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In order to fully utilize the resources of agricultural waste in Gansu Province’s semi-arid area. Local commercial organic fertilizer (ST1) was selected as the control, and four kinds of planting and breeding waste composts (PBCs) were designed with sheep manure (SM), cow manure (CM), tail vegetable (TV), mushroom residue (MR), and corn straw (CS) to study the effects of the different PBC formulations on the yield and quality of mini Chinese cabbage. In contrast to local commercial organic fertilizer, the STS (SM:TV:CS = 6:3:1) treatment increased the economic yield by 5.56%. Additionally, STS also significantly increased the VC content of mini Chinese cabbage, increased the organic acid by 14.66%, increased the free amino acid by 38.98%, and the nitrate concentration was significantly reduced by 41.05%. Meanwhile, the STS formula also increased the concentrations of polyphenols and essential amino acids of mini Chinese cabbage and also had excellent performance in volatile compounds. As a result, the STS formula can make full use of local planting and breeding waste resources and produce high yield and high quality of local mini Chinese cabbage. The study provided a theoretical foundation and technical guidance for screening suitable local compost formulas, as well as for the achievement of high-yield and high-quality mini Chinese cabbage production in the semi-arid areas of central Gansu province.

The low specific speed centrifugal pump plays a crucial role in industrial applications, and ensuring its efficient and stable operation is extremely important for the safety of the whole system. The pump must operate with an extremely high head, an extremely low flow rate, and a very fast speed. The internal flow structure is complex and there is a strong interaction between dynamic and static components; consequently, the hydraulic excitation force produced becomes a significant factor that triggers abnormal vibrations in the pump. Therefore, this study focuses on a low specific speed centrifugal pump and uses a single-stage model pump to conduct PIV and pressure pulsation tests. The findings reveal that the PIV tests successfully captured the typical jet-wake structure at the outlet of the impeller, as well as the flow separation structure at the leading edge of the guide vanes and the suction surface. On the left side of the discharge pipe, large-scale flow separation and reverse flow happen as a result of the flow-through effect, producing a strong vortex zone. The flow field on the left side of the pressure chamber is relatively uniform, and the low-speed region on the suction surface of the guide vanes is reduced due to the reverse flow. The results of the pressure pulsation test showed that the energy of pressure pulsation in the flow passage of the guide vane occurs at the fBPF and its harmonics, and the interaction between the rotor and stator is significant. Under the same operating condition, the RMS value distribution and amplitude at fBPF of each measurement point are asymmetric in the circumferential direction. The amplitude of fBPF near the discharge pipe is lower, while the RMS value is higher. A complex flow structure is shown by the larger amplitude and RMS value of the fBPF on the left side of the pressure chamber. With the flow rate increasing, the energy at fBPF of each measurement point increases first and then decreases, while the RMS value decreases, indicating a more uniform flow field inside the pump.

With the growing demand for new energy sources, electrochemical water splitting for hydrogen production is a technology that must be vigorously promoted. Therefore, to improve the efficiency of the oxygen evolution reaction (OER) at the anode, high-performance OER catalysts are essential. Given their advantages in electrocatalysis, nanoporous materials have garnered considerable attention in previous studies for OER applications. This review provides a comprehensive overview of various strategies to optimize active site utilization in nanoporous materials. These strategies include regulating pore size and porosity, constructing hierarchical nanoporous structures, and enhancing material conductivity. Additionally, it covers approaches to boost the intrinsic OER activity of nanoporous materials, such as tuning the composition of anions and cations, creating vacancies, constructing interfaces, and forming boundary active sites. While nanoporous materials offer significant potential for advancing OER, challenges remain, including difficulties in quantifying activity within nanopores, the unclear impact of nanoporous material morphology, challenges in accessing nanopore interiors with in situ techniques, and a lack of theoretical calculations on pore structure. However, these challenges also present opportunities, and we hope this review provides a fresh perspective to inspire future research.

This study addresses the challenges faced by coal-fired power plants in adapting to energy fluctuations following the integration of renewable energy sources into the power grid. The flexible operation of thermal power plants has become a focal point in academic research. A numerical model of a coal-fired power plant was developed in this study using the Long Short-Term Memory (LSTM) algorithm and the Particle Swarm Optimization (PSO) algorithm based on actual operation data analysis. The combined PSO-LSTM approach improved the accuracy of the model by optimizing parameters. Validation of the model was performed using a Dymola physical simulation model, demonstrating that the PSO-LSTM coupled numerical model accurately simulates coal-fired power plant operations with a goodness of fit reaching 0.998. Overall system performance for comprehensively evaluating the rate and accuracy of unit operation is proposed. Furthermore, the model’s capability to simulate the load variation process of automatic generation control (AGC) under different load command groups was assessed, aiding in optimizing the best load command group. Optimization experiments show that the performance index of output power is optimal within the experimental range when the set load starts and stops are the same and the power of load command γ = 1.8. Specifically, the 50–75% Turbine Heat Acceptance (THA) load rise process enhanced the overall system performance index by 55.1%, while the 75–50% THA load fall process improved the overall system performance index by 54.2%. These findings highlight the effectiveness of the PSO-LSTM approach in optimizing thermal power plant operations and enhancing system performance under varying load conditions.

