Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained promine...Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.展开更多
Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowad...Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowadays,the groundwater vulnerability assessment(GVA)has become an essential task to identify the current status and development trend of groundwater quality.In this study,the Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)models are integrated to realize the spatio-temporal prediction of regional groundwater vulnerability by introducing the Self-attention mechanism.The study firstly builds the CNN-LSTM modelwith self-attention(SA)mechanism and evaluates the prediction accuracy of the model for groundwater vulnerability compared to other common machine learning models such as Support Vector Machine(SVM),Random Forest(RF),and Extreme Gradient Boosting(XGBoost).The results indicate that the CNNLSTM model outperforms thesemodels,demonstrating its significance in groundwater vulnerability assessment.It can be posited that the predictions indicate an increased risk of groundwater vulnerability in the study area over the coming years.This increase can be attributed to the synergistic impact of global climate anomalies and intensified local human activities.Moreover,the overall groundwater vulnerability risk in the entire region has increased,evident fromboth the notably high value and standard deviation.This suggests that the spatial variability of groundwater vulnerability in the area is expected to expand in the future due to the sustained progression of climate change and human activities.The model can be optimized for diverse applications across regional environmental assessment,pollution prediction,and risk statistics.This study holds particular significance for ecological protection and groundwater resource management.展开更多
Diatomic metasurfaces designed for interferometric mechanisms possess significant potential for the multidimensional manipulation of electromagnetic waves,including control over amplitude,phase,frequency,and polarizat...Diatomic metasurfaces designed for interferometric mechanisms possess significant potential for the multidimensional manipulation of electromagnetic waves,including control over amplitude,phase,frequency,and polarization.Geometric phase profiles with spin-selective properties are commonly associated with wavefront modulation,allowing the implementation of conjugate strategies within orthogonal circularly polarized channels.Simultaneous control of these characteristics in a single-layered diatomic metasurface will be an apparent technological extension.Here,spin-selective modulation of terahertz(THz)beams is realized by assembling a pair of meta-atoms with birefringent effects.The distinct modulation functions arise from geometric phase profiles characterized by multiple rotational properties,which introduce independent parametric factors that elucidate their physical significance.By arranging the key parameters,the proposed design strategy can be employed to realize independent amplitude and phase manipulation.A series of THz metasurface samples with specific modulation functions are characterized,experimentally demonstrating the accuracy of on-demand manipulation.This research paves the way for all-silicon meta-optics that may have great potential in imaging,sensing and detection.展开更多
All-season thermal management with zero energy consumption and emissions is more crucial to global decarbonization over traditional energy-intensive cooling/heating systems.However,the static single thermal management...All-season thermal management with zero energy consumption and emissions is more crucial to global decarbonization over traditional energy-intensive cooling/heating systems.However,the static single thermal management for cooling or heating fails to self-regulate the temperature in dynamic seasonal temperature condition.Herein,inspired by the dual-temperature regulation function of the fur color changes on the backs and abdomens of penguins,a smart thermal management composite hydrogel(PNA@H-PM Gel)system was subtly created though an"on-demand"dual-layer structure design strategy.The PNA@H-PM Gel system features synchronous solar and thermal radiation modulation as well as tunable phase transition temperatures to meet the variable seasonal thermal requirements and energy-saving demands via self-adaptive radiative cooling and solar heating regulation.Furthermore,this system demonstrates superb modulations of both the solar reflectance(ΔR=0.74)and thermal emissivity(ΔE=0.52)in response to ambient temperature changes,highlighting efficient temperature regulation with average radiative cooling and solar heating effects of 9.6℃in summer and 6.1℃in winter,respectively.Moreover,compared to standard building baselines,the PNA@H-PM Gel presents a more substantial energy-saving cooling/heating potentials for energy-efficient buildings across various regions and climates.This novel solution,inspired by penguins in the real world,will offer a fresh approach for producing intelligent,energy-saving thermal management materials,and serve for temperature regulation under dynamic climate conditions and even throughout all seasons.展开更多
The traditional von Neumann architecture faces inherent limitations due to the separation of memory and computa-tion,leading to high energy consumption,significant latency,and reduced operational efficiency.Neuromorph...The traditional von Neumann architecture faces inherent limitations due to the separation of memory and computa-tion,leading to high energy consumption,significant latency,and reduced operational efficiency.Neuromorphic computing,inspired by the architecture of the human brain,offers a promising alternative by integrating memory and computational func-tions,enabling parallel,high-speed,and energy-efficient information processing.Among various neuromorphic technologies,ion-modulated optoelectronic devices have garnered attention due to their excellent ionic tunability and the availability of multi-dimensional control strategies.This review provides a comprehensive overview of recent progress in ion-modulation optoelec-tronic neuromorphic devices.It elucidates the key mechanisms underlying ionic modulation of light fields,including ion migra-tion dynamics and capture and release of charge through ions.Furthermore,the synthesis of active materials and the proper-ties of these devices are analyzed in detail.The review also highlights the application of ion-modulation optoelectronic devices in artificial vision systems,neuromorphic computing,and other bionic fields.Finally,the existing challenges and future direc-tions for the development of optoelectronic neuromorphic devices are discussed,providing critical insights for advancing this promising field.展开更多
High temperature piezoelectric energy harvester(HTPEH)is an important solution to replace chemical battery to achieve independent power supply of HT wireless sensors.However,simultaneously excellent performances,inclu...High temperature piezoelectric energy harvester(HTPEH)is an important solution to replace chemical battery to achieve independent power supply of HT wireless sensors.However,simultaneously excellent performances,including high figure of merit(FOM),insulation resistivity(ρ)and depolarization temperature(Td)are indispensable but hard to achieve in lead-free piezoceramics,especially operating at 250°C has not been reported before.Herein,well-balanced performances are achieved in BiFeO3–BaTiO3 ceramics via innovative defect engineering with respect to delicate manganese doping.Due to the synergistic effect of enhancing electrostrictive coefficient by polarization configuration optimization,regulating iron ion oxidation state by high valence manganese ion and stabilizing domain orientation by defect dipole,comprehensive excellent electrical performances(Td=340°C,ρ250°C>10^(7)Ωcm and FOM_(250°C)=4905×10^(–15)m^(2)N^(−1))are realized at the solid solubility limit of manganese ions.The HT-PEHs assembled using the rationally designed piezoceramic can allow for fast charging of commercial electrolytic capacitor at 250°C with high energy conversion efficiency(η=11.43%).These characteristics demonstrate that defect engineering tailored BF-BT can satisfy high-end HT-PEHs requirements,paving a new way in developing selfpowered wireless sensors working in HT environments.展开更多
