ANN modeling of nickel base super alloys for time dependent deformation

Alloys 617 and 276 are nickel-based super alloys with excellent mechanical properties, oxidation, creepresistance, and phase stability at high temperatures. These alloys are used in complex and stochastic applications. Thus, it is difficult to predict their output characteristics mathematically. Therefore, the non-conventional methods for modeling become more effective. These two alloys have been subjected to time-dependent deformation at high temperatures under sustained loading of different values. The creep results have been used to develop the new models. Artificial neural network (ANN) was applied to predict the creep rate and the anelastic elongation for the two alloys. The neural network contains twenty hidden layer with feed forward back propagation hierarchical. The neural network has been designed with MATLAB Neural Network Toolbox. The results show a high correlation between the predicted and the observed results which indicates the validity of the models

From green remediation to polymer hybrid fabrication with improved optical band gaps

The present work proposed a novel approach for transferring high-risk heavy metals tometal complexes via green chemistry remediation. The method of remediation of heavy metals developed in the present work is a great challenge for global environmental sciences and engineering because it is a totally environmentally friendly procedure in which black tea extract solution is used. The FTIR study indicates that black tea contains enough functional groups (OH and NH), polyphenols and conjugated double bonds. The synthesis of copper complex was confirmed by the UV-vis, XRD and FTIR spectroscopic studies. The XRD and FTIR analysis reveals the formation of complexation between Cu metal complexes and Poly (Vinyl Alcohol) (PVA) host matrix. The study of optical parameters indicates that PVA-based hybrids exhibit a small optical band gap, which is close to inorganic-based materials. It was noted that the absorption edge shifted to lower photon energy. When Cu metal complexes were added to PVA polymer, the refractive index was significantly tuned. The band gap shifts from 6.2 eV to 1.4 eV for PVA incorporated with 45 mL of Cu metal complexes. The nature of the electronic transition in hybrid materials was examined based on the Taucs model, while a close inspection of the optical dielectric loss was also performed in order to estimate the optical band gap. The obtained band gaps of the present work reveal that polymer hybrids with sufficient film-forming capability could be useful to overcome the drawbacks associated with conjugated polymers. Based on the XRD results and band gap values, the structure-property relationships were discussed in detail. © 2019 by the authors. Licensee MDPI, Basel, Switzerland

Identification of Allium cepa compounds as Promising Inhibitors against Lung Cancer: An in-Silico Study

Background: Lung cancer is one of the primary causes of cancer-related deaths, and treatment options for advanced-stage disease remain restricted. Overexpression of the epidermal growth factor receptor (EGFR) has been linked to the development of certain cancers. Double-mutated EGFR is an important oncogenic protein in many lung cancer instances. Allium cepa, a common condiment herb, is known for its medical and pharmacological benefits.Methods: The bioactive compound of A. cepa was obtained from the LOTUS database in ‘sdf’ format, and then converted into ‘pdbqt’ format. The prepared compounds library was screened against the double-mutated EGFR using the insilico tool PyRx 0.8 to determine the binding conformations with the lowest binding energies.Result: Eighteen compounds were found to strongly bind with the EGFR protein and have lower binding energy than the cocrystal ligand, with the top five hits being LTS0258243, LTS0042303, LTS0058192, LTS0104946, and LTS0145270. The Asn842, Asp855, Lys745, Met790, Gln791, Leu792, Met793, Ala743, Leu844, Leu718, Val726, Thr854, and Phe723 residues of EGFR were important in binding to these hit compounds. In addition, these compounds have good drug-like properties.Conclusion: The compounds LTS0258243, LTS0042303, LTS0058192, LTS0104946, and LTS0145270 can be used as EGFR inhibitors to manage lung cancer. However, additional experimental studies are required to validate these compounds as EGFR inhibitors.Keywords:  Lung cancer, EGFR, Allium cepa, bioactive compounds, virtual screening

Minimizing makespan of multimachine production system in flow shop environment by means of mixed integer programming model

