Active gap reduction in comb drive of three axes capacitive micro accelerometer for enhancing sense capacitance and sensitivity

The performance of micro-machined sensors is primarily determined via the sensitivity of sensing electrode to displacement. This paper presents the design, modelling, optimization and fabrication of an active gap reduction mechanism used on a conventional comb drive to enhance the capacitance in a three axes capacitive micro accelerometer. The design parameters of the active gap reduced comb drive (AGRCD) are optimized for best performance. The finite element analysis of the AGRCD is performed for design verification. The modeling and simulation results demonstrated a 534 % increase in sensitivity of the three axes capacitive micro accelerometer. The three axes capacitive micro accelerometer with AGRCD is fabricated using a commercially available standard metal-multi user MEMS processes.     

Sentiment analysis of tweets using a unified convolutional neural network‐long short‐term memory network model

Sentiment analysis focuses on identifying and classifying the sentiments expressed in text messages and reviews. Social networks like Twitter, Facebook, and Instagram generate heaps of data filled with sentiments, and the analysis of such data is very fruitful when trying to improve the quality of both products and services alike. Classic machine learning techniques have a limited capability to efficiently analyze such large amounts of data and produce precise results; they are thus supported by deep learning models to achieve higher accuracy. This study proposes a combination of convolutional neural network and long short‐term memory (CNN‐LSTM) deep network for performing sentiment analysis on Twitter datasets. The performance of the proposed model is analyzed with machine learning classifiers, including the support vector classifier, random forest (RF), stochastic gradient descent (SGD), logistic regression, a voting classifier (VC) of RF and SGD, and state‐of‐the‐art classifier models. Furthermore, two feature extraction methods (term frequency‐inverse document frequency and word2vec) are also investigated to determine their impact on prediction accuracy. Three datasets (US airline sentiments, women's e‐commerce clothing reviews, and hate speech) are utilized to evaluate the performance of the proposed model. Experiment results demonstrate that the CNN‐LSTM achieves higher accuracy than those of other classifiers.     

Spatial interpolation in wireless sensor networks: localized algorithms for variogram modeling and Kriging

Wireless sensor networks (WSNs) are rapidly emerging as the prominent technology for monitoring physical phenomena. However, large scale WSNs are known to suffer from coverage holes, i.e., large regions of deployment area where no sensing coverage can be provided. Such holes are the result of hardware failures, extensive costs for redeployment or the hostility of deployment areas. Coverage holes can adversely affect the accurate representation of natural phenomena that are monitored by a WSN. In this work, we propose to exploit the spatial correlation of physical phenomena to make monitoring systems more resilient to coverage holes. We show that a phenomenon can be interpolated inside a coverage hole with a high level of accuracy from the available nodal data given a model of its spatial correlation. However, due to energy limitations of sensor nodes it is imperative to perform this interpolation in an energy efficient manner that minimizes communication among nodes. In this paper, we present highly energy efficient methods for spatial interpolation in WSNs. First, we build a correlation model of the phenomenon being monitored in a distributed manner. Then, a purely localized and distributed spatial interpolation scheme based on Kriging interpolates the phenomenon inside coverage holes. We test the cost and accuracy of our scheme with extensive simulations and show that it is significantly more energy efficient than global interpolations and remarkably more accurate than simple averaging.     

The kinetics of epoxidation of trimethylolpropane ester

Kinetics pertaining epoxidation reaction of a palm oil‐based synthetic lubricant trimethylolpropane (TMP) ester were investigated. The epoxidation reaction of TMP ester was carried out utilizing peracetic acid generated by an in situ technique. The analysis of the reaction kinetics was performed within the low temperature (30, 50, and 60°C) and high temperature (70, 80, and 90°C) regions, owing to the nature of the reactions. The maximum conversion of the unsaturated carbon to oxirane ring was achieved in 1 h at high temperature region, while epoxidation of TMP esters took more than 4 h to reach the maximum conversion at the low temperature region. From the experimental data, the kinetics of epoxidation of TMP esters fitted well with both the second‐order and pseudo first‐order models. The rate constants for pseudo first‐order model increased from 0.0009 to 0.0055 by increasing temperature at the low temperature region, and from 0.0129 to 0.0209 within the high temperature region. The values of activation energies at low temperature and high temperature regions were found to be 69.4 and 53.3 kJ/mol, respectively.     

