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Abstract The amount of actively cultivated land in China is increasingly threatened by rapid urbanization and rural population aging. Quantifying the extent and changes of active cropland and cropping intensity is crucial to global food security. However, national-scale datasets for smallholder agriculture are limited in spatiotemporal continuity, resolution, and precision. In this paper, we present updated annual Cropland Use Intensity maps in China (China-CUI10m) with descriptions of the extent of fallow/abandoned, actively cropped fields and cropping intensity at a 10-m resolution in recent six years (2018–2023). The dataset is produced by robust algorithms with no requirements for regional adjustments or intensive training samples, which take full advantage of the Sentinel-1 (S1) SAR and Sentinel-2 (S2) MSI time series. The China-CUI10m maps have achieved high accuracy when compared to ground truth data (Overall accuracy = 90.88%) and statistical data (R2 > 0.94). This paper provides the recent trends in cropland abandonment and agricultural intensification in China, which contributes to facilitating geographic-targeted cropland use control policies towards sustainable intensification of smallholder agricultural systems in developing countries.

Abstract In order to accurately predict the content and variation trend of dissolved gas in transformer oil and guide the condition maintenance of power transformers, a combined prediction model based on multi‐information fusion is proposed and its effectiveness is analysed. First of all, based on the possibility of pathological and missing historical sample data, a detection and filling method based on variable weight combination samples is established. Second, the authors propose two models. Aiming at the non‐linear and non‐stationary characteristics of gas content, a univariate decomposition prediction mode HBA‐VMD‐TCN which based on the Honey Badger algorithm, variational mode decomposition and time convolutional network (TCN) is established. Then the multivariate Informer prediction model is established for gas content affected by multiple variables. Third, the cross‐entropy theory is used to determine the weight coefficients of the two models, and the multi‐information fusion combined prediction model is formed. Finally, on the basis of the above, a method to determine the time step and the position information of the transition point adaptively in the process of prediction is proposed to further improve the prediction accuracy. The results show that, through a series of simulation experiments of model comparison and transformer anomaly prediction, the accuracy and effectiveness of the combined prediction model are verified.

The atmospheric low-level vortex is an important weather system that causes rainstorm and flood in southern China. The vortex induced rainstorm is sudden and local, and its occurence time, intensity and falling area have always been difficult for meteorological forecasting. In order to improve the accuracy of rainstorm prediction in Zhejiang Province and improve the localization application of the prediction model, based on daily precipitation data of Zhejiang Province from 1979 to 2020 and reanalysis dataset from the National Centers for Environmental Prediction Climate Forecast System, climatic characteristics of the heavy rain in Zhejiang Province induced by low-level vortex is studied, the vortex induced rainstorm in Zhejiang mostly occurs in the rainy season and has obvious interannual variation characteristics. Then, the 850 hPa vertical velocity, water vapor flux divergence, velocity divergence, low-level jet, shear line and 700 hPa vertical velocity are used as ingredient factors, and the potential falling area forecast for heavy rain induced by vortex is constructed by using logistic regression algorithm. Finally, the forecast products of the European Medium-Range Weather Prediction Center are used to forecast the rainstorm falling area based on this model, and the actual data of the same period are used for systematic test and weather process test. Results show that the overall performance of the model is good, and the rainstorm prediction ability of 24 h and 12 h ahead is improved, which can increase from 12% accuracy and 20% to around 14% and 40% respectively, and the omission rate drops from more than 75% to around 60%, but there is a certain degree of empty report. The forecast model of Zhejiang Province based on logistic regression method has a good indicator for the prediction of such rainstorm areas, and is of great significance for meteorological disaster prevention and mitigation.

Background: Osteoarthritis (OA) is a common chronic degenerative joint disease. Due to the limited understanding of its complex pathological mechanism, there is currently no effective treatment that can alleviate or even reverse cartilage damage associated with OA. With improvement in public databases, researchers have successfully identified the key factors involved in the occurrence and development of OA through bioinformatics analysis. The aim of this study was to screen for the differentially expressed genes (DEGs) between the normal and OA cartilage through bioinformatics, and validate the function of the TGF-β1/Smad2/3 pathway-related neuron regeneration related protein (NREP) in the articular cartilage. Methods: The DEGs between the cartilage tissues of OA patients and healthy controls were screened by bioinformatics, and functionally annotated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The expression levels of the DEG in human and murine OA cartilage was verified by reverse transcription-quantitative PCR (RT-qPCR), Western blotting and immunohistochemistry (IHC). RT-qPCR, Western-blotting, Cell Counting Kit-8(CCK8) and EdU assays were used to evaluate the effects of knocking down NREP in normal chondrocytes, and the molecular mechanisms were investigated by RT-qPCR, Western blotting and IHC. Results: In this study, we identified NREP as a DEG in OA through bioinformatics analysis, and found that NREP was downregulated in the damaged articular cartilage of OA patients and mouse model with surgically-induced OA. In addition, knockdown of NREP in normal chondrocytes reduced their proliferative capacity, which is the pathological basis of OA. At the molecular level, knock-down of NREP inactivated the TGF-β1/Smad2/3 pathway, resulting in the downregulation of the anabolic markers Col2a1 and Sox9, and an increase in the expression of the catabolic markers MMP3 and MMP13. Conclusion: NREP plays a key role in the progression of OA by regulating the TGF-β1/Smad2/3 pathway in chondrocytes, and warrants further study as a potential therapeutic target.

This review examines China’s educational system the “two-track system” and rural education challenges through the lens of Bronfenbrenner's Ecology System Theory. Using a theoretical framework to review related literature can support readers in understanding the intricate interplay between societal factors, cultural influences, policy decisions, and specific issues. Therefore, this aims to understand how the ecological environment shapes rural education’s development until today, and the Ecological System Theory is used as a theoretical guidance. To find the potential answer to proposed research questions (RQ): RQ 1. What is the background of rural China and the "two-track system" of education? RQ 2. What is the difference between China's urban education and rural education? RQ 3. How does the related environment influence China's educational system? By addressing this fundamental issue, a comprehensive review is provided by following the ecology system theory from the macro-system level to the micro-system

