Traditional digitizers for signal readout of PET detectors are based on commercial analog-to-digital converters(ADC).However,the cost and power consumption of an entire electronic readout system based on digitizers fo...Traditional digitizers for signal readout of PET detectors are based on commercial analog-to-digital converters(ADC).However,the cost and power consumption of an entire electronic readout system based on digitizers for a PET scanner are high.To address this problem,a soft-core ADC based on a field-programmable gate array(FPGA)was proposed.An FPGA-based ADC(FPGA-ADC)combines low loss and high performance.To achieve good performance,the FPGA-ADC requires three calibrations:time-to-digital converter(TDC)length calibration,TDC alignment calibration,and TDC-to-ADC calibration.A prototype front-end electronics based on FPGA-ADC was built to evaluate the performance of time-of-flight positron emission tomography(TOF PET)detectors.Each PET detector consists of a LYSO crystal single-ended coupled to a silicon photomultiplier(SiPM).The experimental results show that the full-width at half-maximum(FWHM)energy resolution for 511 keV gamma photons after saturation correction of the SiPM was 12.3%.The FWHM coincidence timing resolution(CTR)of the TOF PET detector with the readout of the front-end electronic prototype is 385.2 ps.FPGA-ADCbased front-end electronics are very promising for multichannel,low-cost,highly integrated,and power-efficient readout electronic systems for radiation detector applications.展开更多
In the version of the article originally published in the volume 68,issue 12,2025 of Sci China Mater(pages 4413-4422,https://doi.org/10.1007/s40843-025-3667-7),the Chinese name of the co-first author(肖天孝)was incorr...In the version of the article originally published in the volume 68,issue 12,2025 of Sci China Mater(pages 4413-4422,https://doi.org/10.1007/s40843-025-3667-7),the Chinese name of the co-first author(肖天孝)was incorrect.The corrected Chinese name is:肖天笑.展开更多
Researchers are increasingly focused on enabling groups of multiple unmanned vehicles to operate cohesively in complex,real-world environments,where coordinated formation control and obstacle avoidance are essential f...Researchers are increasingly focused on enabling groups of multiple unmanned vehicles to operate cohesively in complex,real-world environments,where coordinated formation control and obstacle avoidance are essential for executing sophisticated collective tasks.This paper presents a Distributed Formation Control and Obstacle Avoidance(DFCOA)framework for multi-unmanned ground vehicles(UGV).DFCOA integrates a virtual leader structure for global guidance,an improved A^(*)path planning algorithm with an advanced cost function for efficient path planning,and a repulsive-force-based improved vector field histogram star(VFH^(*))technique for collision avoidance.The virtual leader generates a reference trajectory while enabling distributed execution;the improved A^(*)algorithm reduces planning time and number of nodes to determine the shortest path from the starting position to the goal;and the improved VFH^(*)uses 2D LiDAR data with inter-agent repulsive force to simultaneously avoid collision with obstacles and maintain safe inter-vehicle distances.The formation stability of the proposed DFCOA reaches 95.8%and 94.6%in two scenarios,with root mean square(RMS)centroid errors of 0.9516 and 1.0008 m,respectively.Velocity tracking is precise(velocity centroid error RMS of 0.2699 and 0.1700 m/s),and linear velocities closely match the desired 0.3 m/s.Safety metrics showed average collision risks of 0.7773 and 0.5143,with minimum inter-vehicle distances of 0.4702 and 0.8763 m,confirming collision-free navigation of four UGVs.DFCOA outperforms conventional methods in formation stability,path efficiency,and scalability,proving its suitability for decentralized multi-UGV applications.展开更多
To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework ba...To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.展开更多
Phytomelatonin,an emerging plant hormone,plays vital roles in plant growth,development,and stress adaptation(Arnao et al.,2022;Ullah et al.,2024).It acts both as a direct antioxidant and a signaling molecule,engaging ...Phytomelatonin,an emerging plant hormone,plays vital roles in plant growth,development,and stress adaptation(Arnao et al.,2022;Ullah et al.,2024).It acts both as a direct antioxidant and a signaling molecule,engaging complex networks and interacting with other phytohormones(Liu et al.,2022;Khan et al.,2023).Although phytomelatonin receptors(PMTRs)have been identified in many plants(Wei et al.,2018;Wang et al.,2022;Liu et al.,2025),the downstream signaling mechanisms,particularly receptor-mediated protein modifications and transcriptional regulation,remain poorly characterized.展开更多