A new type of assembled integral multi-ribbed composite floor system with novel wet joint and steel sleeve connections, which exhibits satisfactory strength and stiffness, was proposed in the previous study. To further study the flexural performances of the joints, six groups of specimens, including two cast in situ concrete slabs and four composite slabs sized 4700 mm × 1200 mm × 300 mm and 2450 mm × 1200 mm × 300 mm, were investigated under four-point flexural tests. Four main influence factors were experimentally studied, i.e., casting methods, joint amounts, shear span lengths, and steel sleeve layout directions, on the failure modes, crack distributions, and deflection–load carrying capacity relationship. Test results indicated that the proposed composite slab system could provide the ultimate bearing capacity lower by 7% than that of the cast in situ concrete slabs, largely exceeding the code-predicted strength. No strain difference between the steel sleeve connections and steel rebars indicated good wet joint connection behavior. More hollow-core sections and long shear spans increased the potential of interfacial splitting cracks, leading to a shorter elastic stage and lower elastic stiffness. A finite element model was further parametrically conducted to explore the structural performances. Finite element results also indicate that the precast concrete slab had a more significant influence on the failure loads than the influences of concrete compressive strength and lap-splice steel rebar strength. These findings indicate that the proposed composite slab systems possess a satisfactory performance in the ultimate bearing capacity and deformability. Thus, such an assembled integral multi-ribbed composite floor system can be widely applied in construction.

We present a non-traditional finite element method for the electromagnetic scattering of the partly covered groove filled with multiple media. The non-local boundary condition is introduced to make the unbounded scattering into a bounded domain problem. Non-body-fitted mesh is applied to save the cost of discretizing groove domain greatly. In addition, the level set functions are utilized to describe complex media interfaces and boundaries. Numerical tests demonstrate the effectiveness and second-order convergence rate of the proposed method.

Non-contact voltage sensors based on the principle of electric field coupling have the advantages of simple loading and unloading, high construction safety, and the fact that they are not affected by line insulation. They can accurately measure line voltage without the need to connect to the measured object. Starting from the principle of non-contact voltage measurement, this article abstracts a non-contact voltage measurement model into the principle of capacitive voltage sharing and deduces its transfer relationship. Secondly, it is theoretically inferred that the edge effect of the traditional symmetric structure sensor plate will cause the actual capacitance value between the sensor plates to be greater than the theoretically calculated capacitance value, resulting in a certain measurement error. Therefore, the addition of an equipotential ring structure is proposed to eliminate the edge additional capacitance caused by the edge effect in order to design the sensor structure. In addition, due to the influence of sensor volume, material dielectric constant, and other factors, the capacitance value of the sensor itself is only at pF level, resulting in poor low-frequency performance and imbuing the sensor with a low voltage division ratio. In this regard, this article analyzes the measurement principle of non-contact voltage sensors. By paralleling ceramic capacitors between the two electrode plates of the sensor, the capacitance of the sensor itself is effectively increased, improving the low-frequency performance of the sensor while also increasing the sensor’s voltage division ratio. In addition, by introducing a single pole double throw switch to switch parallel capacitors with different capacitance values, the sensor can have different voltage division ratios in different measurement scenarios, giving it a certain degree of adaptability. The final sensor prototype was made, and a high and low voltage experimental platform was built to test the sensor performance. The experimental results showed that the sensor has good linearity and high measurement accuracy, with a ratio error of within ±3%.

The accurate voltage measurement of distribution networks is of great significance in power dispatching and fault diagnosis. Voltage sensors based on the spatial electric field effect do not require grounding, which provides the possibility for the distributed measurement of transmission line voltages. However, the divider ratio of suspension grounding voltage sensors is affected by the height between the sensor and the ground, as well as the distance between the sensor and the telegraph pole. In this paper, a self-calibration method based on internal capacitance transformation is proposed to realize the on-line calibration of suspension grounding voltage sensors. The calibration is accomplished by switching different parameters in the conditioning circuit, and the calibration process does not require power failure or known input excitation. In addition, the impact of electric fields in the other two phases of three-phase transmission lines on measurement through simulation research is quantified in this paper. In order to reduce the impact of interference electric fields, an equipotential shielding structure is designed. The circuit topology and probe prototype have been developed and testing has been conducted in laboratory conditions; the experimental results show that the maximum relative error of voltage amplitude is 1.65%, and the phase relative error is 0.94%. The measurement accuracy is not limited by the height to ground or the distance to the telegraph pole. In addition, in the application of an equipotential shielding probe, the maximum deviation of measured voltage is 0.7% with and without interference electric fields.

Data-driven approaches predict infectious disease dynamics by considering various factors that influence severity and transmission rates. However, these factors may not fully capture the dynamic nature of disease transmission, limiting prediction accuracy and consistency. Our proposed data-driven approach integrates spatiotemporal human mobility patterns from detailed point-of-interest clustering and population flow data. These patterns inform the creation of mobility-informed risk indices, which serve as auxiliary factors in data-driven models for detecting outbreaks and predicting prevalence trends. We evaluated our approach using real-world COVID-19 outbreaks in Beijing and Guangzhou, China. Incorporating the risk indices, our models successfully identified 87% (95% Confidence Interval: 83–90%) of affected subdistricts in Beijing and Guangzhou. These findings highlight the effectiveness of our approach in identifying high-risk areas for targeted disease containment. Our approach was also tested with COVID-19 prevalence data in the United States, which showed that including the risk indices reduced the mean absolute error and improved the R-squared value for predicting weekly case increases at the county level. It demonstrates applicability for spatiotemporal forecasting of widespread diseases, contributing to routine transmission surveillance. By leveraging comprehensive mobility data, we provide valuable insights to optimize control strategies for emerging infectious diseases and facilitate proactive measures against long-standing diseases.

