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IntroductionPine wilt disease spreads rapidly, leading to the death of a large number of pine trees. Exploring the corresponding prevention and control measures for different stages of pine wilt disease is of great significance for its prevention and control.MethodsTo address the issue of rapid detection of pine wilt in a large field of view, we used a drone to collect multiple sets of diseased tree samples at different times of the year, which made the model trained by deep learning more generalizable. This research improved the YOLO v4(You Only Look Once version 4) network for detecting pine wilt disease, and the channel attention mechanism module was used to improve the learning ability of the neural network.ResultsThe ablation experiment found that adding the attention mechanism SENet module combined with the self-designed feature enhancement module based on the feature pyramid had the best improvement effect, and the mAP of the improved model was 79.91%.DiscussionComparing the improved YOLO v4 model with SSD, Faster RCNN, YOLO v3, and YOLO v5, it was found that the mAP of the improved YOLO v4 model was significantly higher than the other four models, which provided an efficient solution for intelligent diagnosis of pine wood nematode disease. The improved YOLO v4 model enables precise location and identification of pine wilt trees under changing light conditions. Deployment of the model on a UAV enables large-scale detection of pine wilt disease and helps to solve the challenges of rapid detection and prevention of pine wilt disease.

The objective of this study is mainly to analyze the drying kinetic parameters, effective diffusivity, and thermodynamic performance indicators (energy, exergy, heat, and mass transfer coefficients) of tea under different drying conditions of different drying temperatures (DT) and thin layer thicknesses (TT). Experimental drying was conducted at drying temperatures of 70°C, 80°C, and 90°C with thin layer thicknesses of 10 mm, 15 mm, and 20 mm. The results show that a higher drying temperature and a lower thin layer thickness can increase evaporation moisture content and shorten drying time. By evaluating and comparing the fitting of five drying models adopted by the coefficient of determination (R2) and chi-square (χ2), it can be found that the logarithmic model is the best to describe drying behavior. The effective moisture diffusivity shows a positively correlated trend with the increase in DT and TT, with the value of activation energy ranging from 14.030 to 22.344 kJ mol−1 K−1. The specific moisture evaporation rate (SMER), energy efficiency, exergy efficiency, and sustainability index (SI) descend as the TT increases at all DT; the specific energy consumption (SEC) and improvement potential (IP) rate perform in an opposite manner, but the TT remains unchange. The conclusion drawn from the thermodynamic parameters is opposite to the aforementioned. As the TT increases, the heat and mass transfer coefficients show a significant decrease trend. In addition, the heat and mass transfer coefficients are given as functions of DT and TT, and further knowledge shows that the mass transfer coefficient is positively correlated with DT but the heat transfer coefficient is negatively correlated. In conclusion, this article provides new insights into the effects of drying characteristics, energy consumption characteristics and heat and mass transfer characteristics in the process of tea drying under different drying conditions, and provides certain theoretical reference bases for promoting the optimization of industrialized tea drying production machinery design and drying process optimization.

Fruit- and vegetable-harvesting robots are a great addition to Agriculture 4.0 since they are gradually replacing human labor in challenging activities. In order to achieve the harvesting process accurately and efficiently, the picking robot’s end-effector should be the first part to come into close contact with the crops. The design and performance requirements of the end-effectors are affected by the fruit and vegetable variety as well as the complexity of unstructured surroundings. This paper summarizes the latest research status of end-effectors for fruit- and vegetable-picking robots. It analyzes the characteristics and functions of end-effectors according to their structural principles and usage, which are classified into clamp, air suction, suction holding, and envelope types. The development and application of advanced technologies, such as the structural design of end-effectors, additional sensors, new materials, and artificial intelligence, were discussed. The typical applications of end-effectors for the picking of different kinds of fruit and vegetables were described, and the advantages, disadvantages, and performance indexes of different end-effectors were given and comparatively analyzed. Finally, challenges and potential future trends of end-effectors for picking robots were reported. This work can be considered a valuable guide to the latest end-effector technology for the design and selection of suitable end-effectors for harvesting different categories of fruit and vegetable crops.

To have a deeper understanding on the moisture migration patterns and microstructural changes of Green Sichuan Pepper during the hot-air drying process, the low-field nuclear magnetic resonance (LF-NMR) technology and scanning electron microscopy (SEM) methodology were adopted to analyze the moisture distribution, migration patterns and microscopic structural changes under different drying temperatures (45, 55 and 65 °C). The LF-NMR scanning results showed that the internal moisture of the Green Sichuan Pepper mainly includes bound water, immobilized water and free water, which can be respectively symbolized by the relaxation time ranges of T21 (0.1–10 ms), T22 (10–500 ms) and T23 (500–10,000 ms). The immobilized water accounts for 83.72% of the internal water, resulting in the significant drying difficulty of Green Sichuan Pepper. During the drying process, the content of immobilized water and free water exhibited a decreasing trend, while the bound moisture content initially increased and then decreased. In addition, the LF-NMR analysis showed that the parameters peak area A2 demonstrated a high correlation with the moisture content of Green Sichuan Pepper, enabling the prediction of moisture content changes during the drying process. Additionally, the SEM results showed that the pore degree and pore density on the pericarp surface of Green Sichuan Pepper perform significant changes during the drying process, which might be a good explanation for revealing some commonly recognized drying phenomena on Green Sichuan Pepper hot-air drying. In summary, the findings presented in the present work provide some new insights into the moisture distribution, migration patterns and microstructural changes of Green Sichuan Pepper, which can offer theoretical guidance for optimizing the drying process of Green Sichuan Pepper.

