Microstructure and Wear Behaviour of Aluminium Metal Matrix Composites Reinforced With Mg-Sic-TiO2-flyash

Al6061 was particulate with various reinforcement composite metal matrix materials adding different matrices using stir casting method. Reinforcement of hybrid composites used to various kinds of applications such as good wear resistance, electrical semiconductor and development of mechanical properties. The characteristics of various combinations of Aluminium reinforcement material were investigated. Wear behavior has investigated by using pin-on- disc mechanism. Using Scanning Electron Microscope (SEM) examined in this investigation. Using optical microscopic to identifying the various worn surfaces such as Ploughing, debris and oxide layer. Pin-on-wear mechanism were carried out the reinforcement of Aluminium with different prepared composites of wear parameters such as sliding distance 300mm, sliding speed 2.5m/s and applying load 5N, 10N, 15N. This analysis result focused on minimum wear, coefficient of friction, frictional load and temperature effect. Experimental result shows the amount of adding composite particles to increasing the wear resistance and decreasing wear losses. From the effect of wear as curve fitting technique was applied for the polynomial and power law equation. These equations of R2 value higher than power law equations and much agreed with the experimental observation

Predicting cervical cancer biopsy results using demographic and epidemiological parameters: a custom stacked ensemble machine learning approach

The human papillomavirus (HPV) is responsible for most cervical cancer cases worldwide. This gynecological carcinoma causes many deaths, even though it can be treated by removing malignant tissues at a preliminary stage. In many developing countries, patients do not undertake medical examinations due to the lack of awareness, hospital resources and high testing costs. Hence, it is vital to design a computer aided diagnostic method which can screen cervical cancer patients. In this research, we predict the probability risk of contracting this deadly disease using a custom stacked ensemble machine learning approach. The technique combines the results of several machine learning algorithms on multiple levels to produce reliable predictions. In the beginning, a deep exploratory analysis is conducted using univariate and multivariate statistics. Later, the one-way ANOVA, mutual information and Pearson’s correlation techniques are utilized for feature selection. Since the data was imbalanced, the Borderline-SMOTE technique was used to balance the data. The final stacked machine learning model obtained an accuracy, precision, recall, F1-score, area under curve (AUC) and average precision of 98%, 97%, 99%, 98%, 100% and 100%, respectively. To make the model explainable and interpretable to clinicians, explainable artificial intelligence algorithms such as Shapley additive values (SHAP), local interpretable model agnostic explanation (LIME), random forest and ELI5 have been effectively utilized. The optimistic results indicate the potential of automated frameworks to assist doctors and medical professionals in diagnosing and screening potential cervical cancer patients

Inertial impedance of coalescence during collision of liquid drops

The fluid dynamics of the collision and coalescence of liquid drops has intrigued scientists and engineers for more than a century owing to its ubiquitousness in nature, e.g. raindrop coalescence, and industrial applications, e.g. breaking of emulsions in the oil and gas industry. The complexity of the underlying dynamics, which includes occurrence of hydrodynamic singularities, has required study of the problem at different scales – macroscopic, mesoscopic and molecular – using stochastic and deterministic methods. In this work, a multi-scale, deterministic method is adopted to simulate the approach, collision, and eventual coalescence of two drops where the drops as well as the ambient fluid are incompressible, Newtonian fluids. The free boundary problem governing the dynamics consists of the Navier–Stokes system and associated initial and boundary conditions that have been augmented to account for the effects of disjoining pressure as the separation between the drops becomes of the order of a few hundred nanometres. This free boundary problem is solved by a Galerkin finite element-based algorithm. The interplay of inertial, viscous, capillary and van der Waals forces on the coalescence dynamics is investigated. It is shown that, in certain situations, because of inertia two drops that are driven together can first bounce before ultimately coalescing. This bounce delays coalescence and can result in the computed value of the film drainage time departing significantly from that predicted from existing scaling theories

Freeze-thaw Resistance of an Alluvial Soil Stabilized with EcoSand and Asbestos-free Fiber Powder

Stabilization of poor soils subjected to large daily temperature variations requires careful selection of suitable stabilizer for improvement of such soils. This study investigated the freeze-thaw resistance of an alluvial soil stabilized with EcoSand and asbestos-free fiber powder (AFP). Physical and mechanical properties of the soil were determined. The soil sample was stabilized with 5 variants of equal mixtures of the EcoSand and AFP in proportions of 2, 4, 6, 8 and 10%, with 1% sodium silicate and 1% fly ash, by weight of the soil. UCS tests were conducted before and after three freeze-thaw cycles, while keeping the sample at 0ºC for 8 hours and later at 30ºC for 8 hours for each cycle. It was found that the 8% EcoSand + AFP with 1% sodium silicate and 1% fly ash content provided an optimized increase of the freeze-thaw resistance of the soil. The use of a mixture of EcoSand and AFP as a soil stabilizer for regions of the world experiencing large temperature variation has the potential to improve the resistance of sand to freezing and thawing

An environmental sustainability roadmap for partially substituting agricultural waste for sand in cement blocks

Agricultural waste can be used in cement block production for a number of reasons, including its environmental, economic, and labor benefits. This study examines the mechanical, durability, and cost-effectiveness characteristics of cement blocks. A cement block made from agriculture waste promotes sustainable construction practices, since waste agriculture is often dumped in landfills and regarded as a waste material. Carbon dioxide (CO2) emissions produced by the construction sector, either from the firing of clay bricks or from the production of cement, contribute significantly to global warming. In many developing countries, air pollution from agricultural activities is primarily accounted for the emissions from agricultural machinery and openly burning agro-waste. Farming is one of the leading causes of water and soil pollution. Hence, adopting agricultural waste into cement production would significantly reduce the environmental impact of concrete structures. The goal of this research is to determine whether agricultural waste products, such as vermiculite, pistachio shells, sugarcane bagasse, and coconut husks, can be used to substitute sand in concrete blocks. The water absorption capacity of waste materials, density, flexural strength, fire resistance, and compressive strength of waste materials as admixtures in concrete were evaluated using experimental tests. In most cases, the concrete blocks made from agricultural waste were strong enough to satisfy ASTM standards. The specimens containing coconut husks and pistachio shells, among others, were found to be fairly strong and durable, even when isolating them from water

SYMBOL LEVEL DECODING FOR DUO-BINARY TURBO CODES

This paper investigates the performance of three different symbol level decoding algorithms for Duo-Binary Turbo codes. Explicit details of the computations involved in the three decoding techniques, and a computational complexity analysis are given. Simulation results with different couple lengths, code-rates, and QPSK modulation reveal that the symbol level decoding with bit-level information outperforms the symbol level decoding by 0.1 dB on average in the error floor region. Moreover, a complexity analysis reveals that symbol level decoding with bit-level information reduces the decoding complexity by 19.6 % in terms of the total number of computations required for each half-iteration as compared to symbol level decoding