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There are many factors involved in the assembly process of complex products, but only a few of the key influencing factors really affect the performance of the product assembly. In order to ensure the performance of complex products, it is necessary to model the deviation transfer flow of the assembly process of complex products and identify key processes and characteristics and determine its scope of influence. This paper establishes the set of assembly performance influencing factors by analyzing the complex product assembly process and uses complex networks combined with entropy weight method to identify the influencing factors of product performance to achieve the identification of key influencing factors. Based on the key factor identification results using an Apriori algorithm to mine the association rules between key factors and performance to provide guidance for the assembly of complex products and achieve transparent and controllable performance. The assembly process of a certain type of aeroengine low-pressure fan rotor is verified as the research object. The results show that the proposed method can identify the key factors in the multistage assembly process and establish an association rule library to provide decision support for process adjustment.

As an important component of the machining system, the influence of fixtures on the machining deformation of the workpiece cannot be ignored. By controlling the clamping force during the machining process is an effective means to suppress or improve the machining deformation. However, due to the dynamic coupling of part geometry, clamping method, manufacturing process and time-varying cutting forces, it is difficult to obtain accurate clamping forces, which hinders the realization of fixture-based deformation control. In this paper, the variation of clamping force is considered as the response of the joint action of cutting force and other working conditions in spatial and temporal terms, and a clamping force prediction method based on deep spatio-temporal network is proposed. The part geometry model is first parameterized based on voxels, after which the cutting forces are dynamically correlated with the clamping forces in spatial and temporal terms. Then, a convolutional network was designed to capture the spatial correlation between the working conditions such as cutting force and clamping force, and a gated recurrent cell network to capture the temporal correlation to predict the clamping force during machining. Finally, an experiment of milling a cylindrical thin-walled part illustrates the effectiveness of the proposed method.

Serving as one of the core component, the excitation coil can exert remarkable influence on the output performance of giant magnetostrictive actuator (GMA) for electronic controlled fuel injector. In this paper, a multi-objective coil optimization scheme is proposed to balance the conflicting response speed and magnetic field intensity determined by the coil parameters. Firstly, a physics-based coil model is established for optimization, whose parameters can be directly calculated by the coil dimensions. Then, with the current response of the coil calculated, a multi-objective optimization framework is conducted attempting to get the selection guideline for the enameled wire diameter of the coil. The optimal choices exist when the outer diameter of the enameled wire falls in 0.9∼1.6 mm, and the inner/outer diameter ratio is relatively high. Finally, a series of experiments are conducted and the results indicate that the proposed model is able to accurately describe the current response throughout the operating frequencies, and the optimization scheme can provide a valuable roadmap to design coil for high performance GMA.

PVDF-based polymers with polar covalent bonds are next-generation dielectric materials for electric energy storage applications. Several types of PVDF-based polymers, such as homopolymer, copolymers, terpolymers and tetrapolymers, were synthesized by...

High-performance two-stage bipolar cascade 905nm vertical cavity surface emitting lasers (VCSELs) are designed and fabricated. Three different designs are investigated to compare the electrical and optical properties. Experimental results show that the current spreading and thermal problems are important factors affecting the device performance. The device with two oxide apertures accompanied with a $1.5\lambda $ -long active region spacing exhibits a very high slope efficiency of 2.27W/A and a relatively high power conversion efficiency of 52.4%. Such high-performance bipolar cascade VCSELs with 905nm are very important for LiDAR and automotive applications.

At present, the research of electronically controlled injectors is mostly limited to the non-direct drive structure. Although the research on the direct drive structure is involved, it mostly stays in the conceptual machine or simulation stage. In this paper, based on the direct-drive structure, the giant magnetostrictive material is used as the energy conversion material, the prototype of the direct-drive giant magnetostrictive fuel injector is designed and manufactured, and the experimental test system and AMESim simulation model are built. By means of experiment and simulation, the injection characteristics of Giant magnetostrictive injector (GMI) are tested. It is found that the minimum single injection quantity of GMI is 5.9 mm3 under the condition of 30 MPa rail pressure, which shows high injection accuracy. The experimental results are in good agreement with the simulation results under different driving pulse widths and voltages. When the driving pulse width is not less than 650 µs, the relative errors are all less than 5%, which verifies the effectiveness of the simulation model. The injection performance of GMI is analyzed. The results show that this injector has a stable injection performance, fast response speed (the shortest injection pulse width is about 200 µs), and the injection process can be completed five times in 5 ms.