Turbulent flow, mainly originating from the rotor-stator interaction (RSI), is closely associated with the normal and safe operation of the centrifugal pump. In the current research, to clarify turbulent flow in the centrifugal pump with a vaned diffuser, the non-intrusive LDA (Laser Doppler Anemometry) system is applied to measure velocity pulsation signals at different regions when the pump operates at various flow rates. Time and frequency domain analysis methods are combined to investigate the velocity signals, and the velocity distribution around the volute tongue region is reconstructed from twenty measuring points. Results show that the velocity spectrum is characterized by the discrete components at the blade passing frequency and its higher harmonics, and it is caused by the RSI between the impeller and the diffuser. For the points in the volute spiral and diffusion sections, due to the significantly reduced RSI effect, the velocity spectrum shows an evident difference from comparison with the points between the impeller and diffuser, and the blade passing frequency is not always the dominant frequency. The comparison of velocity amplitudes and RMS* (root mean square of velocity) values at different points proves that the measuring position and flow rate affect velocity pulsations. As observed from velocity distribution reconstructed by LDA signals, high velocity regions are developed downstream of the diffuser channel for all the measured flow rates.

In the authors’ previous research, a new method for partial discharge detection in the switchgear based on near-field detection was proposed. The content of this paper is the continuation of the authors’ previous research. In order to realize the reasonable layout of the near-field magnetic field probe for partial discharge detection in the switchgear, this paper simulates and analyzes the influence of the internal structure of the switchgear on the near-field propagation characteristics of the electromagnetic wave signal generated by partial discharge, and determines the installation position of the near-field probe in the switchgear. Firstly, the propagation characteristics of electromagnetic wave signals in the different media of the switchgear are analyzed, and the switchgear model is established. Then, based on the finite difference time domain method, the influence of different devices in the switchgear on the near-field propagation of the partial discharge electromagnetic wave signal is simulated. The simulation results show that the current transformer, insulator, busbar and cabinet all obviously attenuate the amplitude of the near-field electromagnetic wave signals generated by partial discharge, and the insulator causes obvious signal distortion. Finally, it is determined that the near-field probe can be installed on the inner wall or the right wall near the bottom plate of the switchgear.

At present, most of the winding fault detections of transformers use offline methods, which require the entire line to be powered off or after the transformer has been shut down due to a serious fault, and they cannot detect the running status of the windings in real time and online. Since the transformer will be impacted by VFTO during operation, the VFTO is used as the excitation, and the basic principle of the frequency response method is used to propose an online detection method of transformer winding deformation based on VFTO characteristics. The finite element software is used to calculate the equivalent circuit parameters of normal and deformed windings, and the equivalent circuit model of the transformer windings before and after deformation is established. Due to the uncertainty of the VFTO waveform, combined with the frequency response method, the VFTO waveform is injected into the transformer winding health, and the frequency response curve is obtained from the deformed equivalent circuit, which is used to analyze the change of the resonance point in the case of radial deformation, axial deformation, and axial displacement of the winding. The simulation results show that with VFTO as the excitation, the obtained resonance point change can comprehensively reflect the deformation information of the winding. At the same time, the feasibility of the method is verified by experiments.

Noncontact voltage measurement has the advantages of simple handling, high construction safety, and not being affected by line insulation. However, in practical measurement of noncontact voltage, sensor gain is affected by wire diameter, wire insulation material, and relative position deviation. At the same time, it is also subject to interference from interphase or peripheral coupling electric fields. This paper proposes a noncontact voltage measurement self-calibration method based on dynamic capacitance, which realizes self-calibration of sensor gain through unknown line voltage to be measured. Firstly, the basic principle of the self-calibration method for noncontact voltage measurement based on dynamic capacitance is introduced. Subsequently, the sensor model and parameters were optimized through error analysis and simulation research. Based on this, a sensor prototype and remote dynamic capacitance control unit that can shield against interference are developed. Finally, the accuracy test, anti-interference ability test, and line adaptability test of the sensor prototype were conducted. The accuracy test showed that the maximum relative error of voltage amplitude was 0.89%, and the phase relative error was 1.57%. The anti-interference ability test showed that the error offset was 0.25% when there were interference sources. The line adaptability test shows that the maximum relative error in testing different types of lines is 1.01%.

In the present work, volatile organic compounds (VOCs) from vehicle exhaust and cooking fumes were investigated via simulation experiments, which covered engine emissions produced during gasoline direct injection (GDI) using two kinds of fuels and cooking emissions produced by preparing three domestic dishes. The distinct characteristics of VOCs emitted during the two processes were identified. Alkanes (73% mass fraction on average) and aromatics (15% on average) dominated the vehicle VOCs, while oxygenated VOCs (49%) and alkanes (29%) dominated the cooking VOCs. Isopentane (22%) was the most abundant species among the vehicle VOCs. N-hexanal (20%) dominated the cooking VOCs. The n-hexanal-to-n-pentanal ratio (3.68 ± 0.64) was utilized to identify cooking VOCs in ambient air. The ozone formation potential produced by cooking VOCs was from 1.39 to 1.93 times higher than that produced by vehicle VOCs, which indicates the significant potential contribution of cooking VOCs to atmospheric ozone. With the equivalent photochemical age increasing from 0 h to 72 h, the secondary organic aerosol formation by vehicle VOCs was from 3% to 38% higher than that of cooking VOCs. Controlling cooking emissions can reduce SOA pollution in a short time due to its higher SOA formation rate than that of vehicle VOCs within the first 30 h. However, after 30 h of oxidation, the amount of SOAs formed by vehicle exhaust emissions exceeded the amount of SOAs produced by cooking activities, implying that reducing vehicle emissions will benefit particle pollution for a longer time. Our results highlight the importance of VOCs produced by cooking fumes, which has not been given much attention before. Further, our study suggested that more research on semi-volatile organic compounds produced by cooking emissions should be conducted in the future.