High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging ...High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).展开更多
Multi-organ-on-a-chip(MOOC)technology represents a pivotal direction in the organ-on-a-chip field,seeking to emulate the complex interactions of multiple human organs in vitro through microfluidic systems.This technol...Multi-organ-on-a-chip(MOOC)technology represents a pivotal direction in the organ-on-a-chip field,seeking to emulate the complex interactions of multiple human organs in vitro through microfluidic systems.This technology overcomes the limitations of traditional single-organ models,providing a novel platform for investigating complex disease mechanisms and evaluating drug efficacy and toxicity.Although it demonstrates broad application prospects,its development still faces critical bottlenecks,including inadequate physiological coupling between organs,short functional maintenance durations,and limited real-time monitoring capabilities.Contemporary research is advancing along three key directions,including functional coupling,sensor integration,and full-process automation systems,to propel the technology toward enhanced levels of physiological relevance and predictive accuracy.展开更多
The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measu...The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measures in a multi-family house building in Greece.The energy efficiency measures include different heating/cooling systems(such as low-temperature and high-temperature heat pumps,natural gas boilers,split units),building envelope components for floor,walls,roof and windows of variable heat transfer coefficients,the installation of solar thermal collectors and PVs.The calculations of the building loads and investment and operating and maintenance costs of the measures are based on the methodology defined in Directive 2010/31/EU,while economic assumptions are based on EN 15459-1 standard.Typically,multi-objective optimization of energy efficiency measures often requires the simulation of very large numbers of cases involving numerous possible combinations,resulting in intense computational load.The results of the study indicate that ANN-driven GA methods can be used as an alternative,valuable tool for reliably predicting the optimal measures which minimize primary energy consumption and life cycle cost of the building with greatly reduced computational requirements.Through GA methods,the computational time needed for obtaining the optimal solutions is reduced by 96.4%-96.8%.展开更多
The China initiative Accelerator Driven System,CiADS,physics design adopts 162.5 MHz,325 MHz,and 650 MHz cavities,which are driven by the corresponding radio frequency(RF)power system,requiring frequency translation f...The China initiative Accelerator Driven System,CiADS,physics design adopts 162.5 MHz,325 MHz,and 650 MHz cavities,which are driven by the corresponding radio frequency(RF)power system,requiring frequency translation front-end for the RF station.For that application,a general-purpose design front-end prototype has been developed to evaluate the multi-frequency point supported design feasibility.The difficult parts to achieve the requirements of the general-purpose design are reasonable device selection and balanced design.With a carefully selected low-noise wide-band RF mixer and amplifier to balance the performance of multi-frequency supported down-conversion,specially designed LO distribution net to increase isolation between adjacent channels,and external band-pass filter to realize expected up-conversion frequencies,high maintenance and modular front-end generalpurpose design has been implemented.Results of standard parameters show an R2 value of at least 99.991%in the range of-60-10 dBm for linearity,up to 18 dBm for P1dB,and up to 89 dBc for cross talk between adjacent channels.The phase noise spectrum is lower than 80 dBc in the range of 0-1 MHz;cumulative phase noise is 0.006°;and amplitude and phase stability are 0.022%and 0.034°,respectively.展开更多