Abstract Over the past few years, there has been a significant increase in the utilization of artificial intelligence (AI)-based educational applications in education. As pre-service teachers’ attitudes towards educational technology that utilizes AI have a potential impact on the learning outcomes of their future students, it is essential to know more about pre-service teachers’ acceptance of AI. The aims of this study are (1) to discover what factors determine pre-service teachers’ intentions to utilize AI-based educational applications and (2) to determine whether gender differences exist within determinants that affect those behavioral intentions. A sample of 452 pre-service teachers (325 female) participated in a survey at one German university. Based on a prominent technology acceptance model, structural equation modeling, measurement invariance, and multigroup analysis were carried out. The results demonstrated that eight out of nine hypotheses were supported; perceived ease of use (β = 0.297***) and perceived usefulness (β = 0.501***) were identified as primary factors predicting pre-service teachers’ intention to use AI. Furthermore, the latent mean differences results indicated that two constructs, AI anxiety (z = − 3.217**) and perceived enjoyment (z = 2.556*), were significantly different by gender. In addition, it is noteworthy that the paths from AI anxiety to perceived ease of use (p = 0.018*) and from perceived ease of use to perceived usefulness (p = 0.002**) are moderated by gender. This study confirms the determinants influencing the behavioral intention based on the Technology Acceptance Model 3 of German pre-service teachers to use AI-based applications in education. Furthermore, the results demonstrate how essential it is to address gender-specific aspects in teacher education because there is a high percentage of female pre-service teachers, in general. This study contributes to state of the art in AI-powered education and teacher education.

To ensure precise positioning of the macro–micro platform with a symmetrical structure, it is crucial to mitigate the impact of various perturbations, including disturbances, as well as complex factors such as external loads, electrical noise, and model parameter variations. This paper proposes a novel macro–micro master–slave control structure that incorporates adaptive fuzzy linear active disturbance rejection control (AFLADRC). The Kp and Kd parameters of the linear state error feedback (LSEF) are dynamically tuned and adjusted using fuzzy reasoning. This approach enhances the robustness of the system and simplifies the tuning process. In addition, this paper also analyzes the symmetry of the coupling effect between macro and micro, as the coupling will affect the motor force and the reaction potential of the motor. The macro–micro platform adopts a symmetric design; the macro stage is driven by a permanent magnet synchronous linear motor (PMLSM), and the micro stage is driven by a voice coil motor. Finally, we built the macro–micro linear motion experimental platform to verify the control effect of the proposed method by conducting trajectory tracking experiments and comparison experiments.

On 31 July 2021, Ningbo, an eastern coast city in China, experienced a severe convective rainstorm, characterized by intense short-duration precipitation extremes with a maximum rainfall rate of 130 mm h−1. In this research, we first analyzed this rainstorm using Doppler radar and precipitation observation and then conducted high-resolution simulation for it. A three-dimensional precipitation diagnostic equation is introduced to quantitatively analyze the microphysical processes during the rainstorm. It is shown that this rainstorm was triggered and developed locally in central Ningbo under favorable large-scale quasi-geostrophic conditions and local conditions. In the early stage, the precipitation increase is mainly driven by the strong convergence of water vapor, and a noticeable increase in both the intensity and spatial extent of uplift promotes the upward transportation of water vapor. As the water vapor flux and associated convergence weaken in the later stage, the precipitation reduces accordingly. Cloud microphysical processes are also important in the entire precipitation process. The early stage updraft supports the escalations in raindrops, with the notable fluctuations in raindrop concentrations directly linked to variations in ground precipitation intensity. The behavior of graupel particles is intricately connected to their melting as they fall below the zero-degree layer. Although cloud water and snow exhibit changes during this period, the magnitudes of these adjustments are considerably less pronounced than those in raindrops and graupels, highlighting the differentiated response of various condensates to the convective dynamics. These results can help deepen the understanding of local severe rainstorms and provide valuable scientific references for practical forecasting.

The objective of this research was to prepare an optimized bio-composite for packaging based on alkali-treated banana pseudo-stem fiber (BPSF), PBAT, and PLA using response surface methodology (RSM). The effects of three factors, i.e., alkali-treated BPSF (0.8 to 2.4 g), PBAT (0.75 to 2.25 g), and PLA (1.6 to 3.2 g) on two dependent variables, i.e., bending strength and tensile strength of bio-composite, were investigated. Box-Behnken design (BBD) provided the combination for an optimum composite, which was 1.15 g of alkali-treated BPSF, 2.09 g of PBAT, and 2.66 g of PLA, respectively. The bending strength and tensile strength for alkali-treated BPSF/PBAT/PLA composite were 32.62 MPa and 30.91 MPa, which was 20.50% and 16.51% higher than native BPSF/PBAT/PLA composite. The bio-composite prepared using the optimized results was further characterized by Fourier transform infrared spectroscopy (FTIR), thermogravimetry, scanning electron microscopy (SEM), mechanical testing, contact angles, and water absorption tests. Analyses showed that alkali-treatment could improve the adhesion and compatibility of BPSF in the polymer matrix. These outcomes were associated with the use of treated-BPSF for better mechanical strength and lower hygroscopicity. This result demonstrated that alkali-treated agricultural residue and degradable polymer could be used to prepare composite materials for green packaging application.

Summary: Seven in absentia homolog 1 (SIAH1) was reported to be downregulated in hepatocellular carcinoma (HCC) and played an important role in HCC progression; however, the underlying reason remains unknown. Here, we found that Cathepsin K (CTSK), a protein potentially interacting with SIAH1, inhibits SIAH1 protein level. CTSK was highly expressed in HCC tissues. CTSK inhibition or downregulation suppressed HCC cell proliferation, whereas CTSK overexpression had the opposite effect; it promotes HCC cell proliferation by regulating the SIAH1/protein kinase B (AKT) pathway, wherein promotes SIAH1 ubiquitination. Neural precursor cells expressing developmentally downregulated 4 (NEDD4) was found to be a potential upstream ubiquitin ligase of SIAH1. Further, CTSK could mediate SIAH1 ubiquitination and degradation by increasing SIAH1 autoubiquitination and recruiting NEDD4 to ubiquitinate SIAH1. Finally, the roles of CTSK were confirmed in a xenograft mouse model. In conclusion, oncogenic CTSK was upregulated in human HCC tissues and accelerated HCC cell proliferation by downregulating SIAH1.

Reflective writing is a relevant aspect of pre-service teachers’ professionalization. Evaluating reflective writing in teacher education is demanding due to a shortage of resources. Hence, this study explores the practical possibilities of evaluating reflective writing using a mixed-methods approach to analyze reflective writing from 198 pre-service teachers at a German university. We used qualitative content analysis, computational linguistic approaches, and BERTopic. Results of qualitative content analysis results indicated primarily descriptive and low-level participants’ reflective writing. Next, computational linguistic analyses revealed that affective and cognitive terminology utilization differed across varying levels of reflection, with a higher frequency of such terms correlating with deeper levels of reflection. BERTopic results showed that reflective content mainly centered on learning materials and shifted toward affective and motivational themes related to higher levels of reflection. This study demonstrates that reflective writing can be evaluated across reflection levels and cognitive, affective, and thematic dimensions, combining qualitative content analysis, computational linguistic approaches, and BERTopic.

