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The research on high power 190 GHz doubler based on the GaAs Schottky diodes is proposed in this paper. The frequency doubler comprises a improved diode configuration that increases the number of anodes by changing the diode arrangement to improve power handling capacity. Electromagnetic and thermal simulation is utilized to demonstrate that the doubler can carry more power. The input power is gradually pumping from 200 mW to 500 mW with an applied DC bias of −15 V. And the peak efficiency of the doubler is measured to be 17%, while the maximum output power is 85 mW at 190 GHz.

Withering is one of the major processing steps critical for the quality of black tea. In this study, we investigated the mechanisms underlying the physicochemical changes in metabolites and gene expression during the withering process of black tea using metabolomic and transcriptomic approaches, respectively. Based on gas chromatography/mass spectrometry non-targeted metabolomic approaches (GC-MS) and ultra-high performance liquid chromatograph–tandem mass spectrometry (UHPLC-MS/MS), a total of 76 volatile compounds and 160 non-volatile compounds were identified from tea leaves, respectively. RNA-seq analysis revealed that the number of differentially expressed genes (DEGs) for the comparative combination of withering time (i.e., W4h, W6h, W8h, W10h, and W12h) compared with CK (i.e., fresh leaves) were 3634, 2906, 4127, 5736, and 7650, respectively. The core genes in starch metabolism, namely alpha-amylase (AMY) and beta-amylase (BAM), were upregulated as withering time increased. AMY and BAM contributed to the decomposition of starch to increase the soluble sugars. The content of tea leaf alcohols and aldehydes, which are the vital contributors for greenish aroma, gradually decreased as withering time increased due to the downregulation of associated genes while the compounds related to sweet and fruity characteristics increased due to the upregulated expression of related genes. Most DEGs involved in amino acids were significantly upregulated, leading to the increase in free amino acids content. However, DEGs involved in catechins metabolism were generally downregulated during withering, and resulted in a reduction in catechins content and the accumulation of theaflavins. The same trend was observed in alpha-linolenic acid metabolism-related genes that were downregulated and enhanced the reduction in grassy aroma in black tea. The weighted gene co-expression network analysis (WGCNA) of DEGs showed that one module can be associated with more components and one component can be regulated by various modules. Our findings provide new insights into the quality formation of black tea during the withering process.

BackgroundThe worldwide human monkeypox (mpox) outbreak in 2022 mainly affected men who have sex with men (MSM). In China, young men who have sex with men (YMSM) were at a potential high risk of mpox infection due to their sexual activeness and the eased COVID-19 restrictions at the end of 2022. ObjectiveThis study aimed to investigate the behavioral intention of receiving mpox vaccination and undergoing mpox testing in 4 different scenarios and explore their associations with background and behavioral theory–related factors among Chinese YMSM. MethodsAn online cross-sectional survey was conducted among YMSM aged 18-29 years from 6 representative provinces of China in September 2022. Participants recruited (recruitment rate=2918/4342, 67.2%) were asked to self-administer an anonymous questionnaire designed based on prior knowledge about mpox and classic health behavior theories. Data on the participants’ background, mpox knowledge and cognition, mpox vaccination and testing cognition, and the behavioral intention of receiving mpox vaccination and undergoing mpox testing were collected. Descriptive analysis and univariate and multivariate linear regressions were performed. Geodetector was used to measure the stratified heterogeneity of behavioral intention. ResultsA total of 2493 YMSM with a mean age of 24.6 (SD 2.9) years were included. The prevalence of having a behavioral intention of receiving mpox vaccination ranged from 66.2% to 88.4% by scenario, varying in epidemic status and cost. The prevalence of having an mpox testing intention was above 90% in all scenarios regardless of the presence of symptoms and the cost. The positive factors related to vaccination intention included mpox knowledge (ba=0.060, 95% CI 0.016-0.103), perceived susceptibility of mpox (ba=0.091, 95% CI 0.035-0.146), perceived severity of mpox (ba=0.230, 95% CI 0.164-0.296), emotional distress caused by mpox (ba=0.270, 95% CI 0.160-0.380), perceived benefits of mpox vaccination (ba=0.455, 95% CI 0.411-0.498), self-efficacy of mpox vaccination (ba=0.586, 95% CI 0.504-0.668), and having 1 male sex partner (ba=0.452, 95% CI 0.098-0.806), while the negative factor was perceived barriers to vaccination (ba=–0.056, 95% CI –0.090 to –0.022). The positive factors related to testing intention were perceived severity of mpox (ba=0.283, 95% CI 0.241-0.325), perceived benefits of mpox testing (ba=0.679, 95% CI 0.636-0.721), self-efficacy of mpox testing (ba=0.195, 95% CI 0.146-0.245), having 1 male sex partner (ba=0.290, 95% CI 0.070-0.510), and having in-person gatherings with MSM (ba=0.219, 95% CI 0.072-0.366), while the negative factor was emotional distress caused by mpox (ba=–0.069, 95% CI –0.137 to –0.001). ConclusionsAmong Chinese YMSM, the intention of undergoing mpox testing is optimal, while the mpox vaccination intention has room for improvement. A future national response should raise YMSM’s mpox knowledge, disseminate updated information about mpox and preventive measures, improve preventive service accessibility and privacy, and provide advice on positively coping with the associated emotional distress.

