New Strategy for Polysulfide Protection Based on Atomic Layer Deposition of TiO2 onto Ferroelectric‐Encapsulated Cathode: Toward Ultrastable Free‐Standing Room Temperature Sodium–Sulfur Batteries

The room temperature (RT) sodium–sulfur batteries (Na–S) hold great promise for practical applications including energy storage and conversion due to high energy density, long lifespan, and low cost, as well based on the abundant reserves of both sodium metal and sulfur. Herein, freestanding (C/S/BaTiO₃)@TiO₂ (CSB@TiO₂) electrode with only ≈3 wt% of BaTiO₃ additive and ≈4 nm thickness of amorphous TiO₂ atomic layer deposition protective layer is rational designed, and first used for RT Na–S batteries. Results show that such cathode material exhibits high rate capability and excellent durability compared with pure C/S and C/S/BaTiO₃ electrodes. Notably, this CSB@TiO₂ electrode performs a discharge capacity of 524.8 and 382 mA h g^?1 after 1400 cycles at 1 A g^?1 and 3000 cycles at 2 A g^?1, respectively. Such superior electrochemical performance is mainly attributed from the “BaTiO₃‐C‐TiO₂” synergetic structure within the matrix, which enables effectively inhibiting the shuttle effect, restraining the volumetric variation and stabilizing the ionic transport interface.     

The influence of gender on cough reflex sensitivity

BACKGROUND: The more common occurrence in women of cough due to angiotensin-converting enzyme inhibitors raises the possibility of gender-related differences in the sensitivity of the cough reflex. Of two recent studies that evaluated cough response to inhaled capsaicin in normal subjects, one demonstrated heightened sensitivity of the cough reflex in women compared with men, while the other revealed no gender-related differences. To further investigate this question, we reviewed our experience with cough challenge testing in normal volunteers. STUDY OBJECTIVE: To compare cough reflex sensitivity in healthy adult female and male subjects. DESIGN: Retrospective data analysis. SETTING: Academic medical center. PARTICIPANTS: One hundred healthy volunteers (50 male, 50 female). Interventions: Subjects inhaled capsaicin in ascending, doubling concentrations until the concentration inducing five or more coughs (C5) was reached. In addition, the concentration inducing two or more coughs (C2; cough threshold) was measured. Results: Mean log C5 was significantly lower in women than in men: 1.02+/-0.09 (SEM) microM vs 1.41+/-0.08 microM, respectively (p=0.002). Log C2 (cough threshold) was also significantly lower in female subjects: 0.534+/-0.068 microM vs 0.870+/-0.065 microM in male subjects (p=0.00058). CONCLUSION: Healthy women have a more sensitive cough reflex than do healthy men. The reasons for this significant gender difference remain to be elucidated, but may involve a heightened sensitivity, in women, of the sensory receptors within the respiratory tract that mediate cough.     

Virtual plasma equipment model: a tool for investigating feedbackcontrol in plasma processing equipment

As microelectronics device feature sizes continue to shrink and wafers continue to increase in size, it is necessary to have tighter tolerances during the fabrication process to maintain high yields. Feedback control has, therefore, become an important issue in plasma processing equipment design; comprehensive plasma equipment models linked to control algorithms would greatly aid in the investigation and optimal selection of control strategies. This paper reports on a numerical plasma simulation tool, the virtual plasma equipment model (VPEM), which addresses this need to test feedback control strategies and algorithms on plasma processing equipment. The VPEM is an extension of the hybrid plasma equipment model which has been augmented by sensors and actuators, linked together through a programmable controller. The sensors emulate experimental measurements of species densities, fluxes, and energies, while the actuators change process parameters such as pressure, inductive power, capacitive power, electrode voltages, and mole fraction of gases. Controllers were designed using a response surface based methodology. Results are presented from studies in which these controllers were used to compensate for a leak of N₂ into an Ar discharge, to stably control drifts in process parameters such as pressure and power in Ar and Ar/Cl₂, and to nullify the effects of long term changes in wall conditions in Cl₂ containing plasmas. A new strategy for improving the ion energy flux uniformity in capacitively coupled discharges using feedback control techniques is also explored     

Determination of optimum operating conditions of carmine decoloration by UV/H2O2 using response surface methodology

In this study, the photolytic decoloration of carmine (C.I. Natural Red 4) via UV radiation in the presence of H2O2 was optimized using response surface methodology (RSM). According to analysis of variance (ANOVA) results, the proposed model can be used to navigate the design space. It was found that the response of carmine degradation is very sensitive to the independent factors of carmine concentration, H2O2 concentration, pH and reaction time. The proposed model for D-optimal design fitted very well with the experimental data with R2 and R(adj)2 correlation coefficients of 0.998 and 0.997, respectively.     