Three-phase four-wire power cables are a primary kind of power transmission method in low-voltage distribution networks. This paper addresses the problem that calibration currents are not easily electrified during the transporting of three-phase four-wire power cable measurements, and proposes a method for obtaining the magnetic field strength distribution in the tangential direction around the cable, finally enabling online self-calibration. The simulation and experimental results show that this method can self-calibrate the sensor arrays and reconstruct the phase current waveforms in three-phase four-wire power cables without calibration currents, and this method is not affected by disturbances such as wire diameter, current amplitudes, and high-frequency harmonics. This study reduces the time and equipment costs required to calibrate the sensing module compared to related studies using calibration currents. This research offers the possibility of fusing sensing modules directly with running primary equipment, and the development of hand-held measurement devices.

The traditional slewing bearing with circular raceway has the problem of stress concentration under the large overturning moment. In this paper, a new non-circular raceway of a slewing ball bearing was presented to conquer this problem. To obtain the new non-circular raceway, first, the static equilibrium equations of the slewing ball bearing was established by the vector method, which is a constraint condition for life optimization; secondly, the life optimization function was established to calculate the contact load distribution in the bearing when the bearing life is at its maximum; finally, through the contact deformation with the contact load, the non-circular inner raceway corresponding to the maximum bearing life was obtained, and the non-circular shape corresponding to the raceway under axial load and overturning moment was studied. The results show that the non-circular raceway devised by this method can evenly reduce the contact force of the raceway and effectively improve the bearing capacity. Moreover, the position where the non-circular raceway deformation occurs is the position where the contact force is different before and after optimization. Therefore, the overturning moment has an effect on the shape of the raceway, whereas the axial load only affects the amplitude of the deformation, and has no obvious effect on the shape of the raceway.

Traditional contact voltage measurement requires a direct electrical connection to the system, which is not easy to install and maintain. The voltage measurement based on the electric field coupling plate capacitance structure does not need to be in contact with the measured object or the ground, which can avoid the above problems. However, most of the existing flat-plate structure voltage measurement sensors are not only expensive to manufacture, but also bulky, and when the relative position between the wire under test and the sensor changes, it will bring great measurement errors, making it difficult to meet actual needs. Aiming to address the above problems, this paper proposes a multi-electrode array structure non-contact voltage sensor and signal processing algorithm. The sensor is manufactured by the PCB process, which effectively reduces the manufacturing cost and process difficulty. The experimental and simulation results show that, when the relative position of the wire and the sensor is offset by 10 mm in the 45° direction, the relative error of the traditional single-electrode voltage sensor is 17.62%, while the relative error of the multi-electrode voltage sensor designed in this paper is only 0.38%. In addition, the ratio error of the sensor under the condition of power frequency of 50 Hz is less than ±1% and the phase difference is less than 4°. The experimental results show that the sensor has good accuracy and linearity.

According to the requirements of weak current measurement in power grid, a weak current sensor with anti-low frequency interference ability is developed. The sensor adopts the principle of fluxgate detection and adds a magnetic ring on the original basis. The structure of the magnetic ring is simulated using comsol to further improve detection sensitivity. In order to solve the problem that the electromagnetic current sensor is vulnerable to the interference of geomagnetic field and power frequency magnetic field in weak current measurement, a magnetic shielding method with low cost is selected, and the shielding shell structure is designed using a finite element analysis method. The experimental results show that the minimum measurable current is 1 mA, the measurement range is 1 mA–1 A, and the bandwidth is DC-16 kHz. The designed magnetic shielding shell can effectively reduce 97.3% of the DC magnetic field interference and 95.7% of the power frequency magnetic field interference. The sensor can realize accurate measurement of weak current in power grid.

With the accelerated construction of the smart grid, new energy sources such as photovoltaic and wind power are connected to the grid. In addition to power frequency, the current signal of power grid also includes several DC signals, as well as medium-high and high-frequency transient signals. Traditional current sensors for power grids are bulky, have a narrow measurement range, and cannot measure both AC and DC at the same time. Therefore, this paper designs a non-intrusive, AC-DC wide-bandwidth current sensor based on the composite measurement principle. The proposed composite current detection scheme combines two different isolation detection technologies, namely tunneling reluctance and the Rogowski coil. These two current sensing techniques are complementary (tunneling magnetoresistive sensors have good low-frequency characteristics and Rogowski coils have good high-frequency characteristics, allowing for a wide detection bandwidth). Through theoretical and simulation analysis, the feasibility of the composite measurement scheme was verified. The prototype of composite current sensor was developed. The DC and AC transmission characteristics of the sensor prototype were measured, and the sensitivity and linearity were 11.96 mV/A, 1.14%, respectively. Finally, the sweep current method and pulse current method experiments prove that the designed composite current sensor can realize the current measurement from DC to 17 MHz.

