A recent theoretical work indicates that intermetallic materials LiMnZ (Z = N, P) with a half-Heusler structure exhibit half-metallic (HM) behaviors at their strained lattice constants, and the ...magnetic moments of these alloys are expected to reach as high as 5 μ
per formula unit. (Damewood et al.
,
, 064409). This work inspired us to find new Heusler-based half-metals with the largest magnetic moment. With the help of the first-principles calculation, we reveal that XCrZ (X = K, Rb, Cs; Z = S, Se, Te) alloys show a robust, half-metallic nature with a large magnetic moment of 5 μ
at their equilibrium and strained lattice constants in their most stable phases, while the excellent HM nature of LiCrZ (Z = S, Se, Te) alloys can be observed in one of their metastable phases. Moreover, the effects of uniform strain in LiCrZ (Z = S, Se, Te) alloys in type II arrangement have also been discussed.
Optical frequency domain reflectometry (OFDR) is a research hotspot in fiber optic sensing technology. This technology can be used for strain, vibration and temperature sensing and has great ...application prospects in fields such as deformation analysis of aerospace components and bridge monitoring. This article analyzes the reasons for strain demodulation errors under large strains. In response to the problem of reduced similarity between the reference state signal and the measured state signal, a strain measurement method based on the similarity feature of a double-segment Rayleigh scattering spectrum is proposed. Local segments at both ends of the reference state signal are used as new fingerprint spectra, and the offset of the measured state signal similarity spectrum is synchronously searched after extension. At the same time, by revealing the mechanism of strain edge demodulation errors, a strain edge optimization method based on automatic adjustment of the sliding window center position is proposed. A comparison experiment was conducted with traditional methods to verify the effectiveness of the above method. Finally, a sensing unit length of 32.6 mm was achieved with a frequency modulation bandwidth of 5 nm, and the measurement range was from ± 2000 µɛ to ± 2500 µɛ. The measurable spectral offset was increased from 48% to 60%, with a maximum standard deviation of 1.9 µɛ.
This review (with 187 refs.) summarizes the progress that has been made in the design of lateral flow biosensors (LFBs) based on the use of micro- and nano-materials. Following a short introduction ...into the field, a first section covers features related to the design of LFBs, with subsections on strip-based, cotton thread-based and vertical flow- and syringe-based LFBs. The next chapter summarizes methods for sample pretreatment, from simple method to membrane-based methods, pretreatment by magnetic methods to device-integrated sample preparation. Advances in flow control are treated next, with subsections on cross-flow strategies, delayed and controlled release and various other strategies. Detection conditionst and mathematical modelling are briefly introduced in the following chapter. A further chapter covers methods for reliability improvement, for example by adding other validation lines or adopting different detection methods. Signal readouts are summarized next, with subsections on color-based, luminescent, smartphone-based and SERS-based methods. A concluding section summarizes the current status and addresses challenges in future perspectives.
Graphical abstract
Recent development and breakthrough points of lateral flow biosensors.
