Hybrid MWCNT and TiO2 Nanoparticle-Suspended Waste Tyre Oil Biodiesel for CI Engines.

Nowadays, scarcity arises in almost all our basic needs, including water, fuel, and food. Recycling used and scrapped things for a valuable commodity is highly appreciable for compensating for the globally fast-growing demand. This paper aims to investigate waste tyre oil for preparing biodiesel for CI engines by enhancing their performance with hybrid nanoparticles for preparing nanofuel and hybrid nanofuel. The nanoparticles (30-40 nm) of MWCNT and TiO2 were utilized to prepare nanofuels with nanoparticle concentrations of MWCNT (300 ppm) and TiO2 (300 ppm), respectively. In the case of hybrid nanofuel, the nanoparticle concentration of MWCNT (150 ppm) and TiO2 (150 ppm) was preferred. The performance of the proposed nanofuel and hybrid nanofuel with pure diesel was evaluated. The proposed fuel performance outperforms the combustion performance, has higher engine efficiency, and has fewer emissions. The best performances were noticed in hybrid nanofuel that has 32% higher brake thermal efficiency than diesel and 24% and 4% lower BSFC and peak pressure than diesel, respectively. The emission performance is also 29%, 50%, and 13% lower in CO, HC, and CO2 emissions than that in pure diesel.

Lung Cancer Prediction from Text Datasets Using Machine Learning.

Lung cancer is the major cause of cancer-related death in this generation, and it is expected to remain so for the foreseeable future. It is feasible to treat lung cancer if the symptoms of the disease are detected early. It is possible to construct a sustainable prototype model for the treatment of lung cancer using the current developments in computational intelligence without negatively impacting the environment. Because it will reduce the number of resources squandered as well as the amount of work necessary to complete manual tasks, it will save both time and money. To optimise the process of detection from the lung cancer dataset, a machine learning model based on support vector machines (SVMs) was used. Using an SVM classifier, lung cancer patients are classified based on their symptoms at the same time as the Python programming language is utilised to further the model implementation. The effectiveness of our SVM model was evaluated in terms of several different criteria. Several cancer datasets from the University of California, Irvine, library were utilised to evaluate the evaluated model. As a result of the favourable findings of this research, smart cities will be able to deliver better healthcare to their citizens. Patients with lung cancer can obtain real-time treatment in a cost-effective manner with the least amount of effort and latency from any location and at any time. The proposed model was compared with the existing SVM and SMOTE methods. The proposed method gets a 98.8% of accuracy rate when comparing the existing methods.

Synthesis and Workability Behavior of Cu-X wt.% TiC (x = 0, 4, 8, and 12) Powder Metallurgy Composites.

In this work, copper (Cu) matrix composite reinforced with titanium carbide (TiC) was fabricated by powder metallurgy (PM) method with the varying TiC content from 0% to 12% by weight in the step of 4%. The required weight percentage of powders was milled in an indigenously developed ball milling setup. Green compacts were made using a computer-controlled hydraulic press (400 kN) and sintered in a muffle furnace at a temperature of 950°C. Scanning electron microscope (SEM) was used to analyze the distribution of TiC particles in Cu matrix in as-sintered conditions. X-ray diffraction (XRD) analysis resulted in the existence of respective phases in the produced composites. The structural characteristics such as stress, strain, dislocation density, and grain size of the milled composites were evaluated. Cold upsetting was conducted for the sintered composites at room temperature to evaluate the axial (σ z ), hoop (σ Ó© ), hydrostatic (σ m ), and effective (σ eff ) true stresses. These stresses were analyzed against true axial strain (ε z ). Results showed that the increase in the inclusion of weight percentage of TiC into the Cu matrix increases density, hardness, (σ z ), (σ Ó© ), (σ m ), (σ eff ), and stress ratio parameters such as (σ z /σ eff ), (σ θ /σ eff ), (σ m /σ eff ), and (σ z /σ θ ) of the composites.

Human Centered Decision-Making for COVID-19 Testing Center Location Selection: Tamil Nadu-A Case Study.

This paper proposes a blend of three techniques to select COVID-19 testing centers. The objective of the paper is to identify a suitable location to establish new COVID-19 testing centers. Establishment of the testing center in the needy locations will be beneficial to both public and government officials. Selection of the wrong location may lead to lose both health and wealth. In this paper, location selection is modelled as a decision-making problem. The paper uses fuzzy analytic hierarchy process (AHP) technique to generate the criteria weights, monkey search algorithm to optimize the weights, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to rank the different locations. To illustrate the applicability of the proposed technique, a state named Tamil Nadu, located in India, is taken for a case study. The proposed structured algorithmic steps were applied for the input data obtained from the government of India website, and the results were analyzed and validated using the government of India website. The ranks assigned by the proposed technique to different locations are in aligning with the number of patients and death rate.

Investigating the Role of Image Fusion in Brain Tumor Classification Models Based on Machine Learning Algorithm for Personalized Medicine.