The oolong tea aroma is shown to consist of cultivar aroma and technical aroma in this study based on the aroma differences between oolong tea products of cultivars of different suitability, as determined by correlation analysis of olfactory, sensory, electronic nose, and GC-MS data. Human senses were significantly affected by the aroma components, which included eight terpene metabolites (β-Ocimene, (Z)-Furan linalool oxide, linalool, (3E)-4,8-Dimethyl-1,3,7-nonatriene, (E)-Pyranoid linalool oxide, γ-Elemene, Humulene, (Z,E)-α-Farnesene), three carotenoid metabolites (β-Ionone, (Z)-Geranylacetone and 6-methyl-5-Hepten -2-one), three lipid metabolites ((Z)-3-Hexenyl (Z)-3-hexenoate, Butanoic acid hexyl ester, and (Z)-Jasmone), four amino acid metabolites (Methyl salicylate, Geranyl isovalerate, indole, and Phenylethyl alcohol), and six thermal reaction products (2-Pentylfuran, Octanal, Decanal, (E,E)-2,4-Nonadienal, (Z)-2-Decenal, and (E)-2-Undecenal). Meanwhile, several aroma compounds (such as (E)-Nerolidol and α-Farnesene), mainly comprising the “technical aroma” formed in the processing mode, were noted to be less closely related to cultivar suitability. This study sheds light on the aroma characteristics of different tea cultivars for oolong tea processing.

Bolted connections are the main method of connecting the components of an aero-engine low pressure rotor. Due to the influence of the elastic interaction relationship, it is easy to cause uneven distribution of the preload force of the bolt group, although it can meet the stiffness needs; however, it may lead to the deflection of the spatial relative position of the components, which is can easily cause coaxiality overrun. In view of the contradictory problem of optimization between coaxiality and stiffness of rotor assembly, this paper proposes a semi-physical simulation optimization method for the bolt tightening process based on reinforcement learning. Firstly, by studying the elastic interaction mechanism between the bolts, the elastic interaction matrix was established using finite element simulation data. On this basis, a coaxiality prediction model for the bolt tightening process was established using a GRU (gate recurrent unit) network to realize the prediction of coaxiality in the bolt tightening process. Then, through the analysis of the bolt connection stiffness, a stiffness calculation model containing the bolt stiffness, the stiffness of the connected parts, and the contact stiffness of the joint surface in series was constructed to realize the calculation of the stiffness during the bolt tightening process. Finally, with the bolt preload force as the optimization variable, coaxiality and stiffness as the optimization target, and the tightening torque and preload force of the installed bolts in the actual assembly process as the constraints, a semi-physical simulation optimization model of the bolt tightening process was established using reinforcement learning to realize the optimization of the bolt tightening process. Moreover, through the semi-physical simulation optimization method, the bolt tightening process can be installed and adjusted at the same time.

Greater than 190 °C continuous wave (CW) lasing is achieved from 905 nm high-efficiency vertical cavity surface-emitting lasers (VCSELs). The maximum power conversion efficiency (PCE) of 46.3% for ${4}~\mu \text{m}$ VCSELs was demonstrated under the ambient temperature of 30 °C. The maximum PCE decreases only 10% of its value from 30 °C to 90 °C, showing stable high-temperature characteristics. The thermal resistances and active region temperatures of the device versus ambient temperature are determined experimentally. Besides, size-dependent thermal characteristics are also analyzed by characterizing the spectral and spatial properties of devices with various apertures. It is found that the higher the ambient temperature is, the less the number of high-order modes is, and single fundamental mode operation can be realized in ${4}~\mu \text{m}$ VCSELs even at a relatively high temperature of 190 °C. The design and characterization of these high-temperature VCSEL devices might present the basis for future laser radar application.