El Niño-Southern Oscillation(ENSO)is a major driver of climate change in middle and low latitudes and thus strongly influences the terrestrial carbon cycle through land-air interaction.Both the ENSO modulation an...El Niño-Southern Oscillation(ENSO)is a major driver of climate change in middle and low latitudes and thus strongly influences the terrestrial carbon cycle through land-air interaction.Both the ENSO modulation and carbon flux variability are projected to increase in the future,but their connection still needs further investigation.To investigate the impact of future ENSO modulation on carbon flux variability,this study used 10 CMIP6 earth system models to analyze ENSO modulation and carbon flux variability in middle and low latitudes,and their relationship,under different scenarios simulated by CMIP6 models.The results show a high consistency in the simulations,with both ENSO modulation and carbon flux variability showing an increasing trend in the future.The higher the emissions scenario,especially SSP5-8.5 compared to SSP2-4.5,the greater the increase in variability.Carbon flux variability in the middle and low latitudes under SSP2-4.5 increases by 30.9%compared to historical levels during 1951-2000,while under SSP5-8.5 it increases by 58.2%.Further analysis suggests that ENSO influences mid-and low-latitude carbon flux variability primarily through temperature.This occurrence may potentially be attributed to the increased responsiveness of gross primary productivity towards regional temperature fluctuations,combined with the intensified influence of ENSO on land surface temperatures.展开更多
Oxygen evolution reaction(OER)is often regarded as a crucial bottleneck in the field of renewable energy storage and conversion.To further accelerate the sluggish kinetics of OER,a cation and anion modulation strategy...Oxygen evolution reaction(OER)is often regarded as a crucial bottleneck in the field of renewable energy storage and conversion.To further accelerate the sluggish kinetics of OER,a cation and anion modulation strategy is reported here,which has been proven to be effective in preparing highly active electrocatalyst.For example,the cobalt,sulfur,and phosphorus modulated nickel hydroxide(denoted as NiCoPSOH)only needs an overpotential of 232 mV to reach a current density of 20 mA cm^(–2),demonstrating excellent OER performances.The cation and anion modulation facilitates the generation of high-valent Ni species,which would activate the lattice oxygen and switch the OER reaction pathway from conventional adsorbate evolution mechanism to lattice oxygen mechanism(LOM),as evidenced by the results of electrochemical measurements,Raman spectroscopy and differential electrochemical mass spectrometry.The LOM pathway of NiCoPSOH is further verified by the theoretical calculations,including the upshift of O 2p band center,the weakened Ni–O bond and the lowest energy barrier of rate-limiting step.Thus,the anion and cation modulated catalyst NiCoPSOH could effectively accelerate the sluggish OER kinetics.Our work provides a new insight into the cation and anion modulation,and broadens the possibility for the rational design of highly active electrocatalysts.展开更多
Two-terminal(2-T)perovskite(PVK)/CuIn(Ga)Se_(2)(CIGS)tandem solar cells(TSCs)have been considered as an ideal tandem cell because of their best bandgap matching regarding to Shockley–Queisser(S–Q)limits.However,the ...Two-terminal(2-T)perovskite(PVK)/CuIn(Ga)Se_(2)(CIGS)tandem solar cells(TSCs)have been considered as an ideal tandem cell because of their best bandgap matching regarding to Shockley–Queisser(S–Q)limits.However,the nature of the irregular rough morphology of commercial CIGS prevents people from improving tandem device performances.In this paper,D-homoserine lactone hydrochloride is proven to improve coverage of PVK materials on irregular rough CIGS surfaces and also passivate bulk defects by modulating the growth of PVK crystals.In addition,the minority carriers near the PVK/C60 interface and the incompletely passivated trap states caused interface recombination.A surface reconstruction with 2-thiopheneethylammonium iodide and N,N-dimethylformamide assisted passivates the defect sites located at the surface and grain boundaries.Meanwhile,LiF is used to create this field effect,repelling hole carriers away from the PVK and C60 interface and thus reducing recombination.As a result,a 2-T PVK/CIGS tandem yielded a power conversion efficiency of 24.6%(0.16 cm^(2)),one of the highest results for 2-T PVK/CIGS TSCs to our knowledge.This validation underscores the potential of our methodology in achieving superior performance in PVK/CIGS tandem solar cells.展开更多
A healthy brain is vital to every person since the brain controls every movement and emotion.Sometimes,some brain cells grow unexpectedly to be uncontrollable and cancerous.These cancerous cells are called brain tumor...A healthy brain is vital to every person since the brain controls every movement and emotion.Sometimes,some brain cells grow unexpectedly to be uncontrollable and cancerous.These cancerous cells are called brain tumors.For diagnosed patients,their lives depend mainly on the early diagnosis of these tumors to provide suitable treatment plans.Nowadays,Physicians and radiologists rely on Magnetic Resonance Imaging(MRI)pictures for their clinical evaluations of brain tumors.These evaluations are time-consuming,expensive,and require expertise with high skills to provide an accurate diagnosis.Scholars and industrials have recently partnered to implement automatic solutions to diagnose the disease with high accuracy.Due to their accuracy,some of these solutions depend on deep-learning(DL)methodologies.These techniques have become important due to their roles in the diagnosis process,which includes identification and classification.Therefore,there is a need for a solid and robust approach based on a deep-learning method to diagnose brain tumors.The purpose of this study is to develop an intelligent automatic framework for brain tumor diagnosis.The proposed solution is based on a novel dense dynamic residual self-attention transfer adaptive learning fusion approach(NDDRSATALFA),carried over two implemented deep-learning networks:VGG19 and UNET to identify and classify brain tumors.In addition,this solution applies a transfer learning approach to exchange extracted features and data within the two neural networks.The presented framework is trained,validated,and tested on six public datasets of MRIs to detect brain tumors and categorize these tumors into three suitable classes,which are glioma,meningioma,and pituitary.The proposed framework yielded remarkable findings on variously evaluated performance indicators:99.32%accuracy,98.74%sensitivity,98.89%specificity,99.01%Dice,98.93%Area Under the Curve(AUC),and 99.81%F1-score.In addition,a comparative analysis with recent state-of-the-art methods was performed and according to the comparative analysis,NDDRSATALFA shows an admirable level of reliability in simplifying the timely identification of diverse brain tumors.Moreover,this framework can be applied by healthcare providers to assist radiologists,pathologists,and physicians in their evaluations.The attained outcomes open doors for advanced automatic solutions that improve clinical evaluations and provide reasonable treatment plans.展开更多