The main purpose of this work is to develop a transient analytical model of an industrial tea dryer based on the theory of heat and mass balance, and provide the variation characteristics in tea moisture content and drying air temperature throughout the whole drying process in the drying chamber. A numerical simulation focusing on the established transient analytical model was conducted, and the simulation results show that the moisture content of tea in the drying chamber performs a descend trend along the flow direction over drying time, while the temperature of the dry air performs an ascend trend along the flow direction of tea. The variations coefficient for the tea moisture content and the dry air temperature in the whole drying chamber were determined to be 0.009–0.09 and 0.065–0.126, respectively, indicating that the overall drying process is relatively stable. An actual production drying test was conducted to validate the analytical model, the test results showed that under dynamic drying conditions, the dynamic changes in tea moisture content and dry air temperature in the drying warehouse are consistent with the corresponding trends predicted by the model. Besides, the determination coefficient of tea moisture content is greater than 0.99, the determination coefficient of dry air in the initial drying period is 0.8027, while it is determined to be 0.9872 in the redrying period. The predicted results of the model are in good agreement with the experimental results, which confirms the effectiveness and reliability of the model. The developed transient analytical model can provide a certain reference for the anaphase development of energy optimization, dynamic state monitoring, and parameter matching systems for tea dryer.

In order to precisely reproduce the precise seeding process of the population in the air-suction seed-metering device, it is necessary to execute accurate modeling of seed particles using the bonded-particle model, in combination with the discrete element method (DEM) and computational fluid dynamics (CFD). Through the repose angle, slope screening, rotating container, and particle sedimentation experiments, in this paper, the influence of the filling accuracy of the bonded-particle model on the flow behavior and mixing characteristics of the seed population was first explored based on EDEM software. The viability of the suggested modeling approach for pelleted vegetable seeds, as described in this study, was confirmed by comparing experimental and simulation outcomes. The surface roughness values obtained from the studies above were utilized to assess the accuracy of the bonded-particle model in filling. Additionally, a mathematical technique for determining the surface roughness was provided. Furthermore, an analysis of the multiple contacts in the bonded-particle model was also performed. The results indicated that the simulation results closely matched the experimental data when the number of sub-spheres in the bonded-particle model was equal to or more than 70, as measured by the standard deviation. In addition, the most optimal modeling scheme for the pelletized vegetable seed bonded-particles, based on the cost of coupling simulation, was found to be the bonded-particle surface roughness (BS) with a value of 0.1. Ultimately, a practical example was utilized to demonstrate the utilization of the pelleted vegetable seed bonded-particle model and the DEM-CFD coupling approach in analyzing the accuracy of the seeding process in the air-suction seed-metering device. This example will serve as a valuable reference point for future field studies.

Abstract Background Rapidly progressive glomerulonephritis (RPGN) is clinically manifestations as a rapidly progressive renal failure and pathologically as crescentic and necrotizing lesions with infiltration of inflammatory cells in the glomeruli. Uremic encephalopathy (UE) usually develops in patients who are suffering from acute or chronic renal failure. Objective The purpose of this article is to provide reference for clinical diagnosis and treatment of renal disease complicated with seizures. Patients Two cases of anti‐glomerular basement membrane type rapidly progressive glomerulonephritis complicated with seizures were reported. Materials & Methods In case 1, a 40‐year‐old woman was hospitalized for the treatment of nausea, anorexia, and fever. On admission, she presented with elevated serum inflammatory indicators, moderate anemia, and advanced acute kidney injury requiring hemodialysis. Her anti‐glomerular basement membrane (GBM) antibody in serum and renal tissues was found to be extremely high. She was finally diagnosed with anti‐GBM disease. She was treated with a combination of corticosteroid pulse therapy, oral cyclophosphamide and prednisolone, and plasma exchange, while continued to require maintenance hemodialysis for end‐stage kidney disease. During treatment, she suddenly suffered blindness, seizure, and consciousness disturbance. She was diagnosed as posterior reversible leukoencephalopathy syndrome by magnetic resonance imaging (MRI). The posterior reversible leukoencephalopathy syndrome subsided quickly after control of her hypertension and reinforcement of immunosuppressive treatment. In case 2, the patient also developed epileptic symptoms on the basis of GBM disease, and was given treatment similar to that of Case 1, so that the epileptic symptoms were controlled. Result Reversible posterior leukoencephalopathy syndrome, especially when accompanied by cerebral hemorrhage, may lead to irreversible and lethal neurological abnormalities, and nephrologists should, therefore, be aware of the potential risk of reversible posterior leukoencephalopathy syndrome in patients with anti‐GBM disease. We can discuss the current two cases in the light of the previous literature.