The output characteristics of vertical cavity surface emitting lasers (VCSELs) arrays are largely influenced by temperature. We systematically studied thermal properties in 20-element oxide-confined VCSEL arrays via both theory and experiment. The output power, conversion efficiency, and threshold current dependent on ambient temperatures were measured. According to the relationship of wavelength shift and dissipated power, the device thermal resistance dependent on ambient temperature was obtained. The actual temperature in active region was successfully extracted. A 3-D heat diffusion model was established to simulate temperature distribution accurately.

An aero engine fan rotor is composed of a multi-stage disk and multi-stage blades. Excessive unbalance of the aero engine fan rotor after assembly is the main cause of aero engine vibration. In the rotor assembly process, blade sequencing optimization and multi-stage blade set assembly phase optimization are important for reducing the overall rotor unbalance. To address this problem, this paper proposes a semi-physical simulation method based on reinforcement learning to optimize the balance in the fan rotor assembly process. Firstly, based on the mass moments of individual blades, the diagonal mass moment difference is introduced as a constraint to build a single-stage blade sorting optimization model, and reinforcement learning is used to find the optimal sorting path so that the balance of the single-stage blade after sorting is optimal. Then, on the basis of the initial unbalance of the disk and the unbalance of the single-stage blade set, a multi-stage blade assembly phase optimization model is established, and reinforcement learning is used to find the optimal assembly phase so that the overall balance of the rotor is optimal. Finally, based on the collection of data during the assembly of the rotor, the least-squares method is used to fit and calculate the real-time assembly unbalance to achieve a semi-physical simulation of the optimization of balance during the assembly process. The feasibility and effectiveness of the proposed method are verified by experiments.

The internal magnetic field characteristics of giant magnetostrictive actuators have an important influence on their output performance. Aiming at the deficiency of current scholars’ research, based on the electromagnetic theory and finite element method, this paper analyzes the magnetic field intensity on a giant magnetostrictive cylinder by using COMSOL Multiphysics software. Considering the inhomogeneity of magnetic field intensity along the radial direction of giant magnetostrictive cylinders, a new averaging method is introduced to calculate the magnetic field intensity in the axial section of the cylinder. The influence of the magnetic permeability of the displacement conversion mechanism (shell) and the size of the air gap inside the device on the magnetic field intensity of the giant magnetostrictive cylinder are analyzed. The prototype of the actuator is manufactured, and the correctness and accuracy of the simulation data are verified by experiments. In order to make the magnetic field on the cylinder strong and uniform, the displacement conversion mechanism and the shell should be made of low permeability and high permeability materials, respectively, and the air gap size should be reduced as much as possible under the condition of meeting the size requirement of the actuator pre-tightening force applying device.

Implant-defined coherent vertical-cavity surface-emitting laser (VCSEL) arrays with an integrated phase shifter that can convert the out-of-phase mode into the in-phase mode were demonstrated. The measured angular full-width at half-maximum (FWHM) from a ${9} \times {9}$ coherent VCSEL array in the far-field lobes is only 0.82°. The intensity of the central lobe is about 2.9 times than that of surrounding sidelobes, showing excellent beam quality. The FWHM of the spectrum of a ${9} \times {9}$ array is only 0.02 nm, showing a single-mode operation. It provides a potential method to fabricate larger in-phase coherent VCSEL arrays. A remaining challenge is enhancing output power via solving the heat problem.

Electrochemical profiles of Lycors radiata, L. aurea, L. sprengeri, L. longituba and L. chinensis were recorded from the embryo tissue of seeds. Differential pulse voltammetry (DPV) measurements re...

Tool wear prediction is essential to ensure part quality and machining efficiency. Tool wear is affected by factors such as the material, structure, process, and processing time of the parts. Tool wear under the variable working conditions and the above factors show a complex coupling and timing correlation, which makes it challenging to predict tool wear under variable working conditions. This article aims to resolve this issue. First, we establish a unified representation of working condition factors. The stacked autoencoder (SAE) model adaptively extracts tool wear features from the machining signal. The extracted wear features and respective working conditions then combine into a working condition feature sequence for predicting tool wear. Finally, the advantages of the long short-term memory (LSTM) model to solve memory accumulation effects learn the regular wear pattern of the working condition feature sequence to realize the prediction of the tool wear. An experiment illustrates the effectiveness of the proposed method.

