Predicting deleterious missense genetic variants via integrative supervised nonnegative matrix tri-factorization

Among an assortment of genetic variations, Missense are major ones which a small subset of them may led to the upset of the protein function and ultimately end in human diseases. Various machine learning methods were declared to differentiate deleterious and benign missense variants by means of a large number of features, including structure, sequence, interaction networks, gene disease associations as well as phenotypes. However, development of a reliable and accurate algorithm for merging heterogeneous information is highly needed as it could be captured all information of complex interactions on network that genes participate in. In this study we proposed a new method based on the non-negative matrix tri-factorization clustering method. We outlined two versions of the proposed method: two-source and three-source algorithms. Two-source algorithm aggregates individual deleteriousness prediction methods and PPI network, and three-source algorithm incorporates gene disease associations into the other sources already mentioned. Four benchmark datasets were employed for internally and externally validation of both algorithms of our predictor. The results at all datasets confirmed that, our method outperforms most state of the art variant prediction tools. Two key features of our variant effect prediction method are worth mentioning. Firstly, despite the fact that the incorporation of gene disease information at three-source algorithm can improve prediction performance by comparison with two-source algorithm, our method did not hinder by type 2 circularity error unlike some recent ensemble-based prediction methods. Type 2 circularity error occurs when the predictor annotates variants on the basis of the genes located on. Secondly, the performance of our predictor is superior over other ensemble-based methods for variants positioned on genes in which we do not have enough information about their pathogenicity.

Predictive Modeling and Correlated Response Optimization of Polymethylmethacrylate (PMMA)-Based Bio-Nano-Composite Material Using a Hybrid Module

Polymethylmethacrylate (PMMA) is commonly known as bone cement, having good biocompatibility, mechanical qualities. It is extensively used in the biomedical sector as a synthetic bone material, orthopedic surgery and dental applications. However, some primary machining is required to achieve the tailored shape, size and finish before application in the human body. This study focuses on the machining (drilling) behavior of the developed PMMA-based Hydroxyapatite (PMMA-HA) bio-nano- composites. The machining efficiency and parametric control were estimated using a combined principal component analysis (PCA) module and evaluation based on distance from average solution (EDAS). The Hydroxyapatite (HA) weight percentage (wt.%), spindle speed (SPEED) and tool material (TOOL) viz. HSS, Carbide and TiAlN are chosen according to the Taguchi-based experimental array. The objective is to get the best possible machining responses, such as the material removal rate (MRR), mean surface roughness (Ra) and circularity error ([Formula: see text] using the PCA-EDAS hybrid module. The optimal condition is found as the HSS drilling bit, 10%[Formula: see text]wt.%, SPEED-1428[Formula: see text]rpm with an improvement of 30.53%, 21.15% and 41.9% in MRR, Ra and [Formula: see text]-ERROR, respectively. The microstructural investigation scanning electron microscope (SEM) shows the excellent morphology and quality of the drilled hole in the proposed composites. Also, an X-ray diffraction (XRD) analysis of the prepared sample was done to ensure the proper reinforcement. The flexural test shows a significant expansion in the mechanical property due to the presence of HA in PMMA

Analysis of the Optical Quartz Lens Centering Process Based on Acoustic Emission Signal Processing and the Support Vector Machine

A method was proposed for analyzing the optical glass lens centering process, and experiments on biplane quartz lenses were performed to determine the material removal rate (MRR) for the hard, brittle material. This study used acoustic emission–sensing technology to monitor the MRR and reconstruct the original shape of the lens. The MRR was evaluated, and an error of 17.87% was obtained. A Taguchi experiment was combined with signal analysis to optimize the process parameters, and a support-vector machine was trained to classify the quality of the grinding wheel; the model had accuracy 98.8%. By using the proposed analysis method, workpiece quality was controlled to an edge surface roughness of <2 μm, a lens circularity error of <0.01 mm, a crack length of <E0.1, and an optical axis error of <150 μrad.