ObjectiveThere is considerable debate as to whether the continuum of major psychiatric disorders exists and to what extent the boundaries extend. Converging evidence suggests that alterations in hippocampal volume are a common sign in psychiatric disorders; however, there is still no consensus on the nature and extent of hippocampal atrophy in schizophrenia (SZ), major depressive disorder (MDD) and bipolar disorder (BD). The aim of this study was to verify the continuum of SZ – BD – MDD at the level of hippocampal subfield volume and to compare the volume differences in hippocampal subfields in the continuum.MethodsA total of 412 participants (204 SZ, 98 MDD, and 110 BD) underwent 3 T MRI scans, structured clinical interviews, and clinical scales. We segmented the hippocampal subfields with FreeSurfer 7.1.1 and compared subfields volumes across the three diagnostic groups by controlling for age, gender, education, and intracranial volumes.ResultsThe results showed a gradual increase in hippocampal subfield volumes from SZ to MDD to BD. Significant volume differences in the total hippocampus and 13 of 26 hippocampal subfields, including CA1, CA3, CA4, GC-ML-DG, molecular layer and the whole hippocampus, bilaterally, and parasubiculum in the right hemisphere, were observed among diagnostic groups. Medication treatment had the most effect on subfields of MDD compared to SZ and BD. Subfield volumes were negatively correlated with illness duration of MDD. Positive correlations were found between subfield volumes and drug dose in SZ and MDD. There was no significant difference in laterality between diagnostic groups.ConclusionThe pattern of hippocampal volume reduction in SZ, MDD and BD suggests that there may be a continuum of the three disorders at the hippocampal level. The hippocampus represents a phenotype that is distinct from traditional diagnostic strategies. Combined with illness duration and drug intervention, it may better reflect shared pathophysiology and mechanisms across psychiatric disorders.

Fused deposition modeling and sintering (FDMS) is a novel 3D printing technique that combines fused deposition modeling with catalytic debinding and sintering processes to enable the rapid production of metal parts with low energy consumption and costs. Firstly, a 316L/POM composite filament is prepared. Subsequently, test specimens are printed using a fused deposition modeling (FDM) printer, followed by catalytic debinding and sintering processes to create dense metal parts. The process parameters show an influence on the part structure and, subsequently, the properties, and this study examines the microstructural characteristics of the 316L/POM composite filament at each process stage. Using tensile strength as the indicator, an orthogonal experiment is designed to identify suitable combinations of process parameters. Experimental results demonstrate that the FDMS process can manufacture 316L stainless steel parts; moreover, they influence the structure and, consequently, the mechanical behavior, as these are strongly related. By appropriately adjusting the process parameters, this method can be suitable for applications requiring functional parts with less stringent strength requirements.

Obstacle avoidance control and navigation in unstructured agricultural environments are key to the safe operation of autonomous robots, especially for agricultural machinery, where cost and stability should be taken into account. In this paper, we designed a navigation and obstacle avoidance system for agricultural robots based on LiDAR and a vision camera. The improved clustering algorithm is used to quickly and accurately analyze the obstacle information collected by LiDAR in real time. Also, the convex hull algorithm is combined with the rotating calipers algorithm to obtain the maximum diameter of the convex polygon of the clustered data. Obstacle avoidance paths and course control methods are developed based on the danger zones of obstacles. Moreover, by performing color space analysis and feature analysis on the complex orchard environment images, the optimal H-component of HSV color space is selected to obtain the ideal vision-guided trajectory images based on mean filtering and corrosion treatment. Finally, the proposed algorithm is integrated into the Three-Wheeled Mobile Differential Robot (TWMDR) platform to carry out obstacle avoidance experiments, and the results show the effectiveness and robustness of the proposed algorithm. The research conclusion can achieve satisfactory results in precise obstacle avoidance and intelligent navigation control of agricultural robots.

