ALBERT

Description: A brand-new gas sensor nanocomposite, In2O3-InN, was synthesized by in-situ partial oxidation of InN and presented fast response–recovery property for NO2 detecting. The structure and morphology of the samples were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS) and energy-dispersive X-ray detection (EDX) analysis. The results show that the final In2O3-InN composites were composed of hexagonal type In2O3 and hexagonal type InN, which exhibited bottle nanotube structure on the relative macroscopic level. Microscopically, at the interface of In2O3 and InN, n–n hetero junction formed. Works form gas sensing property found that it is obviously that In2O3-InN got a quite stronger response, 1021, at relatively lower temperature, 100 °C, comparing to pure In2O3, 279.1 at 150 °C. After doping, the gas-sensing performance was improved. By analyzing the concentration of oxygen vacation and n–n hetero junctions mechanism, it was verified that the superiority of gas sensing properties of the In2O3-InN can be attributed to the high concentration of oxygen vacancies and the formation of n–n hetero junctions.

Description: Exact solutions were derived for a time-fractional Levi equation with Riemann–Liouville fractional derivative. The methods involve, first, the reduction of the time-fractional Levi equation to fractional ordinary differential equations with Erdélyi-Kober fractional differential operator with respect to point symmetry groups, and second, use of the invariant subspace to reduce the time-fractional Levi equation into a system of fractional ordinary differential equations, which were solved by the symmetry group method. The obtained explicit solutions have interesting analytic behaviors connected with blow-up and dispersion. The conservation laws generated by the point symmetries of the time-fractional Levi equation are shown via nonlinear self-adjointness method.

Description: The aim of this study was to verify the applicability of the critical nitrogen concentration dilution curve (Nc) of wheat grown under different irrigation conditions in the field, and discuss the feasibility of using the N nutrition index (NNI) to optimize N fertilizer application. The high-yield, medium-protein wheat varieties Zhoumai 27 and Zhoumai 22 were used in field experiments in two different locations (Zhengzhou and Shangshui) in Huang-Huai, China. Plants were grown under rainfed and irrigation conditions, with five N application rates. Nc models of the leaves, stems, and whole plant were constructed, followed by establishment of an NNI model and accumulative N deficit model (Nand). As previous research reported, our results also showed that the critical N concentration and biomass formed a power function relationship (N = aDW−b). When the biomass was the same, the critical N concentration was higher under irrigation than rainfed treatment. Meanwhile, the fitting accuracy (R2) of the Nc model was also higher under irrigation than rainfed treatment in both sites, and was higher in the stems and whole plant. The NNI calculated using the Nc model increased with increasing N application, reflecting N deficiency. Moreover, there was a significant negative linear correlation between NNI and Nand, and both indices could be uniformly modeled between locations and water treatments. The accuracy of the Nand model was highest in the whole plant, followed by the leaves and stems. The models constructed in this paper provide a theoretical basis for accurate management of N fertilizer application in wheat production.

Description: Since the observation precision of the Global Navigation Satellite System (GNSS) carrier phase is on the order of millimeters, if the phase ambiguity is correctly solved, while calibrating the receiver inter-frequency bias, time synchronization on the order of tens of picoseconds is expected. In this contribution, a method that considers the prior constraints of the between-receiver inter-frequency bias (IFB) and its random variation characteristics is proposed for the estimation of the between-receiver clock difference, based on the uncombined GNSS carrier phase and pseudorange observations of the zero and short baselines. The proposed method can rapidly achieve the single-difference ambiguity resolution of the zero and short baselines, and then obtain the high-precision relative clock offset, by using only the carrier phase observations, along with the between-receiver IFBs being simultaneously determined. Our numerical tests, carried out using GNSS observations sampled every 30 s by a dedicatedly selected set of zero and short baselines, show that the method can fix the between-receiver single-difference ambiguity successfully within an average of fewer than 2 epochs (interval 30 s). Then, a clock difference between two receivers with millimeter precision is obtained, achieving time synchronization on tens of picoseconds level, and deriving a frequency stability of 5 × 10−14 for averaging times of 30,000 s. Furthermore, the proposed approach is compared with the precise point positioning (PPP) time transfer method. The results show that, for different types of receivers, the agreement between the two methods is between −6.7 ns and 0.2 ns.

Description: Remote sensing images classification is the key technology for monitoring forest changes. Texture features have been demonstrated to have better effectiveness than spectral features in the improvement of the classification accuracy. The accuracy of extracting texture information by window-based method depends on the choice of the window size. Moreover, the size should ideally match the spatial scale of the object or class under consideration. However, most of the existing texture feature extraction methods are all based on a single window and do not adequately consider the scale of different objects. Our first proposition is to use a composite window for extracting texture features, which is a small window surrounded by a larger window. Our second proposition is to reinforce the performance of the trained ensemble classifier by training it using only the most important features. Considering the advantages of random forest classifier, such as fast training speed and few parameters, these features feed this classifier. Measures of feature importance are estimated along with the growth of the base classifiers, here decision trees. We aim to classify each pixel of the forest images disturbed by hurricanes and fires in three classes, damaged, not damaged, or unknown, as this could be used to compute time-dependent aggregates. In this study, two research areas—Nezer Forest in France and Blue Mountain Forest in Australia—are utilized to validating the effectiveness of the proposed method. Numerical simulations show increased performance and improved monitoring ability of forest disturbance when using these two propositions. When compared with the reference methods, the best increase of the overall accuracy obtained by the proposed algorithm is 4.77% and 2.96% on the Nezer forest data and Blue Mountain forest data, respectively.

