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Aiming to tackle the problems of low classification accuracy, difficult data processing, long analysis time, and the influence of subjective consciousness of traditional Baijiu (Chinese liquor), this paper reports a method based on the combination of laser-induced breakdown spectroscopy with high-frequency ultrasonic atomization system (AS-LIBS) and optimized grid search cross validation (Opt-GSCV) for high-precision and rapid classification of Baijiu of different dimensions. Multi-dimensional high-precision classification of 23 Baijiu samples with different series, degrees, and production dates from Shanxi Xinghuacun Fen Wine Factory Co., Ltd was performed. By studying the growth and evolution mechanism of plasma generated by laser excitation, a new method of Baijiu spectral signal acquisition was realized by combining LIBS with Baijiu detection. Aiming at addressing the shortcomings of high breakdown thresholds and poor signal stability due to the direct breakdown of Baijiu with high ethanol content, a high-frequency atomization system based on ultrasonic technology is proposed to realize the efficient extraction of Baijiu plasma spectral signals. With regard to the selection of the number of nodes, weights and thresholds of the implicit layer of the back-propagation (BP) network, an optimization model based on the combination of the particle swarm optimization (PSO) algorithm combined with the genetic algorithm (GA) is proposed to realize the construction of the BP optimal network architecture. By constructing a secondary optimization seeking network model based on OptGSCV, the technical problem of mismatch between optimal parameter selection and optimal network construction is solved, and a new network integrating an intelligent qualitative analysis algorithm and a traditional classification algorithm is realized. The experimental results show that the proposed OptGSCV quadratic optimization network combined with the AS-LIBS multidimensional Baijiu high-precision classification model not only introduces an innovative and high-efficiency detection method for the Baijiu industry, but also provides important technical support and new ideas for the key areas of food safety production and Baijiu quality control. [ABSTRACT FROM AUTHOR]

Tunable diode laser absorption spectroscopy (TDLAS) technology, combined with chromatographic imaging algorithms, is commonly used for two-dimensional temperature and concentration measurements in combustion fields. However, obtaining critical temperature information from limited detection data is a challenging task in practical engineering applications due to the difficulty of deploying sufficient detection equipment and the lack of sufficient data to invert temperature and other distributions in the combustion field. Therefore, we propose a sparse projection multi-view synthesis model based on U-Net that incorporates the sequence learning properties of gated recurrent unit (GRU) and the generalization ability of residual networks, called GMResUNet. The datasets used for training all contain projection data with different degrees of sparsity. This study shows that the synthesized full projection data had an average relative error of 0.35%, a PSNR of 40.726, and a SSIM of 0.997 at a projection angle of 4. At projection angles of 2, 8, and 16, the average relative errors of the synthesized full projection data were 0.96%, 0.19%, and 0.18%, respectively. The temperature field reconstruction was performed separately for sparse and synthetic projections, showing that the application of the model can significantly improve the reconstruction accuracy of the temperature field of high-energy combustion. [ABSTRACT FROM AUTHOR]

For the inner-temperature-field reconstruction of a fireball, a detecting method was proposed, using multi-channel visible spectral remote sensing theory. In our proposed method, the reconstructive algorithm based on multi-channel-detection was considered as a multi-objective optimization problem (MOOP), and a fast non-dominated sorting genetic algorithm based on reference-point strategy (NSGA-III) was employed as the solution of this problem. Besides, a so-called ambient pressure operator, based on the unique detecting model, was proposed and employed during the iteration process, for dynamic genetic parameter adjustment. To verify some performance of our proposed method, several numerical reconstructive simulations were carried out, from simple GA to NSGA-III, using several artificial 2-D virtual data, with different kinds of crossover and mutation functions. The simulation results show that, limited to our problems, the NSGA-III can effectively reconstruct different 2-D data, by a fixed crossover rate and a dynamic mutation rate under the proposed ambient pressure operator and an adaptive mutation rate function. The algorithm, limited to the field-reconstruction, also performs well on stability, but still has some deficiencies to be optimized.