In active heave compensation, in order to realize the smooth control of the heave compensation platform, it is necessary to use the ship motion measurement system to accurately obtain the ship displacement signal, invert the ship displacement signal, and then control the expansion and contraction of the electric cylinder so that the compensation platform remains horizontal. The ship displacement measurement system generally adopts the second integral of the acceleration sensor to obtain the ship displacement signal. During the acquisition process of the ship displacement signal, the quadratic integration process of the acceleration, and the communication process of the output control command, there is a processing lag which makes the error accumulate, resulting in a delay in the measurement of the ship motion. In order to collect the ship motion more accurately and control the heave compensation platform more precisely, this paper proposes a ship motion prediction method based on a variable step-variable sampling frequency characteristic LSTM (Long Short-Term Memory) neural network. First, we use the autocorrelation function algorithm to calculate the inherent delay of the lag in the process of signal acquisition by the measurement system. Secondly, the LSTM neural network is used to predict the inherent delay step of the lagging ship displacement signal. During the prediction process, it is found that the difference in the sampling frequency of the displacement signal will lead to a change in the step of the inherent delay—experiment in the laboratory to verify. By controlling the motion platform to simulate the motion of the ship and using the ship motion measurement system and the laser sensor system to measure the displacement signal of the motion platform synchronously, it is verified that the ship motion measurement system does have an inherent delay. Thirdly, on a sailing ship, ship displacement signals are collected by setting multiple sets of ship motion measurement systems. Finally, multiple sets of sampling frequency and multiple steps are set, and the ship motion is predicted based on the variable step-variable sampling frequency LSTM neural network. It is verified that the prediction accuracy is related to the sampling frequency of the signal collector and the prediction step of the LSTM neural network, which improves the prediction accuracy of the model and the timeliness of ship motion acquisition.

Due to the fact that there is no static reference point in ocean space, relative measurement sensors such as laser sensors cannot be applied to the measurement of ship motion. In order to collect the motion signal of a ship in the ocean more accurately, an absolute measurement method for the motion of a ship is proposed. Firstly, the acceleration signal in the heave direction of the ship is obtained by using an acceleration sensor; secondly, the time–frequency domain integration algorithm is used to avoid the problem of the quadratic trend item and the influence of the low-frequency signal, and the ship’s heave displacement value is obtained by integration; finally, the displacement signal measured by the laser sensor and the integrated displacement signal of the acceleration sensor are compared with each other to verify the effectiveness of the absolute measurement of the heave displacement of the ship based on the acceleration sensor. At the same time, an angle sensor is used to measure the roll angle and pitch angle in the motion of the ship, form an absolute measurement system for the motion of the ship, and verify that the measurement points are arranged on the ship, which proves that the system can collect data from the ship in sea areas far from the coast. Motion signals are more advantageous.

Machine vision technology has dramatically improved the efficiency, speed, and quality of fruit-picking robots in complex environments. Target recognition technology for fruit is an integral part of the recognition systems of picking robots. The traditional digital image processing technology is a recognition method based on hand-designed features, which makes it difficult to achieve better recognition as it results in dealing with the complex and changing orchard environment. Numerous pieces of literature have shown that extracting special features by training data with deep learning has significant advantages for fruit recognition in complex environments. In addition, to realize fully automated picking, reconstructing fruits in three dimensions is a necessary measure. In this paper, we systematically summarize the research work on target recognition techniques for picking robots in recent years, analyze the technical characteristics of different approaches, and conclude their development history. Finally, the challenges and future development trends of target recognition technology for picking robots are pointed out.

Detection rapidity and model accuracy are the keys to hyperspectral nondestructive testing technology, especially for Pennisetum sinese Roxb (PsR) due to its extremely high adsorptive heavy metal content. The study of the resolution of PsR is conducive to the analysis of the accumulated heavy metal content in its different parts. In this paper, the contents of Cd, Cu and Zn accumulated in the old leaves, young leaves, upper stem, middle stem and lower stem, as well as the hyperspectral data of the corresponding parts, were measured simultaneously in both fresh and dry states. To begin, the spectral data of PsR were preprocessed by using Ensemble Empirical Mode Decomposition-Daubechies3 (EEMD-db3), Savitzky–Golay (SG), Symlet3 (sym3), Symlet5 (sym5), and multiplicative scatter correction (MSC). The 40 samples were divided into 32 training sets and 8 validation sets. The preprocessed spectral data were transformed by the first derivative (FD) and reciprocal logarithm (log(1/R)) to highlight the singularities using binary wavelet decomposition. After screening the significant bands from the correlation curve, the competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA) were applied to extract the spectral characteristic variables, which were used to establish the partial least-squares (PLS) regression and multiple stepwise linear regression (MSLR) inversion models of Cd, Cu, and Zn contents. Based on EEMD-db3 pretreatment, the inversion model of Zn in the dry (fresh) state had R2 values of 0.884 (0.880), NRMSE values of 0.179 (0.253) and RPD values of 3.191 (3.221), indicating excellent stability and predictive performance. The findings of this study can not only aid in the rapid nondestructive detection of heavy metal adsorption in various parts of PsR, but can also be applied to guide the development and use of animal feed.