A knowledge graph can structure heterogeneous knowledge in the field of power faults, construct the correlation between different pieces of knowledge, and solve the diversification, complexity, and island of fault data. There are many kinds of entities in power fault defect text, the relationship between entities is complex, and the data are often mixed with noise. It is necessary to research how to effectively mine the target data and separate the salient knowledge from the noise. Moreover, the traditional entity and relationship extraction methods used in the construction of a power fault knowledge graph cannot fully understand the text semantics, and the response accuracy is low. The Log system usually contains all kinds of information related to faults and a log analysis helps us collect fault information and perform association analysis. Therefore, a Bidirectional Sliced GRU with Gated Attention mechanism (BiSGRU-GA) model is proposed to detect the anomalous logs in the power system, this enriches the fault knowledge base and provides a good data resource for the construction of the knowledge graph. A new Bidirectional GRU with Gated Attention mechanism and Conditional Random Fields and a BERT input layer (BBiGRU-GA-CRF) model is proposed by introducing a BERT layer and Attention Mechanism into the Bidirectional GRU (BiGRU) model to more fully understand the context information of fault sentences and improve the accuracy of entity recognition of fault sentences. Aiming to solve the problems of large calculation cost and propagation error which occur in the traditional relationship extraction model, an improved Bidirectional Gated Recurrent Unit neural network with fewer parameters and the Gated Attention Mechanism (BiGRU-GA) model is proposed. This new model introduces an improved Gated Attention Mechanism to achieve better effects in relationship extraction. Compared with Bidirectional Long Short-Term Memory with Attention Mechanism (BiLSTM-Attention), the accuracy, recall, and F-measure of the model were improved by 1.79%, 13.83%, and 0.30% respectively, and the time cost is reduced by about 16%. The experimental results show that the BiGRU-GA model can capture local features, reduce the training time cost, and improve the model recognition effect.

Inverted magnetorheological (MR) polishing device mainly use a magnetic sealing ring to collect polishing fluid. This collection method wears the wheel surface of the polishing wheel, affects the surface accuracy of the polishing wheel, and introduces machining error. In order to reduce this wear and improve recovery efficiency, a new type of collector using an air seal is proposed in this paper. Furthermore, testing method using six factors and a three-level orthogonal test table is used to study the structural parameters of the new collector. The flow fields affected by the different structural parameters were simulated, and the corresponding collection efficiency was analyzed. The results show that the air nozzle diameter has the greatest impact on the fluctuation value of the collector outlet flow, followed by the airflow velocity and nozzle spacing. Moreover, the structural parameters obtained from the orthogonal test were optimized using the control variable method. The minimum flow fluctuation and maximum flow at the collector outlet can be obtained when the nozzle diameter is 2.5 mm and the nozzle airflow velocity is 31 m/s.

Bumblebee pollination is crucial to the production of tomato in protected cultivation. Both tomato yield and flavor play important roles in attracting attentions from growers and consumers. Compared with yield, much less work has been conducted to investigate whether and how pollination methods affect tomato flavor. In this study, the effects of bumblebee pollination, vibrator treatment, and plant growth regulator (PGR) treatment on tomato yield and flavor were tested in Gobi Desert greenhouses. Compared with vibrator or PGR treatments, bumblebee pollinated tomato had higher and more stable fruit set, heavier fruit weight, and more seed. We also found that the seed quantity positively correlated with fruit weight in both bumblebee pollinated, and vibrator treated tomato, but not in PGR treated tomato. Besides enhancing yield, bumblebee pollination improved tomato flavor. Bumblebee pollinated tomato fruits contained more fructose and glucose, but less sucrose, citric acid, and malic acid. Furthermore, the volatile organic compounds of bumblebee pollinated tomato were distinctive with vibrator or PGR treated tomato, and more consumer liking related compounds were identified in bumblebee pollinated tomato. Our findings provide new insights into the contributions of bee pollinator towards improving crop yield and quality, emphasizing the importance of bumblebee for tomato pollination.

Arc fault is an important cause of electrical fire. At present, the arc-fault detection method based on current and voltage is vulnerable to the influence of a nonlinear load and switching operation in the line, resulting in misjudgment and omission. Therefore, an arc-fault detection method based on the characteristics of electromagnetic radiation is proposed. A low-voltage AC series arc-fault simulation platform is built, and a simple annular antenna is designed to receive an electromagnetic radiation signal. It is proved by experiments that electromagnetic radiation signals have similar characteristic frequencies (13.6–14.2 MHz) under different currents, loads, arc positions and arc occurrence times. At the same time, the electromagnetic radiation signal of a low-voltage AC series arc and normal switching operations are compared. The pulse oscillation time of the radiation signals of the operating arc (2 μs) is far shorter than that of the faulty arc (4 μs), and the characteristic frequency of the radiation signal generated by the switching operation (9.35 MHz) is significantly lower than that of the series arc radiation signal (14 MHz). Compared with the existing methods, this method does not need to consider the influence of current, nonlinear load and other factors in the line, and can accurately distinguish the operating arc and faulty arc.

This study was first conducted to comprehensively investigate the potential of yak manure as a raw material to prepare fuel pellets. The effect of different parameters such as binder, pressure, and moisture content on pellet density and diametric compressing strength was investigated using a laboratory single pelleting press unit. Results showed that increasing the pressure can help to obtain high-quality fuel pellets. The pellet properties (density and diametric compressing strength) initially increased and then decreased with moisture content increase. By contrast, binder was not identified to promote density and diametric compressing strength. The parameters were optimized using the response surface method with central composite design to obtain high-quality pellets. In conclusion, the use of yak manure as a raw material to make fuels can be considered a sustainable approach and can effectively be used to fulfill the energy and heating requirements of rural areas.

The tracking and positioning of transmission lines is a key element for UAVs (Unmanned Aerial Vehicles) to achieve autonomous inspection of transmission lines. Current methods are vulnerable to weather and environmental factors, have high costs, and have difficulties in data processing. Therefore, this paper proposes a transmission line tracking and localization method based on the electric field sensor array, which calculates the current UAV’s heading angle deflection angle, the distance between the transmission line and the UAV, and the elevation angle, providing a new idea to solve the problem of UAV inspection of transmission lines. At the same time, the electric field distribution of different arrangements of three-phase transmission lines was analyzed using COMSOL to determine the flight area of the UAV. By comparing the electric field distribution of the UAV flight area and single-phase transmission lines, it was verified that the current method is also applicable in the three-phase transmission line scenario, and it was further verified that the sensor array used can sense the change of the UAV position in the flight area, indicating that the electric field sensor array can realize the transmission line tracking and localization of transmission lines. The experimental results showed that, in the three-phase transmission line scenario, when the sensor array moves along the transmission straight wire, the maximum absolute error of the heading angle deflection angle calculated according to this method was 8.2°, the maximum absolute error of the distance between the array and the transmission line was 19.3 cm, and the maximum absolute error of the elevation angle was 11.37°; the error was within a reasonable range and can be used for the UAV to realize autonomous inspection.

