Multi-Response Optimization of Burnishing Variables for Minimizing Environmental Impacts

The purpose of this investigation is to optimize minimum quantity lubrication (MQL) variables, including the nozzle diameter (D), inclined angle (A), air pressure (P), oil quantity (F), and spraying distance (S) for decreasing the energy consumption in the burnishing time (EB) and particulate matter index (PI) of the interior burnishing process. The optimal adaptive neuro-based-fuzzy inference system (ANFIS) models of the performance measures were proposed in terms of the MQL variables with the aid of the Taguchi method. The non-dominated sorting genetic algorithm based on the grid partitioning (NSGA-G) and TOPSI were employed to produce feasible solutions and determine the best optimal point. The obtained results indicated that the optimal values of the D, A, P, F, and S are 1.0 mm, 35 deg., 3 Bar, 70 ml/h, and 10 mm, respectively, while the EB and PI are decreased by 8.0% and 15.7% at the optimal solution. The optimal ANFIS models were trustworthy and ensure accurate predictions. The optimization technique comprising the ANFIS, NSGA-G, and TOPSIS could be extensively utilized to determine the optimal outcomes instead of the trial-error and/or human experience. The outcomes could help to decrease environmental impacts in the practical burnishing process

Some coincidence point results for T-contraction mappings on partially ordered b-metric spaces and applications to integral equations

In this paper, we prove some fixed point results for T-contraction mappings in partially ordered b-metric spaces, that generalize the main results of [H. Huang, S. Radenovič, J. Vujakovič, On some recent coincidence and immediate consequences in partially ordered b-metric spaces, Fixed Point Theory Appl., 2015, Paper No. 63]. As an application, we discuss the existence for a solution of a nonlinear integral equation

A Study on viral etiologies of lower respiratory infections and molecular characterization of influenza virus H1N1 2009 circulating in central Vietnam

Respiratory infections are the most important cause of worldwide burden of disease in children and a major cause of mortality. In this study, viral etiologies of lower respiratory infections from children hospitalized in central Vietnam were detected by polymerase amplification assays. Total viral etiology was 46% and relative frequencies were 45% for influenza A virus, 42% for respiratory syncytial virus, 12% for adenoviruses, 8% for influenza B virus, and 5% both for parainfluenza type 1 and 3 virus. Influenza A (H1N1) 2009 virus was mainly responsible for infections in children from 3 months to 5 years of age. Detection of influenza A (H1N1) 2009 virus was performed both by isolation on MDCK cell culture and embryonated eggs and by amplification assays. In a group of 53 suspected patients A (H1N1) 2009 virus was detected in 32(60.4%) with a combination of both methods, and virus was isolated by cell culture in 24 patients. Characterization of HA and NA genes from representative isolates of influenza A (H1N1) 2009 virus was performed. Similarity of HA gene among Hue representative isolates are from 99.48% to 99.77% and similarity of NA sequences are from 99.57% to 99.86%. The sequences of HA and NA of Hue representative isolates were compared with 18 reference isolates from different countries showing similarity from 99.07% to 99.77% for HA and from 98.65% to 99.36% for NA sequences. Phylogenetic trees based on HA and NA sequences were constructed

Double RIS-Assisted MIMO Systems Over Spatially Correlated Rician Fading Channels and Finite Scatterers

This paper investigates double RIS-assisted MIMO communication systems over Rician fading channels with finite scatterers, spatial correlation, and the existence of a double-scattering link between the transceiver. First, the statistical information is driven in closed form for the aggregated channels, unveiling various influences of the system and environment on the average channel power gains. Next, we study two active and passive beamforming designs corresponding to two objectives. The first problem maximizes channel capacity by jointly optimizing the active precoding and combining matrices at the transceivers and passive beamforming at the double RISs subject to the transmitting power constraint. In order to tackle the inherently non-convex issue, we propose an efficient alternating optimization algorithm (AO) based on the alternating direction method of multipliers (ADMM). The second problem enhances communication reliability by jointly training the encoder and decoder at the transceivers and the phase shifters at the RISs. Each neural network representing a system entity in an end-to-end learning framework is proposed to minimize the symbol error rate of the detected symbols by controlling the transceiver and the RISs phase shifts. Numerical results verify our analysis and demonstrate the superior improvements of phase shift designs to boost system performance.Comment: 15 pages, 9 figures, accepted by IEEE Transactions on Communication

Current medical product development for diagnosis, surgical planning and treatment in the areas of Neurosurgery, Orthopeadic and Dental-Cranio-Maxillofacial surgery in Vietnam

With the population of 86 million and good GDP growth in recent decades, the medical market in Vietnam is growing fast. However, most of the medical technology products are imported, and the number of locally manufactured ones is limited and they do not have the high competition capability in term of quality, quantity and types. In this paper, the current product development in Vietnam for diagnosis, surgical planning and treatment in the areas of Rehabilitation, Neurosurgery, Orthopeadic and Dental-Cranio-Maxillofacial surgery is presented. A roadmap for medical technology development in Vietnam is propose

Optimization of Rough Self-Propelled Rotary Turning Parameters in terms of Total Energy Consumption and Surface Roughness

The self-propelled rotary tool turning (SPRT) process is an economic and effective solution for machining difficult-to-cut materials. This work optimized SPRT parameters, including the inclination angle (A), depth of cut (D), feed rate (f), and turning speed (V) to decrease the total energy consumption (TE) and surface roughness (SR). The turning experiments of the hardened AISI 4150 steel were executed to obtain the experimental data, while the regression method was applied to develop the TE and SR correlations. The entropy method and quantum-behaved particle swarm optimization (QPSO) were utilized to select the weights and optimal factors. The results indicated that the optimal A, D, f, and V were 34 deg., 0.40 mm, 0.47 mm/rev., and 177 m/min, respectively, while the TE and SR were saved by 9.7% and 35.4%, respectively. The f and V were found to be the most effective parameters, followed by the D and A. The outcomes provide valuable data to determine optimal SPRT factors for minimizing energy consumption and maximizing machining quality.The optimizing technique could be applied to solve other issues for different SPRT operations