The purpose of this research work is to perform detailed exergetic, energetic and techno-economic analysis of the black tea drying process in the gas-type industrial dryer. Exergy–energy and techno-economic methodology was applied to investigate the heat loss, exergetic and energetic performance, exergy efficiency, improvement potential rate, sustainability index and techno-economic performance of a drying system. The results showed that the heat loss of exhaust air in the late drying process played a main contributing role in the heat loss and exergy loss of the whole drying system. Therefore, the exergy efficiency of the initial drying period and the redrying period varied from 38.08% to 65.09% and 24.76% to 26.97%, respectively. In addition, the improvement potential rate and sustainability index of the whole system varied from 6.93 kW to 12.94 kW and 1.33 to 2.86, respectively. The improvement potential obtained in the present work indicated that the drying operation is greatly in need of exergy performance improvement. Finally, the net present value and payback period obtained from techno-economic analysis were 179,442.03 USD and 5.3 years, the result is useful for investors or contractors to refer to and make investment decisions.

This paper presents a method for compiling the load spectrum of the transmission assembly of plug-in hybrid electric vehicles (PHEVs). Based on the analysis of the control strategy of the test vehicle, the power flow transmission route in the transmission assembly is different under different operation modes, so it is necessary to divide different load spectrum blocks according to the operation mode. Based on the big data survey of China’s national standard, it is determined that the typical working conditions are urban road working conditions, high-speed road working conditions, provincial road working conditions and poor road conditions. The mileage proportion of the various working conditions is 55:30:10:5, and the mileage of one cycle is 300 km. A total of three cycles are collected. After data processing and time-domain verification, based on the principle of maximum damage, the cycle with the largest pseudo damage is selected as the sample load data for load spectrum extrapolation. The rain flow counting method is used to count the sample load, and a two-dimensional kernel density estimation mathematical model with adaptive bandwidth is established to estimate the probability density function of the data. The extrapolated rain flow matrix is obtained through Monte Carlo simulation. The load spectrum of the two-dimensional rain flow matrix is transformed into a one-dimensional eight-stage program load spectrum by using a variable mean method, Goodman equation and equal damage principle theory. Finally, the fatigue life of the transmission assembly is simulated and calculated under the environment of Romax Designer simulation software. The two-dimensional kernel density estimation model with adaptive bandwidth is used to fit and extrapolate the load rain flow matrix of each hybrid mode of the PHEV, which solves the problem wherein the shape of the rain flow matrix of each hybrid mode of the hybrid electric vehicle is complex and difficult to fit. Finally, taking the after-sales maintenance data of this model from 2020 to the present as auxiliary proof, the failure components and the failure mileage life of the simulation test results are consistent with the results used by the actual users. This shows that the kernel density estimation model proposed in this paper can well fit the rain flow matrix of the PHEV load spectrum. The extrapolated load spectrum based on this model has high accuracy and authenticity. The method of compiling the load spectrum of the transmission assembly of a hybrid electric vehicle in this paper is effective.

White clover is an important temperate legume forage with high nutrition. In the present study, 448 worldwide accessions were evaluated for the genetic variation and polymorphisms using 22 simple sequence repeat (SSR) markers. All the markers were highly informative, a total of 341 scored bands were amplified, out of which 337 (98.83%) were polymorphic. The PIC values ranged from 0.89 to 0.97 with an average of 0.95. For the AMOVA analysis, 98% of the variance was due to differences within the population and the remaining 2% was due to differences among populations. The white clover accessions were divided into different groups or subgroups based on PCoA, UPGMA, and STRUCTURE analyses. The existence of genetic differentiation between the originally natural and introduced areas according to the PCoA analysis of the global white clover accessions. There was a weak correlation between genetic relationships and geographic distribution according to UPGMA and STRUCTURE analyses. The results of the present study will provide the foundation for future breeding programs, genetic improvement, core germplasm collection establishment for white clover.

The dolomite reservoir of the fourth member of Dengying Formation in Moxi area of Sichuan Basin is thin, is fast in lateral variation, and has P-impedance difference from the surrounding rock; it is difficult to identify and predict the dolomite reservoir and fluid properties by conventional poststack seismic inversion. Through the correlation analysis of core test data and logging P-S-wave velocity, this work proposed a formula to calculate the shear wave velocity in different porosity ranges and solved the issue that some wells in the study area have no S-wave logging data. AVO forward analysis reveals that whether the gas reservoir of dolomite reservoir is located at the top of the fourth member of Dengying Formation is the main factor affecting the variation of AVO type. Through cross-plotting analysis of elastic parameters, it is found that P-S-wave velocity ratio and fluid factor are sensitive parameters to gas-bearing property of dolomite reservoir in the study area. By comparing the inversion results of prestack parameters such as density, P-wave impedance, S-wave impedance, P-S-wave velocity ratio, and fluid factor, it is found that the gas-bearing prediction of dolomite reservoir by using P-S-wave velocity ratio and fluid factor obtained from simultaneous prestack inversion had the highest coincidence rate with actual drilling data. At last, according to the distribution characteristics of fluid factor and P-S-wave velocity ratio, the favorable gas-bearing areas of dolomite reservoir in the fourth member of Dengying Formation in the study area are finely predicted, and the next favorable exploration areas were pointed out.