Evaluation of the Surface Defects and Dimensional Tolerances in Multi-Hole Drilling of AA5083, AA6061, and AA2024

Drilling is one of the most performed machining operations for riveting and assembly operations in many industrial sectors. The accuracy of the drilled holes and their surface finish play a vital role in the longevity and performance of the machined components, which, in turn, increase productivity. Therefore, this study investigated the effect of the multi-spindle drilling process on dimensional hole tolerances, such as hole size, circularity, cylindricity, and perpendicularity. In addition, the surface defects formed in the holes were examined using scanning electron microscopy. Three aluminium alloys, AA2024, AA6061, and AA5083, which are commonly used in the aerospace, automotive, and marine sectors, were chosen as the study materials. The results showed that the holes drilled in AA2024 gave less circularity error, cylindricity error, and perpendicularity error. In the case of hole size, the holes drilled in AA6061 were less deviated from the nominal size following holes drilled in AA2024 and AA5083 alloys. Surface damage in the form of metal debris adhesion, smeared material, side flow, and feed marks was found on the inner hole surface. Holes drilled in AA5083 alloy had the worst surface finish and were the most oversized, which was associated with noticeable damage and deformations in their inner surface. The ANOVA results revealed that the spindle speed was more influential than feed and mainly affected the hole size and cylindricity errors. However, in the case of circularity error and perpendicularity error, drilling parameters were found to be insignificant.

Investigating the Efficacy of Adhesive Tape for Drilling Carbon Fibre Reinforced Polymers

In the present research work, an effort has been made to explore the potential of using the adhesive tapes while drilling CFRPs. The input parameters, such as drill bit diameter, point angle, Scotch tape layers, spindle speed, and feed rate have been studied in response to thrust force, torque, circularity, diameter error, surface roughness, and delamination occurring during drilling. It has been found that the increase in point angle increased the delamination, while increase in Scotch tape layers reduced delamination. The surface roughness decreased with the increase in drill diameter and point angle, while it increased with the speed, feed rate, and tape layer. The best low roughness was obtained at 6 mm diameter, 130° point angle, 0.11 mm/rev feed rate, and 2250 rpm speed at three layers of Scotch tape. The circularity error initially increased with drill bit diameter and point angle, but then decreased sharply with further increase in the drill bit diameter. Further, the circularity error has non-linear behavior with the speed, feed rate, and tape layer. Low circularity error has been obtained at 4 mm diameter, 118° point angle, 0.1 mm/rev feed rate, and 2500 RPM speed at three layers of Scotch tape. The low diameter error has been obtained at 6 mm diameter, 130° point angle, 0.12 mm/rev feed rate, and 2500 rpm speed at three layer Scotch tape. From the optical micro-graphs of drilled holes, it has been found that the point angle is one of the most effective process parameters that significantly affects the delamination mechanism, followed by Scotch tape layers as compared to other parameters such as drill bit diameter, spindle speed, and feed rate.

An experimental study on hole quality and different delamination approaches in the drilling of CARALL, a new FML composite

In this study, the hole quality was investigated in the drilling of CARALL composite. In addition, the delamination factor calculation approaches of Chen, Davim, and Machado were compared in terms of the delamination damage at the hole entrance surface. Chen's approach is based on the conventional delamination factor (F d) and Davim's on the adjusted delamination factor (F da). Finally, Machado's approach is based on the minimum delamination factor (F min). The values closest to the nominal hole diameter value were obtained with the uncoated (T1), followed by the TiN-TiAlN-coated (T2) and TiAl/TiAlSiMoCr-coated (T3) carbide drills, respectively. The average circularity error values for the hole top and bottom surfaces were 6.184 µm, 7.647 µm, and 8.959 µm for T1, T2, and T3 tools, respectively. Delamination factor values varied between 1.174 and 1.804. The F da values were found to be the highest, followed by F d values, with F dmin values determined as the lowest.

Ultrasonic-assisted drilling of laminated aluminum 2024 metal matrix composite reinforced with SiC nanoparticles: Experimental investigation and grey relational optimization

Aluminum metal matrix composites are widely used in various engineering areas due to their excellent mechanical properties. However, due to their heterogeneous structure, efficient machining of these materials is still a challenging task. Therefore, in the present study the drilling performance of aluminium-copper alloy (Al 2024) reinforced with SiC nanoparticles was experimentally investigated, in the presence of ultrasonic vibration.In this regard, the influence of ultrasonic vibration, SiC weight fraction and drilling parameters was assessed on circularity error and drilling thrust force. Also, the optimization of process parameters was investigated using grey relational analysis. The performed calculations revealed that ultrasonic vibration, SiC content of 2 %wt, feed rate of 20 mm/min and spindle speed of 1400 rpm is the optimal parameters setting in the present study.