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Abstract Spin-orbitronics, exploiting electron spin and/or orbital angular momentum, offers a powerful route to energy-efficient spintronic applications. Recent research on orbital currents in light metals broadens the scope of spin-orbit torque (SOT). However, distinguishing and manipulating orbital torque in heavy metal/ferromagnet (HM/FM) remains a challenge, limiting the promising synergy of spin and orbital currents. Here, we design a HM/FM/FMSOC heterostructure and experimentally separate orbital torque contribution from spin torque by utilizing the distinct diffusion length of spin and orbital currents. Furthermore, we achieve the synergy of spin and orbital torques by controlling their relative strength, and obtain a 110% improvement in torque efficiency compared to the representative Pt/Co bilayer. Our findings not only contribute to a deeper understanding of SOT mechanisms and orbital current transport in HM/FM multilayers, but also highlight the promising prospect of orbital and spin torque synergy for optimizing the efficiency of next-generation spintronic devices.

Real time recognition and detection of tomato fruit maturity is a key function of tomato picking robots. Existing recognition and detection algorithms have slow speed and low recognition accuracy for small tomatoes. Here, a tomato fruit maturity detection model YOLOv5s3 based on improved YOLOv5s was proposed and its accuracy was verified through comparative experiments. On the basis of YOLOv5s, an SC module was proposed based on channel shuffle packet convolution. Then, A C3S module is constructed, which replaced the original C3 module with this C3S module to reduce the number of parameters while maintaining the feature expression ability of the original network. And a 3-feature fusion FF module was put forward, which accepted inputs from three feature layers. The FF module fused two feature maps from the backbone network. The C2 layer of the backbone was integrated, and the large target detection head was removed to use dual head detection to enhance the detection ability of small targets. The experimental results showed that the improved model has a detection accuracy of 94.8%, a recall rate of 96%, a parameter quantity of 3.02M, and an average accuracy (mAP0.5) of 93.3% for an intersection over union (IoU) of 0.5. The detection speed reaches 9.4ms. It can quickly and accurately identify the maturity of tomato fruits, and the detection speed is 22.95%, 33.33%, 48.91%, 68.35%, 15%, and 25.98% higher than the original YOLOv5s, YOLOv5m, YOLOv5l, YOLOv5x, YOLOv5n, and YOLOv4, respectively. The real-time testing visualization results of different models indicated that the improved model can effectively improve detection speed and solve the problem of low recognition rate for small tomatoes, which can provide reference for the development of picking robots.

The influence of carrier mobility on the space charge transport behavior inside the oil-impregnated pressboard insulation of converter transformers cannot be neglected. However, at present, current knowledge is usually derived from empirical or theoretical values, lacks experimental studies, and often ignores the effects of temperature and field strength under actual operating conditions. In this paper, based on the variable-temperature surface potential decay (SPD) method, a carrier mobility measurement platform for oil-impregnated pressboard is established, and the carrier mobility values for different combinations of oil and oil-impregnated pressboard are obtained experimentally to analyze and reveal the influence mechanisms of temperature and field strength on the carrier mobility. The results indicate the following: (1) The positive and negative carrier mobilities of oil-impregnated pressboard are in the range of 10−12–10−13 m2·V−1·s−1, and the negative carrier mobility is always higher than the positive carrier mobility. (2) The carrier mobility is positively correlated with the changes of temperature and field strength, and when the temperature increases from 20 °C to 80 °C, the positive and negative carrier mobilities increase by 4.01 times and 4.72 times, respectively; when the field strength increases from 1 kV/mm to 7 kV/mm, the positive and negative carrier mobility increases by 2.53 and 2.72 times, respectively. (3) The carrier mobility of the pressboard with a higher oil absorption rate changes more significantly with temperature; when the field strength is 7 kV/mm and the temperature increases from 20 °C to 80 °C, the positive polarity carrier mobility increases from 3.96 × 10−13 m2·V−1·s−1 to 2.64 × 10−11 m2·V−1·s−1, an increase of 66.67 times, while the increase in the carrier mobility of the pressboard with a lower oil absorption rate is only 1.59 times. (4) The carrier mobility of the naphthenic transformer oil-impregnated pressboard is higher than that of the paraffin-based transformer oil-impregnated pressboard, and the carrier mobility of two kinds of naphthenic transformer oil-impregnated pressboard is 3.16 times and 2.47 times higher than that of the paraffin-based transformer oil-impregnated pressboard, respectively, under the conditions of 60 °C and 7 kV/mm. (5) Utilizing the Darcy model and microscopic scanning results of the pressboard morphology, it was revealed that permeability and fiber structure are key factors influencing the variation in carrier mobility. The research results of this paper can provide theoretical basis for the calibration and optimization of the oil-pressboard insulation structure of converter transformers.

