Kinetic and prediction studies of ascorbic acid degradation in normal and concentrate local lemon juice during storage

Kinetics of the loss of ascorbic acid (AA) in local lemon juice of 9° and 50° Brix stored at 25, 35, and 45 °C for 4 months have been investigated. The results indicate that increases of concentrations and temperatures increase the rate of AA degradation. The calculated values of activation energies (E^≠) and frequency factors (A) at different Brix imply that the concentration of juice does not change the mechanism of degradation. Thermodynamic functions of activation (ΔG^≠, ΔH^≠, ΔS^≠ and K^≠) have been determined and considered briefly. A direct equation for estimation of the shelf life of stored juice with respect to first-order losses of AA, at any specific temperature and degradation ratio, has been derived and programmed successfully.     

Properties of TiN for Detector and Amplifier Applications

We have experimentally explored and carefully characterized the important properties of TiN, including the resistivity, nonlinear kinetic inductance, the anomalous electro-dynamical response, and the two-level-system induced frequency shift and noise. We suggest that some of these properties, which are not well understood and are different from conventional superconductors, need further study and special consideration in kinetic inductance detector and parametric amplifier applications.     

Measuring the instability of AC arc discharges

A digital approach using a microprocessor has been developed to give accurate values for AC arc discharge instabilities. This case study is based on theoretically interpreting the instability of arcs in terms of an instability factor related to the deviation of instantaneous values of arc voltages from a predetermined average value. A simple AC atmospheric pressure low-power arc discharge simulation circuit is used and the microprocessor-based measurements applied were found to give accurate indications of instability factors at different arc gaps. This article describes the hardware and software aspects of the system, accounts for the results and provides final critical remarks.     

A Millimeter and Submillimeter Kinetic Inductance Detector Camera

We present results from a demonstration camera using Microwave Kinetic Inductance Detectors (MKIDs) (Day et al. in Nature 425, 817–821, [2003]) at the Caltech Submillimeter Observatory. The focal plane consists of 16 two-color (240 and 350 GHz) pixels. Each pixel is a phased-array of slot dipole antenna whose output power is coupled to MKIDs via in-line color-defining bandpass filters. A prototype software-defined radio system was used to read out up to four MKIDs simultaneously. We obtained maps of Jupiter, Saturn, and G34.3 and demonstrated sensitivities of approximately 1 Jy s^1/2 and 10 Jy s^1/2 in the two bands, respectively, limited by detector noise due to a low-efficiency optical train. We anticipate that a second engineering run in 2008 with a 36-element, 4-color array and an optimized optical train will be background limited at 240, 270, 350, and 400 GHz. We are undertaking the construction of a full-size MKID camera with 576 four-color spatial pixels and using 2304 MKIDs readout by an expanded software-defined radio system.      

Artificial Intelligence Based Sentence Level Sentiment Analysis of COVID-19

Web-blogging sites such as Twitter and Facebook are heavily influenced by emotions, sentiments, and data in the modern era. Twitter, a widely used microblogging site where individuals share their thoughts in the form of tweets, has become a major source for sentiment analysis. In recent years, there has been a significant increase in demand for sentiment analysis to identify and classify opinions or expressions in text or tweets. Opinions or expressions of people about a particular topic, situation, person, or product can be identified from sentences and divided into three categories: positive for good, negative for bad, and neutral for mixed or confusing opinions. The process of analyzing changes in sentiment and the combination of these categories is known as “sentiment analysis.” In this study, sentiment analysis was performed on a dataset of 90,000 tweets using both deep learning and machine learning methods. The deep learning-based model long-short-term memory (LSTM) performed better than machine learning approaches. Long short-term memory achieved 87% accuracy, and the support vector machine (SVM) classifier achieved slightly worse results than LSTM at 86%. The study also tested binary classes of positive and negative, where LSTM and SVM both achieved 90% accuracy.