Authenticity green coffee bean species and geographical origin using near-infrared spectroscopy combined with chemometrics

To prevent the adulteration of agricultural resources and provide a solution to enhance the green coffee bean supply chain, authentication using the near-infrared spectroscopy (NIRS) technique was investigated. Partial least square with discrimination analysis (PLS-DA) models combined with various preprocessing methods were built from NIR spectra of 153 Vietnamese green coffee samples. The model combined with the standard normal variate and the first order of derivative yielded excellent performance in predicting coffee species with the error cross-validation of 0.0261. PLS-DA model of mean centre and first-order derivative spectra also yielded good performance in verifying geographical indication of green coffee with the error of 0.0656. By contrast, the predicting abilities of post-harvest methods were poor. The overall results showed a high potential of the NIRS in online authentication practices.     

Synthesis and Application of Novel Nano Fe-BTC/GO Composites as Highly Efficient Photocatalysts in the Dye Degradation

Topics in Catalysis - Nano Fe-BTC/graphene oxide (GO) composites were successfully synthesized by hydrothermal treatment with a microwave-assisted method. Samples were characterized by X-ray...     

A Comprehensive Performance Analysis of Random Early Detection Mechanism

One of the most promising active queue management schemes being proposed for deployment in the Internet is the Random Early Detection (RED) scheme. However, research results on RED performance are highly mixed, especially in the field of tuning its parameters. In this paper, a comprehensive performance analysis of RED is presented. We revisit some features in RED and study them in greater detail. We point out that RED, in general, does not possess proportional loss between flows as claimed and widely adopted in previous research. We suggest the generalization of the PASTA property and give a proof for TCP flows. We also evaluate the performance of the Exponential Weighted Moving Average (EWMA) algorithm in RED. We find that EWMA in RED is an unbiased estimator of the average queue-length, regardless of the weighting value w _q . We also point out the theoretical and practical limits of EWMA in RED. Finally, we propose the use of fuzzy EWMA to RED (fuzzy RED) to alleviate the inflexibility of RED tuning. We use simulations to evaluate the performance of fuzzy RED and compare it with other versions of RED. Our simulations show that, in the case of a high workload and a high level of variation, fuzzy RED, by tracking system variation in an on-line manner, improves RED performance in a number of important router-based metrics like packet loss rate, average queueing delay, link utilization, and global power.     

Metabolic impact of adenovirus-mediated overexpression of the glucose-6-phosphatase catalytic subunit in hepatocytes

Glucose-6-phosphatase (G6Pase) catalyzes the hydrolysis of glucose 6-phosphate (Glu-6-P) to free glucose and, as the last step in gluconeogenesis and glycogenolysis in liver, is thought to play an important role in glucose homeostasis. G6Pase activity appears to be conferred by a set of proteins localized to the endoplasmic reticulum, including a glucose-6-phosphate translocase, a G6Pase phosphohydrolase or catalytic subunit, and glucose and inorganic phosphate transporters in the endoplasmic reticulum membrane. In the current study, we used a recombinant adenovirus containing the cDNA encoding the G6Pase catalytic subunit (AdCMV-G6Pase) to evaluate the metabolic impact of overexpression of the enzyme in primary hepatocytes. We found that AdCMV-G6Pase-treated liver cells contain significantly less glycogen and Glu-6-P, but unchanged UDP-glucose levels, relative to control cells. Further, the glycogen synthase activity state was closely correlated with Glu-6-P levels over a wide range of glucose concentrations in both G6Pase-overexpressing and control cells. The reduction in glycogen synthesis in AdCMV-G6Pase-treated hepatocytes is therefore not a function of decreased substrate availability but rather occurs because of the regulatory effects of Glu-6-P on glycogen synthase activity. We also found that AdCMV-G6Pase-treated-cells had significantly lower rates of lactate production and [3-3H]glucose usage, coupled with enhanced rates of gluconeogenesis and Glu-6-P hydrolysis. We conclude that overexpression of the G6Pase catalytic subunit alone is sufficient to activate flux through the G6Pase system in liver cells. Further, hepatocytes treated with AdCMV-G6Pase exhibit a metabolic profile resembling that of liver cells from patients or animals with non-insulin-dependent diabetes mellitus, suggesting that dysregulation of the catalytic subunit of G6Pase could contribute to the etiology of the disease.     