Oil-pressboard/paper insulation materials are essential in transformers for ensuring their safe and stable operation, primarily due to their roles in spatial electric field distribution and charge migration mechanisms. Current spatial distribution analyses rely on computational methods that lack empirical validation, particularly for oil-pressboard/paper composites. This study leverages the principles of the Kerr electro-optic effect to develop a rapid measurement platform for electric fields within oil-pressboard/paper insulation under impulse voltage conditions, which measures the spatial electric field characteristics using Cu-Cu and Al-Al electrodes under various scenarios: with asymmetric and symmetric pressboard coverage and different numbers of insulating paper layers. Findings indicated: (1) In asymmetric pressboard models, Cu-Cu electrodes exhibit a consistent peak electric field of approximately 16 kV/mm, while Al-Al electrodes show peak values of 18.13 kV/mm and −14.98 kV/mm. Charge density patterns are similar, with Cu-Cu at about 68 μC/m2 and Al-Al at 11.2 μC/m2 and −124.8 μC/m2. (2) Symmetric models present consistent peak electric fields and charge densities for both polarities. (3) Increasing insulating paper layers elevates electric field strengths. Both electrodes show the similar peak field of about 17 kV/mm with differing paper layers due to higher charge injection from the Al electrode. (4) Utilizing the Schottky effect and field emission principles, the study clarifies charge generation and migration mechanisms. These insights could provide a theoretical foundation for designing and verifying oil-pressboard/paper insulation structures in transformers.

Global warming caused by excess carbon dioxide (CO2) emission has been a focus of the world. The development of neutral carbon technologies becomes a strategic choice for the sustainable human society. Integrating CO2 capture and conversion (iCCC) technology can simultaneously convert the captured CO2 from flue gas into value-added chemicals, which saves great energies and expenses incurred in CO2 compression and transportation processes of conventional carbon capture, utilization, and storage (CCUS) technology. The present review critically discusses the dual-function materials (DFMs) and the iCCC technology at intermediate temperature for methane production and high temperature for syngas production. The design of reactor and optimization of operation conditions are emphasized from the perspective of industrial applications. The dual-fixed-bed reactors mode by switching the flue gas and reactant gases, and the dual-fluidized-bed reactors mode by the circulation of DFMs particles are comparatively reviewed. We hope this review can stimulate further studies including designing and fabricating feasible DFMs, exploring realistic catalytic process for CO2 conversion to high value-added chemicals, developing workable reactor modes and optimizing operation conditions, and establishing industrial demonstration for real applications of iCCC technology in the future.

Infrared image recognition plays an important role in the inspection of power equipment. Existing technologies dedicated to this purpose often require manually selected features, which are not transferable and interpretable, and have limited training data. To address these limitations, this paper proposes an automatic infrared image recognition framework, which includes an object recognition module based on a deep self-attention network and a temperature distribution identification module based on a multi-factor similarity calculation. First, the features of an input image are extracted and embedded using a multi-head attention encoding–decoding mechanism. Thereafter, the embedded features are used to predict the equipment component category and location. In the located area, preliminary segmentation is performed. Finally, similar areas are gradually merged, and the temperature distribution of the equipment is obtained to identify a fault. Our experiments indicate that the proposed method demonstrates significantly improved accuracy compared with other related methods and, hence, provides a good reference for the automation of power equipment inspection.

New-energy vehicles (NEV), particularly electric vehicles, are globally popular with political and financial support from governments, which aim at reducing energy consumption and environmental pollution in the transportation sector. This paper designs a matrix model which identifies the factors influencing the decision-making process on purchasing NEVs, and applies the model on a Chinese case to investigate the impact of influencing factors on Chinese consumers’ purchase behavior to NEVs. The influencing factors are divided into five groups: economic, political, social, technological and environmental. Through a detailed questionnaire survey of 526 consumers in China, this paper conducts a correlation and difference analysis between these consumers’ status and the influencing factors on the purchase decision-making of NEVs through SPSS software. The results indicate that economic, technological and political factors have a strong influence on purchase behavior, while social and environmental factors have a weaker influence. Additional principal component analysis on influencing factors’ preference reveals that consumers’ preferences have two tendencies: practicability or appearance, with the former is attracting far more attention. Rather than practicability or appearance, consumers care more about economic and political factors. According to these results, this paper suggests several policy implications on enhancing the consumers’ willingness to purchase NEVs.

The on-load tap changer (OLTC) is one of the key components of converter transformer. The energy generated during tap switching process of the OLTC will cause thermal expansion and surging of the insulating oil in the chamber, resulting in the mis-operation of protective relay and threatening the safe and stable operation of the power system as well. Therefore, taking the typical dual-transition resistance-type OLTC as an example, a numerical modeling method of OLTC oil flow surging based on the realizable k-ε model is proposed, and the fluid simulation model is established based on the FLUENT software. The quantitative relationship between the OLTC transition resistance value and the oil velocity and oil pressure at the top of the oil chamber is analyzed, that is, the smaller the transition resistance, the greater the oil flow velocity and oil pressure during switching. In addition, an equivalent experimental platform is built to carry out the static action characteristic experiment of OLTC oil flow relay, the corresponding relationship between the change range of oil pressure at the top of the chamber, and the oil velocity under typical value (1.5 m/s, 2.0 m/s, and 2.5 m/s) are revealed. Based on which, the optimization suggestions on the protection relay threshold configuration of OLTC oil velocity and oil pressure are put forward.