Evaluation of tool wear is vital in manufacturing system, since early detections on worn-out condition can ensure workpiece quality, improve machining efficiency. With the development of intelligent manufacturing, tool wear prediction technology plays an increasingly important role. However, traditional tool wear prediction methods rely on experience and knowledge of experts and are labor-extensive. Deep learning provides an effective way to extract features of raw data and establish the mapping relationship between features and targets automatically. In this paper, a new local-feature and global-dependency based tool wear prediction method is proposed. It is a hybrid approach combining manual features with automatic features. Firstly, an enhanced CNN network is designed and applied on the transformed wavelet scalogram to learn the local single-scale specific features and multi-scale correlation features automatically. Secondly, sequence of local feature vectors combining manual features with automatic features are fed into multi-layer LSTM step by step for the global dependency. A fully connected layer is then trained to predict tool wear. Finally, two statistics are proposed to illustrate the overall prediction performance and generalization ability of the model. An experiment illustrates the effectiveness of our proposed method under multiple working conditions.

Green tea processed by Echa 10 was shown to have a fresh and mellow taste as well as clean aroma with a clear honeysuckle fragrance. The colors of different Enshi green teas are closely related with the content of chlorophyll and chlorophyllide. The five green teas also vary in their aroma style. Echa 10 imparts a special honeysuckle fragrance, which was further analyzed by molecular sensory analysis and the formation of this honeysuckle fragrance was attributed to the key components of dodecane, octadecane, phenethyl alcohol, and jasmonone. In aroma evaluation, Echa 10 green tea showed the best performance, which is mainly related with the content of geraniol, linalool, phenethyl alcohol, and benzyl alcohol. Additionally, Echa 10 scored the highest in taste evaluation, which is mainly determined by the contents and ratios of tea polyphenols, amino acids, caffeine, and soluble sugars.

Magnetostrictive transducer, as a kind of high-efficiency transducer, has a broad application in the field of energy harvesting and sensor. However, the voltage generating performance of the transducer limits its application. This paper will endeavor to quantify the load-induced voltage of magnetostrictive transducer by developing a holistic theoretical model and deriving the corresponding analytical solution. On this basis, this paper proposed the concept of voltage sensitivity in transducer optimization, which could quantitatively analyze the normalized voltage generated by a unit excitation. Based on the theoretical simulation and experimental validation, the voltage sensitivities of magnetostrictive transducer under different mechanical loads are investigated. The parametric analysis will be conducted to further optimize the transducer structure to enhance its open-circuit and load voltage. The works of this paper could be instructive to guide magnetostrictive transducer design in sensing and energy harvesting applications.

A high-performance terahertz Schottky barrier diode (SBD) with an inverted trapezoidal epitaxial cross-sectional structure featuring high varactor characteristics and reverse breakdown characteristics is reported in this paper. Inductively coupled plasma dry etching and dissolution wet etching are used to define the profile of the epitaxial layer, by which the voltage-dependent variation trend of the thickness of the metal-semiconductor contact depletion layer is modified. The simulation of the inverted trapezoidal epitaxial cross-section SBD is also conducted to explain the physical mechanism of the electric field and space charge region area. Compared with the normal structure, the grading coefficient M increases from 0.47 to 0.52, and the capacitance modulation ratio (C max/C min) increases from 6.70 to 7.61. The inverted trapezoidal epitaxial cross-section structure is a promising approach to improve the variable-capacity ratio by eliminating the accumulation of charge at the Schottky electrode edge. A 190 GHz frequency doubler based on the inverted trapezoidal epitaxial cross-section SBD also shows a doubling efficiency of 35% compared to that 30% of a normal SBD.

Aiming at the inherent problems of non-direct-drive injectors, such as long oil circuits and a difficult precise control of the needle stroke, based on the structure of a direct-drive injector and combined with the demand for the needle drive of a truck diesel injector, this study designs and fabricates a giant magnetostrictive actuator for direct-drive injectors. A simplified model of a giant magnetostrictive actuator is established, which mainly simplifies the magnetization model to facilitate the subsequent integrated modeling of the fuel injector. An actuator output displacement test system was built, and the output waveform and frequency characteristics of the actuator were analyzed. It was found that the experimental results were in good agreement with the calculated results of the model, and the average relative error of the amplitude was 3.26%, while the average relative error of the phase difference was 3.83%, which verifies the correctness of the model. This research enriches the modeling method of giant magnetostrictive actuators and has an important reference value for the research and design of fuel injectors.