Description: Computer-based fully-automated cell tracking is becoming increasingly important in cell biology, since it provides unrivalled capacity and efficiency for the analysis of large datasets. However, automatic cell tracking’s lack of superior pattern recognition and error-handling capability compared to its human manual tracking counterpart inspired decades-long research. Enormous efforts have been made in developing advanced cell tracking packages and software algorithms. Typical research in this field focuses on dealing with existing data and finding a best solution. Here, we investigate a novel approach where the quality of data acquisition could help improve the accuracy of cell tracking algorithms and vice-versa. Generally speaking, when tracking cell movement, the more frequent the images are taken, the more accurate cells are tracked and, yet, issues such as damage to cells due to light intensity, overheating in equipment, as well as the size of the data prevent a constant data streaming. Hence, a trade-off between the frequency at which data images are collected and the accuracy of the cell tracking algorithms needs to be studied. In this paper, we look at the effects of different choices of the time step interval (i.e., the frequency of data acquisition) within the microscope to our existing cell tracking algorithms. We generate several experimental data sets where the true outcomes are known (i.e., the direction of cell migration) by either using an effective chemoattractant or employing no-chemoattractant. We specify a relatively short time step interval (i.e., 30 s) between pictures that are taken at the data generational stage, so that, later on, we may choose some portion of the images to produce datasets with different time step intervals, such as 1 min, 2 min, and so on. We evaluate the accuracy of our cell tracking algorithms to illustrate the effects of these different time step intervals. We establish that there exist certain relationships between the tracking accuracy and the time step interval associated with experimental microscope data acquisition. We perform fully-automatic adaptive cell tracking on multiple datasets, to identify optimal time step intervals for data acquisition, while at the same time demonstrating the performance of the computer cell tracking algorithms.

Description: This work reveals first principle results of the endohedral fullerenes made from neutral or charged single atomic lithium (Li or Li+) encapsulated in fullerenes with various cage sizes. According to the calculated binding energies, it is found that the encapsulation of a single lithium atom is energetically more favorable than that of lithium cation. Lithium, in both atomic and cationic forms, exhibits a clear tendency to depart from the center in large cages. Interaction effects dominate the whole encapsulation process of lithium to carbon cages. Further, the nature of the interaction between Li (or Li+) and carbon cages is discussed based on reduced density gradient, energy decomposition analysis, and charge transfer.

Description: A phosphomolybdic acid/polyaniline (PMoA/PANI) optical-light photochromic inorganic/organic hybrid thin film was successfully synthesized by protonation between the the multiprotonic acid phosphomolybdic acid (H3PO4·12MoO3) and the conductive polymer polyaniline. The stable Keggin-type structure of PMoA was maintained throughout the process. Protonation and proton transfer successfully transformed the quinone structure of eigenstate PANI into the benzene structure of single-polarized PANI in the PMoA/PANI hybridized thin film, and proton transfer transformed the benzene structure of single-polarized PANI back to the quinone structure of eigenstate PANI in the PMoA/PANI hybrid thin film, as verified by Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). The average distribution of PMoA/PANI was observed by atom force microscopy (AFM). Interestingly, protonation of PMoA caused PANI to trigger transformation of the quinone structure into the single-polarized benzene structure, which enhanced the electron delocalization ability and vastly enhanced the maximum light absorption of the PMoA/PANI hybrid thin film as confirmed by density functional theory (DFT), electrochemistry, and ultraviolet-visible spectroscopy (UV-Vis) studies. Under optical-light illumination, the pale-yellow PMoA/PANI hybrid thin film gradually turned deep blue, thus demonstrating a photochromic response, and reversible photochromism was also observed in the presence of hydrogen peroxide (H2O2) or oxygen (O2). After 40 min of optical-light illumination, 36% of the Mo5+ species in PMoA was photoreduced via a protonation-induced proton transfer mechanism, and this proton transfer resulted in a structural change of PANI, as observed by XPS, generating a dominant structure with high maximum light absorption of 3.46, when compared with the literature reports.

Description: Chemical research of the medicinal plant Hemsleya amabilis (Cucurbitaceae) yielded five new cucurbitane-type triterpenes hemslelis A–E (1–5) by silica gel column, ODS column, and semi-HPLC techniques. Their structures were determined by spectroscopic analysis and examined alongside existing data from prior studies. Compounds 1–5 were evaluated for their cytotoxic activities against three human tumor cell lines, Hela, HCT-8, and HepG-2, with the IC50 ranging from 5.9 to 33.9 μM compared to Cisplatin.