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.     

Preferential Oxidation of CO in H2-Rich Gas at Low Temperatures over Au Nanoparticles Supported on Metal Oxides

We investigated Au catalysts supported on TiO₂, Fe₂O₃, and ZnO for their preferential oxidation of CO in a H₂-rich atmosphere. Both full conversion and selectivity were achieved over Au/Fe₂O₃ and Au/ZnO around room temperature, but at higher temperatures the CO conversion was suppressed due to competition between CO and H₂.     

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.     

A revised method for ranking fuzzy numbers using maximizing set and minimizing set

A large number of methods have been proposed for ranking fuzzy numbers in the last few decades. Nevertheless, none of these methods can always guarantee a consistent result for every situation. Some of them are even non-intuitive and not discriminating. Chen proposed a ranking method in 1985 to overcome these limitations and simplify the computational procedure based on the criteria of total utility through maximizing set and minimizing set. However, there were some shortcomings associated with Chen’s ranking method. Therefore, we propose a revised ranking method that can overcome these shortcomings. Instead of considering just a single left and a single right utility in the total utility, the proposed method considers two left and two right utilities. In addition, the proposed method also takes into account the decision maker’s optimistic attitude of fuzzy numbers. Several comparative examples and an application demonstrating the usage, advantages, and applicability of the revised ranking method are presented. It can be concluded that the revised ranking method can effectively resolve the issues with Chen’s ranking method. Moreover, the revised ranking method can be used to differentiate different types of fuzzy numbers.     

A decision tree using ID3 algorithm for English semantic analysis

Natural language processing has been studied for many years, and it has been applied to many researches and commercial applications. A new model is proposed in this paper, and is used in the English document-level emotional classification. In this survey, we proposed a new model by using an ID3 algorithm of a decision tree to classify semantics (positive, negative, and neutral) for the English documents. The semantic classification of our model is based on many rules which are generated by applying the ID3 algorithm to 115,000 English sentences of our English training data set. We test our new model on the English testing data set including 25,000 English documents, and achieve 63.6% accuracy of sentiment classification results.     

Solving distribution problems in content-based recommendation system with gaussian mixture model

Applied Intelligence - Recommendation systems play an important role in boosting purchasing consumption for many manufacturers by helping consumers find the most appropriate items. Furthermore,...     

Barrier effect of flame retardant systems in poly(methyl methacrylate): Study of the efficiency of the surface treatment by octylsilane of silica nanoparticles in combination with phosphorous fire retardant additives

In this work, flame retardant systems comprising ammonium polyphosphate (AP423) and hydrophilic (A200) or hydrophobic (R805) nanometric silica were incorporated into PMMA. The following techniques were performed to detail the fire behaviour of the composites: mass loss cone calorimetry, pyrolysis‐combustion flow calorimetry, pyrolysis‐gas chromatography–mass spectrometry, thermogravimetric analysis, X‐ray diffraction analysis, Fourier transform infrared spectroscopy and microscopic observations. The best fire behaviour was obtained with the surface‐treated silica in the presence of AP423. The formation of a new crystalline phase from the interactions between AP423 and R805 silica and a strong barrier effect due to a layered residue were the main modes of action of this system. Moreover, we have shown that the difference between the AP423 + R805 and AP423 + A200 systems was due to poor dispersion of the silica into the PMMA matrix in the latter formulation. Copyright © 2011 John Wiley & Sons, Ltd.     

Advances in optical engineering for future telescopes

Significant optical engineering advances at the University of Arizona are being made for design,fabrication,and con-struction of next generation astronomical te...     

外源性雄激素连续三年生理诱导雄麝二次泌香的研究

本文报道了应用外源性雄激素连续三年生理诱导雄麝二次泌香实验过程中,雄麝诱导泌香期的生理特征及诱导产香量的变化规律,探讨了雄激素生理诱导技术对雄麝自然泌香量的影响。实验结果表明:应用外源性雄激素连续三年诱导雄麝均可使其产生诱导香,提高麝香年产量,雄麝诱导泌香期的行为及体征和自然泌香期一样:雄麝连续三年诱导产香量呈逐年下降趋势。实验中还发现:雄激素连续三年诱导雄麝产香实验不影响雄麝自然产香量。     

Estimating aboveground biomass of a mangrove plantation on the Northern coast of Vietnam using machine learning techniques with an integration of ALOS-2 PALSAR-2 and Sentinel-2A data

ABSTRACT

Aboveground biomass (AGB) of mangrove forest plays a crucial role in global carbon cycle by reducing greenhouse gas emissions and mitigating climate change impacts. Monitoring mangrove forests biomass accurately still remains challenging compared to other forest ecosystems. We investigated the usability of machine learning techniques for the estimation of AGB of mangrove plantation at a coastal area of Hai Phong city (Vietnam). The study employed a GIS database and support vector regression (SVR) to build and verify a model of AGB, drawing upon data from a survey in 25 sampling plots and an integration of Advanced Land Observing Satellite-2 Phased Array Type L-band Synthetic Aperture Radar-2 (ALOS-2 PALSAR-2) dual-polarization horizontal transmitting and horizontal receiving (HH) and horizontal transmitting and vertical receiving (HV) and Sentinel-2A multispectral data. The performance of the model was assessed using root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R²), and leave-one-out cross-validation. Usability of the SVR model was assessed by comparing with four state-of-the-art machine learning techniques, i.e. radial basis function neural networks, multi-layer perceptron neural networks, Gaussian process, and random forest. The SVR model shows a satisfactory result (R² = 0.596, RMSE = 0.187, MAE = 0.123) and outperforms the four machine learning models. The SVR model-estimated AGB ranged between 36.22 and 230.14 Mg ha^?1 (average = 87.67 Mg ha^?1). We conclude that an integration of ALOS-2 PALSAR-2 and Sentinel-2A data used with SVR model can improve the AGB accuracy estimation of mangrove plantations in tropical areas.