To face the challenges of industrial globalization and sustain in the competitive market, the manufacturers have to gratify the customer demand by launching the products on time having variable design and volume at low price. In this regards, the necessity of adopting the epitome of flexible mass production flow shop structure along with the appropriate production planning tools and techniques like scheduling knows no bound. As a consequence, numerous approaches have already proposed for scheduling the production flow shop. However, before the adoption of any of these conventional approaches it is an utmost need for the manufacturer to realize its consequences and the appropriateness. Therefore, in this endeavour, we anticipated mixed integer linear-programming model for machine scheduling in flow shop environment based on multi-machine and multi-product scenario. Real data from industry has been collected by conducting several site visits at a local production system. The model then was analysed using What’s Best Excel Solver. The result shown by adopting the appropriate sequence, it is possible to achieve the minimum completion time compared to other possible sequence combination of products. By minimizing the makespan, the idle times of some of the machine will be reduced meanwhile the utilization of the machine will be maximized consequently

An Examination of the Effects of the America Reads Tutoring Program and Tutor Training on the Attitude and Academic Achievement of Urban At-Risk Minority Students

The American educational system is struggling to identify methods of preventing early reading failure. Many schools are implementing tutoring intervention programs to supplement classroom instruction and to help meet the needs of struggling at-risk readers. Although there is substantial research on tutoring programs that employ professional teachers, there is a dearth of research on the effectiveness of non-professional volunteer tutoring programs. The purpose of this study was to investigate the effectiveness of the America Reads tutoring program and tutor training on the reading achievement and reading attitude of urban, at-risk, K–3 minority students. The population sample was drawn from four inner-city urban schools of similar racial composition and academic achievement level. Two schools received America Reads tutoring services and two schools served as comparison schools. Numerous standardized tests in place in the school system were used to gauge reading achievement and The Elementary Reading Attitude Survey was used to measure reading attitude. Six research questions were addressed: (1) Is there a significant difference in reading achievement between students who received America Reads tutoring and a comparison group of similar students who did not receive America Reads tutoring? (2) Is there a difference between the reading scores of students who were taught by moderately-trained tutors and those who were taught by minimally-trained tutors? (3) Is there a change over the course of an academic year in the America Reads tutee\u27s attitude in contrast to a comparison group? (4) Is there a relationship between the student\u27s reading attitude and reading achievement? (5) Is there a difference in female and male students attitudes toward reading after participating in a tutoring intervention program? (6) Is there a difference in the strategies that moderately-trained and minimally-trained tutors implement in their tutoring sessions? One-way between groups analysis of covariance, multivariate analysis of covariance, and Pearson Product Moment correlations were employed. Results indicated that: (1) the tutored group achieved significantly higher mean scores on five of the ten reading achievement tests; (2) only a significant negative correlation in grade three was found between reading attitude and reading achievement; (3) there were no significant changes in participants reading attitudes; (4) there were no significant differences in female and male attitudes toward reading; (5) there were some differences in strategies that moderately-trained tutors implemented in their tutoring sessions compared to minimally-trained tutors; (6) that reading tutoring intervention programs that employ non-professional tutors can have a significant impact upon tutee reading achievement