As the electrification process of aircrafts continues to advance, the propulsion motor system, as its core component, has received more attention and research. This paper summarizes and analyzes the development status, research focus and typical cases in this field in recent years. Firstly, it analyzes the basic structure and principle of five common motors, summarizes the current status of their respective applications in electric aircrafts, and compares them to determine the most suitable type of motor for use as a propulsion motor, focusing on various performance indexes. Then, the optimized design of propulsion motors is generally divided into two categories, namely high power density and fault tolerance. Starting from the basic relationship equation of motor design, the basic method to improve the power density of motors is pointed out; at the same time, according to the basic principles and objectives of the fault tolerance of motors, the fault tolerance design is divided into two aspects, namely the redundant design and the design to improve the fault tolerance capability. Finally, this paper summarizes the current development status of the propulsion motor system and the existing problems and points out the main development direction of this field in the future, so as to provide reference for the further development of the electric propulsion system of aircraft.

Abstract This study presents an improved sub‐domain analytical model for the characteristic evaluation of the flux reversal permanent magnet machine with Halbach array magnets (FRPMM‐HAM) in rotor slot. The influence of iron saturation is considered by equating iron reluctances into airgap. Based on the different material magnetic permeabilities, the FRPMM‐HAM is divided into several regions, including slots, permanent magnets (PMs), airgap and so on. By solving the Maxwell equations in polar coordinate for each region according to the boundary conditions, the vector magnetic potential can be derived. According to the vector magnetic potential, the airgap flux density distributions are predicted and the electromagnetic characteristics are calculated. The average magnetic density of the tooth and yoke is combined with the Bertotti model to calculate the iron loss. Besides, the eddy current loss of PMs is also calculated. The accuracy of analytical results is validated via the finite element method (FEM).

In order to study the volatile and semivolatile components changes of cut tobacco in the drying process, the cut tobacco were determined by headspace-gas chromatography/mass spectrometry (HS-GC/MS). Principal component analysis (PCA) and similarity analysis were used to analyze the HS-GC/MS results. Based on the similarity, the drying conditions and drying mechanism have been studied. The results showed that the increase in water content would reduce the dying rate, and if the temperature increased, the dying rate increased. The preliminary study on the drying kinetics of the drying process shows that Henderson and Pabis model appeared to be the most suitable for describing the drying process of cut tobacco, and the drying activation energy is 111.9 kJ.

PurposeTo systematically review the clinical value of three imaging examinations (Magnetic Resonance Imaging, Computed Tomography, and myelography) in the diagnosis of Lumbar Disc Herniation.MethodsDatabases including PubMed, Embase, The Cochrane Library, Web of Science, CBM, CNKI, WanFang Data, and VIP were electronically searched to collect relevant studies on three imaging examinations in the diagnosis of Lumbar Disc Herniation from inception to July 1, 2021. Two reviewers using the Quality Assessment of Diagnostic Accuracy Studies-2 tool independently screened the literature, extracted the data, and assessed the risk of bias of included studies. Then, meta-analysis was performed by using Meta-DiSc 1.4 software and Stata 15.0 software.ResultsA total of 38 studies from 19 articles were included, involving 1,875 patients. The results showed that the pooled Sensitivity, pooled Specificity, pooled Positive Likelihood Ratio, pooled Negative Likelihood Ratio, pooled Diagnostic Odds Ratio, Area Under the Curve of Summary Receiver Operating Characteristic, and Q* were 0.89 (95%CI: 0.87–0.91), 0.83 (95%CI: 0.78–0.87), 4.57 (95%CI: 2.95–7.08), 0.14 (95%CI: 0.09–0.22), 39.80 (95%CI: 18.35–86.32), 0.934, and 0.870, respectively, for Magnetic Resonance Imaging. The pooled Sensitivity, pooled Specificity, pooled Positive Likelihood Ratio, pooled Negative Likelihood Ratio, pooled Diagnostic Odds Ratio, Area Under the Curve of Summary Receiver Operating Characteristic, and Q* were 0.82 (95%CI: 0.79–0.85), 0.78 (95%CI: 0.73–0.82), 3.54 (95%CI: 2.86–4.39), 0.19 (95%CI: 0.12–0.30), 20.47 (95%CI: 10.31–40.65), 0.835, and 0.792, respectively, for Computed Tomography. The pooled Sensitivity, pooled Specificity, pooled Positive Likelihood Ratio, pooled Negative Likelihood Ratio, pooled Diagnostic Odds Ratio, Area Under the Curve of Summary Receiver Operating Characteristic, and Q* were 0.79 (95%CI: 0.75–0.82), 0.75 (95%CI: 0.70–0.80), 2.94 (95%CI: 2.43–3.56), 0.29 (95%CI: 0.21–0.42), 9.59 (95%CI: 7.05–13.04), 0.834, and 0.767 respectively, for myelography.ConclusionThree imaging examinations had high diagnostic value. In addition, compared with myelography, Magnetic Resonance Imaging had a higher diagnostic value.

Post-traumatic osteoarthritis is a special type of osteoarthritis and a common disease, with few effective treatments available. α2-Macroglobulin (α2M) is important to chondral protection in post-traumatic osteoarthritis. However, its injection into xenogeneic joint cavities involves safety hazards, limiting clinical applications. Exploring serum α2M-enriching strategies and the therapeutic effect and mechanism of α2M-rich serum (α2MRS) autologous joint injection to treat post-traumatic osteoarthritis has significant value. In the present study, a unique filtration process was used to obtain α2MRS from human and mini pig serum. We evaluated the potential of α2MRS in protecting against post-surgery cartilage degeneration. We identify the potential of α2MRS in reducing the expression of inflammatory cytokines and factors that hasten cartilage degeneration in post-operative conditions leading to post-traumatic osteoarthritis. The potential of α2MRS was analyzed in interleukin-1β induced human chondrocytes and mini pig models. In the chondrocyte model, α2MRS significantly promoted human chondrocyte proliferation and reduced apoptosis and chondrocyte catabolic cytokine gene transcription and secretion. The anterior cruciate ligament autograft reconstruction model of mini pigs was randomized into groups, operated on, and injected with α2MRS or saline. The results showed that α2MRS injection significantly suppressed the levels of inflammatory factors, improved gait, and showed significantly lower cartilage degeneration than the groups that did not receive α2MRS injections. This study highlights the chondroprotective effects of α2MRS, elucidated its potential applications against cartilage degeneration, and could provide a basis for the clinical translation of α2MRS.