Past studies have already determined that selenium (Se) is very effective in alleviating cell oxidative damage caused by various abiotic stresses in plants. Past studies have also indicated other ...physiological pathways by which Se may benefit plants. In order to better understand the full array of potential applications for Se in agriculture, this study investigated the influence of Se on carbohydrate and nitrogen (N) metabolism in potato plants (Solanum tuberosum L. cv. Sante) grown under cadmium (Cd) and/or arsenic (As) toxicity. Potato plants were grown in a growth chamber and fertigated with Hoagland nutrient solution with or without Se (9 μM). After 48-d of growth under Cd (40 μM) and/or As (40 μM) stress, carbohydrate and N metabolism in leaves, roots and stolons were measured. For carbohydrate metabolism, various sugars—i.e., sucrose, starch, glucose, fructose, and total soluble sugar contents (TSSC)—and the activities of enzymes associated with sucrose metabolism and glycolysis—i.e., acid invertase (AI), neutral invertase (NI), sucrose-synthetase (SS), sucrose phosphatesynthetase (SPS), fructokinase (FK), hexokinase (HK), phosphofructokinase (PFK), and pyruvatekinase (PK)—were measured. For N metabolism, NO3−, NO2− and NH4+ contents along with the enzymatic activities of nitrate reductase (NRA), nitrite reductase (NiRA), glutamine-synthetase (GS), and glutamate-synthetase (GOGAT) were measured. Overall, Cd and/or As treatments had reduced plant growth relative to those plants grown without heavy metal toxicity, due to hindered photosynthesis and alterations in N metabolism and glycolysis. Regarding N metabolism, heavy metal toxicity caused a reduction in NO3− and NO2− content and NRA and NiRA enzymatic activity and enhanced NH4+ content and GDH activity in leaves, roots and stolons. Regarding glycolysis, the activity of enzymes of glycolysis—i.e., FK, HK, PFK, and PK—were also reduced. In the C metabolism study, plants combatted Cd and As toxicity naturally by an adaptation mechanism which caused an increase in soluble sugars (fructose, glucose, sucrose) by increasing NI, SS and SSP enzymatic activity. Supplementation with Se in the Cd and/or As treatments in the carbohydrate and N metabolism studies improved plant growth. Selenium supplementation in the Cd and As treatments decreased Cd and/or As content in the plant tissue and alleviating the Cd- and/or As-induced toxicity by enhancing the C-metabolism adaptation mechanism. Applying Se to Cd and As treatments also decreased nitrogen losses by hindering Cd- and As-induced changes in the N-metabolism. Se also limited Cd and As accumulation in the plant tissue by the antagonistic effect between Cd/Se and As/Se in the roots. The results of this study indicate that in the presence of Cd and/or As. soil toxicity, Se may be a powerful tool for promoting plant growth.
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•Cd and/or As enhanced the enzymes of carbohydrate metabolism except AI activity.•Sugars and starch contents were lowered by Cd and/or As stress.•Cd and/or As toxicity limited the NO3− and NO2− contents but enhanced NH4+ content.•Se alleviated Cd and/or As toxicity by modulating the glycolysis.•Se reduced oxidative damage by limiting the Cd and As uptake through roots.
Abstract
The topological materials have attracted much attention for their unique electronic structure and peculiar physical properties. ZrTe
5
has host a long-standing puzzle on its anomalous ...transport properties manifested by its unusual resistivity peak and the reversal of the charge carrier type. It is also predicted that single-layer ZrTe
5
is a two-dimensional topological insulator and there is possibly a topological phase transition in bulk ZrTe
5
. Here we report high-resolution laser-based angle-resolved photoemission measurements on the electronic structure and its detailed temperature evolution of ZrTe
5
. Our results provide direct electronic evidence on the temperature-induced Lifshitz transition, which gives a natural understanding on underlying origin of the resistivity anomaly in ZrTe
5
. In addition, we observe one-dimensional-like electronic features from the edges of the cracked ZrTe
5
samples. Our observations indicate that ZrTe
5
is a weak topological insulator and it exhibits a tendency to become a strong topological insulator when the layer distance is reduced.
•The deep learning GRU network is first used as surrogate model in hydrology.•High-dimensional parameter auto-calibration is performed.•Time-variant features of MODFLOW parameters are captured via ...proposed method.•Proposed GRU surrogate technique considerably reduces the computational cost.
The correlations of the multiple time-series outputs of an original simulation model are difficult to take into account using traditional surrogate model techniques. This study proposes a novel surrogate model based on a deep learning structure called gated recurrent unit (GRU) network, with the aim of developing a substitute for an original simulation model with large temporal and spatial variations and of improving the computational efficiency of studies that require thousands of model executions. First, a numerical groundwater flow model was established as the original simulation model, and then, a GRU network was trained using the two-dimensional outputs of the original simulation model. After this, the parameter was auto-calibrated by combining the GRU surrogate with the particle swarm optimization (PSO) algorithm. Furthermore, a Sobol’ sensitivity analysis was conducted for multiple time nodes. The results demonstrate that the GRU-based surrogate model exhibits a high accuracy and the ability to manage problems with multiple time-series outputs. The GRU surrogate combined with the PSO algorithm has an excellent ability to implement high-dimensionality parameter calibration tasks. In addition, the Sobol’ sensitivity analysis based on the GRU surrogate exhibits a sufficient capacity to capture the temporal characteristics of the simulation model parameters. The surrogate based on the GRU also significantly reduces the computational costs. The GRU-based surrogate technique not only can facilitate the groundwater studies, but can also have an excellent application potential for other long-term water resource managements.