Corrosion protection of commercial steel using stainless steel coatings deposited by Cathodic Arc Plasma Deposition technique

The commercial steel AISI 1010 was coated with AISI 316L steel using Cathodic Arc Plasma Deposition (CAPD) technique. The coatings were deposited in vacuum and in the presence of nitrogen, acetylene and mixture of the two as reactive gases. The coatings were deposited as a function of time while other parameters remained constant. The coatings 0.75 to 1.3 μm thick were adherent and amorphous. The aqueous corrosion properties of the coated samples in 3.5 wt % NaCl solution were studied by Tafel, cyclic and potentiodynamic polarization measurements. The derived corrosion parameters were then compared among the various uncoated and coated samples. The study revealed that the coated samples were more corrosion resistant than the uncoated one. Similarly, the samples coated in the nitrogen + acetylene mixture atmosphere were more corrosion resistant than the samples coated in only nitrogen and acetylene atmospheres. The corrosion parameters were also compared as a function of coating time to ascertain best coating thickness.     

Effect of zirconium addition on the structure and properties of CuO/CeO2 catalysts for high-temperature water–gas shift in an IGCC system

A series of Ce_xZr_1−xO₂-based Cu catalysts was synthesized by the co-precipitation method. The influences of copper content, zirconium addition, and ratio of ceria to zirconia on the catalytic activity were investigated. BET, N₂O decomposition, XRD, TEM, SEM, EDS, Raman spectroscopy, H₂-TPR, TG/DTA, and XPS were used to characterize the catalysts. The catalytic activity was tested in terms of CO conversion and H₂ selectivity in H₂-rich coal-derived synthesis gas, simulating the actual gas composition of an integrated gasification combined cycle (IGCC) system. The long-term catalyst stability was also examined at 450 °C for 196 h. The addition of zirconium was found to be very important in enhancing catalytic performance. The surface area, copper dispersion, oxygen storage and mobility capacity, reducibility, as well as resistance to sintering all improved after zirconium addition.     

Transfer Learning-based Computer-aided Diagnosis System for Predicting Grades of Diabetic Retinopathy

Diabetic retinopathy (DR) diagnosis through digital fundus images requires clinical experts to recognize the presence and importance of many intricate features. This task is very difficult for ophthalmologists and time-consuming. Therefore, many computer-aided diagnosis (CAD) systems were developed to automate this screening process of DR. In this paper, a CAD-DR system is proposed based on preprocessing and a pre-train transfer learning-based convolutional neural network (PCNN) to recognize the five stages of DR through retinal fundus images. To develop this CAD-DR system, a preprocessing step is performed in a perceptual-oriented color space to enhance the DR-related lesions and then a standard pre-train PCNN model is improved to get high classification results. The architecture of the PCNN model is based on three main phases. Firstly, the training process of the proposed PCNN is accomplished by using the expected gradient length (EGL) to decrease the image labeling efforts during the training of the CNN model. Secondly, the most informative patches and images were automatically selected using a few pieces of training labeled samples. Thirdly, the PCNN method generated useful masks for prognostication and identified regions of interest. Fourthly, the DR-related lesions involved in the classification task such as micro-aneurysms, hemorrhages, and exudates were detected and then used for recognition of DR. The PCNN model is pre-trained using a high-end graphical processor unit (GPU) on the publicly available Kaggle benchmark. The obtained results demonstrate that the CAD-DR system outperforms compared to other state-of-the-art in terms of sensitivity (SE), specificity (SP), and accuracy (ACC). On the test set of 30,000 images, the CAD-DR system achieved an average SE of 93.20%, SP of 96.10%, and ACC of 98%. This result indicates that the proposed CAD-DR system is appropriate for the screening of the severity-level of DR.     

Climate change and the UN-2030 agenda: Do mitigation technologies represent a driving factor? New evidence from OECD economies

Clean Technologies and Environmental Policy - The United Nations 2030 agenda states various sustainable development goals (SDGs) to counter climate change affecting individuals' health and...     

Intelligent Machine Learning with Metaheuristics Based Sentiment Analysis and Classification

Sentiment Analysis (SA) is one of the subfields in Natural Language Processing (NLP) which focuses on identification and extraction of opinions that exist in the text provided across reviews, social media, blogs, news, and so on. SA has the ability to handle the drastically-increasing unstructured text by transforming them into structured data with the help of NLP and open source tools. The current research work designs a novel Modified Red Deer Algorithm (MRDA) Extreme Learning Machine Sparse Autoencoder (ELMSAE) model for SA and classification. The proposed MRDA-ELMSAE technique initially performs preprocessing to transform the data into a compatible format. Moreover, TF-IDF vectorizer is employed in the extraction of features while ELMSAE model is applied in the classification of sentiments. Furthermore, optimal parameter tuning is done for ELMSAE model using MRDA technique. A wide range of simulation analyses was carried out and results from comparative analysis establish the enhanced efficiency of MRDA-ELMSAE technique against other recent techniques.