In order to optimize the performance of data transmission in multiple input multiple output (MIMO) wireless communication systems, a spread spectrum self-interference system based on channel inverse matrix modulation is proposed. First of all, we improved the spread spectrum method and proposed the DIMMSS(channel inverse matrix modulation spread spectrum) spread spectrum method based on the channel inverse matrix. And a simulation comparison experiment was carried out for the anti-interference ability and anti-eavesdropping ability of this method. Secondly, this paper also proposes two blind despreading methods based on Walsh-hadamard algorithm and cluzeau algorithm. We studied the anti-jamming performance of these two algorithms, and optimized the blind despreading process according to their different characteristics of computational complexity. Experimental results show that compared with ordinary direct sequence spread spectrum, the spread spectrum self-interference system can not only improve the confidentiality and security of the transmitted signal, but also greatly simplify the receiving equipment. It has certain application value in the military and civilian fields.

Large-scale renewable energy integration decreases the system inertia and restricts frequency regulation. To maintain the frequency stability, allocating adequate frequency-sup-port sources poses a critical challenge to planners. In this context, we propose a frequency-constrained coordination planning model of thermal units, wind farms, and battery energy storage systems (BESSs) to provide satisfactory frequency supports. Firstly, a modified multi-machine system frequency response (MSFR) model that accounts for the dynamic responses from both synchronous generators and grid-connected inverters is constructed with preset power-headroom. Secondly, the rate-of-change-of-frequency (ROCOF) and frequency response power are deduced to construct frequency constraints. A data-driven piecewise linearization (DDPWL) method based on hyperplane fitting and data classification is applied to linearize the highly nonlinear frequency response power. Thirdly, frequency constraints are inserted into our planning model, while the unit commitment based on the coordinated operation of the thermal-hydro-wind-BESS hybrid system is implemented. At last, the proposed model is applied to the IEEE RTS-79 test system. The results demonstrate the effectiveness of our co-planning model to keep the frequency stability.

We discover three new weak pulse components in two known pulsars, one in PSR J0304+1932 and two in PSR J1518+4904. These components are emitted about halfway between the main emission beam and the interpulse beam (beam from the opposite pole). They are separated from their main pulse peak by 99° ± 3° for J0304+1932 and 123.°6 ± 0.°7 (leading) and 93° ± 0.°4 (trailing) for J1518+4904. Their peak-intensity ratios to main pulses are ∼ 0.06% for J0304+1932 and ∼0.17% and ∼0.83% for J1518+4904. We also analyzed the flux fluctuations and profile variations of the emissions for the two pulsars. The results show correlations between the weak pulses and their main pulses, indicating that these emissions come from the same pole. We estimated the emission altitude of these weak pulses and derived a height of about half of the pulsar’s light-cylinder radius. These pulse components are a unique sample of high-altitude emissions from pulsars, and challenge the current pulsar emission models.

Authenticity assessment of (E)-cinnamic acid, vanillin, and benzoic acid from various origins (n = 26) was performed using gas chromatography-isotope ratio mass spectrometry coupled with combustion and pyrolysis mode (GC-C/P-IRMS). For that reason, the above three compounds (1–3) from synthetic, natural, and Sumatra benzoin balsam (laboratory prepared, adulterated, and commercial) were investigated. The δ13CV-PDB and δ2HV-SMOW values for compounds 1–3 from synthetic samples (S1–S5) ranging from −26.9 to −31.1‰ and 42 to 83‰, respectively, were clearly different from those of authentic samples (N1–N4, L1–L9) varying from −29.8 to −41.6‰ and −19 to −156‰. In adulteration verification testing, for compounds 1 and 3, both δ13CV-PDB and δ2HV-SMOW data of A1 (5.0% added) and A2 (2.5% added) except A3 (0.5% added) fell into the synthetic region, whereas for compound 2, the δ2HV-SMOW data of adulterated samples (A1–A3) fell into the synthetic region, and even the lowest adulterated sample A3 is no exception. With this conclusion, some commercial Sumatra benzoin balsam samples were identified to be adulterated with synthetic 1 (C1, C3, and C5) and synthetic 2 (C3, C4, and C5) but not with synthetic 3. GC-C/P-IRMS allowed clear-cut differentiation of the synthetic and natural origin of 1, 2, and 3 and definite identification of whether a Sumatra benzoin balsam was adulterated or not.

The effects of gravity, capillary force, and viscous force on the migration characteristics of oil and gas interface in oxygen-reduced air-assisted gravity drainage (OAGD) were studied through a two-dimensional visualization model. The effects of bond number, capillary number and low-temperature oxidation on OAGD recovery were studied by long core displacement experiments. On this basis, the low-temperature oxidation number was introduced and its relationship with the OAGD recovery was established. The results show that the shape and changing law of oil and gas front are mainly influenced by gravity, capillary force and viscous force. When the bond number is constant (4.52×10−4), the shape of oil-gas front is controlled by capillary number. When the capillary number is less than 1.68×10−3, the oil and gas interface is stable. When the capillary number is greater than 2.69×10−2, the oil and gas interface shows viscous fingering. When the capillary number is between 1.68×10−3 and 2.69×10−2, the oil and gas interface becomes capillary fingering. The core flooding experiments results show that for OAGD stable flooding, before the gas breakthrough, higher recovery is obtained in higher gravity number and lower capillary number. In this stage, gravity is predominant in controlling OAGD recovery and the oil recovery could be improved by reducing injection velocity. After gas breakthrough, higher recovery was obtained in lower gravity and higher capillary numbers, which means that the viscous force had a significant influence on the recovery. Increasing gas injection velocity in this stage is an effective measure to improve oil recovery. The low-temperature oxidation number has a good correlation with the recovery and can be used to predict the OAGD recovery.

