Plant synthetic biology has emerged as a transformative field in agriculture,offering innovative solutions to enhance food security,provide resilience to climate change,and transition to sustainable farming practices....Plant synthetic biology has emerged as a transformative field in agriculture,offering innovative solutions to enhance food security,provide resilience to climate change,and transition to sustainable farming practices.By integrating advanced genetic tools,computational modeling,and systems biology,researchers can precisely modify plant genomes to enhance traits such as yield,stress tolerance,and nutrient use efficiency.The ability to design plants with specific characteristics tailored to diverse environmental conditions and agricultural needs holds great potential to address global food security challenges.Here,we highlight recent advancements and applications of plant synthetic biology in agriculture,focusing on key areas such as photosynthetic efficiency,nitrogen fixation,drought tolerance,pathogen resistance,nutrient use efficiency,biofortification,climate resilience,microbiology engineering,synthetic plant genomes,and the integration of artificial intelligence with synthetic biology.These innovations aim to maximize resource use efficiency,reduce reliance on external inputs,and mitigate environmental impacts associated with conventional agricultural practices.Despite challenges related to regulatory approval and public acceptance,the integration of synthetic biology in agriculture holds immense promise for creating more resilient and sustainable agricultural systems,contributing to global food security and environmental sustainability.Rigorous multi-field testing of these approaches will undoubtedly be required to ensure reproducibility.展开更多
An efficient and practical synthetic process for Daprodustat was developed.Starting with N,N'-dicyclohexylcarbodiimide(DCC)and malonic acid,the key intermediate 1,3-dicyclohexylpyrimidine-2,4,6(1H,3H,5H)-trione wa...An efficient and practical synthetic process for Daprodustat was developed.Starting with N,N'-dicyclohexylcarbodiimide(DCC)and malonic acid,the key intermediate 1,3-dicyclohexylpyrimidine-2,4,6(1H,3H,5H)-trione was synthesized via condensation reaction with 91%yield.Subsequent activation of this intermediate by 1,1'-carbonyldiimidazole(CDI),followed by a one-pot reaction with glycine ethyl ester hydrochloride,directly afforded Daprodustat in 92%yield with>99.8%HPLC purity.The process achieved an overall yield of 84%upon validation at 62-gram scale.Structural confirmation of the key intermediate was accomplished through nuclear magnetic resonance(NMR)spectroscopy and high-resolution mass spectrometry(HRMS).Compared with existing methods,this streamlined protocol demonstrates advantages including simplified operation,reduced reaction time,and lower production costs,offering significant potential for industrial-scale synthesis of Daprodustat.展开更多
The use of synthetic biology technology to develop cosmetic ingredients is attracting widespread attention due to its effectiveness,safety,and environmental friendliness.This article explains the concept of synthetic ...The use of synthetic biology technology to develop cosmetic ingredients is attracting widespread attention due to its effectiveness,safety,and environmental friendliness.This article explains the concept of synthetic biology and its key technologies and current status in the production of cosmetic ingredients.It also briefly analyzes the regulatory approaches to synthetic biology-based cosmetic ingredients in different countries and regions,providing guidance for the management of this field in China.The goal is to ensure product safety,enhance consumer trust,and promote the healthy development of the industry.展开更多
There is a growing body of clinical research on the utility of synthetic data derivatives,an emerging research tool in medicine.In nephrology,clinicians can use machine learning and artificial intelligence as powerful...There is a growing body of clinical research on the utility of synthetic data derivatives,an emerging research tool in medicine.In nephrology,clinicians can use machine learning and artificial intelligence as powerful aids in their clinical decision-making while also preserving patient privacy.This is especially important given the epidemiology of chronic kidney disease,renal oncology,and hypertension worldwide.However,there remains a need to create a framework for guidance regarding how to better utilize synthetic data as a practical application in this research.展开更多
