Tunable structural and electrical impedance properties of ordered and disordered iron oxide phases for capacitive applications

Having extensive knowledge of room temperature and temperature dependent dielectric and impedance properties of iron oxide nanostructures will help in extending the field of application from biomedical sciences to microelectronics industry. This aspect of iron oxide has long been neglected and the attention is mostly focused on magnetic investigations. To explore and extend the field of application of iron oxide this study is focused on detailed investigation of structural as well as temperature dependent (30–210?°C) dielectric along with impedance analysis. Iron oxide nanostructures are prepared using template free oleic acid assisted sol-gel method with variation in molarity of the finally synthesized sol in the range of 0.2–2.0?mM (interval 0.2?mM). Magnetite (Fe₃O₄) phase is observed at molarity of 0.2?mM whereas, vacancy ordered and disordered maghemite (γ-Fe₂O₃) phases are observed at molarities of 0.8–1.0?mM and 1.4–2.0?mM, respectively. Dielectric constant of 104.6, 74.5 and 98.43 (log f = 5.0) is observed at molarities of 0.2?mM, 1.0?mM and 2.0?mM for Fe₃O₄ and vacancy ordered (V_o) & disordered (V_d) γ-Fe₂O₃ phase, respectively. Zview software is used for the fitting of Nyquist plots. Fitted data reveals that dielectric constant strongly depends on grain boundary resistance (R_gb). Activation energy of 0.25?eV and 0.296?eV (log f = 5) is observed for Fe₃O₄ and V_d γ-Fe₂O₃ phase at 0.2?mM and 2.0?mM molarity of the final iron oxide sol.     

A new auxiliary information based cumulative sum median control chart for location monitoring

Control charts are commonly used tools in statistical process control for the detection of shifts in process parameters. Shewhart-type charts are efficient for large shift values, whereas cumulative sum (CUSUM) charts are effective in detecting medium and small shifts. Control chart use commonly assumes that data are free of outliers and parameters are known or correctly estimated based on an in-control process. In practice, these assumptions are not often true because some processes occasionally have outliers. Monitoring the location parameter is usually based on mean charts, which are seriously affected by violations of these assumptions. In this paper we propose several CUSUM median control charts based on auxiliary variables, and offer comparisons with their corresponding mean control charts. To monitor the location parameter, we examined the performance of mean and median control charts in the presence and absence of outliers. Both symmetric and non-symmetric processes were studied to examine the properties of the proposed control charts to monitor the location parameter using CUSUM control charts. We used different run length measures to study in-control and out-of-control performances of CUSUM charts. Results revealed that our proposed control charts perform much better than the traditional charts in the presence of outliers. A real application of our study was provided using data on concrete compressive strength as it relates to the quality of cement manufacturing.

     

On improved monitoring of linear profiles under modified successive sampling

Control charts are often used for the monitoring of quality characteristics of interest. There may exist some other characteristics that are associated with the main quality characteristic. A model that quantifies the relationship between them is termed as a profile, for instance, the relation between charge and capacitance. The monitoring of the main variable that is linearly related with an associated explanatory variable is termed as simple linear profiles' monitoring. It is a common practice to use simple random sampling (SRS) for profile monitoring. This study intends to enhance profile monitoring by considering modified successive sampling (MSS) approach. The performance of the existing and the proposed schemes are evaluated using the well‐known metrics average run length. The comparative analysis revealed that the proposed structure outperforms the existing ones in terms of efficiency. A real application from electrical engineering is used to show the implementation of our proposal in practice.     

Mixed convection flow over a stretching sheet of variable thickness: Analytical and numerical solutions of self‐similar equations

In this study, a mixed convection flow over a nonlinearly stretching sheet of variable thickness is examined. Governing equations are modeled and transformed into dimensionless forms by utilizing dimensionless variables. For further investigation, dimensionless, coupled nonlinear differential equations with suitable boundary conditions are numerically solved using the Matlab built‐in function bvp5c tool, and analytical solutions are also computed using the homotopy analysis method. A comparative study is carried out to check the efficiency and accurateness of the proposed solution methodologies. Convergence of the derived series solutions is carefully checked. The impact of wall thickness parameter, velocity index parameter, Prandtl number, and mixed convection parameter on nondimensional velocity, temperature, skin friction coefficient, and local Nusselt number is examined. The novelty of this examination is that the dimensionless equations are self‐similar in the presence of mixed convection. These self‐similar equations are acquired by establishing a relationship between velocity and temperature power index parameters, and similarity solutions exist only for a particular form of variable surface temperature.     

An adaptive approach to EWMA dispersion chart using Huber and Tukey functions

Random causes are vital part of every process in manufacturing and nonmanufacturing environments, and these do not affect the product features. Special causes, on the other hand, come because of some burden(s) in a process and requires special attention; otherwise, it ruins the products excellence. Special causes are categorized into small, moderate, and large shifts and are handled by statistical quality control charts. The Shewhart control chart is well known for large shifts, while the cumulative sum and exponentially weighted moving average are more effective in detecting small to moderate shifts. However, in practice, many processes require the simultaneous monitoring of both the small to the large shifts. In this study, we have designed an adaptive EWMA for dispersion parameter in connection with Huber and Tukey's bisquare functions. The performance measures used in this study include average run length, extra quadratic loss, relative average run length, and performance‐comparison index. We have observed that the study proposals are good competitors to the other counter parts for an efficient monitoring of shifts of varying amounts. An illustrative example using real data is given to demonstrate the implementation of the study proposal.     