Tight oil reservoir development is faced with the key technical problem that “water cannot be injected and oil cannot be produced” yet. With the diphenyl ethers water-soluble (gemini) surfactants as water phase shell and C10–C14 straight-chain hydrocarbon compounds as oil phase kernel, a nanofluids permeation flooding system was prepared by microemulsion technology, and its characteristics and EOR mechanisms were evaluated through experiments. The system has the following five characteristics: (1) “Small-size liquid”: the average particle size of the system is less than 30 nm, which can greatly reduce the starting pressure gradient of water injection, and effectively enter and expand the sweep volume of micro-nano matrix; (2) “Small-size oil” : the system can break the crude oil into “small-size oil” under the flow condition, which can greatly improve the percolation ability and displacement efficiency of the crude oil in the micro-nano matrix; (3) Dual-phase wetting: the system has contact angles with the water-wet and oil-wet interfaces of (46±1)° and (68±1)° respectively, and makes it possible for capillarity to work fully under complex wetting conditions of the reservoir; (4) High surface activity: the interfacial tension between the system and crude oil from a tight oil reservoir in Xinjiang is 10−3–10−2 mN/m, indicating the system can effectively improve the displacement efficiency of oil in fine pore throats; (5) Demulsification and viscosity reduction: the system has a demulsification and viscosity reduction rate of more than 80% to inversely emulsified crude oil from a tight oil reservoir in Xinjiang, so it can improve the mobility of crude oil in the reservoir and wellbore. The system can be used to increase oil production by fracturing in tight reservoirs, replenish formation energy by reducing injection pressure and increasing injection rate, and enhance oil recovery by displacement and cyclic injection, providing key technical support for effective production and efficient development and recovery enhancement of tight reservoirs.

To solve the problem that the propulsion motor of the solar-powered aircraft is difficult to meet the high-torque-density requirement, the mass optimization method of a surface-mounted permanent magnet synchronous motor (SPMSM) based on a lightweight structure is proposed in this paper. According to the motor load generated by propeller, the strength and stiffness analytical models of the mechanical structure are established. By correlating the mechanical dimensions with the electromagnetic dimensions, a full-scale parametric model of SPMSM is obtained. A 4kW and a 167W SPMSMs are designed based on the parametric model. Analysis results show that with the increase of the ratio of motor diameter to length, the motor mass firstly decreases and then increases, and there is a minimum value. The validity of the lightweight structure and analytical models are verified by the finite element models (FEM) and prototypes.

In order to improve the thrust density of the electric actuator in aircraft, a double primary tubular permanent magnet synchronous linear motor is designed. By establishing the magnetic circuit model, the air gap magnetic density and thrust characteristics of outer and inner primary motors are analyzed. Considering that it works in the wide temperature range environment, a multi-objective optimization of motor is carried out. The surrogate model established by neural network is used to simplify the mathematical model of the motor. In order to improve the average thrust, restrain the thrust fluctuation and reduce the difference of output thrust under different environment temperatures, the Pareto front method is used to determine the target range of the optimal parameters, and gradually strengthen conditions, so as to obtain the optimal scheme of motor design.

In high performance electric machines, the increase of fundamental frequency leads to additional losses in the winding due to parasitic effects such as the associated skin and proximity effects. In the first part, this paper presents an investigation into accurate modelling of AC losses in the winding using numerical methods and their experimental verification. Then, using experimental motorette setups, this research provides a comparative study between fixed strand positioning and fixed conductor shapes on the AC losses in the winding. It is shown that the exact position of strands in the conductor is not a critical factor; however, it is very important to control the conductor shape inside the slot. In the final section of this paper, an investigation into the relationship between AC losses in the winding and copper filling factor is presented. It is shown experimentally that counter-intuitive design choices such as using a lower copper fill factor and thicker strand diameters may be beneficial in achieving the highest overall efficiency.

Twitter exhibits several characteristics, including a limited number of features and noisy text information. Extracting valuable information from Twitter has made hot topic detection a challenging task. In this paper, a novel four-stage framework is proposed to improve the performance of topic detection. Data preprocessing is the first stage. Deep learning is then exploited to enrich short text information via image understanding. Next, improved latent Dirichlet allocation is used to optimize the image effective word pairs, which improves the accuracy of the extracted topic words. Finally, both short text and images are integrated for topic detection, in which the corresponding topics are mined based on fuzzy matching of topic words. A large number of experiments show that the proposed framework significantly improves the performance of topic detection and outperforms the selected baseline methods.

Recent years have witnessed increasing research interest in human behavior recognition as it provides attractive applications in various sensing scenarios. Among these encouraging implementations, device-free behavior recognition based on WiFi channel state information (CSI) has attracted significant attention due to the popularity of WiFi devices and abundant channel characteristics from CSI. Meanwhile, the CSI signal provides us with additional benefits because it can propagate through a wall. This through-the-wall and device-free scheme not only enables us to identify specific human actions but also to infer person activities by collecting the data from different rooms. This paper presents a survey on the state-of-art progresses in device-free through-the-wall human behavior recognition based on CSI. Specifically, this paper first introduces the basic concept of CSI and describes the signal variation caused by human behavior. Then, it illustrates that different human behaviors can cause signal transformation. Therefore, the unique map relationship between action and signal variation can be leveraged to recognize human behavior. Next, it provides the general architecture of through-the-wall behavior recognition and highlights its core characteristic. It investigates the state-of-art applications in various scenarios and analyzes specific design schemes and implementations. Afterward, it discusses the various across wall applications and makes a detailed comparison between non-through-the-wall and through-the-wall applications. Meanwhile, it analyzes many factors that affect recognition accuracy and emphasizes performance differences under across wall scenarios. Finally, this paper concludes by summarizing the issues and challenges faced and providing insights into the possible solution and future research trend.

Recently, device-free human behavior recognition has become a hot research topic and has achieved significant progress in the field of ubiquitous computing. Among various implementation, behavior recognition based on WiFi CSI (channel state information) has drawn wide attention due to its major advantages. This paper investigates more than 100 latest CSI based behavior recognition applications within the last 6 years and presents a comprehensive survey from every aspect of human behavior recognition. Firstly, this paper reviews general behavior recognition applications using the WiFi signal and presents the basic concept of CSI and the fundamental principle of CSI-based behavior recognition. This paper analyzes the key components and core characteristics of the system architecture of human behavior recognition using CSI. Afterward, we divide the sensing procedures into many steps and summarize the typical studies from these steps, including base signal selection, signal preprocessing, and identification approaches. Next, based on the recognition technique, we classify the applications into three groups, including pattern-based, model-based, and deep learning-based approach. In every group, we categorize the state-of-the-art applications into three groups, including coarse-grained specific behavior recognition, fine-grained specific behavior recognition, and activity inference. It elaborates the typical behavior recognition applications from five aspects, including experimental equipment, experimental scenario, behavior, classifier, and system performance. Then, this paper presents comprehensive discussions about representative applications from the implementation view and outlines the major consideration when developing a recognition system. Finally, this article concludes by analyzing the open issues of CSI-based behavior recognition applications and pointing out future research directions.