Imbalanced multiclass datasets pose challenges for machine learning algorithms.They often contain minority classes that are important for accurate predictions.However,when the data is sparsely distributed and overlaps...Imbalanced multiclass datasets pose challenges for machine learning algorithms.They often contain minority classes that are important for accurate predictions.However,when the data is sparsely distributed and overlaps with data points fromother classes,it introduces noise.As a result,existing resamplingmethods may fail to preserve the original data patterns,further disrupting data quality and reducingmodel performance.This paper introduces Neighbor Displacement-based Enhanced Synthetic Oversampling(NDESO),a hybridmethod that integrates a data displacement strategy with a resampling technique to achieve data balance.It begins by computing the average distance of noisy data points to their neighbors and adjusting their positions toward the center before applying random oversampling.Extensive evaluations compare 14 alternatives on nine classifiers across synthetic and 20 real-world datasetswith varying imbalance ratios.This evaluation was structured into two distinct test groups.First,the effects of k-neighbor variations and distance metrics are evaluated,followed by a comparison of resampled data distributions against alternatives,and finally,determining the most suitable oversampling technique for data balancing.Second,the overall performance of the NDESO algorithm was assessed,focusing on G-mean and statistical significance.The results demonstrate that our method is robust to a wide range of variations in these parameters and the overall performance achieves an average G-mean score of 0.90,which is among the highest.Additionally,it attains the lowest mean rank of 2.88,indicating statistically significant improvements over existing approaches.This advantage underscores its potential for effectively handling data imbalance in practical scenarios.展开更多
The immune system is the body's main cancer surveillance system.Unlike surgery,radiation,and chemotherapy,which are typically nonspecific,cancer immunotherapy holds tremendous promise as it harnesses the high spec...The immune system is the body's main cancer surveillance system.Unlike surgery,radiation,and chemotherapy,which are typically nonspecific,cancer immunotherapy holds tremendous promise as it harnesses the high specificity of a person's immune system to kill cancer cells selectively.This promising approach includes checkpoint inhibitors,chimeric antigen receptor (CAR)-T cell therapy,cancer vaccines,cytokines,and monoclonal antibodies,among others.Cancer immunotherapy has progressed tremendously,resulting from basic science discoveries in the molecular and cellular biology of T cells.展开更多
The earthquake early warning(EEW)system provides advance notice of potentially damaging ground shaking.In EEW,early estimation of magnitude is crucial for timely rescue operations.A set of thirty-four features is extr...The earthquake early warning(EEW)system provides advance notice of potentially damaging ground shaking.In EEW,early estimation of magnitude is crucial for timely rescue operations.A set of thirty-four features is extracted using the primary wave earthquake precursor signal and site-specific information.In Japan's earthquake magnitude dataset,there is a chance of a high imbalance concerning the earthquakes above strong impact.This imbalance causes a high prediction error while training advanced machine learning or deep learning models.In this work,Conditional Tabular Generative Adversarial Networks(CTGAN),a deep machine learning tool,is utilized to learn the characteristics of the first arrival of earthquake P-waves and generate a synthetic dataset based on this information.The result obtained using actual and mixed(synthetic and actual)datasets will be used for training the stacked ensemble magnitude prediction model,MagPred,designed specifically for this study.There are 13295,3989,and1710 records designated for training,testing,and validation.The mean absolute error of the test dataset for single station magnitude detection using early three,four,and five seconds of P wave are 0.41,0.40,and 0.38 MJMA.The study demonstrates that the Generative Adversarial Networks(GANs)can provide a good result for single-station magnitude prediction.The study can be effective where less seismic data is available.The study shows that the machine learning method yields better magnitude detection results compared with the several regression models.The multi-station magnitude prediction study has been conducted on prominent Osaka,Off Fukushima,and Kumamoto earthquakes.Furthermore,to validate the performance of the model,an inter-region study has been performed on the earthquakes of the India or Nepal region.The study demonstrates that GANs can discover effective magnitude estimation compared with non-GAN-based methods.This has a high potential for wide application in earthquake early warning systems.展开更多