Abstract 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.

When the operating state of the power system changes, a modular multilevel converter power electronic transformer (MMC-PET) based on modular multilevel converters cannot perform efficient energy transfer and power conversion under conventional control strategies. To address the above problems, this paper proposes a passive, second-order super-helical sliding mode control strategy for MMC-PET by combining passive control and second-order super-helical sliding mode control with a stronger anti-interference capability. First, a Euler–Lagrange model based on positive and negative sequence separation is established according to the mathematical model of the MMC; second, the model of the system is passively analyzed, and a passive controller is designed according to its passivity, and the passive controller is further optimized by using the super-helical second-order sliding mode control, which improves the overall robustness and interference immunity; finally, the effectiveness and superiority of the super-twisting second-order sliding mode passive control strategy is demonstrated by verifying it through the construction of the MMC-PET simulation model and testing it under various non-ideal working conditions.

Nine tea cultivars planted in Enshi were selected and processed into “Lichuan black tea”. Sensory evaluation showed that cultivar had the greatest influence on taste and aroma quality, including sweetness, umami and concentration of taste, as well as sweet and floral fragrances of aroma. The non-volatile and volatile components were identified by UPLC-Q-TOF/MS and GC–MS, and PCA analysis showed good separation between cultivars, which could cause the difference in quality. Baiyaqilan, Meizhan and Echa 10 had a floral aroma, with obvious difference in their aromatic composition from other cultivars. Moreover, Echa 10 also had a strong sweet aroma. The key aroma components in Echa 10 (with the largest cultivation area) were further investigated by GC-O-MS combined with odor activity value (OAV) analysis, included β-damascenone, phenylethylaldehyde, nonenal, geraniol, linalool, jasmonone, (E)-2-nonenal, β-cyclocitral, (E)-β-ocimene, methyl salicylate, β-ionone, 2,6,10,10-tetramethyl-1-oxaspiro[4.5]dec-6-ene, citral, β-myrcene, nerol, phenethyl alcohol, benzaldehyde, hexanal, nonanoic acid, and jasmin lactone.

Abstract Impedance matching is one outstanding issue for the electret‐based vibration energy harvester (eVEH) due to its small equivalent capacitance. In this paper, the authors developed a synchronized charge extraction (SCE) circuit for eVEH, to improve its performance in small load resistance conditions. An electromagnetic detector is embedded with the eVEH to detect its maximum vibration amplitude. The synchronized switch is controlled by a microcontroller unit. According to the authors’ simulation study, the power output of eVEH interfaced with the SCE circuit is three times larger than that of the standard circuit, which proved the effectiveness of our SCE circuit.

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.

Abstract 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.

This study delved into the aroma characteristics of “Qingxiang” oolong tea, analyzing six different cultivars and their processing modes. The findings showed that both cultivars and processing modes have a significant impact on the oolong tea aroma system. The study identified 18 terpenoid volatiles (VTs), 11 amino-acid-derived volatiles (AADVs), 15 fatty-acid-derived volatiles (FADVs), 3 carotenoid-derived volatiles (CDVs), and 10 other compounds in oolong tea that differentiate it from green and black tea. The turn-over stage was found to be the primary processing stage for oolong tea aroma formation. Molecular sensory analysis revealed that the “fresh” odor attribute is the basis for its aroma, while “floral and fruity” fragrances are its aroma characteristics. The perception of oolong tea as “fresh” and “floral and fruity” is influenced by the interactions of its aroma components. These findings provide a new basis for breed improvement and process enhancement in oolong tea production.