The performance evaluation and optimization of an energy conversion system design of an energy intensive drying system applied the method of combining exergy and economy is a theme of global concern. In this study, a gas-type industrial drying system of black tea with a capacity of 100 kg/h is used to investigate the exergetic and economic performance through the exergy and exergoeconomic methodology. The result shows that the drying rate of tea varies from the maximum value of 3.48 gwater/gdry matter h to the minimum 0.18 gwater/gdry matter h. The highest exergy destruction rate is found for the drying chamber (74.92 kW), followed by the combustion chamber (20.42 kW) in the initial drying system, and 51.83 kW and 21.15 kW in the redrying system. Similarly, the highest cost of the exergy destruction rate is found for the drying chamber (18.497 USD/h), followed by the combustion chamber (5.041 USD/h) in the initial drying system, and 12.796 USD/h and 5.222 USD/h in the redrying system. Furthermore, we analyzed the unit exergy rate consumed and the unit exergy cost of water removal in different drying sections of the drying system, and determined the optimal ordering of each component. These results mentioned above indicate that, whether from an energy or economic perspective, the component improvements should prioritize the drying chamber. Accordingly, minimizing exergy destruction and the cost of the exergy destruction rate can be considered as a strategy for improving the performance of energy and economy. Overall, the main results provide a more intuitive judgment for system improvement and optimization, and the exergy and exergoeconomic methodology can be commended as a method for agricultural product industrial drying from the perspective of exergoeconomics.

This article proposes a method for compiling the load spectra of reducers for hybrid electric vehicles. Selecting typical working conditions for real vehicle data collection, the load data under each typical working condition were divided into five categories according to the state of the power source and the data were preprocessed. The optimal sample loads for compiling load spectra were obtained based on a multi-criteria decision-making method, rainflow counting for optimal sample loads was performed according to different power source output patterns, non-parametric extrapolation was performed to obtain the full-life two-dimensional load spectrum after dimensionality reduction, and a full-life eight-level programmed load spectrum that could be used for bench tests was obtained. Using the programmed load spectrum and the extracted sample load as the load input, a fatigue life prediction simulation of the reducer gear of a hybrid electric vehicle was carried out. The reducer gear fatigue life from the programmed load spectrum was compared to the gear fatigue life under actual load. The fatigue life of the reducer gear when the programmed load spectrum was used as the input was 1.412 × 103. When the actual load was used as the input load, the fatigue life of the reducer gear was 1.933 × 103. The relative error between the two is only 26%, which is in the normal range. The results show that the programmed load spectrum is effective and reliable and that the load spectrum compilation method provides a basis for accurately evaluating the reliability of the hybrid electric vehicle reducer.

The air-suction precision seed-metering device is prone to the instability of the seed adsorption state, which arises from blockage of the suction hole and leads to uneven seeding. This paper analyzed and determined key structural parameters of the seed-metering plate, then established an adsorption mechanics model of the seed during the migration process and designed the key structure of the air-suction seed-metering device with the aim of improving the uniformity of high-speed direct seeding of vegetables. Furthermore, we used the DEM-CFD coupling method to analyze the influence of the law of seeds on the change of the flow field with different hole types. Results showed that the turbulent kinetic energy (202.65 m2∙s−2) and the coupling force to the seeds (0.029 N) of the B-type hole are the largest, which is the best fluid domain structure for the suction hole of the seed-metering plate. Moreover, we used Adams to analyze the meshing process between the knock-out wheel and the seed-metering plate, affirming the rationality of the knock-out wheel design. Finally, in order to improve the working efficiency of the seed-metering device, we performed one-factor and response surface experiments of seeding performance using the air-suction seed-metering device designed with the optimized structure as the experimental object. Analysis of the influence of weights across each factor on the experimental performance evaluation indicators revealed an optimal combination of seeding performance parameters in the air-suction seed-metering device, namely a seed-throwing angle of 13°, a working speed of 14.5 km/h, and negative pressure of 3.1 kPa. Results from verification experiments revealed the corresponding experimental indicators, namely qualified, multiple, and missing indexes of 95.9, 1.2%, and 2.9%, respectively.