Deformation behavior of styrene-block-butadiene-block-styrene triblock copolymers having different morphologies

Deformation behavior of styrene-block-butadiene-block-styrene (SBS) triblock copolymers having different morphologies was investigated. Due to the combination of different methods which provide information on different deformation levels (macroscopic, microscopic and molecular) complex deformation mechanisms for each type of SBS block copolymer (including glassy-rubber alternating lamellae, rubber cylinders in glassy matrix and hard domains in soft matrix morphology) could be revealed. In combination with tensile tests, Fourier transform infrared (FTIR) spectroscopy was successfully applied to study the change of orientation in individual phases using the absorption bands at 1493 and 966 cm⁻¹ for polystyrene (PS) and polybutadiene (PB) phases, respectively. For all the block copolymers investigated the PB phase always oriented stronger than the PS phase because of its lower Young's modulus. However, differences in orientation in both phases were influenced by an appropriate stress distribution within the specimens during deformation, which, in fact, depends on the morphology of the polymers. Additionally, atomic force microscopy revealed local morphological changes during uniaxial stretching, which, in fact, depend on the arrangement of the structural units.     

Positivity and stability of mixed fractional‐order systems with unbounded delays: Necessary and sufficient conditions

This article provides a comprehensive study on quantitative properties of linear mixed fractional‐order systems with multiple time‐varying delays. The delays can be bounded or unbounded. We first obtain a result on existence and uniqueness of solutions to these systems. Then, we prove a necessary and sufficient condition for their positivity. Finally, we provide a necessary and sufficient criterion to characterize asymptotic stability of positive linear mixed fractional‐order systems with multiple time‐varying delays.     

From big smartphone data to worldwide research: The Mobile Data Challenge

This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of mobile data analysis methodologies. First, we review the Lausanne Data Collection Campaign (LDCC), an initiative to collect unique longitudinal smartphone dataset for the MDC. Then, we introduce the Open and Dedicated Tracks of the MDC, describe the specific datasets used in each of them, discuss the key design and implementation aspects introduced in order to generate privacy-preserving and scientifically relevant mobile data resources for wider use by the research community, and summarize the main research trends found among the 100+ challenge submissions. We finalize by discussing the main lessons learned from the participation of several hundred researchers worldwide in the MDC Tracks.     

Human interaction discovery in smartphone proximity networks

Since humans are fundamentally social beings and interact frequently with others in their daily life, understanding social context is of primary importance in building context-aware applications. In this paper, using smartphone Bluetooth as a proximity sensor to create social networks, we present a probabilistic approach to mine human interaction types in real life. Our analysis is conducted on Bluetooth data continuously sensed with smartphones for over one year from 40 individuals who are professionally or personally related. The results show that the model can automatically discover a variety of social contexts. We objectively validated our model by studying its predictive and retrieval performance.     

Stock-Price Forecasting Based on XGBoost and LSTM

Using time-series data analysis for stock-price forecasting (SPF) is complex and challenging because many factors can influence stock prices (e.g., inflation, seasonality, economic policy, societal behaviors). Such factors can be analyzed over time for SPF. Machine learning and deep learning have been shown to obtain better forecasts of stock prices than traditional approaches. This study, therefore, proposed a method to enhance the performance of an SPF system based on advanced machine learning and deep learning approaches. First, we applied extreme gradient boosting as a feature-selection technique to extract important features from high-dimensional time-series data and remove redundant features. Then, we fed selected features into a deep long short-term memory (LSTM) network to forecast stock prices. The deep LSTM network was used to reflect the temporal nature of the input time series and fully exploit future contextual information. The complex structure enables this network to capture more stochasticity within the stock price. The method does not change when applied to stock data or Forex data. Experimental results based on a Forex dataset covering 2008–2018 showed that our approach outperformed the baseline autoregressive integrated moving average approach with regard to mean absolute error, mean squared error, and root-mean-square error.