The assembly, as the core part of low-pressure rotor manufacturing, is of great importance to ensure its unbalance. Low-voltage rotor assembly is a multi-process process influenced by the quality of part machining, assembly process, and assembly quality, resulting in unbalance that is difficult to predict during the assembly process. The unbalance measurement in the assembly process is important for the subsequent process optimization. Therefore, in order to achieve the prediction of unbalance measurement in the assembly process, this paper proposes an unbalance measurement prediction method based on mechanism and data fusion. Firstly, through research and analysis, the influencing factors of unbalance are determined, the low-pressure rotor blade sequencing mechanism model is established, and the blade sequencing optimization is realized by using reinforcement learning. Then, since the unbalance is formed after all the processes are completed and the subsequent work steps in the assembly process have not been carried out yet, the actual process parameters cannot be obtained, the semi-physical simulation method is used to combine the actual data of the assembled work steps with the theoretical data of the unassembled work steps to build a prediction model of the unbalance based on the BRNN (bidirectional recurrent neural network) network to achieve the prediction of the unbalance measurement in the assembly process. Finally, the model was validated using actual assembly process data, which proved the feasibility and effectiveness of the method.

The freshness and color quality of postharvest tea leaves can be markedly prolonged and retained by proper preservation measures. Here, we investigated the dynamic changes of chlorophyll and its derivatives in postharvest tea leaves under different low-temperature treatments using natural withering as a control. Chlorophyll decomposition was found closely related with chlorophyllide, pheophorbide, and pheophytin. Low-temperature withering could slow chlorophyll degradation in postharvest tea leaves via significant inhibition on the enzyme activity and gene expression of Mg-dechelatase, chlorophyllase, and pheophorbide a oxygenase. At the initial stage of withering, a significant increase was observed in the chlorophyll content, expression of chlorophyll-synthesis-related enzymes (such as glutamyl-tRNA synthetase, etc.), and chlorophyll synthase activity in newly picked tea leaves. Moreover, an obvious decrease was found in the content of l-glutamate as the foremost precursor substance of chlorophyll synthesis. Hence, our findings revealed that the chlorophyll synthesis reaction was induced by the light-dehydration-stress in the initial withering of tea leaves. This study provides a theoretical basis for exploring preservation technology in actual green tea production.

A kind of giant magnetostrictive rotary actuator (GMRA) based on inchworm motion is designed in this paper, which can realize high precision rotation motion in large scale and high speed. Applying a series of square wave signals in strict order, the clamping mechanism and driving mechanism of the actuator are alternately operating to realize high precision stepping rotation motion. Finite element method (FEM) is used to analyze the mechanical property of GMRA, suggesting that the designed structure possesses enough mechanical strength. To test the overall performance of the designed actuator, a prototype is fabricated and a series of experiments are conducted. The results indicate that the actuator can function well under driving voltage between 2 V and 4.5 V with driving frequency no higher than 160 Hz. The minimal and maximum stepping angles are 124.2μrad and 308.5μrad respectively. The peak angular velocity and output torque can reach 57.75 mrad/s and 250 N·mm respectively. The experimental consequences prove that the designed actuator can produce highly precise output under different currents and frequencies.

A high-performance terahertz Schottky barrier diode (SBD) with a film-supported single mesa structure featuring a low structural parasitic with extremely low dielectric loss is reported in this brief. The fabricated substrate-free SBD is supported with a 2–3 µm polymer film using a low parasitic coupling capacitor of about 0.12 fF. It significantly reduces the electromagnetic coupling effect between the anode and cathode pads by 90% compared with the traditional structure of the same size. To reduce the Schottky junction capacitance at the anode, a submicron T-junction process is used with a low junction capacitance of 0.15 fF. The cutoff frequency calculated by the total capacitance of the diode is up to 4 THz. The substrate-free terahertz monolithic integration circuit based on this film-supported single mesa SBD has better performance in reducing the circuit dielectric loss, exhibiting a remarkable potential for high-frequency applications.

