ALBERT

Description: In order to meet the dual requirements of economic development and energy consumption, the Chinese government has adopted a series of measures and policies to improve energy efficiency. However, the developing characteristics are not the same in different regions. Thus, it is necessary to analyze the actual energy efficiency levels in a more targeted manner. In this paper, 30 provinces in China will be adopted to study energy efficiency based on the statistical data from National Energy Administration and National Bureau of Statistics. With the trends of Chinese energy consumption and economy development, the Lorenz curve between Chinese energy consumption and GDP is fitted firstly. The Lorenz coefficient (0.1562) shows that the energy allocation in China is neither reasonable nor balanced. Then, by cluster analysis, the regions of different provinces are newly divided into five divisions from the dimensions of economy and energy, i.e., (high development level and high consumption level, HH), (moderate development level and moderate consumption level, MM), (moderate development level and low consumption level, ML), (low development level and low consumption level, LL), and (low development level and moderate consumption level, LM). Based on the division results, the provincial promotion potentialities are estimated quantitatively according to the absolute convergence feature of energy efficiency promotion. The provinces that have more promotion potentialities of energy efficiency are located, such as Hebei (56.29%) in division (HH), Liaoning (38.15%) in division (MM), Anhui (44.17%) in division (ML), Ningxia (71.63%) in division (LL), and Xinjiang (35.26%) in division (LM), According to the different provincial potentialities, energy efficiency in China needs to improve more, but the improvement approaches should be specific and differential. Driven by technology, policy and mechanism, and industrial restructuring, the Chinese economy and energy resources should be integrated afresh to promote the energy efficiency level. Some optimal strategies on energy efficiency promotion are discussed, finally, aiming to provide effective references for the Chinese government to make relative policies. The study proposes a new perspective for provincial energy efficiency research which can also provide a reference for other countries.

Description: An amorphous complex precursor with uniform Mn/Ni cation distribution is attempted for preparing a nano-structured layered Li-rich oxide (Li1.2Mn0.6Ni0.2O2)cathode material, using diethylenetriaminepentaacetic acid (DTPA) as a chelating agent. The materials are characterized by powder X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and electrochemical tests. The crystal structure of Li-rich materials is found to be closely related to synthesis temperature. As-obtained nano materials sintered at 850 °C for 10 h show an average size of 200 nm with a single crystal phase and good crystallinity. At a current density of 20 mA·g−1, the specific discharge capacity reaches 221 mAh·g−1 for the first cycle and the capacity retention is 81% over 50 cycles. Even at a current density of 1000 mA·g−1, the capacity is as high as 118 mAh·g−1. The enhanced rate capability can be ascribed to the nano-sized morphology and good crystal structure.

Description: Peanut meal is the byproduct of high-temperature peanut oil extraction; it is mainly composed of proteins, which have complex tastes after enzymatic hydrolysis to free amino acids and small peptides. The enzymatic hydrolysis method was adopted by using two compound proteases of trypsin and flavorzyme to hydrolyze peanut meal aiming to provide a flavor base. Hence, it is necessary to assess the taste attributes and assign definite taste scores of peanut meal double enzymatic hydrolysis hydrolysates (DEH). Conventionally, sensory analysis is used to assess taste intensity in DEH. However, it has disadvantages because it is expensive and laborious. Hence, in this study, both taste attributes and taste scores of peanut meal DEH were evaluated using an electronic tongue. In this regard, the response characteristics of the electronic tongue to the DEH samples and standard five taste samples were researched to qualitatively assess the taste attributes using PCA and DFA. PLS and RBF neural network (RBFNN) quantitative prediction models were employed to compare predictive abilities and to correlate results obtained from the electronic tongue and sensory analysis, respectively. The results showed that all prediction models had good correlations between the predicted scores from electronic tongue and those obtained from sensory analysis. The PLS and RBFNN prediction models constructed using the voltage response values from the sensors exhibited higher correlation and prediction ability than that of principal components. As compared with the taste performance by PLS model, that of RBFNN models was better. This study exhibits potential advantages and a concise objective taste assessment tool using the electronic tongue in the assessment of DEH taste attributes in the food industry.

Description: An electronic nose (E-nose) consisting of 14 metal oxide gas sensors and one electronic chemical gas sensor has been constructed to identify four different classes of wound infection. However, the classification results of the E-nose are not ideal if the original feature matrix containing the maximum steady-state response value of sensors is processed by the classifier directly, so a novel pre-processing technique based on supervised locality preserving projections (SLPP) is proposed in this paper to process the original feature matrix before it is put into the classifier to improve the performance of the E-nose. SLPP is good at finding and keeping the nonlinear structure of data; furthermore, it can provide an explicit mapping expression which is unreachable by the traditional manifold learning methods. Additionally, some effective optimization methods are found by us to optimize the parameters of SLPP and the classifier. Experimental results prove that the classification accuracy of support vector machine (SVM combined with the data pre-processed by SLPP outperforms other considered methods. All results make it clear that SLPP has a better performance in processing the original feature matrix of the E-nose.