Synthetic biology(SynBio)is an emerging field of study with great potential in designing,engineering,and constructing new microbial synthetic cells that do not pre-exist in nature or re-engineering existing cells to a...Synthetic biology(SynBio)is an emerging field of study with great potential in designing,engineering,and constructing new microbial synthetic cells that do not pre-exist in nature or re-engineering existing cells to accomplish industrial purposes.Systems biology seeks to understand biology at multiple dimensions,beginning with the molecular and cellular level and progressing to the tissues and organismal level and characterizes cells as complex information-processing systems.SynBio,on the other hand,toggles further and strives to develop and create its systems from scratch.SynBio is now applied in the development of novel therapeutic drugs for the prevention of human diseases,scale up industrial processes,and accomplish previously unfeasible industrial outcomes.This is made possible through significant breakthroughs in DNA sequencing and synthesis technology,as well as insights gained from synthetic chemistry and systems biology.SynBio technologies have allowed for the introduction of improved and synthetic metabolic functionalities in microorganisms to enable the synthesis of a range of pharmacologically-relevant compounds for pharmaceutical exploration.SynBio applications range from finding new ways to making industrial chemical synthesis processes more sustainable as well as the microbial synthesis of improved therapeutic modalities.Hence,this study underpins several innovations,auspicious potentials,and future directions afforded by SynBio that proposes improved industrial microbial synthesis for pharmaceutical exploration.展开更多
Synthetic phenolic antioxidants(SPAs)and parabens,particularly the high-molecularweight(HMW)SPAs and long alkyl chain(LAC)parabens with higher environmental persistence and toxicities,are widely produced and applied w...Synthetic phenolic antioxidants(SPAs)and parabens,particularly the high-molecularweight(HMW)SPAs and long alkyl chain(LAC)parabens with higher environmental persistence and toxicities,are widely produced and applied worldwide.However,their occurrence and potential risks in aquatic environments remain largely unknown.This work investigated 11 HMW SPAs and 3 LAC parabens in the river and treated industrial wastewater samples along the Yangtze River,the largest river and most important source water in China.For convenience of comparison,6 short alkyl chain(SAC)parabens were also included.In 15 industrial wastewater treatment plant effluents(effluent-IWTPs)and 34 surface water monitoring sections along the river,19 out of 20 target compounds were detected with total concentrations(TCs)from 152.5 to 1955.5 ng/L and 141.3 to 1364.9 ng/L in effluent-IWTPs and surface water,respectively.HMW SPAs 1,3,5-tris-[(3,5-di–tert–butyl–4-hydroxyphenyl)methyl]-1,3,5-triazinane-2,4,6-trione(AO3114)and octadecyl 3-(3,5-di–tert–butyl–4-hydroxyphenyl)propionate(AO1076)were the dominant ones.HMWSPAs bis(3–tert–butyl–4–hydroxy-5-methylphenyl)propionate(AO245)and 4,4'-sulfanediylbis(2–tert–butyl–5-methylphenol)(AO-TBM6)and SAC parabens propylparaben(PrP)and butylparaben(BuP)posed medium to high potential ecological risks in 27 surface water monitoring sections.Additionally,AO-TBM6 also exhibited potential health risks in 2 out of 11 drinking water sources.Though the ecotoxicity data are very limited,the ubiquitous presence of the LAC parabens including hexylparaben(HeP)and octylparaben(OcP)in surface water should be of concern since these compounds usually exhibit stronger estrogen potencies than the SAC ones.Finally,kernel density analysis revealed that regulation of industrial discharges is necessary to mitigate the HMW SPA and paraben contamination.展开更多
Synthetic phenolic antioxidants(SPAs)are widely used in diverse industries due to their exceptional antioxidant characteristics.However,human exposure to SPAs may cause health problems.In this study,226 dust samples w...Synthetic phenolic antioxidants(SPAs)are widely used in diverse industries due to their exceptional antioxidant characteristics.However,human exposure to SPAs may cause health problems.In this study,226 dust samples were collected from 10 provinces in China,and six SPAs(three parent SPAs and their three transformation products)were analyzed.The concentrations of6SPAs(the sum of six target compounds)ranged from 15.4 to 3210 ng/g(geometric mean(GM):169 ng/g).The highest concentration of6SPAswas found in Sichuan Province(GM:349 ng/g),which was approximately 4 times higher than that in Hubei Province(81.6 ng/g)(p<0.05).The concentrations of butylated hydroxytoluene(BHT),2,2'-methylene bis(4-methyl-6–tert-butylphenol)(AO2246),2,6-di–tert–butyl–1,4-benzoquinone(BHT-Q),2,6-di–tert–butyl–4-(hydroxymethyl)phenol(BHT-OH),and ∑_(p)-SPAs were substantially higher in dust from urban areas than rural areas(p<0.05).AO2246 concentration in dust from homes(GM:0.400 ng/g)was about 4 times higher than that in workplaces(0.116 ng/g)(p<0.01).Significantly higherp-SPAs concentrations were found in dust from homes(GM:17.5 ng/g)than workplaces(11.4 ng/g)(p<0.01).The estimated daily intakes(EDIs)of ∑_(6)SPAs exposed through dust ingestion were 0.582,0.342,0.197,0.076,and 0.080 ng/kg bw/day in different age groups,and exposed through dermal contact was 0.358,0.252,0.174,0.167,and 0.177 ng/kg bw/day.EDIs showed that the exposure risks of SPAs decreased with age.This is the first work to determine SPAs in dust from10 provinces in China and investigate the spatial distribution of SPAs in those regions.展开更多