This paper presents a reconfigurable RF front-end for multi-mode multi-standard(MMMS) applications. The designed RF front-end is fabricated in 0.18 μm RF CMOS technology. The low noise characteristic is achieved by t...This paper presents a reconfigurable RF front-end for multi-mode multi-standard(MMMS) applications. The designed RF front-end is fabricated in 0.18 μm RF CMOS technology. The low noise characteristic is achieved by the noise canceling technique while the bandwidth is enhanced by gate inductive peaking technique. Measurement results show that, while the input frequency ranges from 100 MHz to 2.9 GHz, the proposed reconfigurable RF front-end achieves a controllable voltage conversion gain(VCG) from 18 dB to 39 dB. The measured maximum input third intercept point(IIP3) is-4.9 dBm and the minimum noise figure(NF) is 4.6 dB. The consumed current ranges from 16 mA to 26.5 mA from a 1.8 V supply voltage. The chip occupies an area of 1.17 mm^2 including pads.展开更多
The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can caus...The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.展开更多
Introduction-Nuclei near and beyond the proton drip line represent a fascinating frontier in the nuclear landscape. Proton-rich nuclei exhibit intriguing phenomena, such as the Thomas-Ehrman shift and proton-halo stru...Introduction-Nuclei near and beyond the proton drip line represent a fascinating frontier in the nuclear landscape. Proton-rich nuclei exhibit intriguing phenomena, such as the Thomas-Ehrman shift and proton-halo structure. Beyond the proton dripline, nuclei become unbound, allowing protons to be emitted and giving rise to novel radioactive decay modes. Single-proton radioactivity, a process in which some nuclei with an odd number of protons(Z) decay by ejecting a proton, was discovered several decades ago and has been extensively studied [1, 2].展开更多
An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyram...An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.展开更多
Traditionally,a continuous-wave(CW)signal is used to simulate RF circuits during the design procedure,while the fabricated circuits are measured by modulated signals in the test phase,because modulated signals are use...Traditionally,a continuous-wave(CW)signal is used to simulate RF circuits during the design procedure,while the fabricated circuits are measured by modulated signals in the test phase,because modulated signals are used in reality.It is almost impossible to use a CW signal to predict system performances,such as error vector magnitude(EVM),bit error rate(BER),etc.,of a transceiver front-end when dealing with complex modulated signals.This paper develops an integrated system evaluation engine(ISEE)to evaluate the system performances of a transceiver front-end or its sub-circuits.This crossdomain simulation platform is based on Matlab,advanced design system(ADS),and Cadence simulators to link the baseband signals and transceiver frond-end.An orthogonal frequency division multiplex(OFDM)modem is implemented in Matlab for evaluating the system performances.The modulated baseband signal from Matlab is dynamically fed into ADS,which includes transceiver front-end for co-simulation.The sub-block circuits of the transceiver front-end can be implemented using ADS and Cadence simulators.After system-level circuit simulation in ADS,the output signal is dynamically delivered to Matlab for demodulation.To simplify the use of the co-simulation platform,a graphical user interface(GUI)is constructed using Matlab.The parameters of the OFDM signals can be easily reconfigured on the GUI to simulate RF circuits with different modulation schemes.To demonstrate the effectiveness of the ISEE,a 3.5 GHz power amplifier is simulated and characterized using 20 MHz 16-and 64-QAM OFDM signals.展开更多
In response to the pain points of rapid iteration of front-end education technology,large differences in learner foundations,and a lack of practical scenarios,this paper combines generative artificial intelligence and...In response to the pain points of rapid iteration of front-end education technology,large differences in learner foundations,and a lack of practical scenarios,this paper combines generative artificial intelligence and AI agents to analyze the empowerment logic from three dimensions:knowledge ecology reconstruction,cognitive collaborative upgrading,and teaching methodology innovation.It explores its application scenarios in teaching and learning,sorts out challenges such as technology adaptation and learning dependence,and proposes paths such as building an exclusive AI ecosystem and optimizing the guidance mechanism of intelligent agents to provide support for the digital transformation of front-end education.展开更多