The application of agricultural robots can liberate labor. The improvement of robot sensing systems is the premise of making it work. At present, more research is being conducted on weeding and harvesting systems of field robot, but less research is being conducted on crop disease and insect pest perception, nutritional element diagnosis and precision fertilizer spraying systems. In this study, the effects of the nitrogen application rate on the absorption and accumulation of nitrogen, phosphorus and potassium in sweet maize were determined. Firstly, linear, parabolic, exponential and logarithmic diagnostic models of nitrogen, phosphorus and potassium contents were constructed by spectral characteristic variables. Secondly, the partial least squares regression and neural network nonlinear diagnosis model of nitrogen, phosphorus and potassium contents were constructed by the high-frequency wavelet sensitivity coefficient of binary wavelet decomposition. The results show that the neural network nonlinear diagnosis model of nitrogen, phosphorus and potassium content based on the high-frequency wavelet sensitivity coefficient of binary wavelet decomposition is better. The R2, MRE and NRMSE of nn of nitrogen, phosphorus and potassium were 0.974, 1.65% and 0.0198; 0.969, 9.02% and 0.1041; and 0.821, 2.16% and 0.0301, respectively. The model can provide growth monitoring for sweet corn and a perception model for the nutrient element perception system of an agricultural robot, while making preliminary preparations for the realization of intelligent and accurate field fertilization.

Background/Aims: CD24 is a highly glycosylated mucin-like antigen on the cell surface, which has recently emerged as a novel oncogene and metastasis promoter. We performed bioinformatics analysis to investigate whether CD24 can serve as a prognostic indicator in breast cancer. Methods: CD24 expression was assessed using SAGE Genie tools and Oncomine analysis. The PrognoScan database, Kaplan-Meier Plotter, and bc-GenExMiner were used to identify the prognostic roles of CD24 in breast cancer. Results: We found that CD24 was more frequently overexpressed in breast cancer than in normal breast tissue and correlated with worse prognosis. Meanwhile, high CD24 expression was associated with increased risk of HER2, basal-like, triple-negative breast cancer, and higher Scarff-Bloom-Richardson grade. Data mining in multiple big databases confirmed a positive correlation between CD24 mRNA expression and SDC1 mRNA expression in breast cancer tissue. Conclusions: Our findings suggest that CD24 overexpression is more common in breast cancer than in corresponding normal tissue. In addition, CD24 and SDC1 can serve as prognostic indicators for breast cancer. However, large-scale and comprehensive research is needed to further confirm these results.

Laser-induced graphene (LIG) has been widely used in flexible sensors due to its excellent mechanical properties and high conductivity. In this paper, a flexible pressure sensor prepared by bionic micro/nanostructure design and LIG mass fraction regulation is reported. First, prepared LIG and conductive carbon paste (CCP) solutions were mixed to obtain a conductive polymer. After the taro leaf structure was etched on the surface of the aluminum alloy plate by Nd:YAG laser processing, the conductive polymer was evenly coated on the template. Pressure sensors were packaged with a stencil transfer printing combined with an Ecoflex flexible substrate. Finally, the effects of different laser flux and the proportion of LIG in the composite on the sensitivity of the sensor are discussed. The results show that when the laser flux is 71.66 J·cm−2 and the mass fraction of LIG is 5%, the sensor has the best response characteristics, with a response time and a recovery time of 86 ms and 101 ms, respectively, with a sensitivity of 1.2 kPa−1 over a pressure range of 0–6 kPa, and stability of 650 cycle tests. The LIG/CCP sensor with a bionic structure demonstrates its potential in wearable devices.

The ventilation resistance of air flow through rice grain layers is one of the key parameters affecting drying uniformity as well as the energy consumption of the drying process. In order to reveal the variation of characteristics of the ventilation resistance with paddy grain moisture content, the air velocity and the bed layer depth are needed. A second order model was fitted to pressure drop using the response surface methodology and the results are compared with those of the Ergun model. The results showed that the pressure drop increases with the increase of paddy grain moisture content, air velocity and the bed layer depth, and the interactions between the air velocity and the bed layer depth have the most significant influence on the pressure drop. Moreover, a second-order polynomial pressure drop model based on RSM was established and compared with the Ergun model. The results showed that the pressure drop model established by RSM is similar to that of the Ergun model.

Abstract Mental disorders are severe, disabling conditions with unknown etiology and are commonly misdiagnosed when clinical symptomology criteria are solely used. Our previous work indicated that combination of serum levels of multiple proteins in tissue plasminogen activator (tPA)-brain-derived neurotrophic factor (BDNF) pathway improved accuracy of diagnosis of major depressive disorder (MDD). Here, we measured serum levels of tPA, plasminogen activator inhibitor-1 (PAI-1), BDNF, precursor-BDNF (proBDNF), tropomyosin-related kinase B (TrkB) and neurotrophin receptor p75 (p75NTR) in patients with paranoid schizophrenia (SZ, n = 34), MDD (n = 30), bipolar mania (BM, n = 30), bipolar depression (BD, n = 22), panic disorder (PD, n = 30), and healthy controls (HCs, n = 30) by Enzyme-linked immunosorbent assay kits. We used receiver operating characteristic (ROC) curve to analyze diagnostic potential of these proteins. We found, compared with HCs, that serum tPA and proBDNF were lower in SZ, BM and BD; TrkB was lower in SZ and BD; and p75NTR was declined in SZ and BM. ROC analysis showed that combined serum level of tPA, PAI-1, BDNF, proBDNF, TrkB and p75NTR was better than any single protein in accuracy of diagnosis and differentiation, suggesting that the combination of multiple serum proteins levels in tPA-BDNF pathway may have a potential for a diagnostic panel in mental disorders.