Metabolic profiling on 2D NMR TOCSY spectra using machine learning

Due to the dynamicity of biological cells, the role of metabolic profiling in discovering biological fingerprints of diseases, and their evolution, as well as the cellular pathway of different biological or chemical stimuli is most significant. Two-dimensional nuclear magnetic resonance (2D NMR) is one of the fundamental and strong analytical instruments for metabolic profiling. Though, total correlation spectroscopy (2D NMR 1H -1H TOCSY) can be used to improve spectral overlap of 1D NMR, strong peak shift, signal overlap, spectral crowding and matrix effects in complex biological mixtures are extremely challenging in 2D NMR analysis. In this work, we introduce an automated metabolic deconvolution and assignment based on the deconvolution of 2D TOCSY of real breast cancer tissue, in addition to different differentiation pathways of adipose tissue-derived human Mesenchymal Stem cells. A major alternative to the common approaches in NMR based machine learning where images of the spectra are used as an input, our metabolic assignment is based only on the vertical and horizontal frequencies of metabolites in the 1H-1H TOCSY. One- and multi-class Kernel null foley–Sammon transform, support vector machines, polynomial classifier kernel density estimation, and support vector data description classifiers were tested in semi-supervised learning and novelty detection settings. The classifiers’ performance was evaluated by comparing the conventional human-based methodology and automatic assignments under different initial training sizes settings. The results of our novel metabolic profiling methods demonstrate its suitability, robustness, and speed in automated nontargeted NMR metabolic analysis

Tool life modeling in high speed turning of AISI 4340 hardened steel with mixed ceramic tools by using face central cubic design

Tool life estimation for the cutting tool before the machining process is important due to economic and quality consideration. Thus, developing a model that can predict the tool life with high accuracy is an important issue. This paper deals with developing a new model of tool life for mixed ceramic tools in turning hardened steel AISI 4340 based on experimental tests. The experiments were planned and implemented using Central Composites Design (CCD) of Response Surface Methodology (RSM) with three input factors: cutting speed, feed rate and negative rake angle. The Face Central Cubic Design has been used as a special case of CCD. The analysis of variance (ANOVA) has been conducted to analyze the influence of process parameters and their interaction during machining. The first and second order models have been developed. It was found that the second order model provide higher accuracy prediction than the first order model. It was observed that the cutting speed is the most significant factor that influences the tool life for the two models, followed by the feed rate then the negative rake angle. The predicted values are confirmed by using validation experiments. Copyright © 2013 Praise Worthy Prize S.r.l. - All rights reserved

Flexural behavior of open-cell aluminum foam sandwich under three-point bending

Aluminum foam sandwich (AFS) panels are one of an advanced material that has various advantages such as lightweight, excellent stiffness to weight ratio and high-energy absorption. Due to their advantages, many researchers’ shows an interest in aluminum foam material for expanding the use of foam structure. However, there is still a gap need to be filling in order to develop reliable data on mechanical behavior of AFS with different parameters and analysis method approach. There are two types of aluminum foam that is open-cell and closed-cell foam. Few researchers were focusing on open-cell aluminum foam. Moreover, open-cell metal foam had some advantages compared to closed-cell due to the cost and weight matters. Thus, this research is focusing on aluminum foam sandwich using open-cell aluminum foam core with grade 6101 attached to aluminum sheets skin tested under three point bending. The effect Skin to core ratio investigated on AFS specimens analyzed by constructing load-displacement curves and observing the failure modes of AFS. Design of experiment of three levels skin sheet thickness (0.2mm, 0.4mm, and 0.6mm) and two levels core thickness (3.2mm and 6.35mm). a full factorial of six runs were performed with three time repetition. The results show that when skin to core ratio increase, force that AFS panels can withstand also increase with increasing core thickness

Effect of microwave sintering treatment to the flank wear of titanium carbide tools in milling operations

The paper reports the research on the improvement of tool wear resistant of Titanium Carbide (TiC) cutting tool after microwave post sintering treatment. Titanium Carbide square milling insert was microwave sintered at 600°C with 15 minutes of holding time. The face milling operations were conducted to Carbon Steel S45C block (130 mm x 95 mm x 40 mm) by using both of original and microwave sintered insert at 5 different cutting speed (60, 90 , 120 , 150 and 180 m/min), constant feed rate (0.2 mm/tooth) and constant depth of cut(0.2 mm/tooth). The flank wear of the insert was measured every nearest 10th minute of complete cutting passes. The results of the experiment show that microwave post sintering treatment improves the tool resistant of the TiC insert. The flank wear of the sintered insert is lower at any machining time and all cutting speed. The research also found that the percentage of the improvement is lower at higher cutting speed compare to lower cutting speed