Amplitude stripes imposed by ionospheric scintillation have been frequently observed in many of the equatorial nighttime acquisitions of the Advanced Land Observing Satellite(ALOS)Phased Array-type L-band Synthetic Ap...Amplitude stripes imposed by ionospheric scintillation have been frequently observed in many of the equatorial nighttime acquisitions of the Advanced Land Observing Satellite(ALOS)Phased Array-type L-band Synthetic Aperture Radar(PALSAR).This type of ionospheric artifact impedes PALSAR interferometric and polarimetric applications,and its formation cause,morphology,and negative influence have been deeply investigated.However,this artifact can provide an alternative opportunity in a positive way for probing and measuring ionosphere scintillation.In this paper,a methodology for measuring ionospheric scintillation parameters from PALSAR images with amplitude stripes is proposed.Firstly,sublook processing is beneficial for recovering the scattered stripes from a single-look complex image;the amplitude stripe pattern is extracted via band-rejection filtering in the frequency domain of the sublook image.Secondly,the amplitude spectrum density function(SDF)is estimated from the amplitude stripe pattern.Thirdly,a fitting scheme for measuring the scintillation strength and spectrum index is conducted between the estimated and theoretical long-wavelength SDFs.In addition,another key parameter,the scintillation index,can be directly measured from the amplitude stripe pattern or indirectly derived from the scintillation strength and spectrum index.The proposed methodology is fully demonstrated on two groups of PALSAR acquisitions in the presence of amplitude stripes.Self-validation is conducted by comparing the measured and derived scintillation index and by comparing the measurements of range lines and azimuth lines.Cross-validation is performed by comparing the PALSAR measurements with in situ Global Position System(GPS)measurements.The processing results demonstrate a powerful capability to robustly measure ionospheric scintillation parameters from space with high spatial resolution.展开更多
We identified the antimony species present in a wide variety of plastic samples by X ray absorption spectroscopy(XAS)at the Sb L_(3)-edge.The samples contained different concentrations of antimony(Sb),ranging from PET...We identified the antimony species present in a wide variety of plastic samples by X ray absorption spectroscopy(XAS)at the Sb L_(3)-edge.The samples contained different concentrations of antimony(Sb),ranging from PET bottles in which Sb compounds are used as catalysts,with concentrations around 300 mg/kg,to electrical equipment in which the element is used as a flame retardant,with concentrations of several tens of thousands of mg/kg.Although the shape of the spectra at the L_(3)-edge is quite similar for all Sb reference materials,we were able to identify antimony glycolate or acetate in PET bottles,bound organic Sb in c-PET trays and senarmontite in electrical materials as themain Sb components.In samples with high Ca content(e.g.,electrical objects,some c-PET food trays and textiles)the Ca Ka emission line interferes with the Sb La line by introducing a high background which reduces the signal-to-noise ratio in the Sb XAS spectrum,resulting in noisy and distorted spectra.The element-resolved map on a PET bottle sample revealed both Sb and Ca hot spots of around 10-20 microns in size,with no correlation.展开更多
Synthetic aperture radar(SAR)aboard SEASAT was first launched in 1978.At the beginning of the 21st century,the Chinese remote sensing community recognized the urgent need to develop domestic SAR capabilities.Unlike sc...Synthetic aperture radar(SAR)aboard SEASAT was first launched in 1978.At the beginning of the 21st century,the Chinese remote sensing community recognized the urgent need to develop domestic SAR capabilities.Unlike scatterometers and al-timeters,space-borne SAR offers high-resolution images of the ocean,regardless of weather conditions or time of day.SAR imagery provides rich information about the sea surface,capturing complicated dynamic processes in the upper layers of the ocean,particular-ly in relation to tropical cyclones.Over the past four decades,the advantages of SAR have been increasingly recognized,leading to notable marine applications,especially in the development of algorithms for retrieving wind and wave data from SAR images.This study reviews the history,progress,and future outlook of SAR-based monitoring of sea surface wind and waves.In particular,the ap-plicability of various SAR wind and wave algorithms is systematically investigated,with a particular focus on their performance un-der extreme sea conditions.展开更多
