresses cyber risks and creates value to the financial services sector. The research used the Pragmatism paradigm, which is a philosophy that is closely linked to the Mixed Method Research (MMR). The Focus Group comprised a total of 100 participants from the Masters students attending the Applied Business Informatics module taught by the author at the University of Zimbabwe in 2020. The National Institute of Standards and Technology (NIST) Cybersecurity Framework and the General deterrence theory (GDT) were used to formulate the Cybersecurity strategy. Blockchain cyber vulnerabilities were determined. A model structure for block chain technology and cryptocurrency was developed and an explanation provided on how the framework of Blockchain Technology and Cryptocurrency addresses cyber risks and creates value to financial services sector.

The early compressive strength of concrete is investigated. The influence of complex organic ions and molecules of a number of non-electrolytes, due to the inhibitory effect of dissolved particles on the translational motion of water molecules in a solution, has been established. It was found that these substances alter the formation of concrete strength. The rate of formation of concrete strength in the early stages increases due to the dissolution of cement particles with structured water. It was found that the effect of complex organic ions and molecules of a number of non-electrolytes on the strength of concrete exceeds the effect of solid nanomodifiers, in particular, carbon nanotubes, silicon oxide, and others. When using complex organic ions and molecules of a number of non-electrolytes as individual modifiers of water used for concrete preparation, their optimal amount is 8 10-4...9 10-4% of the cement mass. The increase in the strength of concrete at an early age reaches 170%, and at the age of 28 days - 35%. The mechanism of action of modifiers of this type is that they structure water due to hydrophobic hydration and the effect of "small doses".

In this article, we present a new study for the analysis and classification of atmospheric aerosols in remote sensing LIDAR data. Information on particle size and associated properties are extracted from these remote sensing atmospheric data which are collected by a ground-based LIDAR system. This study first considers optical LIDAR parameter-based classification methods for clustering and classification of different types of harmful aerosol particles in the atmosphere. Since accurate methods for aerosol prediction behaviors are based upon observed data, computational approaches must overcome design limitations, and consider appropriate calibration and estimation accuracy. Consequently, two statistical methods based on generalized linear models (GLM) and regression tree techniques are used to further analyze the performance of the LIDAR parameter-based aerosol classification methods. The goal of GLM and regression tree analyses is to compare and contrast distinct classification data schemes, and compare the results with the measured aerosol reflection data in the atmosphere. The detailed statistical comparisons and analyses shows that the optical methods adopted in this study for classification and prediction of various harmful aerosol types such as soot, carbon monoxide (CO), sulfates (SOx), and nitrates (NOx) are efficient under appropriate functional distributions. The articleoffers a method for natural ordering of the aerosol types.