The detection of cotton impurity rates can reflect the cleaning effect of cotton impurity removal equipment, which plays a vital role in improving cotton quality and economic benefits. Therefore, several studies are being carried out to improve detection accuracy. Image processing technology is increasingly used in cotton impurity detection, in which deep learning technology based on convolution neural networks has shown excellent results in image classification, segmentation, target detection, etc. However, most of these applications focus on detecting foreign fibers in lint, which is of little significance to the parameter adjustment of cotton impurity removal equipment. For this reason, our goal was to develop an impurity detection system for seed cotton. In image segmentation, we propose a multi-channel fusion segmentation algorithm to segment the machine-picked seed cotton image. We collected 1017 images of machine-picked seed cotton as a dataset to train the detection model and tested and recognized 100 groups of samples, with an average recognition rate of 94.1%. Finally, the image segmented by the multi-channel fusion algorithm is input into the improved YOLOv4 network model for classification and recognition, and the established V–W model calculates the content of all kinds of impurities. The experimental results show that the impurity content in machine-picked cotton can be obtained effectively, and the detection accuracy of the impurity rate can increase by 5.6%.

Robotic welding often uses vision-based measurement to find the correct placement of the welding seam. Traditional machine vision methods work well in many cases but lack robustness when faced with variations in the manufacturing process or in the imaging conditions. While supervised deep neural networks have been successful in increasing accuracy and robustness in many real-world measurement applications, their success relies on labeled data. In this paper, we employ semi-supervised learning to simultaneously increase accuracy and robustness while avoiding expensive and time-consuming labeling efforts by a domain expert. While semi-supervised learning approaches for various image classification tasks exist, we purpose a novel algorithm for semi-supervised key-point detection for seam placement by a welding robot. We demonstrate that our approach can work robustly with as few as fifteen labeled images. In addition, our method utilizes full image resolution to enhance the accuracy of the key-point detection in seam placement.

At present, the detection of transformer winding deformation faults is carried out in an offline state, which requires the transformer to cooperate with the implementation of planned power outages, or it takes place after the sudden failure of the transformer when it is out of operation. It is difficult to obtain the status information of the windings online in time. Since the transformer will suffer very fast transient overvoltage (VFTO) impact during operation, combined with the principle of the frequency response method, an online detection method of transformer winding deformation based on VFTO is proposed. In order to study the frequency response characteristics of transformer winding under the impact of VFTO, the generation process of VFTO is simulated by simulation software, and the equivalent circuit model of transformer winding before and after deformation is established. The VFTO signal is injected into the transformer circuit model as an excitation source, and the changes of resonant frequencies of frequency response curve under different deformation types and different deformation degrees of winding are analyzed. The simulation results show that the frequency response curves of different winding deformation types are different. Different deformation degrees are simulated by increasing the radial capacitance by 4%, 13%, and 23%, series inductance by 2%, 4%, and 6%, and longitudinal capacitance by 3%, 6%, and 9%, and the change of resonance frequencies can comprehensively reflect the deformation information of winding. At the same time, the tests of different deformation types and deformation degrees of the simulated winding are carried out. The results show that with the deepening of the change degree of the simulated fault inductance value, the frequency response curve shifts to the low-frequency direction, confirming the feasibility of the online detection method of transformer winding deformation based on VFTO.

In order to ensure the safe and stable operation of a power system, the performance evaluation of transformer windings after a short-circuit test can predict whether the windings are deformed in order to provide a useful reference for the operation and maintenance of the power sector. This paper proposes a method for evaluating the performance of transformer windings in order to improve the overall effectiveness of a winding evaluation. The index data obtained based on a short-circuit impedance method, frequency response method, and oscillation wave method are used in the algorithm proposed in this paper. First, the transformer winding performance evaluation index system is constructed. Second, the weight of each index is determined by analytic hierarchy process, and then the fuzzy comprehensive assessment method is introduced, and the fuzzy evaluation matrix is established, the evaluation results are calculated using the evaluation formula. Finally, the maximum membership principle is used to determine the performance level of the transformer winding on the evaluation results, and the evaluation results of the transformer winding state are obtained. The example shows that the evaluation level of the measured transformer winding performance can be obtained by this method as “good”. Compared with the traditional method, this method can simplify the evaluation while maintaining higher accuracy.

As for industrial robots’ point-to-point joint motion planning with constrained velocity, cubic polynomial planning has the problem of discontinuous acceleration; quintic polynomial planning requires acceleration to be specified in advance, which will likely cause velocity to fluctuate largely because appropriate acceleration assigned in advance is hardly acquired. Aiming at these problems, a modified cubic Hermite interpolation for joint motion planning was proposed. In the proposed methodology, knots of cubic Hermite interpolation need to be reconfigured according to the initial knots. The formulas for how to build new knots were put forward after derivation. Using the newly-built knots instead of initial knots for cubic Hermite interpolation, joint motion planning was carried out. The purpose was that the joint planning not only satisfied the displacement and velocity constraints at the initial knots but also guaranteed C2 continuity and less velocity fluctuation. A study case was given to verify the rationality and effectiveness of the methodology. Compared with the other two planning methods, it proved that the raised problems can be solved effectively via the proposed methodology, which is beneficial to the working performance and service life of industrial robots.