Description: n/a

Description: Currently, accurate information on crop area coverage is vital for food security and industry, and there is strong demand for timely crop mapping. In this study, we used MODIS time series data to investigate the effect of the time series length on crop mapping. Eight time series with different lengths (ranging from one month to eight months) were tested. For each time series, we first used the Random Forest (RF) algorithm to calculate the importance score for all features (including multi-spectral data, Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and phenological metrics). Subsequently, an extension of the Jeffries–Matusita (JM) distance was used to measure class separability for each time series. Finally, the RF algorithm was used to classify crop types, and the classification accuracy and certainty were used to analyze the influence of the time series length and the number of features on classification performance; the features were added one by one based on their importance scores. Results indicated that when the time series was longer than five months, the top ten features remained stable. These features were mainly in July and August. In addition, the NDVI features contributed the majority of the most significant features for crop mapping. The NDWI and data from multi-spectral bands also contributed to improving crop mapping. On the other hand, separability, classification accuracy, and certainty increased with the number of features used and the time series length, although these values quickly reached saturation. Five months was the optimal time series length, as longer time series provided no further improvement in the classification performance. This result shows that relatively short time series have the potential to identify crops accurately, which allows for early crop mapping over large areas.

Description: Impedance plethysmography provides a way to measure respiratory activity by sensing the change of thoracic impedance caused by inspiration and expiration. This measurement imposes little pressure on the body and uses the human body as the sensor, thereby reducing the need for adjustments as body position changes and making it suitable for long-term or ambulatory monitoring. The empirical mode decomposition (EMD) can decompose a signal into several intrinsic mode functions (IMFs) that disclose nonstationary components as well as stationary components and, similarly, capture respiratory episodes from thoracic impedance. However, upper-body movements usually produce motion artifacts that are not easily removed by digital filtering. Moreover, large motion artifacts disable the EMD to decompose respiratory components. In this paper, motion artifacts are detected and replaced by the data mirrored from the prior and the posterior before EMD processing. A novel intrinsic respiratory reconstruction index that considers both global and local properties of IMFs is proposed to define respiration-related IMFs for respiration reconstruction and instantaneous respiratory estimation. Based on the experiments performing a series of static and dynamic physical activates, our results showed the proposed method had higher cross correlations between respiratory frequencies estimated from thoracic impedance and those from oronasal airflow based on small window size compared to the Fourier transform-based method.

Description: A new scan that matches an aided Inertial Navigation System (INS) with a low-cost LiDAR is proposed as an alternative to GNSS-based navigation systems in GNSS-degraded or -denied environments such as indoor areas, dense forests, or urban canyons. In these areas, INS-based Dead Reckoning (DR) and Simultaneous Localization and Mapping (SLAM) technologies are normally used to estimate positions as separate tools. However, there are critical implementation problems with each standalone system. The drift errors of velocity, position, and heading angles in an INS will accumulate over time, and on-line calibration is a must for sustaining positioning accuracy. SLAM performance is poor in featureless environments where the matching errors can significantly increase. Each standalone positioning method cannot offer a sustainable navigation solution with acceptable accuracy. This paper integrates two complementary technologies—INS and LiDAR SLAM—into one navigation frame with a loosely coupled Extended Kalman Filter (EKF) to use the advantages and overcome the drawbacks of each system to establish a stable long-term navigation process. Static and dynamic field tests were carried out with a self-developed Unmanned Ground Vehicle (UGV) platform—NAVIS. The results prove that the proposed approach can provide positioning accuracy at the centimetre level for long-term operations, even in a featureless indoor environment.

Description: Background: Genetic polymorphisms of the transcription factor 7-like 2 (TCF7L2) gene have been reported to be strongly associated with type 2 diabetes mellitus (T2DM) in Icelandic, Danish and American populations and further replicated in other European populations, African Americans, Mexican Americans, and Asian populations. The aim of the present study was to investigate the association of TCF7L2 gene polymorphisms with T2DM in a Uygur population of China. Methods: 877 T2DM patients and 871 controls were selected for the present study. Two single nucleotide polymorphisms (SNPs) (rs12255372 and rs7901695) were genotyped by using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry. The associations of SNPs and haplotypes with T2DM and linkage disequilibrium (LD) structure of the TCF7L2 gene were analyzed. Results: For total participants and male, the distribution of rs12255372 alleles and the dominant model (Guanine Guanine (GG) genotype vs. Guanine Thymine (GT) genotype + Thymine Thymine (TT) genotype) showed significant difference between T2DM and control subjects (for allele: p = 0.013 and p = 0.002, respectively; for dominant model: p = 0.028 and p = 0.008, respectively). The distribution of rs7901695 alleles and the dominant model (TT genotype vs. Thymine Cytosine (TC) genotype + Cytosine Cytosine (CC) genotype) for total participants and male showed significant difference between T2DM and control subjects (for allele: both p = 0.001; for dominant model: p = 0.006 and p = 0.008, respectively). Conclusions: Our data suggested that the genetic polymorphisms of the TCF7L2 gene were associated with T2DM in the Uygur population of China.

Description: The accurate information of the initial rotor position is very critical for successful starting of the Surface-mounted Permanent Magnet Synchronous Motor (SPMSM). In order to solve the problems of low accuracy and unreliability in the conventional estimation strategy, in this paper, an improved initial rotor position estimation strategy without any position sensor for SPMSM at standstill is proposed based on rectangular pulse voltage injection. In the work, when the second series of pulse voltages were applied. By the ways of strengthening the effect of weakening or strengthening magnetic fields and increasing the difference between each current of the vector. The improved strategy enhanced reliability and raised the initial position estimation accuracy from 7.5° to 1.875°. The improved strategy does not need any additional hardware. Experimental results demonstrate the validity and usefulness of the improved strategy.