Image fusion can be performed on images either in spatial domain or frequency domain methods. Frequency domain methods will be most preferred because these methods can improve the quality of edges in an image. In image fusion, the resultant fused images will be more informative than individual input images, thus more suitable for classification problems. Artificial intelligence (AI) algorithms play a significant role in improving patient's treatment in the health care industry and thus improving personalized medicine. This research work analyses the role of image fusion in an improved brain tumour classification model, and this novel fusion-based cancer classification model can be used for personalized medicine more effectively. Image fusion can improve the quality of resultant images and thus improve the result of classifiers. Instead of using individual input images, the high-quality fused images will provide better classification results. Initially, the contrast limited adaptive histogram equalization technique preprocess input images such as MRI and SPECT images. Benign and malignant class brain tumor images are applied with discrete cosine transform-based fusion method to obtain fused images. AI algorithms such as support vector machine classifier, KNN classifier, and decision tree classifiers are tested with features obtained from fused images and compared with the result obtained from individual input images. Performances of classifiers are measured using the parameters accuracy, precision, recall, specificity, and F1 score. SVM classifier provided the maximum accuracy of 96.8%, precision of 95%, recall of 94%, specificity of 93%, F1 score of 91%, and performed better than KNN and decision tree classifiers when extracted features from fused images are used. The proposed method results are compared with existing methods and provide satisfactory results.

Investigation of Mechanical Properties and Salt Spray Corrosion Test Parameters Optimization for AA8079 with Reinforcement of TiN + ZrO2.

This work mainly focuses on increasing the mechanical strength and improving the corrosion resistance of an aluminum alloy hybrid matrix. The composites are prepared by the stir casting procedure. For this work, aluminum alloy 8079 is considered as a base material and titanium nitride and zirconium dioxide are utilized as reinforcement particles. Mechanical tests, such as the ultimate tensile strength, wear, salt spray corrosion test and microhardness test, are conducted effectively in the fabricated AA8079/TiN + ZrO2 composites. L9 OA statistical analysis is executed to optimize the process parameters of the mechanical and corrosion tests. ANOVA analysis defines the contribution and influence of each parameter. In the tensile and wear test, parameters are chosen as % of reinforcement (3%, 6% and 9%), stirring speed (500, 550 and 600 rpm) and stirring time (20, 25 and 30 min). Similarly, in the salt spray test and microhardness test, the selected parameters are: percentage of reinforcement (3%, 6% and 9%), pH value (3, 6 and 9), and hang time (24, 48 and 72 h). The percentage of reinforcement highly influenced the wear and microhardness test, while the stirring time parameter extremely influenced the ultimate tensile strength. From the corrosion test, the hang time influences the corrosion rate. The SEM analysis highly reveals the bonding of each reinforcement particle to the base material.

Optimization of Process Parameters in CNC Turning of Aluminum 7075 Alloy Using L27 Array-Based Taguchi Method.

With the advent of the industrial revolution 4.0, the goal of the manufacturing industry is to produce a large number of products in relatively less time. This study applies the Taguchi L27 orthogonal array methodological paradigm along with response surface design. This work optimizes the process parameters in the turning of Aluminum Alloy 7075 using a Computer Numerical Control (CNC) machine. The optimal parameters influenced the rate of metal removal, the roughness of the machined surface, and the force of cutting. This experimental investigation deals with the optimization of speed (800 rpm, 1200 rpm, and 1600 rpm) and feed (0.15, 0.20, and 0.25 mm/rev) in addition to cutting depth (1.0, 1.5, and 2.0 mm) on the turning of Aluminum 7075 alloy in a CNC machine. The outcome in terms of results such as the removal rate of material (maximum), roughness on the machined surface (minimum), along with cutting force (least amount) were improved by the L27 array Taguchi method. There were 27 specimens of Al7075 alloy produced as per the array, and the corresponding responses were measured with the help of various direct contact and indirect contact sensors. Results were concluded all the way through diagrams of main effects in favor of signal-to-noise ratios and diagrams of surfaces with contour diagrams for various combinations of responses.

Experimental Investigation of the Friction Stir Weldability of AA8006 with Zirconia Particle Reinforcement and Optimized Process Parameters.

A lightweight, highly corrosive resistant, and high-strength wrought alloy in the aluminum family is the Aluminium 8006 alloy. The AA8006 alloy can be formed, welded, and adhesively bonded. However, the recommended welding methods such as laser, TIG (Tungsten Inert Gas welding), and ultrasonic are more costly. This investigation aims to reduce the cost of welding without compromising joint quality by means of friction stir welding. The aluminum alloy-friendly reinforcement agent zirconia is utilized as particles during the weld to improve the performance of the newly identified material AA8006 alloy in friction stir welding (FSW). The objectives of this research are to identify the level of process parameters for the friction stir welding of AA8006 to reduce the variability by the trial-and-error experimental method, thereby reducing the number of samples needing to be characterized to optimize the process parameters. To enhance the quality of the weld, the friction stir processing concept will be adapted with zirconia reinforcement during welding. The friction stir-processed samples were investigated regarding their mechanical properties such as tensile strength and Vickers microhardness. The welded samples were included in the corrosion testing to ensure that no foreign corrosive elements were included during the welding. The quality of the weld was investigated in terms of its surface morphology, including aspects such as the dispersion of reinforced particles on the welded area, the incorporation of foreign elements during the weld, micro defects or damage, and other notable changes through scanning electron microscopy analysis. The process of 3D profilometry was employed to perform optical microscopy investigation on the specimens inspected to ensure their surface quality and finish. Based on the outcomes, the optimal process parameters are suggested. Future directions for further investigation are highlighted.