Compared with ordinary images, each of the remote sensing images contains many kinds of objects with large scale changes, providing more details. As a typical object of remote sensing image, ship ...detection has been playing an essential role in the field of remote sensing. With the rapid development of deep learning, remote sensing image detection method based on convolutional neural network (CNN) has occupied a key position. In remote sensing images, the objects of which small scale objects account for a large proportion are closely arranged. In addition, the convolution layer in CNN lacks ample context information, leading to low detection accuracy for remote sensing image detection. To improve detection accuracy and keep the speed of real-time detection, this paper proposed an efficient object detection algorithm for ship detection of remote sensing image based on improved SSD. Firstly, we add a feature fusion module to shallow feature layers to refine feature extraction ability of small object. Then, we add Squeeze-and-Excitation Network (SE) module to each feature layers, introducing attention mechanism to network. The experimental results based on Synthetic Aperture Radar ship detection dataset (SSDD) show that the mAP reaches 94.41%, and the average detection speed is 31FPS. Compared with SSD and other representative object detection algorithms, this improved algorithm has a better performance in detection accuracy and can realize real-time detection.
•Electrochemical behavior of stainless steels with diverse metallographic phases is studied.•Passivation behavior is examined using potentiodynamic tests and EIS measurements.•Metallographic phases ...affect passivating films and electrochemical machining performances.•SUS316L exhibits distinct behavior in neutral solutions compared to the SUS430 and SUS 440C.
To investigate the anodic dissolution of stainless steels with diverse metallographic phases and its impact on micro electrochemical machining (micro-ECM) performance, the electrochemical behavior of representative ferritic stainless steel (SUS430), martensite stainless steel (SUS440C), dual-phase stainless steel (2205 DSS), and austenite stainless steel (SUS316L) in neutral solutions were examined by analyzing potentiodynamic results and electrochemical impedance spectroscopy (EIS). The growth and chemical compositions of passive films on the stainless steels were evaluated using X-ray photoelectron spectroscopy (XPS) and Auger electron spectroscopy (AES). EIS results indicate that the passive film formed on SUS430 (ferritic phase) and SUS440C (martensite phase) in NaClO3 exhibited greater stability compared to that formed in NaNO3, which is attributed to the thicker passive film formed in NaClO3 and higher Fe2+/Fe3+ ratio as well as Cr2O3/Cr(OH)3 ratio. The SUS316L (austenite phase) exhibits almost the opposite behavior compared to the SUS430 and SUS 440C. The impact of the electrochemical behavior on the evolution of dissolution region and surface topography was discussed from the micro-ECM experimental results. The 2205 DSS exhibits a much higher corrosion resistance, but local corrosion zones were observed at the edges of microgrooves, leading to approximate material removal rate values compared to single-phase steels. The metallographic phases influence the composition, structure, and density of the passive films, and determine the ECM performance. This study demonstrates the relationships between the MRRs and the metallographic phases of stainless steel, which provides a feasible idea for optimizing the matches of the electrolyte composition and stainless steel workpiece.
In this paper, we explore an impulsive stochastic infected predator-prey system with Lévy jumps and delays. The main aim of this paper is to investigate the effects of time delays and impulse ...stochastic interference on dynamics of the predator-prey model. First, we prove some properties of the subsystem of the system. Second, in view of comparison theorem and limit superior theory, we obtain the sufficient conditions for the extinction of this system. Furthermore, persistence in mean of the system is also investigated by using the theory of impulsive stochastic differential equations (ISDE) and delay differential equations (DDE). Finally, we carry out some simulations to verify our main results and explain the biological implications.
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