Multilevel coding(MLC)is a commonly used polar coded modulation scheme,but challenging to implement in engineering due to its high complexity and long decoding delay for high-order modulations.To address these limitat...Multilevel coding(MLC)is a commonly used polar coded modulation scheme,but challenging to implement in engineering due to its high complexity and long decoding delay for high-order modulations.To address these limitations,a novel two-level serially concatenated MLC scheme,in which the bitlevels with similar reliability are bundled and transmitted together,is proposed.The proposed scheme hierarchically protects the two bit-level sets:the bitlevel sets at the higher level are sufficiently reliable and do not require excessive resources for protection,whereas only the bit-level sets at the lower level are encoded by polar codes.The proposed scheme has the advantages of low power consumption,low delay and high reliability.Moreover,an optimized constellation signal labeling rule that can enhance the performance is proposed.Finally,the superiority of the proposed scheme is validated through the theoretical analysis and simulation results.Compared with the bit interleaving coding modulation(BICM)scheme,under 256-quadrature amplitude modulation(QAM),the proposed scheme attains a performance gain of 1.0 dB while reducing the decoding complexity by 54.55%.展开更多
Medical image analysis based on deep learning has become an important technical requirement in the field of smart healthcare.In view of the difficulties in collaborative modeling of local details and global features i...Medical image analysis based on deep learning has become an important technical requirement in the field of smart healthcare.In view of the difficulties in collaborative modeling of local details and global features in multimodal image analysis of ophthalmology,as well as the existence of information redundancy in cross-modal data fusion,this paper proposes amultimodal fusion framework based on cross-modal collaboration and weighted attention mechanism.In terms of feature extraction,the framework collaboratively extracts local fine-grained features and global structural dependencies through a parallel dual-branch architecture,overcoming the limitations of traditional single-modality models in capturing either local or global information;in terms of fusion strategy,the framework innovatively designs a cross-modal dynamic fusion strategy,combining overlappingmulti-head self-attention modules with a bidirectional feature alignment mechanism,addressing the bottlenecks of low feature interaction efficiency and excessive attention fusion computations in traditional parallel fusion,and further introduces cross-domain local integration technology,which enhances the representation ability of the lesion area through pixel-level feature recalibration and optimizes the diagnostic robustness of complex cases.Experiments show that the framework exhibits excellent feature expression and generalization performance in cross-domain scenarios of ophthalmic medical images and natural images,providing a high-precision,low-redundancy fusion paradigm for multimodal medical image analysis,and promoting the upgrade of intelligent diagnosis and treatment fromsingle-modal static analysis to dynamic decision-making.展开更多
Deep Learning-based systems for Finger vein recognition have gained rising attention in recent years due to improved efficiency and enhanced security.The performance of existing CNN-based methods is limited by the pun...Deep Learning-based systems for Finger vein recognition have gained rising attention in recent years due to improved efficiency and enhanced security.The performance of existing CNN-based methods is limited by the puny generalization of learned features and deficiency of the finger vein image training data.Considering the concerns of existing methods,in this work,a simplified deep transfer learning-based framework for finger-vein recognition is developed using an EfficientNet model of deep learning with a self-attention mechanism.Data augmentation using various geometrical methods is employed to address the problem of training data shortage required for a deep learning model.The proposed model is tested using K-fold cross-validation on three publicly available datasets:HKPU,FVUSM,and SDUMLA.Also,the developed network is compared with other modern deep nets to check its effectiveness.In addition,a comparison of the proposed method with other existing Finger vein recognition(FVR)methods is also done.The experimental results exhibited superior recognition accuracy of the proposed method compared to other existing methods.In addition,the developed method proves to be more effective and less sophisticated at extracting robust features.The proposed EffAttenNet achieves an accuracy of 98.14%on HKPU,99.03%on FVUSM,and 99.50%on SDUMLA databases.展开更多
Following publication of the original article[1],the authors found that they pasted the same data when drawing XRD for sample NCO-1 and NCO-2 in Fig.2a,however,the XRD of all four samples in the manuscript was tested,...Following publication of the original article[1],the authors found that they pasted the same data when drawing XRD for sample NCO-1 and NCO-2 in Fig.2a,however,the XRD of all four samples in the manuscript was tested,and XRD raw data were kept and can be offered.The correct Fig.2 has been provided in this Correction.展开更多
As the complexity of scientific satellite missions increases,the requirements for their magnetic fields,magnetic field fluctuations,and even magnetic field gradients and variations become increasingly stringent.Additi...As the complexity of scientific satellite missions increases,the requirements for their magnetic fields,magnetic field fluctuations,and even magnetic field gradients and variations become increasingly stringent.Additionally,there is a growing need to address the alternating magnetic fields produced by the spacecraft itself.This paper introduces a novel modeling method for spacecraft magnetic dipoles using an integrated self-attention mechanism and a transformer combined with Kolmogorov-Arnold Networks.The self-attention mechanism captures correlations among globally sparse data,establishing dependencies b.etween sparse magnetometer readings.Concurrently,the Kolmogorov-Arnold Network,proficient in modeling implicit numerical relationships between data features,enhances the ability to learn subtle patterns.Comparative experiments validate the capability of the proposed method to precisely model magnetic dipoles,achieving maximum Root Mean Square Errors of 24.06 mA·m^(2)and 0.32 cm for size and location modeling,respectively.The spacecraft magnetic model established using this method accurately computes magnetic fields and alternating magnetic fields at designated surfaces or points.This approach facilitates the rapid and precise construction of individual and complete spacecraft magnetic models,enabling the verification of magnetic specifications from the spacecraft design phase.展开更多