Based on the structure of direct-drive injectors, the giant magnetostrictive actuator (GMA) for direct-drive injectors designed with giant magnetostrictive materials (GMMs) shows great application prospects in the field of fuel injection. This paper establishes the mathematical model of GMA for direct-drive fuel injector. The numerical model of the GMA is built by means of the Advanced Modeling Environment for performing Simulation of engineering systems (AMEsim) simulation platform combined with the working process of the actuator. A prototype was fabricated, and the correctness of the numerical model was verified by experiments. Through AMEsim, the magnetic potential distribution law, unit step response, and frequency characteristics were analyzed, it is found that the relationship between the magnetic potential and the current on the GMM is approximately linear, and the ratio of the magnetic potential to the total magnetic potential of the magnetic circuit is about 0.9; the unit step response shows that the actuator has a fast response speed, the rise time is about 240 µs, and when the output displacement enters the 5% and 2% error bands, the adjustment time is 540 and 810 µs, respectively; by inputting the sweep signal, it is obtained that the natural frequency of the actuator is about 700 Hz, and the output amplitude is relatively “smooth” with a bandwidth of 0–250 Hz. This research enriches the research methods of GMAs and provides a novel idea for the development of high-performance fuel injectors.

In this paper, a new method for the parameter extraction of Schottky barrier diode (SBD) is presented to eliminate the influence of parasitic parameters on the intrinsic capacitance-voltage (C-V) characteristics of the Schottky diodes at high frequencies. The method is divided into the de-embedding and parameter extraction, including six auxiliary configurations and, is referred tos as Two-step Six-configuration Parameter Extraction Method (TSPEM). Compared to the traditional junction capacitance extraction method, this method can extract the value of junction capacitance at higher frequencies with higher accuracy. At the same time, compared to the other de-embedding methods, this method shows better performance in de-embedding the contributions of parasitic structures from the transmission line measurements. The intrinsic junction capacitances obtained by this method and the three-dimensional (3-D) electromagnetic model are combined to form a diode simulation model, which accurately characterizes the capacitance characteristics of the SBD. It was verified with a 200 GHz double frequency multiplier, and the simulation results and measurement results showed good consistency.

The stratospheric pathway is a major driver of El Niño–Southern Oscillation (ENSO) impacts on mid-latitude tropospheric circulation and winter weather. The weak vortex induced by El Niño conditions has been shown to increase the risk of cold spells, especially over Eurasia, but its role for North American winters is less clear. This study involved idealized experiments with the Whole Atmosphere Community Climate Model to examine how the weak winter vortex induced by extreme El Niño events is linked to North American coldness in spring. Contrary to the expected mid-latitude cooling associated with a weak vortex, extreme El Niño events do not lead to North American cooling overall, with daily cold extremes actually decreasing, especially in Canada. The expected cooling is absent in most of North America because of the advection of warmer air masses guided by an enhanced ridge over Canada and a trough over the Aleutian Peninsula. This pattern persists in spring as a result of the trapping of stationary waves from the polar stratosphere and troposphere, implying that the stratospheric influence on North America is sensitive to regional downward wave activities.

Inspired by data comics, this paper introduces a novel format for reporting controlled studies in the domain of human-computer interaction (HCI). While many studies in HCI follow similar steps in explaining hypotheses, laying out a study design, and reporting results, many of these decisions are buried in blocks of dense scientific text. We propose leveraging data comics as study reports to provide an open and glanceable view of studies by tightly integrating text and images, illustrating design decisions and key insights visually, resulting in visual narratives that can be compelling to non-scientists and researchers alike. Use cases of data comics study reports range from illustrations for non-scientific audiences to graphical abstracts, study summaries, technical talks, textbooks, teaching, blogs, supplementary submission material, and inclusion in scientific articles. This paper provides examples of data comics study reports alongside a graphical repertoire of examples, embedded in a framework of guidelines for creating comics reports which was iterated upon and evaluated through a series of collaborative design sessions.