Novel derivatives of 3D woven T-shaped composites with improved performance

Conventional 3D woven T-shaped preforms were modified for improving joint/ peel off strength of associated T-shaped polymeric composites. Preforms were modified at weaving level by preferred yarns orientations for better performance in associated composites. Major modifications studied are; the addition of supporting layer, single or double-crossing in joint layers and crossing along with supporting layer (in a single sample). Novel derivatives were compared with conventionally used (on-loom and off-loom) T-shaped 3D woven composites. Microscopic analysis was also carried out to analyze the orientation of yarns and analysis of samples from failure point after testing. By combined variation of crossing and supporting layers improvement of 47.37% in peel off strength and 70.37% in impact strength was observed. Modified T shapes can serve to be a good replacement for conventional T-shaped stiffener (used to avoid folding under loads) and in joints.     

Vertical Handover Necessity Estimation Based on a New Dwell Time Prediction Model for Minimizing Unnecessary Handovers to a WLAN Cell

In this work, we devise a vertical handover necessity estimation (HNE) method to minimize unnecessary handovers for a mobile node (MN) entering a WLAN cell. The method relies on a new model for prediction of dwell time and computation of certain threshold values. By comparing the predicted dwell time with those thresholds, a MN is able to make decision whether it should perform handover to a WLAN cell, while keeping the probability of handover failure and probability of unnecessary handover within bounds. Simulation results obtained from Monte-Carlo experiments prove validity of the proposed model. We also compare this model with existing models for minimizing unnecessary handovers. We further enhance the analytical model by incorporating the throughput gain in HNE and show that this can further optimize handover decision in heterogenous networks.     

Effect of elementary and advanced glycation products of nisin on its preservative efficacy and digestibility

Nisin used as a natural preservative in variety of food systems was subjected to glycation at 95 °C in high molar concentration. The elementary glycation end products (EGEs) and the advanced glycated end products (AGEs) formed were tested as compared to non-glycation nisin (NN) for their antibacterial activity against some food spoilage organisms such as Staphylococcus aureus, Bacillus cereus, Micrococcus luteus, Clostridium botulinum and Lactobacillus sake. The extent of hydrolysis of EGEs and AGEs by trypsin was also compared with the (NN) as control. The results have illustrated that antibacterial activity of nisin was substantially affected by the process of glycation in case of all the five organisms selected for the study, suggesting that the glycation of nisin, whether invivo or invitro will adversely affect the preservation of foods. Moreover glycation also reduced the tryptic digestibility of EGEs by 5.2% and AGEs by 50.325% as compared to NN. The data indicated that amount of nisin, if added to carbohydrate consisting foods should be increased accordingly to claim the reported shelf life of the products and food labelling in such products needs cardinal modification. Although hydrolysis of glycated nisin by trypsin is slightly decreased, however; it will not produce any adverse effect on human digestion in view of the minute amount of nisin present in the food system.     

Microwave-assisted pre-ionization experiments on GLAST-Ⅲ

GLAss Spherical Tokamak(GLAST-Ⅲ) is a spherical tokamak with an insulating vacuum vessel that has a unique single-passage capability for incident microwaves.In this work,electron cyclotron resonance heating(ECRH)-assisted plasma pre-ionization in GLAST-Ⅲ is explored for three radio-frequency(RF) polarizations(the O-,X-,and M-modes) at different toroidal-field(TF) strengths and filled gas pressures.The optimum hydrogen pressure is identified for efficient plasma pre-ionization.A comparison of the plasma pre-ionizations initiated by the O-,X-,and M-modes shows prominent differences in the breakdown time,location,and wave absorption.In the case of O-mode polarization,microwave absorption occurs for a relatively shorter duration,resulting in a bell-shaped electron-temperature(T_e) temporal profile.Microwave absorption is dominant in the case of the X-mode,leading to a broader T_e temporal profile.The M-mode discharge contains features of both the X-and O-modes.Efficient plasma pre-ionization is achieved in the X-mode polarization for the intermediate TF strengths(with a central toroidal magnetic field B_0=0.075 T).Traces of the electron-number density show a similar tendency,as revealed by T_e.These results suggest that the X-mode is the best candidate for efficient plasma pre-ionization at low filled gas pressures(~10~(-2) Pa) in small tokamaks.     

Optimizing Steering Angle Predictive Convolutional Neural Network for Autonomous Car

Deep learning techniques, particularly convolutional neural networks (CNNs), have exhibited remarkable performance in solving vision-related problems, especially in unpredictable, dynamic, and challenging environments. In autonomous vehicles, imitation-learning-based steering angle prediction is viable due to the visual imagery comprehension of CNNs. In this regard, globally, researchers are currently focusing on the architectural design and optimization of the hyperparameters of CNNs to achieve the best results. Literature has proven the superiority of metaheuristic algorithms over the manual-tuning of CNNs. However, to the best of our knowledge, these techniques are yet to be applied to address the problem of imitation-learning-based steering angle prediction. Thus, in this study, we examine the application of the bat algorithm and particle swarm optimization algorithm for the optimization of the CNN model and its hyperparameters, which are employed to solve the steering angle prediction problem. To validate the performance of each hyperparameters’ set and architectural parameters’ set, we utilized the Udacity steering angle dataset and obtained the best results at the following hyperparameter set: optimizer, Adagrad; learning rate, 0.0052; and nonlinear activation function, exponential linear unit. As per our findings, we determined that the deep learning models show better results but require more training epochs and time as compared to shallower ones. Results show the superiority of our approach in optimizing CNNs through metaheuristic algorithms as compared with the manual-tuning approach. Infield testing was also performed using the model trained with the optimal architecture, which we developed using our approach.