High-resolution flame temperature images are essential indicators for evaluating combustion conditions. Tunable diode laser absorption spectroscopy (TDLAS) is an effective combustion diagnostic method. In actual engineering, due to the limitation of line-of-sight (LOS) measurement, TDLAS technology has the problems of small data volume and low dimensionality in measuring combustion fields, which seriously limits the development of TDLAS in combustion diagnosis. This article demonstrates a TDLAS imaging method based on a 64-pixel area array sensor to reconstruct the two-dimensional temperature field of the flame. This paper verifies the robustness of the Algebraic Reconstruction Technique (ART) algorithm through numerical simulation and studies the effects of temperature, concentration, and pressure on the second harmonic intensity based on the HITRAN database. The two-dimensional temperature field of the flame was reconstructed, and reconstruction accuracy was verified using thermocouples. The maximum relative error was 3.71%. The TDLAS detection system based on a 64-pixel area array sensor provides a way to develop high-precision, high-complexity flame temperature measurement technology. [ABSTRACT FROM AUTHOR]

Focusing on the problems of complicated sample processing and long detection time of traditional soil composition testing in mining areas, this paper proposes a rapid soil classification method of grid search and cross validation (GSCV) quadratic optimization-seeking network combined with magnetic field enhanced laser-induced breakdown spectroscopy (MF-LIBS), which can quickly and accurately classify standard soil samples from 10 different mining areas. First, the effects of different magnetic field strengths on the enhancement factor, signal-to-noise ratio (SNR) and plasma electron temperature of the characteristic spectral lines were investigated. Next, 5400 12 248-dimensional spectral data under the 0.98 T magnetic field constraint were preprocessed using principal component analysis (PCA). Then the back propagation (BP) network was optimized by combining the sparrow search algorithm (SSA) external optimization network with the genetic algorithm (GA) internal optimization network. Finally, the hyperparameters of the optimal network architecture are refined by GSCV for secondary optimization. By comparing with the unoptimized network, the results show that the classification accuracy of GSCV-SSA-GA-BP is 0.99997, the precision is 0.99977, the recall is 0.99981, and the F1-score is 0.99979. The proposed technique of GSCV quadratic optimization network combined with MF-LIBS soil classification can not only play an important role in soil research, but also provide a new scheme for rapid detection and high-precision qualitative analysis of soil components. [ABSTRACT FROM AUTHOR]

Wind energy as a renewable energy source is easily available and widely distributed in cities. However, current wind-energy harvesters are inadequate at capturing energy from low-speed winds in urban areas, thereby limiting their application in distributed self-powered sensor networks. A triboelectric–electromagnetic hybrid harvester with a low startup wind speed (LSWS-TEH) is proposed that also provides output power within a wide range of wind speeds. An engineering-implementable propeller design method is developed to reduce the startup wind speed of the harvester. A mechanical analysis of the aerodynamics of the rotating propeller is performed, and optimal propeller parameter settings are found that greatly improved its aerodynamic torque. By combining the high-voltage output of the triboelectric nanogenerator under low-speed winds with the high-power output of the electromagnetic generator under high-speed winds, the harvester can maintain direct current output over a wide wind-speed range after rectification. Experiments show that the harvester activates at wind speeds as low as 1.2 m/s, powers a sensor with multiple integrated components in 1.7 m/s wind speeds, and drives a Bluetooth temperature and humidity sensor in 2.7 m/s wind speeds. The proposed small, effective, inexpensive hybrid energy harvester provides a promising way for self-powered requirements in smart city settings. [ABSTRACT FROM AUTHOR]

With advances in new materials and technologies, there has been increasing research focused on flexible sensors. However, in most flexible pressure sensors made using new materials, it is challenging to achieve high detection sensitivity across a wide pressure range. Although traditional silicon-based sensors have good performance, they are not formable and, because of their rigidity and brittleness, they are not suitable for fitting with soft human skin, which limits their application in wearable devices to collect various signals. Silicon nanomembranes are ultra-thin, flexible materials with excellent piezoresistive properties, and they can be applied in various fields, such as in soft robots and flexible devices. In this study, we developed a flexible pressure sensor based on the use of silicon nanomembranes (with a thickness of only 340 nm) as piezoresistive units, which were transferred onto a flexible polydimethylsiloxane (PDMS) substrate. The flexible pressure sensor operated normally in the range of 0–200 kPa, and the sensitivity of the sensor reached 0.0185 kPa−1 in the low-pressure range of 0–5 kPa. In the high-pressure range of 5–200 kPa, the sensitivity of the sensor was maintained at 0.0023 kPa−1. The proposed sensor exhibited a fast response and excellent long-term stability and could recognize human movements, such as the bending of fingers and wrist joints, while maintaining a stable output. Thus, the developed flexible pressure sensor has promising applications in body monitoring and wearable devices. [ABSTRACT FROM AUTHOR]