Both calcium-independent phospholipase A2 beta (iPLA2β) and endoplasmic reticulum (ER) stress regulate important pathophysiological processes including inflammation, calcium homeostasis and apoptosis. However, their roles in ischemic heart disease are poorly understood. Here, we show that the expression of iPLA2β is increased during myocardial ischemia/reperfusion (I/R) injury, concomitant with the induction of ER stress and the upregulation of cell death. We further show that the levels of iPLA2β in serum collected from acute myocardial infarction (AMI) patients and in samples collected from both in vivo and in vitro I/R injury models are significantly elevated. Further, iPLA2β knockout mice and siRNA mediated iPLA2β knockdown are employed to evaluate the ER stress and cell apoptosis during I/R injury. Additionally, cell surface protein biotinylation and immunofluorescence assays are used to trace and locate iPLA2β. Our data demonstrate the increase of iPLA2β augments ER stress and enhances cardiomyocyte apoptosis during I/R injury in vitro and in vivo. Inhibition of iPLA2β ameliorates ER stress and decreases cell death. Mechanistically, iPLA2β promotes ER stress and apoptosis by translocating to ER upon myocardial I/R injury. Together, our study suggests iPLA2β contributes to ER stress-induced apoptosis during myocardial I/R injury, which may serve as a potential therapeutic target against ischemic heart disease.

Hydrolytic activity for chlorogenic acid (CGA) has been recognized as an important side activity of some types of ferulic acid esterases. The purpose of this work was to enhance the efficient expression of ferulic acid esterase (FAE) and to explore its application in the processing of sunflower seed. Two novel FAEs from Aspergillus aculeatus (AaSD14) were expressed in genetically engineered E. coli BL21 (DE3), and their properties, including temperature, pH, metal ions and substrate specificity, were characterized after purification. Competitive CGA hydrolysis activity was observed in these recombined ferulic acid esterases (reFAEs) with reFAE1 of 246.37 U/g and reFAE2 of 340.95 U/g, which were 56.6 and 78.4 times higher than that of the wild strain (4.35 U/g), respectively. Meanwhile, the fermentation cycle was greatly shortened to 2.0 d. These reFAEs were recognized as type C FAE through substrate specificity assays. Treatment of sunflower seed protein (SSP) using reFAE2 resulted in a remarkable color change, from green to milk-white, confirming the activity of CGA biodegradation. Therefore, it shows certain potential in the processing of sunflower seed and other related foodstuffs.

At present, the method of using unmanned aerial vehicles (UAVs) with traditional navigation equipment for inspection of overhead transmission lines has the limitations of expensive sensors, difficult data processing, and vulnerable to weather and environmental factors, which cannot ensure the safety of UAV and power systems. Therefore, this paper establishes a mathematical model of spatial distribution of transmission lines to study the field strength distribution information around transmission lines. Based on this, research the navigation and positioning algorithm. The data collected by the positioning system are input into the mathematical model to complete the identification, positioning, and safety distance diagnosis of the field source. The detected data and processing results can provide reference for UAV obstacle avoidance navigation and safety warning. The experimental results show that the positioning effect of the positioning navigation algorithm is obvious, and the positioning error is within the range of use error and has good usability and application value.

Quantum private queries can commonly protect important information in a good many of domains, such as finance, business, military, which use quantum effects to achieve unprecedented classical private queries. However, quantum state can be easily affected by environmental noise, which affects the actual effect of quantum private queries. This paper developed a new quantum private query protocol based on four qubits logical Bell state to resist the collective-dephasing noise. The symmetric private information retrieval problem, which is the most influential problem in the process of quantum private query, was solved well by quantum oblivious transfer. It introduces the construction of four qubits logical Bell state. The quantum private query protocol innovates the quantum key distribution process by using the four qubits logical Bell state as the measurement base to measure the logical qubits, and ensures the function of quantum oblivious transmission. The protocol cannot only resist the noise influence of the communication process, but also ensure the security of both sides of the communication.

The traditional method of using electric field sensors to realize early warning of electric power safety distance cannot measure the distance of dangerous sources. Therefore, aiming at the electric field with a frequency of 50 to 60 Hz (AC electric field), a new method for localization of aerial AC target by the capacitive one-dimensional spherical electric field sensor circular array is studied. This method can directly calculate the distance, elevation, and azimuth of the detector from the dangerous source. By combining the measurement principle of the spherical electric field sensor and the plane circular array theory, a mathematical model for the localization of aerial targets in an AC electric field is established. An error model was established using Gaussian noise and the effects of different layout parameters on the localization error were simulated. Based on mutual interference between sensors, minimum induced charge, and localization error, an optimal model for sensor layout was established, and it was solved by using genetic algorithms. The optimization results show that when the number of sensors is 4, the array radius is 20 cm, and the sensor radius is 1.5 cm, the ranging error is 8.4%. The detector was developed based on the layout parameters obtained from the optimization results, and the localization method was experimentally verified at 10 and 35 kV alarm distances. The experimental results show that when the detector is located at 10 kV alarm distance, the distance error is 0.18 m, the elevation error is 6.8°, and the azimuth error is 4.57°, and when it is located at 35 kV alarm distance, the distance error is 0.2 m, the elevation error is 4.8°, and the azimuth error is 5.14°, which meets the safety distance warning requirements of 10 and 35 kV voltage levels.