With the rapid progress of the Internet of Things (IoT) technology, human behavior recognition has become an important research topic in the field of ubiquitous computing and has obtained quite a number of research achievements. Accurate human behavior recognition can enhance the quality of human-computer interaction and facilitate the development of various sensing applications. With the popularity of smartphones and the improved performance of sensors such as speakers and microphones built in smartphones, the behavior recognition technique based on ultrasound signal of the smartphone has gained more attention and achieved several research results. In this paper, we first review the common behavior recognition techniques including light, video, sound, and frequency radio and outline their main characteristics. Then, we introduce the fundamental principle of human behavior recognition based on ultrasound signals. Specifically, these systems treat speakers and microphones embedded in smartphones as the transceiver and leverage the received signal changes caused by human movement including phase differences, frequency shift, and time of flight (ToF) to recognize human behavior. Next, we investigate the state-of-the-art studies and applications and analyze the signal processing techniques such as data collection, signal preprocessing, feature description, and behavior recognition approach. Afterward, according to the purpose of these applications, we classify them into five groups and compare them in detail including hand gesture recognition, activity recognition, hand trajectory tracking, vital sign monitoring, and lip reading. Finally, we conclude by discussing the limitations, challenges, and open issues involved in behavior recognition based on ultrasound signal of smartphone.

With the rapid development of Internet of Things, hand gesture recognition has drawn wide attention in the field of ubiquitous computing because it provides us with simple and natural human-computer interaction mode. Among these various implementations, hand gesture recognition using ultrasonic signals of smartphone has become a hot research topic due to its various advantages. In this paper, we consider the smartphone as an active sonar sensing system to identify hand movements. Specifically, the speakers emit ultrasonic signal and the microphone on the same phone receives the changed echo affected by hand movements. This paper investigates the state-of-the-art hand gesture applications and presents a comprehensive survey on the characteristics of studies using the active sonar sensing system. Firstly, we review the existing research of hand gesture recognition based on acoustic signals. After that, we introduce the characteristics of ultrasonic signal and describe the fundamental principle of hand gesture recognition. Then, we focus on the typical methods used in these studies and present a detailed analysis on signal generation, feature extraction, preprocessing, and recognition methods. Next, we investigate the state-of-the-art ultrasonic-based applications of hand gesture recognition using smartphone and analyze them in detail from dynamic gesture recognition and hand tracking. Afterwards, we make a discussion about these systems from signal acquisition, signal processing, and performance evaluation to obtain some insight into development of the ultrasonic hand gesture recognition system. Finally, we conclude by discussing the challenges, insight, and open issues involved in hand gesture recognition based on ultrasonic signal of the smartphone.

In this study, the agricultural traits, alkaloids content and Fourier transform infrared spectroscopy (FT-IR) and two-dimensional correlation infrared spectroscopy (2DCOS-IR) analysis of the tobacco after Berberine Bridge Enzyme-Like Proteins (BBLs) knockout were investigated. The knockout of BBLs has limited effect on tobacco agricultural traits. After the BBLs knockout, nicotine and most alkaloids are significantly reduced, but the content of myosmine and its derivatives increases dramatically. In order to identify the gene editing of tobacco, principal component analysis (PCA) was performed on the FT-IR and 2DCOS-IR spectroscopy data. The results showed that FT-IR can distinguish between tobacco roots and leaves but cannot classify the gene mutation tobacco from the wild one. 2DCOS-IR can enhance the characteristics of the samples due to the increased apparent resolution of the spectra. Using the autopeaks in the synchronous map for PCA analysis, we successfully identified the mutants with an accuracy of over 90%.

Leaf ionome indicates plant phylogenetic evolution and responses to environmental stress, which is a critical influential factor to the structure of species populations in local edaphic sites. However, little is known about leaf ionomic responses of local plant species to natural edaphic mineral variations. In the present study, all plant species and soil samples from a total of 80 soil sites in Shiozuka Highland were collected for multi-elemental analysis. Ioniomic data of species were used for statistical analysis, representing 24 species and 10 families. Specific preferences to ionomic accumulation in plants were obviously affected by the phylogeny, whereas edaphic impacts were also strong but limited within the phylogenetic preset. Correlations among elements resulted from not only elemental synergy and competition but also the adaptive evolution to withstand environmental stresses. Furthermore, ionomic differences of plant families were mainly derived from non-essential elements. The majority of variations in leaf ionome is undoubtedly regulated by evolutionary factors, but externalities, especially environmental stresses also have an important regulating function for landscape formation, determining that the contributions of each factor to ionomic variations of plant species for adaptation to environmental stress provides a new insight for further research on ionomic responses of ecological speciation to environmental perturbations and their corresponding adaptive evolutions.

This paper investigates the thrust ripple of a consequent-pole permanent magnet linear synchronous motor (CP-PMLSM). The components of the thrust ripple are analyzed through the comparison of the thrust waves' constituent frequencies. A two-step design process is then proposed in order to reduce the different components of the thrust ripple. The first step consists of implementing a modular regulating structure and rewinding the coils in the resulting two sectional motors, through which the thrust ripples of the modular CP-PMLSM originating from the armature current and voltage harmonics are suppressed. In the second step, the geometrical parameters defining the magnetic regulating structure and the end-teeth are comprehensively optimized to reduce the detent force. By using the aforesaid procedure, it is shown that the CP-PMLSM can achieve small values of thrust ripple for different loadings.

This paper presents the modular topologies of the dual-stator dual-winding permanent magnet (PM) linear motors for linear compressors used in the electrified transportation application. Compared to the conventional PM linear motor in compressor, the proposed modular model is designed with the same volume but a higher thrust force density and a further higher air pressure in air cylinder, which are competitive in the compressor industry. The proposed compact PM linear motors are constructed with tubular windings in both inner and outer stators, as well as the ring-type magnet in mover. Simulation results of motor characteristics are compared by three-dimensional finite element method (3D FEM). Finally, the prototypes of the proposed PM linear motors are manufactured and tested for the linear compressor application.

A fully superconducting wind generator employs superconductors in stator and rotor to enable high torque density and low weight, that is, enable an ultra-light electric machine for wind application. However, the level of the AC loss of the stator armature coils is a critical issue, which lacks investigations in the design of the fully superconducting generators. In this paper, an in-house model was developed to analyze the potential of a fully superconducting generator by integrating the electromagnetic design with the AC loss estimation. The electromagnetic model was made through analytical equations, which take into consideration the geometry, the magnetic properties of iron, and the nonlinear E–J constitutive law of superconductors. Since the permeability of iron materials and the critical current of the superconductors depend on the magnetic field, an iteration process was proposed to find their operating points for every electromagnetic design. The AC loss estimation was carried out through finite element software based on the T–A formulation of Maxwell’s equations instead of analytical equations, due to the complexity of magnetic fields, currents and rotation. The results demonstrate that the design approach is viable and efficient, and is therefore useful for the preliminary design of the generator. In addition, it is found that smaller tape width, larger distance between the superconducting coils in the same slot, smaller coil number in one slot and lower working temperature can reduce the AC loss of the stator coils, but the reduction of the AC loss needs careful design to achieve an optimum solution.