Multi-electron and multi-orbital effects play a crucial role in the interaction of strong laser fields with complex molecules.Here,multi-electron effects encompass not only electron-electron Coulomb interactions and e...Multi-electron and multi-orbital effects play a crucial role in the interaction of strong laser fields with complex molecules.Here,multi-electron effects encompass not only electron-electron Coulomb interactions and exchangecorrelation effects but also the interference between the dynamics of different electron wave packets.展开更多
Multi-label image classification is a challenging task due to the diverse sizes and complex backgrounds of objects in images.Obtaining class-specific precise representations at different scales is a key aspect of feat...Multi-label image classification is a challenging task due to the diverse sizes and complex backgrounds of objects in images.Obtaining class-specific precise representations at different scales is a key aspect of feature representation.However,existing methods often rely on the single-scale deep feature,neglecting shallow and deeper layer features,which poses challenges when predicting objects of varying scales within the same image.Although some studies have explored multi-scale features,they rarely address the flow of information between scales or efficiently obtain class-specific precise representations for features at different scales.To address these issues,we propose a two-stage,three-branch Transformer-based framework.The first stage incorporates multi-scale image feature extraction and hierarchical scale attention.This design enables the model to consider objects at various scales while enhancing the flow of information across different feature scales,improving the model’s generalization to diverse object scales.The second stage includes a global feature enhancement module and a region selection module.The global feature enhancement module strengthens interconnections between different image regions,mitigating the issue of incomplete represen-tations,while the region selection module models the cross-modal relationships between image features and labels.Together,these components enable the efficient acquisition of class-specific precise feature representations.Extensive experiments on public datasets,including COCO2014,VOC2007,and VOC2012,demonstrate the effectiveness of our proposed method.Our approach achieves consistent performance gains of 0.3%,0.4%,and 0.2%over state-of-the-art methods on the three datasets,respectively.These results validate the reliability and superiority of our approach for multi-label image classification.展开更多
基金supported by the Key R&D Program of Shandong Province(No.2023SFGC0101)Shandong Excellent Young Scientists Fund Program(Overseas)(No.2023HWYQ-047)+1 种基金the Natural Science Foundation of Shandong Province(No.ZR2022QA039)the National Natural Science Foundation of China(NSFC)(No.U2106202).
文摘Traditional digitizers for signal readout of PET detectors are based on commercial analog-to-digital converters(ADC).However,the cost and power consumption of an entire electronic readout system based on digitizers for a PET scanner are high.To address this problem,a soft-core ADC based on a field-programmable gate array(FPGA)was proposed.An FPGA-based ADC(FPGA-ADC)combines low loss and high performance.To achieve good performance,the FPGA-ADC requires three calibrations:time-to-digital converter(TDC)length calibration,TDC alignment calibration,and TDC-to-ADC calibration.A prototype front-end electronics based on FPGA-ADC was built to evaluate the performance of time-of-flight positron emission tomography(TOF PET)detectors.Each PET detector consists of a LYSO crystal single-ended coupled to a silicon photomultiplier(SiPM).The experimental results show that the full-width at half-maximum(FWHM)energy resolution for 511 keV gamma photons after saturation correction of the SiPM was 12.3%.The FWHM coincidence timing resolution(CTR)of the TOF PET detector with the readout of the front-end electronic prototype is 385.2 ps.FPGA-ADCbased front-end electronics are very promising for multichannel,low-cost,highly integrated,and power-efficient readout electronic systems for radiation detector applications.
文摘In the version of the article originally published in the volume 68,issue 12,2025 of Sci China Mater(pages 4413-4422,https://doi.org/10.1007/s40843-025-3667-7),the Chinese name of the co-first author(肖天孝)was incorrect.The corrected Chinese name is:肖天笑.