Synthetic speech detection is an essential task in the field of voice security,aimed at identifying deceptive voice attacks generated by text-to-speech(TTS)systems or voice conversion(VC)systems.In this paper,we propo...Synthetic speech detection is an essential task in the field of voice security,aimed at identifying deceptive voice attacks generated by text-to-speech(TTS)systems or voice conversion(VC)systems.In this paper,we propose a synthetic speech detection model called TFTransformer,which integrates both local and global features to enhance detection capabilities by effectively modeling local and global dependencies.Structurally,the model is divided into two main components:a front-end and a back-end.The front-end of the model uses a combination of SincLayer and two-dimensional(2D)convolution to extract high-level feature maps(HFM)containing local dependency of the input speech signals.The back-end uses time-frequency Transformer module to process these feature maps and further capture global dependency.Furthermore,we propose TFTransformer-SE,which incorporates a channel attention mechanism within the 2D convolutional blocks.This enhancement aims to more effectively capture local dependencies,thereby improving the model’s performance.The experiments were conducted on the ASVspoof 2021 LA dataset,and the results showed that the model achieved an equal error rate(EER)of 3.37%without data augmentation.Additionally,we evaluated the model using the ASVspoof 2019 LA dataset,achieving an EER of 0.84%,also without data augmentation.This demonstrates that combining local and global dependencies in the time-frequency domain can significantly improve detection accuracy.展开更多
Dual Synthetic Jets (DSJ) can directly affect the development of spray through the complex vortex structure. The mechanism of flow control on spray and its thermal management application are studied by combining exper...Dual Synthetic Jets (DSJ) can directly affect the development of spray through the complex vortex structure. The mechanism of flow control on spray and its thermal management application are studied by combining experiment and simulation. The spray characteristics under different injection angles are studied, and the results show that the angle should be controlled in the range of 45°–60°, so that sufficient momentum transfer can be obtained, and meanwhile spray impingement area narrowing can be avoided. The spray characteristics under flow control of DSJ with different Reynolds numbers are studied, and the results show that Reynolds number should be controlled in the range of 2859–3574, so that strong particle streamwise acceleration and wall film disturbing can be achieved. In addition, the DSJ kinetic energy is utilized more efficiently. On the basis of previous research, this paper proposes a novel active heat pipe based on spray controlled by DSJ. The space occupancy has been reduced by more than 60%. Even in a sealed state, the active heat pipe is able to cool a hot surface with heat flux of 22.2 kW/m^(2) from 111℃ to 57℃ only in 20 s. The noise of DSJ is reduced from 85 dB to 60 dB, which is expected to promote the practical application of DSJ in thermal management.展开更多
To delay the vortex breakdown position of the slender delta wing,this study innovativelyproposes the application of control near the Leading-Edge Vortex(LEV)core sweeping path,whichis called Coupled Core Rotation Dual...To delay the vortex breakdown position of the slender delta wing,this study innovativelyproposes the application of control near the Leading-Edge Vortex(LEV)core sweeping path,whichis called Coupled Core Rotation Dual Synthetic Jets(CCR-DSJ)control.The results show that thevortex breakdown points at each angle of attack are moved backward after control,and the max-imum delayed displacement is 32.4%of the root chord at 30°.Besides,there is a linear relationshipbetween the breakdown position and the angle of attack after control,indicating that CCR-DSJcontrol has a significant effect on the pressure gradient of the vortex axis.Furthermore,the lift coef-ficient C_(L)is enhanced after control,with a maximum CLincrement of 0.078 at 27°,and an effectiveincrement interval of[25°,32°].This interval is different from most previous studies,which isdirectly related to the position of the actuators.According to the lift change mechanism,the anglesof attack are divided into three stages:Stage 1(a=15°–25°),Stage 2(a=25°–32°),and Stage 3(a=32°–40°).In conclusion,CCR-DSJ control can significantly change the pressure distribution,thereby offering promising prospects for the flight stage of the slender delta wing.展开更多