The development of the Internet of Things (IoT) plays a very important role for processing data at the edge of a network. Therefore, it is very important to protect the privacy of IoT devices when these devices process and transfer data. A mesh signature (MS) is a useful cryptographic tool, which makes a signer sign any message anonymously. As a result, the signer can hide his specific identity information to the mesh signature, namely his identifying information (such as personal public key) may be hidden to a list of tuples that consist of public key and message. Therefore, we propose an improved mesh signature scheme for IoT devices in this paper. The IoT devices seen as the signers may sign their publishing data through our proposed mesh signature scheme, and their specific identities can be hidden to a list of possible signers. Additionally, mesh signature consists of some atomic signatures, where the atomic signatures can be reusable. Therefore, for a large amount of data published by the IoT devices, the atomic signatures on the same data can be reusable so as to decrease the number of signatures generated by the IoT devices in our proposed scheme. Compared with the original mesh signature scheme, the proposed scheme has less computational costs on generating final mesh signature and signature verification. Since atomic signatures are reusable, the proposed scheme has more advantages on generating final mesh signature by reconstructing atomic signatures. Furthermore, according to our experiment, when the proposed scheme generates a mesh signature on 10 MB message, the memory consumption is only about 200 KB. Therefore, it is feasible that the proposed scheme is used to protect the identity privacy of IoT devices.

Disconnect switch and circuit breakers operations in gas insulated switchgear (GIS) systems may produce very fast transient overvoltage (VFTO). Detecting VFTO is the first step for researchers to reduce the damage to other equipment of the substation caused by VFTO. Most of the existing sensors used for VFTO are generally bulky, complex to install, and require modification of the GIS structure. In this paper, a miniaturized measurement system that uses capacitive voltage divider and differentiating−integrating circuit is proposed. A special sensor structure and optimized differentiating−integrating circuit components arrangement were designed to increase the bandwidth of the measurement system. The frequency-domain, time-domain and voltage divide calibration experiment was performed, and a comparison experiment with an internal VFTO sensor was conducted. The measurement system was applied in the 500 kV GIS substation, and the VFTO measurement under specific conditions was carried out. The measured time domain and frequency domain waveforms conformed to the definition of standard VFTO according to IEC 60,071. It was found that the proposed measurement system meets VFTO measurement requirements and can be applied to actual VFTO measurements.

With the development of the highway transportation and business trade, vehicle Weigh-In-Motion (WIM) technology has become a key technology for measuring traffic loads. In this paper a novel WIM system based on monitoring of pavement strain responses in rigid pavement was investigated. In this WIM system multiple low cost, light weight, small volume and high accuracy embedded concrete strain sensors were used as WIM sensors to measure rigid pavement strain responses. In order to verify the feasibility of the method, a system prototype based on multiple sensors was designed and deployed on a relatively busy freeway. Field calibration and tests were performed with known two-axle truck wheel loads and the measurement errors were calculated based on the static weights measured with a static weighbridge. This enables the weights of other vehicles to be calculated from the calibration constant. Calibration and test results for individual sensors or three-sensor fusions are both provided. Repeatability, sources of error, and weight accuracy are discussed. Successful results showed that the proposed method was feasible and proven to have a high accuracy. Furthermore, a sample mean approach using multiple fused individual sensors could provide better performance compared to individual sensors.

Abstract: This paper presents a new vehicle classification and develops a traffic monitoring detector to provide reliable vehicle classification to aid traffic management systems. The basic principle of this approach is based on measuring the dynamic strain caused by vehicles across pavement to obtain the corresponding vehicle parameters – wheelbase and number of axles – to then accurately classify the vehicle. A system prototype with five embedded strain sensors was developed to validate the accuracy and effectiveness of the classification method. According to the special arrangement of the sensors and the different time a vehicle arrived at the sensors one can estimate the vehicle’s speed accurately, corresponding to the estimated vehicle wheelbase and number of axles. Because of measurement errors and vehicle characteristics, there is a lot of overlap between vehicle wheelbase patterns. Therefore, directly setting up a fixed threshold for vehicle classification often leads to low-accuracy results. Using the machine learning pattern recognition method to deal with this problem is believed as one of the most effective tools. In this study, support vector machines (SVMs) were used to integrate the classification features extracted from the strain sensors to automatically classify vehicles into five types, ranging from small vehicles to combination trucks, along the lines of the Federal Highway Administration vehicle classification guide. Test bench and field experiments will be introduced in this paper. Two support vector machines classification algorithms (one-against-all, one-against-one) are used to classify single sensor data and multiple sensor combination data. Comparison of the two classification method results shows that the classification accuracy is very close using single data or multiple data. Our results indicate that using multiclass SVM-based fusion multiple sensor data significantly improves the results of a single sensor data, which is trained on the whole multisensor data set.

Cooperative downloading is one of the effective methods to improve the amount of downloaded data in vehicular ad hoc networking (VANET). However, the poor channel quality and short encounter time bring about a high packet loss rate, which decreases transmission efficiency and fails to satisfy the requirement of high quality of service (QoS) for some applications. Digital fountain code (DFC) can be utilized in the field of wireless communication to increase transmission efficiency. For cooperative forwarding, however, processing delay from frequent coding and decoding as well as single feedback mechanism using DFC cannot adapt to the environment of VANET. In this paper, a cooperative downloading method for VANET using concatenated DFC is proposed to solve the problems above. The source vehicle and cooperative vehicles encodes the raw data using hierarchical fountain code before they send to the client directly or indirectly. Although some packets may be lost, the client can recover the raw data, so long as it receives enough encoded packets. The method avoids data retransmission due to packet loss. Furthermore, the concatenated feedback mechanism in the method reduces the transmission delay effectively. Simulation results indicate the benefits of the proposed scheme in terms of increasing amount of downloaded data and data receiving rate.