文摘Researchers are increasingly focused on enabling groups of multiple unmanned vehicles to operate cohesively in complex,real-world environments,where coordinated formation control and obstacle avoidance are essential for executing sophisticated collective tasks.This paper presents a Distributed Formation Control and Obstacle Avoidance(DFCOA)framework for multi-unmanned ground vehicles(UGV).DFCOA integrates a virtual leader structure for global guidance,an improved A^(*)path planning algorithm with an advanced cost function for efficient path planning,and a repulsive-force-based improved vector field histogram star(VFH^(*))technique for collision avoidance.The virtual leader generates a reference trajectory while enabling distributed execution;the improved A^(*)algorithm reduces planning time and number of nodes to determine the shortest path from the starting position to the goal;and the improved VFH^(*)uses 2D LiDAR data with inter-agent repulsive force to simultaneously avoid collision with obstacles and maintain safe inter-vehicle distances.The formation stability of the proposed DFCOA reaches 95.8%and 94.6%in two scenarios,with root mean square(RMS)centroid errors of 0.9516 and 1.0008 m,respectively.Velocity tracking is precise(velocity centroid error RMS of 0.2699 and 0.1700 m/s),and linear velocities closely match the desired 0.3 m/s.Safety metrics showed average collision risks of 0.7773 and 0.5143,with minimum inter-vehicle distances of 0.4702 and 0.8763 m,confirming collision-free navigation of four UGVs.DFCOA outperforms conventional methods in formation stability,path efficiency,and scalability,proving its suitability for decentralized multi-UGV applications.
基金supported by the confidential research grant No.a8317。
文摘To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.
基金supported by the grants from the Key Research and Development Program of Xinjiang Uygur autonomous region in China(Grant No.2023B02017)the National Key Research and Development Program of China(Grant No.2024YFD2300703)+1 种基金the financial support from the Beijing Rural Revitalization Agricultural Science and Technology Project(Grant No.NY2401080000),BAIC01-2025the 2115 Talent Development Program of China Agricultural University.
文摘Phytomelatonin,an emerging plant hormone,plays vital roles in plant growth,development,and stress adaptation(Arnao et al.,2022;Ullah et al.,2024).It acts both as a direct antioxidant and a signaling molecule,engaging complex networks and interacting with other phytohormones(Liu et al.,2022;Khan et al.,2023).Although phytomelatonin receptors(PMTRs)have been identified in many plants(Wei et al.,2018;Wang et al.,2022;Liu et al.,2025),the downstream signaling mechanisms,particularly receptor-mediated protein modifications and transcriptional regulation,remain poorly characterized.
文摘High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).
基金supported by the Shenzhen Medical Research Fund(Grant No.A2303049)Guangdong Basic and Applied Basic Research(Grant No.2023A1515010647)+1 种基金National Natural Science Foundation of China(Grant No.22004135)Shenzhen Science and Technology Program(Grant No.RCBS20210706092409020,GXWD20201231165807008,20200824162253002).
文摘Multi-organ-on-a-chip(MOOC)technology represents a pivotal direction in the organ-on-a-chip field,seeking to emulate the complex interactions of multiple human organs in vitro through microfluidic systems.This technology overcomes the limitations of traditional single-organ models,providing a novel platform for investigating complex disease mechanisms and evaluating drug efficacy and toxicity.Although it demonstrates broad application prospects,its development still faces critical bottlenecks,including inadequate physiological coupling between organs,short functional maintenance durations,and limited real-time monitoring capabilities.Contemporary research is advancing along three key directions,including functional coupling,sensor integration,and full-process automation systems,to propel the technology toward enhanced levels of physiological relevance and predictive accuracy.
文摘The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measures in a multi-family house building in Greece.The energy efficiency measures include different heating/cooling systems(such as low-temperature and high-temperature heat pumps,natural gas boilers,split units),building envelope components for floor,walls,roof and windows of variable heat transfer coefficients,the installation of solar thermal collectors and PVs.The calculations of the building loads and investment and operating and maintenance costs of the measures are based on the methodology defined in Directive 2010/31/EU,while economic assumptions are based on EN 15459-1 standard.Typically,multi-objective optimization of energy efficiency measures often requires the simulation of very large numbers of cases involving numerous possible combinations,resulting in intense computational load.The results of the study indicate that ANN-driven GA methods can be used as an alternative,valuable tool for reliably predicting the optimal measures which minimize primary energy consumption and life cycle cost of the building with greatly reduced computational requirements.Through GA methods,the computational time needed for obtaining the optimal solutions is reduced by 96.4%-96.8%.