Inspired by data comics, this paper introduces a novel format for reporting controlled studies in the domain of human-computer interaction (HCI). While many studies in HCI follow similar steps in explaining hypotheses, laying out a study design, and reporting results, many of these decisions are buried in blocks of dense scientific text. We propose leveraging data comics as study reports to provide an open and glanceable view of studies by tightly integrating text and images, illustrating design decisions and key insights visually, resulting in visual narratives that can be compelling to non-scientists and researchers alike. Use cases of data comics study reports range from illustrations for non-scientific audiences to graphical abstracts, study summaries, technical talks, textbooks, teaching, blogs, supplementary submission material, and inclusion in scientific articles. This paper provides examples of data comics study reports alongside a graphical repertoire of examples, embedded in a framework of guidelines for creating comics reports which was iterated upon and evaluated through a series of collaborative design sessions.

Giant magnetostrictive materials (GMMs) have broad application prospects in the field of servo valves, but the giant magnetostrictive actuator (GMA) has problems such as large loss and severe heat generation, which affect the output effect and accuracy. To solve these problems, this paper designs a stacked giant magnetostrictive actuator (SGMA) and analyzes the magnetic circuit and magnetic field distribution of the SGMA. Based on the magnetic field analysis and the Jiles&ndash, Atherton model, we analyze the SGMA magnetization model, simplify the traditional model, and give a solution for the simplified model using the Runge&ndash, Kutta method. We analyze the eddy current loss of the SGMA, and according to Bessel&rsquo, s equation and the Kelvin function, we calculate the relationship among eddy current loss, GMM rod radius, and magnetic field frequency. By analyzing the inherent hysteresis of GMMs, a hysteresis loss model of the SGMA is established in this paper. We also calculate the coil impedance and obtain the coil loss model. Based on the loss model, the SGMA cooling system is designed. Based on the above analysis, we design a SGMA prototype, set-up the corresponding experimental platform, and conduct the necessary experiments. The experimental results show that the SGMA responds well to different signals, but as frequency increases, attenuation, deformation, and hysteresis become more pronounced, which verifies the amplitude and phase changes caused by various losses in the theoretical analysis. The experiment also observes the temperature rise of the oil-cooled SGMA at different frequencies, indicating that the cooling system can effectively control the temperature change of the SGMA, which validates the foregoing analysis.

The present work proposes a 180&ndash, 225 GHz broadband frequency doubler monolithic microwave integrated circuit (MMIC) based on a novel Schottky barrier diode (SBD) terminal structure denoted as a Schottky metal-brim (SMB). Compared with an MMIC adopting the conventional SBD terminal structure, preliminary measurements show that the maximum output power of the MMIC adopting the SMB structure increases from 0.216 mW at 206 GHz to 0.914 mW at 208 GHz. Analysis of the nonlinear current&ndash, voltage and capacitance&ndash, voltage characteristics of the two terminal structures based on an extended one-dimensional drift-diffusion model, indicates that the SMB structure provides significantly better conversion efficiency than the conventional SBD structure by eliminating the accumulation of charge and additional current paths near the Schottky electrode edge. It provides a feasible scheme for the optimization of MMIC applications requiring high power and high efficiency.

A global design method for a terahertz monolithic integrated frequency multiplier is proposed. Compared with a traditional independent design, the method in this paper adopts overall optimization and combines the device with the circuit design. The advantage is that it provides a customized design for frequency multipliers according to specifications. On the basis of the gallium arsenide process of the Institute of Microelectronics, Chinese Academy of Sciences, two types of Schottky diodes have been developed to meet the needs of different designs. On the one hand, a Schottky diode with a 3 &mu, m junction&rsquo, s diameter was used in the design of the 200 GHz balanced doubler and, on the other hand, a diode with a 5 &mu, m diameter was used in the 215 GHz unbalanced tripler. The measured results indicated that the output power of the doubler was more than 250 &mu, W at 180~218 GHz, and the maximum was 950 &mu, W at 198 GHz when driven with 12.3 mW, whereas that of the tripler was above 5 mW at 210~218 GHz and the maximum exceeded 10 mW. Such frequency multiplier sources could be widely used in terahertz imaging, radiometers, and so on.