The development of deep learning has made non-biochemical methods for molecular property prediction screening a reality,which can increase the experimental speed and reduce the experimental cost of relevant experiment...The development of deep learning has made non-biochemical methods for molecular property prediction screening a reality,which can increase the experimental speed and reduce the experimental cost of relevant experiments.There are currently two main approaches to representing molecules:(a)representing molecules by fixing molecular descriptors,and(b)representing molecules by graph convolutional neural networks.Currently,both of these Representative methods have achieved some results in their respective experiments.Based on past efforts,we propose a Dual Self-attention Fusion Message Neural Network(DSFMNN).DSFMNN uses a combination of dual self-attention mechanism and graph convolutional neural network.Advantages of DSFMNN:(1)The dual self-attention mechanism focuses not only on the relationship between individual subunits in a molecule but also on the relationship between the atoms and chemical bonds contained in each subunit.(2)On the directed molecular graph,a message delivery approach centered on directed molecular bonds is used.We test the performance of the model on eight publicly available datasets and compare the performance with several models.Based on the current experimental results,DSFMNN has superior performance compared to previous models on the datasets applied in this paper.展开更多
Currently,most trains are equipped with dedicated cameras for capturing pantograph videos.Pantographs are core to the high-speed-railway pantograph-catenary system,and their failure directly affects the normal operati...Currently,most trains are equipped with dedicated cameras for capturing pantograph videos.Pantographs are core to the high-speed-railway pantograph-catenary system,and their failure directly affects the normal operation of high-speed trains.However,given the complex and variable real-world operational conditions of high-speed railways,there is no real-time and robust pantograph fault-detection method capable of handling large volumes of surveillance video.Hence,it is of paramount importance to maintain real-time monitoring and analysis of pantographs.Our study presents a real-time intelligent detection technology for identifying faults in high-speed railway pantographs,utilizing a fusion of self-attention and convolution features.We delved into lightweight multi-scale feature-extraction and fault-detection models based on deep learning to detect pantograph anomalies.Compared with traditional methods,this approach achieves high recall and accuracy in pantograph recognition,accurately pinpointing issues like discharge sparks,pantograph horns,and carbon pantograph-slide malfunctions.After experimentation and validation with actual surveillance videos of electric multiple-unit train,our algorithmic model demonstrates real-time,high-accuracy performance even under complex operational conditions.展开更多
The simultaneously transmitting and reflecting reconfigurable intelligent surface(STAR-RIS)is regarded as a promising paradigm for enhancing the connectivity and reliability of non-orthogonal multiple access(NOMA)netw...The simultaneously transmitting and reflecting reconfigurable intelligent surface(STAR-RIS)is regarded as a promising paradigm for enhancing the connectivity and reliability of non-orthogonal multiple access(NOMA)networks.However,the transmission of STAR-RIS enhanced NOMA networks performance is severely limited due to the inter-user interference(IUI)on multi-user detections.To mitigate this drawback,we propose a generalized quadrature spatial modulation(GQSM)aided STAR-RIS in conjunction with the NOMA scheme,termed STARRIS-NOMA-GQSM,to improve the performance of the corresponding NGMA network.By STAR-RISNOMA-GQSM,the information bits for all users in transmission and reflection zones are transmitted via orthogonal signal domains to eliminate the IUI so as to greatly improve the system performance.The lowcomplexity detection and upper-bounded bit error rate(BER)of STAR-RIS-NOMA-GQSM are both studied to evaluate its feasibility and performance.Moreover,by further utilizing index modulation(IM),we propose an enhanced STAR-RIS-NOMA-GQSM scheme,termed E-STAR-RIS-NOMA-GQSM,to enhance the transmission rate by dynamically adjusting reflection patterns in both transmission and reflection zones.Simulation results show that the proposed original and enhanced scheme significantly outperform the conventional STAR-RIS-NOMA and also confirm the precision of the theoretical analysis of the upper-bounded BER.展开更多
Under submerged conditions, compared with traditional self-excited oscillating pulsed waterjets(SOPWs), annular fluid-enhanced self-excited oscillating pulsed waterjets(AFESOPWs) exhibit a higher surge pressure throug...Under submerged conditions, compared with traditional self-excited oscillating pulsed waterjets(SOPWs), annular fluid-enhanced self-excited oscillating pulsed waterjets(AFESOPWs) exhibit a higher surge pressure through self-priming. However, their pressure frequency and cavitation characteristics remain unclear, resulting in an inability to fully utilize resonance and cavitation erosion to break coal and rock. In this study, high-frequency pressure testing, high-speed photography, and large eddy simulation(LES) are used to investigate the distribution of the pressure frequency band, evolution law of the cavitation cloud, and its regulation mechanism of a continuous waterjet, SOPW, and AFESOPW. The results indicated that the excitation of the plunger pump, shearing layer vortex, and bubble collapse corresponded to the three high-amplitude frequency bands of the waterjet pressure. AFESOPWs have an additional self-priming frequency that can produce a larger amplitude under a synergistic effect with the second high-amplitude frequency band. A better cavitation effect was produced after self-priming the annulus fluid, and the shedding frequency of the cavitation clouds of the three types of waterjets was linearly related to the cavitation number. The peak pressure of the waterjet and cavitation erosion effect can be improved by modulating the waterjet pressure oscillation frequency and cavitation shedding frequency.展开更多
Two-dimensional(2D)materials are promising for next-generation electronic devices and systems due to their unique physical properties.The interfacial adhesion plays a vital role not only in the synthesis,transfer and ...Two-dimensional(2D)materials are promising for next-generation electronic devices and systems due to their unique physical properties.The interfacial adhesion plays a vital role not only in the synthesis,transfer and manipulation of 2D materials but also in the manufacture,integration and performance of the functional devices.However,the atomic thickness and limited lateral dimensions of 2D materials make the accurate measurement and modulation of their interfacial adhesion energy challenging.In this review,the recent advances in the measurement and modulation of the interfacial adhesion properties of 2D materials are systematically combed.Experimental methods and relative theoretical models for the adhesion measurement of 2D materials are summarized,with their scope of application and limitations discussed.The measured adhesion energies between 2D materials and various substrates are described in categories,where the typical adhesion modulation strategies of 2D materials are also introduced.Finally,the remaining challenges and opportunities for the interfacial adhesion measurement and modulation of 2D materials are presented.This paper provides guidance for addressing the adhesion issues in devices and systems involving 2D materials.展开更多
基金supported by the National Natural Science Foundation of China(No.52277055).
文摘Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.
基金supported by the National Key Research and Development Program of China(No.2021YFA0715900).