文摘The China initiative Accelerator Driven System,CiADS,physics design adopts 162.5 MHz,325 MHz,and 650 MHz cavities,which are driven by the corresponding radio frequency(RF)power system,requiring frequency translation front-end for the RF station.For that application,a general-purpose design front-end prototype has been developed to evaluate the multi-frequency point supported design feasibility.The difficult parts to achieve the requirements of the general-purpose design are reasonable device selection and balanced design.With a carefully selected low-noise wide-band RF mixer and amplifier to balance the performance of multi-frequency supported down-conversion,specially designed LO distribution net to increase isolation between adjacent channels,and external band-pass filter to realize expected up-conversion frequencies,high maintenance and modular front-end generalpurpose design has been implemented.Results of standard parameters show an R2 value of at least 99.991%in the range of-60-10 dBm for linearity,up to 18 dBm for P1dB,and up to 89 dBc for cross talk between adjacent channels.The phase noise spectrum is lower than 80 dBc in the range of 0-1 MHz;cumulative phase noise is 0.006°;and amplitude and phase stability are 0.022%and 0.034°,respectively.
基金Supported by the National Nature Science Foundation of China(No.61674037)the Priority Academic Program Development of Jiangsu Higher Education Institutions,the National Power Grid Corp Science and Technology Project(No.SGTYHT/16-JS-198)the State Grid Nanjing Power Supply Company Project(No.1701052)
文摘This paper presents a reconfigurable RF front-end for multi-mode multi-standard(MMMS) applications. The designed RF front-end is fabricated in 0.18 μm RF CMOS technology. The low noise characteristic is achieved by the noise canceling technique while the bandwidth is enhanced by gate inductive peaking technique. Measurement results show that, while the input frequency ranges from 100 MHz to 2.9 GHz, the proposed reconfigurable RF front-end achieves a controllable voltage conversion gain(VCG) from 18 dB to 39 dB. The measured maximum input third intercept point(IIP3) is-4.9 dBm and the minimum noise figure(NF) is 4.6 dB. The consumed current ranges from 16 mA to 26.5 mA from a 1.8 V supply voltage. The chip occupies an area of 1.17 mm^2 including pads.
基金Projects(U22B2084,52275483,52075142)supported by the National Natural Science Foundation of ChinaProject(2023ZY01050)supported by the Ministry of Industry and Information Technology High Quality Development,China。
文摘The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.
文摘Introduction-Nuclei near and beyond the proton drip line represent a fascinating frontier in the nuclear landscape. Proton-rich nuclei exhibit intriguing phenomena, such as the Thomas-Ehrman shift and proton-halo structure. Beyond the proton dripline, nuclei become unbound, allowing protons to be emitted and giving rise to novel radioactive decay modes. Single-proton radioactivity, a process in which some nuclei with an odd number of protons(Z) decay by ejecting a proton, was discovered several decades ago and has been extensively studied [1, 2].
基金supported by the National Natural Science Foundation of China(No.62241109)the Tianjin Science and Technology Commissioner Project(No.20YDTPJC01110)。
文摘An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.
基金supported by the Project of Hetao Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone(No.HZQB-KCZYB-2020083).