This paper presents a new adaptive small chattering sliding mode control (SCSMC) scheme that uses reinforcement learning (RL) and time-delay estimation (TDE) for the motion control of free-floating space robotic manipulators (FSRM) subject to model uncertainty and external disturbance. The proposed sliding mode control scheme can achieve small chattering effects and improve the tracking accuracy by using a new adaptive law for the switching gain and a RL-based robust term to handle the control inputs. In SCSMC, the complicated multiple-input-multiple-output (MIMO) uncertain system of FSRM is transformed into multiple single-input-single-output (SISO) known subsystems with bounded estimation errors by the TDE technique and state feedback compensation. Subsequently, once the sliding variable is inside the designed manifold, the derivative of the switching gain for each subsystem becomes a negative hyperbolic tangent function of the associated sliding variable, which offers the ability to reduce chattering by decreasing the switching gain. Moreover, the RL based robust term for each subsystem is designed to avoid the loss of tracking accuracy caused by the aforementioned switching gain drop. The tracking errors are proven to be uniformly-ultimately-bounded (UUB) with an arbitrarily small bound by using the Lyapunov theory. The effectiveness of the proposed control scheme is verified by numerical simulations.

Organisms are increasingly tolerant to metal toxicity in the natural ecosystems, which did not match the results of the environmental risk assessment (ERA) of metals based on toxicity data from organisms in the laboratory. Studies have described the effects of pre-exposure to metals on metal toxicity tolerance in terms of the toxicokinetic (TK) process; however, the toxicodynamic (TD) process may be more susceptible to metal pre-exposure. Therefore, to determine whether pre-exposure to low concentrations of silver (Ag) or cadmium (Cd) affects the metal TK and TD processes of zebrafish (Danio rerio) larvae, we investigated four TK–TD model parameters that control tolerance and sensitivity to metal toxicity on the survival. Our results showed that the killing rate (ks) of larvae exposed to high Cd concentrations was significantly lower following pre-exposure to 10 μg/L Cd than that of larvaenot pre-exposed. However, the ks for high Ag concentrations was significantly higher in zebrafish larvae following pre-exposure to 2 μg/L Ag than in larvae not pre-exposed. In other words, a one-day pre-exposure to 2 µg/L Ag rendered the larvae more sensitive to Ag during a subsequent 4-day exposure to higher Ag concentrations, whereas a one-day pre-exposure to 10 µg/L Cd rendered the larvae more tolerance to Cd during a subsequent 4-day exposure to higher Cd concentrations. Our results further the current understanding of toxic metal tolerance mechanisms, both in TK and TD processes, and they will guide future laboratory studies to assess actual pre-exposure scenarios that occur in natural environments. Thus, our study can help reduce uncertainty in testing and improve ecological management concerning metal risk assessments.

In this paper, a bi-level Delta-Sigma modulator-based MEMS gyroscope design is presented based on a Model Predictive Control (MPC) approach. The MPC is popular because of its capability of handling hard constraints. In this work, we propose to combine the 1-bit nature of the bi-level Delta-Sigma modulator output with the MPC to develop a 1-bit processing-based MPC (OBMPC). This paper will focus on the affine relationship between the 1-bit feedback and the in-loop MPC controller, as this can potentially remove the multipliers from the controller. In doing so, the computational requirement of the MPC control is significantly alleviated, which makes the 1-bit MEMS Gyroscope feasible for implementation. In addition, a stable constrained MPC is designed, so that the input will not overload the quantizer while maintaining a higher Signal-to-Noise Ratio (SNR).

The controlled presplitting blasting technique is widely used in mining engineering to improve the permeability and gas extraction efficiency of coal seams. One of the key factors is the appropriate arrangement of the blasting and drainage holes, which can help improve the gas drainage quantity. To optimize the drilling layout to enhance gas-drainage efficiency, a series of controlled presplitting blasting tests were conducted at the Pingdingshan No. 8 coal mine. Based on the analysis of variations in stress and longitudinal-wave velocity of the coal in different blasting ranges, the results show that the stress on the coal at a distance of 1 m from the blasting hole decreased significantly after blasting; thus, the coal exhibited negligible bearing capacity and the longitudinal-wave velocity decreased by 56%. However, the coal exhibited particular bearing capacity at a distance of 3 m away from the blasting hole, and the longitudinal-wave velocity decreased by 35%. The stress and longitudinal-wave velocity at a distance of 5 m from the blasting hole were unaffected by the blasting. The blasting integrity rate of coal kv was defined to characterize the effect of blasting on the coal-seam fracture. The effective cracking and effective influence radii of blasting under these working conditions were predicted to be in the ranges 3.3–3.4 m and 7.2–7.3 m, respectively. According to the test results, the borehole layout was optimized in the field testes for gas drainage in coal roadway strips, and the amounts of pure gas extracted after blasting were thus increased by 1.54–2.24 times the amount before blasting.

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