Giant magnetostrictive actuators (GMA) are widely used in the field of servo valves, but the displacement of GMA is limited, which renders meeting the requirements of large flow direct-drive electro-hydraulic servo valves (DDV) difficult. In order to solve these problems, this study proposes a double-row bow-type micro-displacement amplifier (DBMA), used to increase output displacement of GMA to meet the requirements. This study, by static analysis, analyzes the force of a flexure hinge based on theoretical mechanics and material mechanics, derives the stiffness matrix of the flexure hinge by the influence coefficient method, establishes the pseudo-rigid model, and derives the amplification ratio of a DBMA. Also, by kinetic analysis, using Castigliano&rsquo, s second theorem, a formula of equivalent stiffness and natural frequency of DBMA were derived and the influences of different parameters on them were analyzed, respectively. After that, we analyzed the amplifier using finite element method (FEM) simulation software and verified the model by manufacturing a prototype and building a test system. Theoretical calculations and experimental results showed that the amplification ratio of the DBMA fluctuated between 15.43 and 16.25. The natural frequency was about 305 Hz to 314 Hz and the response bandwidth was up to 300 Hz. The error among the theoretical, simulated, and experimental values was within 8%, supporting the accuracy of the model.

The influence mechanism of different withering methods (CK, indoor natural spreading; LTD, low-temperature plus dark; LTY, low-temperature plus yellow-light; LTCD, low-temperature plus CO2) on non-volatile compounds in postharvest tea leaves was investigated by UHPLC-Q-TOF/MS-based non-targeted metabolomic and transcriptomic analyses. Compared with CK, low-temperature withering could slow down polyphenol oxidation by inhibiting polyphenol oxidase activity and keeping the expression of genes for flavanol synthesis. After withering, the proteinaceous amino acid content increased significantly, especially for LTCD and LTY, mainly due to increased peptidase activity and up-regulation of genes involved in the biosynthesis of valine, leucine, aspartic acid, glutamic acid, phenylalanine, and proline. Moreover, LTCD and LTY enhanced the synthesis of γ-aminobutyric acid and metabolism of phenylalanine-methyl salicylate and tryptophan-indole, respectively. Meanwhile, the transformation of theobromine to caffeine was accelerated under low-temperature withering. This research provides ageneticmetabolicbasis for the application of low-temperature withering to actual green tea processing.

A biosensor has been developed based on disposable screen-printed electrode for recording the electrochemical fingerprint of plant leaf tissue. A thin layer of polydopamine functionalized graphene sheets was coated on the plant tissue modified electrode for signal enhancement. The voltammetric data recorded under different buffer solutions can be derived as patterns for species identification. As the distribution of electrochemical active compounds in plants is controlled by genes, these fingerprints can reflect differences at the genetic level between species. Therefore, the electrochemical fingerprint of plant tissues can be used for phylogenetic research without qualitative analysis. 19 species of Amaryllidaceae including A. africanus, Clivia miniata, Clivia nobilis, Crinum firmifolium, Crinum latifolium, Crinum moorei, Curculiga gracilis, Cyrtanthus breviflorus, Habranthus robustus, Haemanthus albiflos, Haemathus multiflorus, Hippeastrum rutilum, Hymenocallis littoralis, Leucojum aestivum, Sprekelia formosissima, Tulbaghia violacea, Zephyranthes grandiflora, Zephyranthes macrosiphon and Zephyranthes minima have been selected deliberately. The dendrogram deduced from the electrochemical fingerprint was compared with the molecular phylogenetics. The results indicate the electrochemical fingerprint-based phylogenetic study is a persuasive methodology for plant phylogenetic analysis.

Giant magnetostrictive rotary actuators (GMRAs) can realize rotary motion in large scale and with high precision. A series of rectangular voltage signals organized in strict order exerted on GMRA helps its clamping mechanism and driving mechanism complete specific actions such as clamping, loosening, and actuating, thus leading to the stepping output of the rotor. In order to describe the overall performance of GMRA, an angular displacement model is established, which can be divided into four parts, namely, the current model, magnetic field model, output force model, and vibration model. The experimental results indicate that the established model agrees well with the actual performance of GMRA in operating conditions. After that, utilizing the established model, simulations are conducted to analyze the angular response of GMRA. According to the simulation results, the order of driving signals is optimized, making the maximum driving frequency rise from 160 Hz to 210 Hz, while the peak angular speed of GMRA reaches 70.77 mrad/s. The modeling and optimization methods proposed in this paper can be helpful for the structural optimization and controller design of GMRA as well.