文摘Traditionally,a continuous-wave(CW)signal is used to simulate RF circuits during the design procedure,while the fabricated circuits are measured by modulated signals in the test phase,because modulated signals are used in reality.It is almost impossible to use a CW signal to predict system performances,such as error vector magnitude(EVM),bit error rate(BER),etc.,of a transceiver front-end when dealing with complex modulated signals.This paper develops an integrated system evaluation engine(ISEE)to evaluate the system performances of a transceiver front-end or its sub-circuits.This crossdomain simulation platform is based on Matlab,advanced design system(ADS),and Cadence simulators to link the baseband signals and transceiver frond-end.An orthogonal frequency division multiplex(OFDM)modem is implemented in Matlab for evaluating the system performances.The modulated baseband signal from Matlab is dynamically fed into ADS,which includes transceiver front-end for co-simulation.The sub-block circuits of the transceiver front-end can be implemented using ADS and Cadence simulators.After system-level circuit simulation in ADS,the output signal is dynamically delivered to Matlab for demodulation.To simplify the use of the co-simulation platform,a graphical user interface(GUI)is constructed using Matlab.The parameters of the OFDM signals can be easily reconfigured on the GUI to simulate RF circuits with different modulation schemes.To demonstrate the effectiveness of the ISEE,a 3.5 GHz power amplifier is simulated and characterized using 20 MHz 16-and 64-QAM OFDM signals.
基金funded by two 2024 Ministry of Education supply-demand docking employment and education projects(Grant No.2024101679202,Grant No.2024121116066)2024“Innovation Strong Institute Project of Guangdong Polytechnic Institute”(Grant No.2024CQ-29)2022 Guangdong Province Undergraduate Online Open Course Guidance Committee Research Project(Grant No.2022ZXKC612).
文摘In response to the pain points of rapid iteration of front-end education technology,large differences in learner foundations,and a lack of practical scenarios,this paper combines generative artificial intelligence and AI agents to analyze the empowerment logic from three dimensions:knowledge ecology reconstruction,cognitive collaborative upgrading,and teaching methodology innovation.It explores its application scenarios in teaching and learning,sorts out challenges such as technology adaptation and learning dependence,and proposes paths such as building an exclusive AI ecosystem and optimizing the guidance mechanism of intelligent agents to provide support for the digital transformation of front-end education.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFE0134200)the National Natural Science Foundation of China(Grant No.12204214)+1 种基金the Fundamental Research Funds for the Central Universities(Grant No.GK202207012)QCYRCXM-2022-241。
文摘Multi-electron and multi-orbital effects play a crucial role in the interaction of strong laser fields with complex molecules.Here,multi-electron effects encompass not only electron-electron Coulomb interactions and exchangecorrelation effects but also the interference between the dynamics of different electron wave packets.
基金supported by the National Natural Science Foundation of China(62302167,62477013)Natural Science Foundation of Shanghai(No.24ZR1456100)+1 种基金Science and Technology Commission of Shanghai Municipality(No.24DZ2305900)the Shanghai Municipal Special Fund for Promoting High-Quality Development of Industries(2211106).
文摘Multi-label image classification is a challenging task due to the diverse sizes and complex backgrounds of objects in images.Obtaining class-specific precise representations at different scales is a key aspect of feature representation.However,existing methods often rely on the single-scale deep feature,neglecting shallow and deeper layer features,which poses challenges when predicting objects of varying scales within the same image.Although some studies have explored multi-scale features,they rarely address the flow of information between scales or efficiently obtain class-specific precise representations for features at different scales.To address these issues,we propose a two-stage,three-branch Transformer-based framework.The first stage incorporates multi-scale image feature extraction and hierarchical scale attention.This design enables the model to consider objects at various scales while enhancing the flow of information across different feature scales,improving the model’s generalization to diverse object scales.The second stage includes a global feature enhancement module and a region selection module.The global feature enhancement module strengthens interconnections between different image regions,mitigating the issue of incomplete represen-tations,while the region selection module models the cross-modal relationships between image features and labels.Together,these components enable the efficient acquisition of class-specific precise feature representations.Extensive experiments on public datasets,including COCO2014,VOC2007,and VOC2012,demonstrate the effectiveness of our proposed method.Our approach achieves consistent performance gains of 0.3%,0.4%,and 0.2%over state-of-the-art methods on the three datasets,respectively.These results validate the reliability and superiority of our approach for multi-label image classification.