文摘Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowadays,the groundwater vulnerability assessment(GVA)has become an essential task to identify the current status and development trend of groundwater quality.In this study,the Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)models are integrated to realize the spatio-temporal prediction of regional groundwater vulnerability by introducing the Self-attention mechanism.The study firstly builds the CNN-LSTM modelwith self-attention(SA)mechanism and evaluates the prediction accuracy of the model for groundwater vulnerability compared to other common machine learning models such as Support Vector Machine(SVM),Random Forest(RF),and Extreme Gradient Boosting(XGBoost).The results indicate that the CNNLSTM model outperforms thesemodels,demonstrating its significance in groundwater vulnerability assessment.It can be posited that the predictions indicate an increased risk of groundwater vulnerability in the study area over the coming years.This increase can be attributed to the synergistic impact of global climate anomalies and intensified local human activities.Moreover,the overall groundwater vulnerability risk in the entire region has increased,evident fromboth the notably high value and standard deviation.This suggests that the spatial variability of groundwater vulnerability in the area is expected to expand in the future due to the sustained progression of climate change and human activities.The model can be optimized for diverse applications across regional environmental assessment,pollution prediction,and risk statistics.This study holds particular significance for ecological protection and groundwater resource management.
基金supports from National Key Research and Development Program of China(2021YFB2800703)Sichuan Province Science and Technology Support Program(25QNJJ2419)+1 种基金National Natural Science Foundation of China(U22A2008,12404484)Laoshan Laboratory Science and Technology Innovation Project(LSKJ202200801).
文摘Diatomic metasurfaces designed for interferometric mechanisms possess significant potential for the multidimensional manipulation of electromagnetic waves,including control over amplitude,phase,frequency,and polarization.Geometric phase profiles with spin-selective properties are commonly associated with wavefront modulation,allowing the implementation of conjugate strategies within orthogonal circularly polarized channels.Simultaneous control of these characteristics in a single-layered diatomic metasurface will be an apparent technological extension.Here,spin-selective modulation of terahertz(THz)beams is realized by assembling a pair of meta-atoms with birefringent effects.The distinct modulation functions arise from geometric phase profiles characterized by multiple rotational properties,which introduce independent parametric factors that elucidate their physical significance.By arranging the key parameters,the proposed design strategy can be employed to realize independent amplitude and phase manipulation.A series of THz metasurface samples with specific modulation functions are characterized,experimentally demonstrating the accuracy of on-demand manipulation.This research paves the way for all-silicon meta-optics that may have great potential in imaging,sensing and detection.
基金the funding and generous support of the National Natural Science Foundation of China(52103263,52271249)the Key Project of International Science&Technology Cooperation of Shaanxi Province(2023-GHZD-09)+5 种基金the Key Project of Science Foundation of Education Department of Shaanxi Province(22JY011)the Key Project of Scientific Research and Development of Shaanxi Province(2023GXLH-070)the Qinchuangyuan"Scientist+Engineer"Team of Shaanxi Province(2023KXJ-069)the Key Research and Development Program of Shaanxi(2023-YBGY-488)the Sci-tech Innovation Team of Shaanxi Province(2024RS-CXTD-46)the Key Research and Development Program of Shaanxi Province(2020ZDLGY13-11).
文摘All-season thermal management with zero energy consumption and emissions is more crucial to global decarbonization over traditional energy-intensive cooling/heating systems.However,the static single thermal management for cooling or heating fails to self-regulate the temperature in dynamic seasonal temperature condition.Herein,inspired by the dual-temperature regulation function of the fur color changes on the backs and abdomens of penguins,a smart thermal management composite hydrogel(PNA@H-PM Gel)system was subtly created though an"on-demand"dual-layer structure design strategy.The PNA@H-PM Gel system features synchronous solar and thermal radiation modulation as well as tunable phase transition temperatures to meet the variable seasonal thermal requirements and energy-saving demands via self-adaptive radiative cooling and solar heating regulation.Furthermore,this system demonstrates superb modulations of both the solar reflectance(ΔR=0.74)and thermal emissivity(ΔE=0.52)in response to ambient temperature changes,highlighting efficient temperature regulation with average radiative cooling and solar heating effects of 9.6℃in summer and 6.1℃in winter,respectively.Moreover,compared to standard building baselines,the PNA@H-PM Gel presents a more substantial energy-saving cooling/heating potentials for energy-efficient buildings across various regions and climates.This novel solution,inspired by penguins in the real world,will offer a fresh approach for producing intelligent,energy-saving thermal management materials,and serve for temperature regulation under dynamic climate conditions and even throughout all seasons.
基金supported by National Natural Science Foundation of China(62174164,U23A20568,and U22A2075)National Key Research and Development Project(2021YFA1202600)+2 种基金Talent Plan of Shanghai Branch,Chinese Academy of Sciences(CASSHB-QNPD-2023-022)Ningbo Technology Project(2022A-007-C)Ningbo Key Research and Development Project(2023Z021).
文摘The traditional von Neumann architecture faces inherent limitations due to the separation of memory and computa-tion,leading to high energy consumption,significant latency,and reduced operational efficiency.Neuromorphic computing,inspired by the architecture of the human brain,offers a promising alternative by integrating memory and computational func-tions,enabling parallel,high-speed,and energy-efficient information processing.Among various neuromorphic technologies,ion-modulated optoelectronic devices have garnered attention due to their excellent ionic tunability and the availability of multi-dimensional control strategies.This review provides a comprehensive overview of recent progress in ion-modulation optoelec-tronic neuromorphic devices.It elucidates the key mechanisms underlying ionic modulation of light fields,including ion migra-tion dynamics and capture and release of charge through ions.Furthermore,the synthesis of active materials and the proper-ties of these devices are analyzed in detail.The review also highlights the application of ion-modulation optoelectronic devices in artificial vision systems,neuromorphic computing,and other bionic fields.Finally,the existing challenges and future direc-tions for the development of optoelectronic neuromorphic devices are discussed,providing critical insights for advancing this promising field.
基金supported by the National Natural Science Foundation of China(Grant Nos.52272103 and 52072010)Beijing Natural Science Foundation(Grant Nos.2242029 and JL23004).
文摘High temperature piezoelectric energy harvester(HTPEH)is an important solution to replace chemical battery to achieve independent power supply of HT wireless sensors.However,simultaneously excellent performances,including high figure of merit(FOM),insulation resistivity(ρ)and depolarization temperature(Td)are indispensable but hard to achieve in lead-free piezoceramics,especially operating at 250°C has not been reported before.Herein,well-balanced performances are achieved in BiFeO3–BaTiO3 ceramics via innovative defect engineering with respect to delicate manganese doping.Due to the synergistic effect of enhancing electrostrictive coefficient by polarization configuration optimization,regulating iron ion oxidation state by high valence manganese ion and stabilizing domain orientation by defect dipole,comprehensive excellent electrical performances(Td=340°C,ρ250°C>10^(7)Ωcm and FOM_(250°C)=4905×10^(–15)m^(2)N^(−1))are realized at the solid solubility limit of manganese ions.The HT-PEHs assembled using the rationally designed piezoceramic can allow for fast charging of commercial electrolytic capacitor at 250°C with high energy conversion efficiency(η=11.43%).These characteristics demonstrate that defect engineering tailored BF-BT can satisfy high-end HT-PEHs requirements,paving a new way in developing selfpowered wireless sensors working in HT environments.