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.

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.

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.

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.

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.

A comprehensive model is a basis for adequately understanding the energy conversion mechanism of magnetostrictive materials and guides the design of magnetostrictive devices. This paper established a fully coupled model that fully considered the piezomagnetic effect and the electromagnetic effect in its procedure of energy conversion. The proposed model can accurately predict the output voltage with different excitation levels and illustrate the interaction between physical fields. Based on the established model, we quantify the voltage contributed by the piezomagnetic effect and the electromagnetic effect. After a thorough analysis, we discovered that even though the voltage generation is always dominated by the piezomagnetic effect of the magnetostrictive material, the proportion from the electromagnetic effect will increase moderately with larger excitation levels. In addition, the magnetostrictive effect toward the output performance is also discussed, which proved to lead to a marginal impact on the voltage waveform.

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.

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 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 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.

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.

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.

The present study emphasizes the effect of withering time set at 4 ± 0.5 h (WT4), 6 ± 0.5 h (WT6), 8 ± 0.5 h (WT8), 10 ± 0.5 h (WT10), and 12 ± 0.5 h (WT12) on the sensory qualities, chemical components, and nutritional characteristics of black tea. The sensory evaluation revealed high total quality scores at WT8 and WT10. Polysaccharides, amino acids, and soluble sugars significantly increased with an increase in withering time, and an apparent peak value was obtained at WT10. However, polyphenols, flavonoids, glycosides, organic acids, catechins, alkanoids, and theaflavins decreased with an increase in withering time. With an increase in withering time, the content of aromatic substances showed a trend of increasing first and then decreasing. The peaks of alcohols, aldehydes, and acids appeared at 10 ± 0.5 h, 10 ± 0.5 h, and 8 ± 0.5 h, respectively. The content of esters, ketones, and hydrocarbons showed a downward trend with an increase in withering time. Aroma analysis revealed that withering time could not exceed 10 ± 0.5 h. Black tea withered up to WT10 showed enhanced inhibition of α-glucosidase and α-amylase activity with good sensorial attributes. Glucose uptake inhibition capacity increased up 6 ± 0.5 h and then decreased, while antioxidant capacity decreased with an increase in withering time. The overall results show that the 8 ± 0.5 h to 10 ± 0.5 h withering time could improve black tea quality and nutritional characteristics.

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–Atherton model, we analyze the SGMA magnetization model, simplify the traditional model, and give a solution for the simplified model using the Runge–Kutta method. We analyze the eddy current loss of the SGMA, and according to Bessel’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.

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 μm junction’s diameter was used in the design of the 200 GHz balanced doubler and, on the other hand, a diode with a 5 μm diameter was used in the 215 GHz unbalanced tripler. The measured results indicated that the output power of the doubler was more than 250 μW at 180~218 GHz, and the maximum was 950 μW 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’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 continuous refinement of specialization requires that the group manufacturing company must be constantly focused on how to concentrate its core resources in special sphere to form its core competitive advantage. However, the resources in enterprise group are usually distributed in different subsidiary companies, which means they cannot be fully used, constraining the competition and development of the enterprise. Conducted as a response to a need for cloud manufacturing studies, systematic and detailed studies on cloud manufacturing schema for group companies are carried out in this paper. A new hybrid private clouds paradigm is proposed to meet the requirements of aggregation and centralized use of heterogeneous resources and business units distributed in different subsidiary companies. After the introduction of the cloud manufacturing paradigm for enterprise group and its architecture, this paper presents a derivation from the abstraction of paradigm and framework to the application of a practical evaluative working mechanism. In short, the paradigm establishes an effective working mechanism to translate collaborative business process composed by the activities into cloud manufacturing process composed by services so as to create a foundation resulting in mature traditional project monitoring and scheduling technologies being able to be used in cloud manufacturing project management.