Thin graphene oxide (GO) films have been widely explored for their outstanding optical properties, especially their large optical nonlinear coefficients, and high tunability of optical nonlinear coefficients under photoreduction. In this work, an experimental observation of giant nonlinear optical response in GO films under two different photoreduction mechanisms is reported. After ultraviolet (UV) light‐induced photochemical reduction and continuous wave (CW) laser‐induced photothermal reduction, the nonlinear optical response of reduced graphene oxide (rGO) via the Z‐scan method using a femtosecond laser beam at a wavelength of 800 nm is characterized. The rGO exhibits a giant optical nonlinearity, especially two‐photon absorption (TPA). A giant TPA coefficient of rGO on the order of ≈105 cm GW−1 is obtained under two photoreduction mechanisms (photochemistry and photothermal). This is the first time that a large enhancement effect of TPA has been observed in graphene‐based materials, and the TPA coefficient is an order of magnitude higher than the highest value previously reported. The versatile optical nonlinear properties imply a huge potential of the GO film in advanced photonic and optoelectronic devices such as broadband ultrafast optical switching and optical limiting. [ABSTRACT FROM AUTHOR]

The pollution of the surrounding soil can be caused by the development of heavy metal mining areas, which can do a significant harm to the ecological environment and human health. The classification of soil in different mining areas is of great significance for soil management and environmental pollution control. Due to the complex physical property and composition of soil, it is easily affected by matrix effects, which can directly affect the collection and processing of spectral data. It is thus challenging to accurately classify soil. In this article, laser-induced breakdown spectroscopy (LIBS) combined with a convolutional neural network with a two-dimensional (2D-CNN) algorithm, which was based on principal component analysis (PCA), was used to accurately identify and classify standard soil samples from ten different mining areas. The sample data were first standardized and dimensionlessly pre-processed by PCA to remove redundant information and to improve the signal-to-noise ratio (SNR) of the data. Then, the structure of 2D-CNN was designed, and the features of the input data were deeply extracted twice by convolution and pooling operations, so that more feature information could be extracted from the data, which was conducive to improving the classification accuracy of the model. The experiment results show that the classification accuracy of the 2D-CNN test set could reach 99.60% and the loss could be minimized at 0.012. In order to verify the superior performance of the 2D-CNN-based PCA classification model, the 2D-CNN algorithm was compared with other models, including back propagation artificial neural network (BP-ANN), support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF). The results show that 2D-CNN has great potential in the field of soil recognition and classification, and provides a new and reliable data processing method for LIBS to classify materials with similar chemical properties. [ABSTRACT FROM AUTHOR]

In dynamic temperature measurement, thermocouple sensors are widely used, and their dynamic characteristics directly affect the accuracy of the test results. So before applying thermocouple sensors to dynamic temperature measurement, their dynamic characteristics should be obtained, and their dynamic performance parameters should be analyzed. The time constant is the most important dynamic parameter, which reflects the response speed of a thermocouple sensor. Therefore, it is necessary to measure the time constant. The time constant is closely related to the heat transfer mode, so the heat transfer environment of the time constant measurement system should be similar to the application environment of thermocouple sensor. When using the thermocouple to measure the temperature in various kilns, the heat transfer is mainly through the radiative mode, and existing equipment, such as constant temperature water/oil tank, shock wave tube and other devices, cannot be used to measure the time constant to reflect scenarios in actual measurement applications. Therefore, this paper proposed a new method to measure the time constant of the thermocouple by improved high temperature furnace. In the system, the high temperature furnace was used to generate the stable temperature field, and the fast feed device was used to insert the thermocouple into the high temperature furnace and generates the ramp temperature excitation. The temperature can reach 1500 °C in the furnace, and the temperature error in uniform temperature field is ±1 °C. Finally, the time constant of a K-type thermocouple was measured, and the uncertainty of the measurement result was analyzed. [ABSTRACT FROM AUTHOR]

In order to enhance the intensity of emission spectra of laser-induced plasma, a method of combining magnetic field enhanced laser induced breakdown spectroscopy(LIBS) with nanoparticle enhanced LIBS (NELIBS) was proposed. Laser-induced breakdown of copper was respectively performed by using traditional LIBS, magnetic field-enhanced LIBS, NELIBS, and the combination of the magnetic field enhanced LIBS and NELIBS.Some parameters including electron temperature, electron density and signal-to-noise ratio were obtained and analyzed. The experimental results show that when the Au-nanoparticles(Au NPs) are combined with magnetic field in LIBS, the spectral intensity of plasma is effectively enhanced and, the signal-to-noise ratio is significantly improved, and the optimum is achieved when the laser pulse energy is 80 mJ; the electron temperature and the electron density do not change obviously, the ability of using LIBS to detect trace elements is significantly improved. [ABSTRACT FROM AUTHOR]