基金jointly supported by projects of the National Natural Science Foundation of China [grant numbers 42141017 and 41975112]。
文摘El Niño-Southern Oscillation(ENSO)is a major driver of climate change in middle and low latitudes and thus strongly influences the terrestrial carbon cycle through land-air interaction.Both the ENSO modulation and carbon flux variability are projected to increase in the future,but their connection still needs further investigation.To investigate the impact of future ENSO modulation on carbon flux variability,this study used 10 CMIP6 earth system models to analyze ENSO modulation and carbon flux variability in middle and low latitudes,and their relationship,under different scenarios simulated by CMIP6 models.The results show a high consistency in the simulations,with both ENSO modulation and carbon flux variability showing an increasing trend in the future.The higher the emissions scenario,especially SSP5-8.5 compared to SSP2-4.5,the greater the increase in variability.Carbon flux variability in the middle and low latitudes under SSP2-4.5 increases by 30.9%compared to historical levels during 1951-2000,while under SSP5-8.5 it increases by 58.2%.Further analysis suggests that ENSO influences mid-and low-latitude carbon flux variability primarily through temperature.This occurrence may potentially be attributed to the increased responsiveness of gross primary productivity towards regional temperature fluctuations,combined with the intensified influence of ENSO on land surface temperatures.
文摘Oxygen evolution reaction(OER)is often regarded as a crucial bottleneck in the field of renewable energy storage and conversion.To further accelerate the sluggish kinetics of OER,a cation and anion modulation strategy is reported here,which has been proven to be effective in preparing highly active electrocatalyst.For example,the cobalt,sulfur,and phosphorus modulated nickel hydroxide(denoted as NiCoPSOH)only needs an overpotential of 232 mV to reach a current density of 20 mA cm^(–2),demonstrating excellent OER performances.The cation and anion modulation facilitates the generation of high-valent Ni species,which would activate the lattice oxygen and switch the OER reaction pathway from conventional adsorbate evolution mechanism to lattice oxygen mechanism(LOM),as evidenced by the results of electrochemical measurements,Raman spectroscopy and differential electrochemical mass spectrometry.The LOM pathway of NiCoPSOH is further verified by the theoretical calculations,including the upshift of O 2p band center,the weakened Ni–O bond and the lowest energy barrier of rate-limiting step.Thus,the anion and cation modulated catalyst NiCoPSOH could effectively accelerate the sluggish OER kinetics.Our work provides a new insight into the cation and anion modulation,and broadens the possibility for the rational design of highly active electrocatalysts.
基金supported by“National Natural Science Foundation of China(U21A20171,U20A20245)”“Hubei Provincial Natural Science Foundation of China(2023AFA010)”+1 种基金“Independent Innovation Projects of the Hubei Longzhong Laboratory(2022ZZ-09)”“Social Public Welfare and Basic Research Special Project of Zhongshan(2020B2015).”。
文摘Two-terminal(2-T)perovskite(PVK)/CuIn(Ga)Se_(2)(CIGS)tandem solar cells(TSCs)have been considered as an ideal tandem cell because of their best bandgap matching regarding to Shockley–Queisser(S–Q)limits.However,the nature of the irregular rough morphology of commercial CIGS prevents people from improving tandem device performances.In this paper,D-homoserine lactone hydrochloride is proven to improve coverage of PVK materials on irregular rough CIGS surfaces and also passivate bulk defects by modulating the growth of PVK crystals.In addition,the minority carriers near the PVK/C60 interface and the incompletely passivated trap states caused interface recombination.A surface reconstruction with 2-thiopheneethylammonium iodide and N,N-dimethylformamide assisted passivates the defect sites located at the surface and grain boundaries.Meanwhile,LiF is used to create this field effect,repelling hole carriers away from the PVK and C60 interface and thus reducing recombination.As a result,a 2-T PVK/CIGS tandem yielded a power conversion efficiency of 24.6%(0.16 cm^(2)),one of the highest results for 2-T PVK/CIGS TSCs to our knowledge.This validation underscores the potential of our methodology in achieving superior performance in PVK/CIGS tandem solar cells.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia under Grant No.(GPIP:1055-829-2024).
文摘A healthy brain is vital to every person since the brain controls every movement and emotion.Sometimes,some brain cells grow unexpectedly to be uncontrollable and cancerous.These cancerous cells are called brain tumors.For diagnosed patients,their lives depend mainly on the early diagnosis of these tumors to provide suitable treatment plans.Nowadays,Physicians and radiologists rely on Magnetic Resonance Imaging(MRI)pictures for their clinical evaluations of brain tumors.These evaluations are time-consuming,expensive,and require expertise with high skills to provide an accurate diagnosis.Scholars and industrials have recently partnered to implement automatic solutions to diagnose the disease with high accuracy.Due to their accuracy,some of these solutions depend on deep-learning(DL)methodologies.These techniques have become important due to their roles in the diagnosis process,which includes identification and classification.Therefore,there is a need for a solid and robust approach based on a deep-learning method to diagnose brain tumors.The purpose of this study is to develop an intelligent automatic framework for brain tumor diagnosis.The proposed solution is based on a novel dense dynamic residual self-attention transfer adaptive learning fusion approach(NDDRSATALFA),carried over two implemented deep-learning networks:VGG19 and UNET to identify and classify brain tumors.In addition,this solution applies a transfer learning approach to exchange extracted features and data within the two neural networks.The presented framework is trained,validated,and tested on six public datasets of MRIs to detect brain tumors and categorize these tumors into three suitable classes,which are glioma,meningioma,and pituitary.The proposed framework yielded remarkable findings on variously evaluated performance indicators:99.32%accuracy,98.74%sensitivity,98.89%specificity,99.01%Dice,98.93%Area Under the Curve(AUC),and 99.81%F1-score.In addition,a comparative analysis with recent state-of-the-art methods was performed and according to the comparative analysis,NDDRSATALFA shows an admirable level of reliability in simplifying the timely identification of diverse brain tumors.Moreover,this framework can be applied by healthcare providers to assist radiologists,pathologists,and physicians in their evaluations.The attained outcomes open doors for advanced automatic solutions that improve clinical evaluations and provide reasonable treatment plans.