Proximate Composition and Antioxidant Potential of Leaves from Three Varieties of Mulberry (Morus sp.): A Comparative Study

In this study, leaves of three indigenous varieties of Mulberry namely, Morus alba L., Morus nigra L. and Morus rubra L. were investigated for their antioxidant potential and their proximate composition was determined. The yields of 80% methanolic extracts ranged between 8.28–13.89%. The contents of total phenolics (TPC), total flavonoids (TFC) and ascorbic acid (AA) ranged between 16.21–24.37 mg gallic acid equivalent (GAE)/g, 26.41–31.28 mg rutin equivalent (RE)/g and 0.97–1.49 mg/g, respectively. The antioxidant activity of leaf extracts was evaluated by measuring 1,1-diphenyl-2-picrylhydrazyl (DPPH^•) radical scavenging actity, 2,2′-azino-bis-(3-ethylbenzthiazoline-6-sulphonic acid (ABTS^•+) radical cation scavenging capacity and ferric ion reducing power and values ranged between 1.89–2.12, 6.12–9.89 and 0.56–0.97 mM Trolox equivalent/g of dried leaves, respectively. The investigated features reveal good nutritive and antioxidant attributes of all the varieties with mutually significant differences.     

IoT-Enabled Smart Socket

Wireless Personal Communications - Conserving energy efficiently is becoming a challenging problem around the globe, specifically in developing countries. One main reason is the lack of awareness...     

Predicting numeric ratings for Google apps using text features and ensemble learning

Application (app) ratings are feedback provided voluntarily by users and serve as important evaluation criteria for apps. However, these ratings can often be biased owing to insufficient or missing votes. Additionally, significant differences have been observed between numeric ratings and user reviews. This study aims to predict the numeric ratings of Google apps using machine learning classifiers. It exploits numeric app ratings provided by users as training data and returns authentic mobile app ratings by analyzing user reviews. An ensemble learning model is proposed for this purpose that considers term frequency/inverse document frequency (TF/IDF) features. Three TF/IDF features, including unigrams, bigrams, and trigrams, were used. The dataset was scraped from the Google Play store, extracting data from 14 different app categories. Biased and unbiased user ratings were discriminated using TextBlob analysis to formulate the ground truth, from which the classifier prediction accuracy was then evaluated. The results demonstrate the high potential for machine learning-based classifiers to predict authentic numeric ratings based on actual user reviews.     

Binding ability of Allura Red with food proteins and its impact on protein digestibility

Allura Red-40 is a safe colour additive (permissible by the FDA and Health Canada) that is used in a variety of foods to make them more attractive and appealing for consumers. However, limited information is available about its binding to macronutrients that are responsible for its uniform distribution in food products. In the present study, the binding capacity of Allura with food proteins is compared with Coomassie Brillant Blue R 250, which is an established staining agent for visualizing electrophoretically resolved proteins. The data illustrate that Allura is a fast reacting dye and binds with a variety of food proteins including peanut, rice bran, garlic and mixture of proteins [(Takadiastase, nisin, a microbial protein and bovine serum albumin (BSA)]. The Allura bound proteins retained their colour at high and low temperatures and in a wide range of pH. The experiments on the resolution of proteins and staining with Allura have shown that the dye is highly sensitive, rapid, lasting and is easily linked with a variety of proteins. The binding of Allura to various proteins had almost no adverse effect on protein digestibility, as predicted by in vitro digestibility determinations.     

Patterns as a paradigm for theory in community-based learning

Learning about information technology is typically not a first-order goal for community-based volunteer organizations. Nonetheless, information technology is vital to such groups for member recruiting and management, communication and visibility to the community, and for primary group activities. During the past 12 years, we have worked with community groups in Centre County, Pennsylvania, and Montgomery County, Virginia. We have built partnerships with these groups to better understand and address their learning challenges with respect to information technology. In this paper, we suggest that patterns, standard solution schemata for recurring problems (as used in architecture and software engineering, among other design domains), can be a paradigm for codifying and developing an understanding of learning in and by community organizations. Patterns are middle-level abstractions; they capture regularities of practices in ways that are potentially intelligible, verifiable, and perhaps useful to the practitioners themselves. We present two example patterns and discuss issues and directions for developing patterns as a theoretical foundation for community-based learning.