In recent years, with the development of the permanent magnet linear synchronous motor (PMLSM), the application of PMLSM has not been limited only to the high-end equipment field; the primary stator discontinuous segmented-PMLSM (DSPMLSM), which consists of multiple primary stators and one mover, has also been applied in long-distance transportation systems, such as electromagnetic launch, high precision material transport, etc. Compared with the symmetry phase parameters of conventional PMLSM, the stationary electrical parameters vary when the mover enters and leaves the primary stators (the inter-segment region). At the same time, due to the sectional power supply, there will be primary suction or pulling force when the mover enters and exits the inter-segment region, which will lead to large thrust fluctuation and result in lager position error. This paper proposed a related drive and control strategy about the DSPMLSM system, which improved the position tracking accuracy during the full range of DSPMLSM. First, the parameter variation between stator segments has been analyzed through finite element simulation of DSPMLSM. Then, a double closed-loop series control structure of position-current is designed, in which a PI-Lead controller was adopted for the position loop and a PI controller was adopted for the current loop. In order to improve the position tracking accuracy of DSPMLSM, a thrust fluctuation extended state observer (TFESO) was adopted to observe and compensate the complex thrust disturbances such as cogging force, friction and other unmodeled thrust fluctuation. At last, the DSPMLSM experimental stage was established, and the experimental results show that the proposed driver and control theory can effectively improve the position tracking accuracy of the whole stroke of DSPMLSM.

with the onboard diesel generators and energy storage system to meet the propulsion and service load, the all-electric ship (AES) can be viewed as a ‘mobile microgrid’. Nowadays, photovoltaic (PV) generation is gradually integrated into the AES for a ‘greener’ voyage. To fully utilise the PV energy, this paper proposes a joint generation and voyage scheduling method for the PV integrated AES. The overall model is formulated as a bi-level programming problem and solved by column-and-constraint generation method. The simulation results manifest that, the integration of PV into AES leads to reductions on both the fuel consumption and CO2 emission.

High-penetration renewable energy and AC/DC hybrid network are two main basic characteristics of future power systems. Evaluating different networks' structure adaptability is extremely important for the power system to keep economical, safe, reliable and flexible. Firstly, the basic characteristic and the definition of network structure adaptability are given under the background of strong randomness. On this basis, several probable indexes of structure adaptability are proposed. After that, a practical calculation method is put forward, in which the Monte Carlo simulation method and the optimal economic dispatch model are used. Case studies are carried out on the modified IEEE 30-bus system, and the simulation results show that the proposed indexes can effectively reflect the adaptability of different network structures with the integration of high-penetration renewable energy.

Coronary atherosclerosis is one of the major factors causing cardiovascular diseases. However, identifying the tipping point (predisease state of disease) and detecting early-warning signals of human coronary atherosclerosis for individual patients are still great challenges. The landscape dynamic network biomarkers (l-DNB) methodology is based on the theory of dynamic network biomarkers (DNBs), and can use only one-sample omics data to identify the tipping point of complex diseases, such as coronary atherosclerosis. Based on the l-DNB methodology, by using the metabolomics data of plasma of patients with coronary atherosclerosis at different stages, we accurately detected the early-warning signals of each patient. Moreover, we also discovered a group of dynamic network biomarkers (DNBs) which play key roles in driving the progression of the disease. Our study provides a new insight into the individualized early diagnosis of coronary atherosclerosis and may contribute to the development of personalized medicine.

After re-considering the contribution of Jinghan Zhang, Zhongshan Mu, and Tianyu Zhao, respectively, we wish to remove them from the authorship of our paper [...]

Improving the accuracy of edge pixel classification is crucial for extracting the winter wheat spatial distribution from remote sensing imagery using convolutional neural networks (CNNs). In this study, we proposed an approach using a partly connected conditional random field model (PCCRF) to refine the classification results of RefineNet, named RefineNet-PCCRF. First, we used an improved RefineNet model to initially segment remote sensing images, followed by obtaining the category probability vectors for each pixel and initial pixel-by-pixel classification result. Second, using manual labels as references, we performed a statistical analysis on the results to select pixels that required optimization. Third, based on prior knowledge, we redefined the pairwise potential energy, used a linear model to connect different levels of potential energies, and used only pixel pairs associated with the selected pixels to build the PCCRF. The trained PCCRF was then used to refine the initial pixel-by-pixel classification result. We used 37 Gaofen-2 images obtained from 2018 to 2019 of a representative Chinese winter wheat region (Tai’an City, China) to create the dataset, employed SegNet and RefineNet as the standard CNNs, and a fully connected conditional random field as the refinement methods to conduct comparison experiments. The RefineNet-PCCRF’s accuracy (94.51%), precision (92.39%), recall (90.98%), and F1-Score (91.68%) were clearly superior than the methods used for comparison. The results also show that the RefineNet-PCCRF improved the accuracy of large-scale winter wheat extraction results using remote sensing imagery.

Improving the accuracy of edge pixel classification is an important aspect of using convolutional neural networks (CNNs) to extract winter wheat spatial distribution information from remote sensing imagery. In this study, we established a method using prior knowledge obtained from statistical analysis to refine CNN classification results, named post-processing CNN (PP-CNN). First, we used an improved RefineNet model to roughly segment remote sensing imagery in order to obtain the initial winter wheat area and the category probability vector for each pixel. Second, we used manual labels as references and performed statistical analysis on the class probability vectors to determine the filtering conditions and select the pixels that required optimization. Third, based on the prior knowledge that winter wheat pixels were internally similar in color, texture, and other aspects, but different from other neighboring land-use types, the filtered pixels were post-processed to improve the classification accuracy. We used 63 Gaofen-2 images obtained from 2017 to 2019 of a representative Chinese winter wheat region (Feicheng, Shandong Province) to create the dataset and employed RefineNet and SegNet as standard CNN and conditional random field (CRF) as post-process methods, respectively, to conduct comparison experiments. PP-CNN’s accuracy (94.4%), precision (93.9%), and recall (94.4%) were clearly superior, demonstrating its advantages for the improved refinement of edge areas during image classification.