基金supported by the External Cooperation Program of Science and Technology of Fujian Province,China(2024I0016)the Fundamental Research Funds for the Central Universities(ZQN-1005).
文摘Multilevel coding(MLC)is a commonly used polar coded modulation scheme,but challenging to implement in engineering due to its high complexity and long decoding delay for high-order modulations.To address these limitations,a novel two-level serially concatenated MLC scheme,in which the bitlevels with similar reliability are bundled and transmitted together,is proposed.The proposed scheme hierarchically protects the two bit-level sets:the bitlevel sets at the higher level are sufficiently reliable and do not require excessive resources for protection,whereas only the bit-level sets at the lower level are encoded by polar codes.The proposed scheme has the advantages of low power consumption,low delay and high reliability.Moreover,an optimized constellation signal labeling rule that can enhance the performance is proposed.Finally,the superiority of the proposed scheme is validated through the theoretical analysis and simulation results.Compared with the bit interleaving coding modulation(BICM)scheme,under 256-quadrature amplitude modulation(QAM),the proposed scheme attains a performance gain of 1.0 dB while reducing the decoding complexity by 54.55%.
基金funded by the Ongoing Research Funding Program(ORF-2025-102),King Saud University,Riyadh,Saudi Arabiaby the Science and Technology Research Programof Chongqing Municipal Education Commission(Grant No.KJQN202400813)by the Graduate Research Innovation Project(Grant Nos.yjscxx2025-269-193 and CYS25618).
文摘Medical image analysis based on deep learning has become an important technical requirement in the field of smart healthcare.In view of the difficulties in collaborative modeling of local details and global features in multimodal image analysis of ophthalmology,as well as the existence of information redundancy in cross-modal data fusion,this paper proposes amultimodal fusion framework based on cross-modal collaboration and weighted attention mechanism.In terms of feature extraction,the framework collaboratively extracts local fine-grained features and global structural dependencies through a parallel dual-branch architecture,overcoming the limitations of traditional single-modality models in capturing either local or global information;in terms of fusion strategy,the framework innovatively designs a cross-modal dynamic fusion strategy,combining overlappingmulti-head self-attention modules with a bidirectional feature alignment mechanism,addressing the bottlenecks of low feature interaction efficiency and excessive attention fusion computations in traditional parallel fusion,and further introduces cross-domain local integration technology,which enhances the representation ability of the lesion area through pixel-level feature recalibration and optimizes the diagnostic robustness of complex cases.Experiments show that the framework exhibits excellent feature expression and generalization performance in cross-domain scenarios of ophthalmic medical images and natural images,providing a high-precision,low-redundancy fusion paradigm for multimodal medical image analysis,and promoting the upgrade of intelligent diagnosis and treatment fromsingle-modal static analysis to dynamic decision-making.
文摘Deep Learning-based systems for Finger vein recognition have gained rising attention in recent years due to improved efficiency and enhanced security.The performance of existing CNN-based methods is limited by the puny generalization of learned features and deficiency of the finger vein image training data.Considering the concerns of existing methods,in this work,a simplified deep transfer learning-based framework for finger-vein recognition is developed using an EfficientNet model of deep learning with a self-attention mechanism.Data augmentation using various geometrical methods is employed to address the problem of training data shortage required for a deep learning model.The proposed model is tested using K-fold cross-validation on three publicly available datasets:HKPU,FVUSM,and SDUMLA.Also,the developed network is compared with other modern deep nets to check its effectiveness.In addition,a comparison of the proposed method with other existing Finger vein recognition(FVR)methods is also done.The experimental results exhibited superior recognition accuracy of the proposed method compared to other existing methods.In addition,the developed method proves to be more effective and less sophisticated at extracting robust features.The proposed EffAttenNet achieves an accuracy of 98.14%on HKPU,99.03%on FVUSM,and 99.50%on SDUMLA databases.
文摘Following publication of the original article[1],the authors found that they pasted the same data when drawing XRD for sample NCO-1 and NCO-2 in Fig.2a,however,the XRD of all four samples in the manuscript was tested,and XRD raw data were kept and can be offered.The correct Fig.2 has been provided in this Correction.
基金supported by the National Key Research and Development Program of China(2020YFC2200901)。
文摘As the complexity of scientific satellite missions increases,the requirements for their magnetic fields,magnetic field fluctuations,and even magnetic field gradients and variations become increasingly stringent.Additionally,there is a growing need to address the alternating magnetic fields produced by the spacecraft itself.This paper introduces a novel modeling method for spacecraft magnetic dipoles using an integrated self-attention mechanism and a transformer combined with Kolmogorov-Arnold Networks.The self-attention mechanism captures correlations among globally sparse data,establishing dependencies b.etween sparse magnetometer readings.Concurrently,the Kolmogorov-Arnold Network,proficient in modeling implicit numerical relationships between data features,enhances the ability to learn subtle patterns.Comparative experiments validate the capability of the proposed method to precisely model magnetic dipoles,achieving maximum Root Mean Square Errors of 24.06 mA·m^(2)and 0.32 cm for size and location modeling,respectively.The spacecraft magnetic model established using this method accurately computes magnetic fields and alternating magnetic fields at designated surfaces or points.This approach facilitates the rapid and precise construction of individual and complete spacecraft magnetic models,enabling the verification of magnetic specifications from the spacecraft design phase.
文摘The development of deep learning has made non-biochemical methods for molecular property prediction screening a reality,which can increase the experimental speed and reduce the experimental cost of relevant experiments.There are currently two main approaches to representing molecules:(a)representing molecules by fixing molecular descriptors,and(b)representing molecules by graph convolutional neural networks.Currently,both of these Representative methods have achieved some results in their respective experiments.Based on past efforts,we propose a Dual Self-attention Fusion Message Neural Network(DSFMNN).DSFMNN uses a combination of dual self-attention mechanism and graph convolutional neural network.Advantages of DSFMNN:(1)The dual self-attention mechanism focuses not only on the relationship between individual subunits in a molecule but also on the relationship between the atoms and chemical bonds contained in each subunit.(2)On the directed molecular graph,a message delivery approach centered on directed molecular bonds is used.We test the performance of the model on eight publicly available datasets and compare the performance with several models.Based on the current experimental results,DSFMNN has superior performance compared to previous models on the datasets applied in this paper.