Graphene is a new type of carbon material with a flexible, two-dimensional structure. Due to the excellent stability of its lattice structure and its mechanical flexibility, graphene-based materials can be applied in flexible humidity sensors. At present, the application of graphene-based flexible humidity sensors in the fields of medical care and environmental monitoring is attracting widespread attention. In this review, the basic properties of graphene oxide (GO) and reduced graphene oxide (rGO) as moisture-sensitive materials and methods for their preparation were introduced. Moreover, three methods for improving the performance of moisture-sensitive materials were discussed. The working principle of different types of graphene-based humidity sensors were introduced. The progress in the research on graphene-based flexible humidity sensors in four respects: Human respiration, skin moisture, human sweat, and environmental humidity were discussed. Finally, the future research, following the development trends and challenges, to develop the potential of integrated, graphene-based flexible humidity sensors were discussed.

Pine wilt disease (PWD) caused by pine wood nematode (PWN, Bursaphelenchus xylophilus) originated in North America and has since spread to Asia and Europe. PWN is currently a quarantine object in 52 countries. In recent years, pine wilt disease has caused considerable economic losses to the pine forest production industry in China, as it is difficult to control. Thus, one of the key strategies for controlling pine wilt disease is to identify epidemic points as early as possible. The use of hyperspectral cameras mounted on drones is expected to enable PWD monitoring over large areas of forest, and hyperspectral images can reflect different stages of PWD. The trend of applying hyperspectral techniques to the monitoring of pine wilt disease is analyzed, and the corresponding strategies to address the existing technical problems are proposed, such as data collection of early warning stages, needs of using unmanned aerial vehicles (UAVs), and establishment of models after preprocessing.

Midkine (MK) has been reported as the potential novel diagnostic biomarker for cancer in several studies, but their results were controversial. Therefore, we performed a diagnostic meta-analysis to assess the diagnostic value of serum MK in cancer patients. A systematic electronic and manual search was performed for relevant literatures through several databases up to June 1, 2017. The quality of the studies included in the meta-analysis was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. All analyses were conducted using stata12.0 software. Ten studies collectively included 1119 cancer patients and 1441 controls met the eligible criteria. The summary estimates were: sensitivity 0.78 (95% CI = 0.68-0.85), specificity 0.83 (95% CI = 0.72-0.90), positive likelihood ratio 4,54 (95% CI = 2.64-7.80), negative likelihood 0.27 (95% CI = 0.18-0.40), diagnostic odds ratio 16.79 (95% CI = 7.17-39.33), and area under the curve 0.87 (95% CI = 0.84-0.89). Publication bias was suggested by Deeks' funnel plot asymmetry test (P = 0.92). According to our results, serum MK has greater diagnostic value in diagnosing cancer, however, more reliable studies in larger cohort should be conducted to evaluate the diagnostic accuracy of serum MK.

Voltage-gated sodium channels (VGSC) regulate neuronal excitability by governing action potential (AP) generation and propagation. Recent studies have revealed that AMP-activated protein kinase (AMPK) activators decrease sensory neuron excitability, potentially by preventing sodium (Na+) channel phosphorylation by kinases such as ERK or via modulation of translation regulation pathways. The direct positive allosteric modulator A769662 displays substantially greater efficacy than other AMPK activators in decreasing sensory neuron excitability suggesting additional mechanisms of action. Here, we show that A769662 acutely inhibits AP firing stimulated by ramp current injection in rat trigeminal ganglion (TG) neurons. PT1, a structurally dissimilar AMPK activator that reduces nerve growth factor (NGF) -induced hyperexcitability, has no influence on AP firing in TG neurons upon acute application. In voltage-clamp recordings, application of A769662 reduces VGSC current amplitudes. These findings, based on acute A769662 application, suggest a direct channel blocking effect. Indeed, A769662 dose-dependently blocks VGSC in rat TG neurons and in Nav1.7-transfected cells with an IC50 of ~ 10 μM. A769662 neither displayed use-dependent inhibition nor interacted with the local anesthetic (LA) binding site. Popliteal fossa administration of A769662 decreased noxious thermal responses with a peak effect at 5 mins demonstrating an analgesic effect. These data indicate that in addition to AMPK activation, A769662 acts as a direct blocker/modulator of VGSCs, a potential mechanism enhancing the analgesic property of this compound.