When extracting land-use information from remote sensing imagery using image segmentation, obtaining fine edges for extracted objects is a key problem that is yet to be solved. In this study, we developed a new weight feature value convolutional neural network (WFCNN) to perform fine remote sensing image segmentation and extract improved land-use information from remote sensing imagery. The WFCNN includes one encoder and one classifier. The encoder obtains a set of spectral features and five levels of semantic features. It uses the linear fusion method to hierarchically fuse the semantic features, employs an adjustment layer to optimize every level of fused features to ensure the stability of the pixel features, and combines the fused semantic and spectral features to form a feature graph. The classifier then uses a Softmax model to perform pixel-by-pixel classification. The WFCNN was trained using a stochastic gradient descent algorithm; the former and two variants were subject to experimental testing based on Gaofen 6 images and aerial images that compared them with the commonly used SegNet, U-NET, and RefineNet models. The accuracy, precision, recall, and F1-Score of the WFCNN were higher than those of the other models, indicating certain advantages in pixel-by-pixel segmentation. The results clearly show that the WFCNN can improve the accuracy and automation level of large-scale land-use mapping and the extraction of other information using remote sensing imagery.

This paper presents the design and fabrication of a rotary-impact magnetostrictive energy generator, used to harvest the rotation of human knee joint. The harvester consists of twelve movable Terfenol-D rods, surrounded by the picked up coils respectively, and alternate permanent magnet (PM) array sandwiched in each part of the shell. Rotational electromagnetic power generating effect and impacted magnetostrictive power generating effect are designed in the harvester. Modeling and simulation are used to validate the concept. Then, magnetic field and leakage of the harvester are analyzed, electromagnetic force in the harvester is simulated. A prototype of harvester is fabricated, and subjected to the experimental characterization. It can be concluded that huge induced voltage generated in the short-time impact situation and that induced voltage in the harvester can reach up to 60-80 volts at 0.91Hz low frequency rotation. Also, the presented harvester has good harvesting effects at low frequency human walking and periodic swing crus situation, which are suitable to be used for future researches of wearable knee joint applications.

To study puff-by-puff release characteristics of crotonaldehyde in mainstream cigarette smoke under diverse intensive smoking regimens, we designed an RM20H smoking machine with a puff-by-puff smoke collection unit to automatically trap crotonaldehyde in the mainstream cigarette smoke. Using this process, we trapped, puff-by-puff, crotonaldehyde in mainstream smoke generated by different smoking regimens and quantitatively analysed the levels of crotonaldehyde using high-performance liquid chromatography with a modified QuEChERS sample pretreatment method. On the basis of the crotonaldehyde in each puff, we determined crotonaldehyde's puff-by-puff release characteristics. The results showed that crotonaldehyde's puff-by-puff release remained nearly constant for the International Organization for Standardization mode while increased polynomial trend was seen (n ≥ 6) under the Massachusetts and Health Canada smoking regimens. The equation fit for various regimens was good (R2 > 0.9192). Release characteristics by puff were classified into four categories: (1) first, second and third puffs; (2) fourth and fifth puffs; (3) sixth puff; and (4) seventh and eighth puffs.

Anaerobic digestion is considered to be a priority disposal technology for rice straw and sewage sludge. In this study, the synergistic and alkali-treat effect on co-digestion of rice straw and sewage sludge was investigated. The results indicated that the co-digestion of alkali-treated rice straw and sewage sludge had the best biogas yield of 338.9 mL/gVS, which was 1.06 and 1.75 times that of either alkali-treated rice straw or sewage sludge alone, respectively. The actual biogas and methane yields of a co-digestion group with raw rice straw and sewage sludge (G4) increased 26.39% and 24.79% relative to the theoretical calculation based on raw rice straw digestion (group G2) and sewage sludge digestion (group G5), suggesting that a synergistic effect occurred during the co-digestion process. The maximum concentration of volatile fatty acids (VFA) was 4860 mg/L on the 4th day in the sewage sludge group. Xylanase activity reached a maximum of 10.55 U/mL on the 6th day in the alkali-treated rice straw group, while the concentration of protease enzyme was relatively higher in the sewage sludge group than in others. The removal rates of cellulose and hemicellulose in groups with alkali treatment were 32.25% and 36.96% (G1) and 40.86% and 41.61% (G3), higher than that of groups without treatment.

In this paper, the NaOH pretreatment of banana pseudo-stem fiber for biogas production was investigated using a statistically designed set of experiments. A central composite design was used to identify the optimum pretreatment condition for four factors, i.e., NaOH concentration, pretreatment temperature, pretreatment time, and fiber length, on biogas fermentation of banana pseudo-stem fiber. The best pretreatment condition was 7.8% NaOH, 0.2-cm fiber length, and a temperature of 48 °C for 3 days. NaOH pretreatment increased the biogas yield of banana pseudo-stem fiber. The highest biogas yield was 463.0 mL•g-1VSadded, which was 89.2% higher than that of the control, at 244.7 mL•g-1VSadded.

Using satellite remote sensing has become a mainstream approach for extracting crop spatial distribution. Making edges finer is a challenge, while simultaneously extracting crop spatial distribution information from high-resolution remote sensing images using a convolutional neural network (CNN). Based on the characteristics of the crop area in the Gaofen 2 (GF-2) images, this paper proposes an improved CNN to extract fine crop areas. The CNN comprises a feature extractor and a classifier. The feature extractor employs a spectral feature extraction unit to generate spectral features, and five coding-decoding-pair units to generate five level features. A linear model is used to fuse features of different levels, and the fusion results are up-sampled to obtain a feature map consistent with the structure of the input image. This feature map is used by the classifier to perform pixel-by-pixel classification. In this study, the SegNet and RefineNet models and 21 GF-2 images of Feicheng County, Shandong Province, China, were chosen for comparison experiment. Our approach had an accuracy of 93.26%, which is higher than those of the existing SegNet (78.12%) and RefineNet (86.54%) models. This demonstrates the superiority of the proposed method in extracting crop spatial distribution information from GF-2 remote sensing images.

In several industrial sectors, induction machines are being replaced with permanent magnet based alternatives, owing to the potential for higher power density and efficiency. However, high-speed applications feature a wide flux-weakening region, where advanced induction machines could bring benefits in terms of system-level optimization. This paper gives an overview the technological challenges for high-speed drives with induction machines, materials, simulations and future challenges for the power electronics in these applications. The target application is a high-speed induction machine for a naval turbocharging system. The comparison with permanent magnet synchronous machines will demonstrate how the extended flux weakening operation effectively allows for a weight reduction of the overall system.