基金supported by the National Key R&D Program of China(No.2022YFB4301102).
文摘Currently,most trains are equipped with dedicated cameras for capturing pantograph videos.Pantographs are core to the high-speed-railway pantograph-catenary system,and their failure directly affects the normal operation of high-speed trains.However,given the complex and variable real-world operational conditions of high-speed railways,there is no real-time and robust pantograph fault-detection method capable of handling large volumes of surveillance video.Hence,it is of paramount importance to maintain real-time monitoring and analysis of pantographs.Our study presents a real-time intelligent detection technology for identifying faults in high-speed railway pantographs,utilizing a fusion of self-attention and convolution features.We delved into lightweight multi-scale feature-extraction and fault-detection models based on deep learning to detect pantograph anomalies.Compared with traditional methods,this approach achieves high recall and accuracy in pantograph recognition,accurately pinpointing issues like discharge sparks,pantograph horns,and carbon pantograph-slide malfunctions.After experimentation and validation with actual surveillance videos of electric multiple-unit train,our algorithmic model demonstrates real-time,high-accuracy performance even under complex operational conditions.
基金supported in part by Guangdong Basic and Applied Basic Research Foundation under Grants 2023A1515030118 and 2024A1515010012in part by the Guangzhou Science and Technology Project under Grant 2023A03J0110+3 种基金in part by Guangzhou Basic Research Program Municipal School(College)Joint Funding Project under Grant 2025A03J3119in part by National Natural Science Foundation of China under Grant 62173101in part by the Key Discipline Project of Guangzhou Education Bureau under Grant 202255467in part by the Key Laboratory of on-Chip Communication and Sensor Chip of Guangdong Higher Education Institutes under Grant 2023KSYS002。
文摘The simultaneously transmitting and reflecting reconfigurable intelligent surface(STAR-RIS)is regarded as a promising paradigm for enhancing the connectivity and reliability of non-orthogonal multiple access(NOMA)networks.However,the transmission of STAR-RIS enhanced NOMA networks performance is severely limited due to the inter-user interference(IUI)on multi-user detections.To mitigate this drawback,we propose a generalized quadrature spatial modulation(GQSM)aided STAR-RIS in conjunction with the NOMA scheme,termed STARRIS-NOMA-GQSM,to improve the performance of the corresponding NGMA network.By STAR-RISNOMA-GQSM,the information bits for all users in transmission and reflection zones are transmitted via orthogonal signal domains to eliminate the IUI so as to greatly improve the system performance.The lowcomplexity detection and upper-bounded bit error rate(BER)of STAR-RIS-NOMA-GQSM are both studied to evaluate its feasibility and performance.Moreover,by further utilizing index modulation(IM),we propose an enhanced STAR-RIS-NOMA-GQSM scheme,termed E-STAR-RIS-NOMA-GQSM,to enhance the transmission rate by dynamically adjusting reflection patterns in both transmission and reflection zones.Simulation results show that the proposed original and enhanced scheme significantly outperform the conventional STAR-RIS-NOMA and also confirm the precision of the theoretical analysis of the upper-bounded BER.
基金supported by the program for National Natural Science Foundation of China (Nos. 52174173, 52274188, and 52104190)the Joint Funds of the National Natural Science Foundation of China (No. U24A2091)+1 种基金The Natural Science Foundation of Henan Polytechnic University (No. B2021-2)Double FirstClass Initiative of Safety and Energy Engineering (Henan Polytechnic University) (Nos. AQ20240703 and AQ20230304)。
文摘Under submerged conditions, compared with traditional self-excited oscillating pulsed waterjets(SOPWs), annular fluid-enhanced self-excited oscillating pulsed waterjets(AFESOPWs) exhibit a higher surge pressure through self-priming. However, their pressure frequency and cavitation characteristics remain unclear, resulting in an inability to fully utilize resonance and cavitation erosion to break coal and rock. In this study, high-frequency pressure testing, high-speed photography, and large eddy simulation(LES) are used to investigate the distribution of the pressure frequency band, evolution law of the cavitation cloud, and its regulation mechanism of a continuous waterjet, SOPW, and AFESOPW. The results indicated that the excitation of the plunger pump, shearing layer vortex, and bubble collapse corresponded to the three high-amplitude frequency bands of the waterjet pressure. AFESOPWs have an additional self-priming frequency that can produce a larger amplitude under a synergistic effect with the second high-amplitude frequency band. A better cavitation effect was produced after self-priming the annulus fluid, and the shedding frequency of the cavitation clouds of the three types of waterjets was linearly related to the cavitation number. The peak pressure of the waterjet and cavitation erosion effect can be improved by modulating the waterjet pressure oscillation frequency and cavitation shedding frequency.
基金supported by the National Natural Science Foundation of China(Grant Nos.12002133,12372109,and 11972171)the Natural Science Foundation of Jiangsu Province(Grant Nos.BK20200590 and BK20180031)+4 种基金the Fundamental Research Funds for the Central Universities(Grant No.JUSRP121040)the National Key R&D Program of China(Grant No.2023YFB4605101)the 111 project(Grant No.B18027)the Open Fund of Key Laboratory for Intelligent Nano Materials and Devices of the Ministry of Education(Grant No.NJ2020003)the Sixth Phase of Jiangsu Province“333 High Level Talent Training Project”Second Level Talents.
文摘Two-dimensional(2D)materials are promising for next-generation electronic devices and systems due to their unique physical properties.The interfacial adhesion plays a vital role not only in the synthesis,transfer and manipulation of 2D materials but also in the manufacture,integration and performance of the functional devices.However,the atomic thickness and limited lateral dimensions of 2D materials make the accurate measurement and modulation of their interfacial adhesion energy challenging.In this review,the recent advances in the measurement and modulation of the interfacial adhesion properties of 2D materials are systematically combed.Experimental methods and relative theoretical models for the adhesion measurement of 2D materials are summarized,with their scope of application and limitations discussed.The measured adhesion energies between 2D materials and various substrates are described in categories,where the typical adhesion modulation strategies of 2D materials are also introduced.Finally,the remaining challenges and opportunities for the interfacial adhesion measurement and modulation of 2D materials are presented.This paper provides guidance for addressing the adhesion issues in devices and systems involving 2D materials.