Aiming at the existing problems in the dynamic calibration method of temperature sensor, Quasi-δ pulse temperature dynamic calibration method is proposed. The Quasi-δ pulse dynamic calibration principle was described, the characteristics of Quasi-δ pulse function were analyzed, and the relationship among pulse width of Quasi-δ signal, frequency range that can be calibrated, and calibration precision was obtained. Quasi-δ pulse temperature dynamic calibration system was built using the laser as the heat source which generates the excitation temperature required for dynamic calibration. In the system, optical focusing system was designed using ellipsoidal mirror which has a better conjugate imaging property. Applying the dynamic calibration system, a K-type armored thermocouple was calibrated dynamically, and mathematical model of the calibrated thermocouple was established by generalized least squares method based on a special whitening filter. Dynamic characteristics of the thermocouple in time domain and frequency domain were analyzed. [ABSTRACT FROM AUTHOR]

Layered o -LiMnO 2 , synthesized by mechanical alloying (MA) with LiOH·H 2 O and Mn 2 O 3 without any inert atmosphere protection at room temperature, is a potential useful electronic material as cathode for lithium ion batteries. In this study, the effects of solid/liquid weight ratio(S/LWR), ball/powder weight ratio (B/PWR), Li + /Mn 3+ mole ratio (L/MMR), MA time (MAT), process control agent (PCA) and synthesis temperature on the structural properties of layered o -LiMnO 2 were examined, and it was found that the optimum preparation conditions were B/PWR of 40:1, S/LWR of 2:1, L/MMR of 1.5:1, MAT of 10 h and PCA of deionized water, at which the pure o -LiMnO 2 with small particle size and bulk lattice parameter could be synthesized. [ABSTRACT FROM AUTHOR]

Background and Purpose: Fangchinoline is a novel anti-tumour agent with little known of its cellular and molecular mechanisms of action. Here we have investigated the mode of cell death induced by fangchinoline and its underlying mechanism in two human hepatocellular carcinoma cell lines, HepG2 and PLC/PRF/5.Experimental Approach: Apoptosis and autophagy were monitored in fangchinoline-treated HepG2 and PLC/PRF/5 cells by histological methods. The signal transduction pathways involved in activation of autophagy were examined, using immunoblotting, real-time PCR and siRNA techniques.Key Results: Fangchinoline did not induce apoptosis in HepG2 and PLC/PRF/5 cells but triggered, dose-dependently, autophagy, an alternative mode of cell death which may contribute to fangchinoline's anti-tumour action. Nuclear translocation of p53 was involved in induction of autophagy by fangchinoline, followed by selective transactivation of the autophagy-related gene sestrin2 and initiation of the autophagic process. Signalling by the AMP-activated protein kinase was also involved as a downstream target of sestrin2 and induced mTOR-independent autophagic cell death in both cell lines. siRNA for Atg 5 or pharmacological block of p53 abolished fangchinoline-induced autophagy and inhibition of autophagy switched cell death to apoptosis in these cells, suggesting that cell death is irreversible once autophagy is induced by fangchinoline.Conclusions and Implications: Fangchinoline is a highly specific agent inducing autophagic cell death in hepatocellular carcinoma cells with a novel mechanism, which elucidates the potential of fangchinoline to potentiate programmed cell death in cancer cells. [ABSTRACT FROM AUTHOR]

Due to the complexity of, and low accuracy in, iron ore classification, a method of Laser-Induced Breakdown Spectroscopy (LIBS) combined with machine learning is proposed. In the research, we collected LIBS spectra of 10 iron ore samples. At the beginning, principal component analysis algorithm was employed to reduce the dimensionality of spectral data, then we applied k-nearest neighbor model, neural network model, and support vector machine model to the classification. The results showed that the accuracy of three models were 82.96%, 93.33%, and 94.07% respectively. The results also demonstrated that LIBS with machine learning model exhibits an excellent classification performance. Therefore, LIBS technique combined with machine learning can achieve a rapid, precise classification of iron ores, and can provide a completely new method for iron ores' selection in the metallurgical industry. [ABSTRACT FROM AUTHOR]

In dynamic temperature measurement, the dynamic characteristics of the sensor affect the accuracy of the measurement results. Thermocouples are widely used for temperature measurement in harsh conditions due to their low cost, robustness, and reliability, but because of the presence of the thermal inertia, there is a dynamic error in the dynamic temperature measurement. In order to eliminate the dynamic error, two-thermocouple sensor was used to measure dynamic gas temperature in constant velocity flow environments in this paper. Blind system identification of two-thermocouple sensor based on a cross-relation method was carried out. Particle swarm optimization algorithm was used to estimate time constants of two thermocouples and compared with the grid based search method. The method was validated on the experimental equipment built by using high temperature furnace, and the input dynamic temperature was reconstructed by using the output data of the thermocouple with small time constant. [ABSTRACT FROM AUTHOR]