This work focuses on enhancing low frequency seismic data using a convolutional neural network trained on synthetic data.Traditional seismic data often lack both high and low frequencies,which are essential for detail...This work focuses on enhancing low frequency seismic data using a convolutional neural network trained on synthetic data.Traditional seismic data often lack both high and low frequencies,which are essential for detailed geological interpretation and various geophysical applications.Low frequency data is particularly valuable for reducing wavelet sidelobes and improving full waveform inversion(FWI).Conventional methods for bandwidth extension include seismic deconvolution and sparse inversion,which have limitations in recovering low frequencies.The study explores the potential of the U-net,which has been successful in other geophysical applications such as noise attenuation and seismic resolution enhancement.The novelty in our approach is that we do not rely on computationally expensive finite difference modelling to create training data.Instead,our synthetic training data is created from individual randomly perturbed events with variations in bandwidth,making it more adaptable to different data sets compared to previous deep learning methods.The method was tested on both synthetic and real seismic data,demonstrating effective low frequency reconstruction and sidelobe reduction.With a synthetic full waveform inversion to recover a velocity model and a seismic amplitude inversion to estimate acoustic impedance we demonstrate the validity and benefit of the proposed method.Overall,the study presents a robust approach to seismic bandwidth extension using deep learning,emphasizing the importance of diverse and well-designed but computationally inexpensive synthetic training data.展开更多
Background: The population of Fontan patients, patients born with a single functioningventricle, is growing. There is a growing need to develop algorithms for this population that can predicthealth outcomes. Artiffcia...Background: The population of Fontan patients, patients born with a single functioningventricle, is growing. There is a growing need to develop algorithms for this population that can predicthealth outcomes. Artiffcial intelligence models predicting short-term and long-term health outcomes forpatients with the Fontan circulation are needed. Generative adversarial networks (GANs) provide a solutionfor generating realistic and useful synthetic data that can be used to train such models. Methods: Despitetheir promise, GANs have not been widely adopted in the congenital heart disease research communitydue, in some part, to a lack of knowledge on how to employ them. In this research study, a GAN was usedto generate synthetic data from the Pediatric Heart Network Fontan I dataset. A subset of data consistingof the echocardiographic and BNP measures collected from Fontan patients was used to train the GAN.Two sets of synthetic data were created to understand the effect of data missingness on synthetic datageneration. Synthetic data was created from real data in which the missing values were imputed usingMultiple Imputation by Chained Equations (MICE) (referred to as synthetic from imputed real samples). Inaddition, synthetic data was created from real data in which the missing values were dropped (referred to assynthetic from dropped real samples). Both synthetic datasets were evaluated for ffdelity by using visualmethods which involved comparing histograms and principal component analysis (PCA) plots. Fidelitywas measured quantitatively by (1) comparing synthetic and real data using the Kolmogorov-Smirnovtest to evaluate the similarity between two distributions and (2) training a neural network to distinguishbetween real and synthetic samples. Both synthetic datasets were evaluated for utility by training aneural network with synthetic data and testing the neural network on its ability to classify patients thathave ventricular dysfunction using echocardiograph measures and serological measures. Results: Usinghistograms, associated probability density functions, and (PCA), both synthetic datasets showed visualresemblance in distribution and variance to real Fontan data. Quantitatively, synthetic data from droppedreal samples had higher similarity scores, as demonstrated by the Kolmogorov–Smirnov statistic, for all butone feature (age at Fontan) compared to synthetic data from imputed real samples, which demonstrateddissimilar scores for three features (Echo SV, Echo tda, and BNP). In addition, synthetic data from droppedreal samples resembled real data to a larger extent (49.3% classiffcation error) than synthetic data fromimputed real samples (65.28% classiffcation error). Classiffcation errors approximating 50% represent datasetsthat are indistinguishable. In terms of utility, synthetic data created from real data in which the missingvalues were imputed classiffed ventricular dysfunction in real data with a classiffcation error of 10.99%.Similarly, utility of the generated synthetic data by showing that a neural network trained on synthetic dataderived from real data in which the missing values were dropped could classify ventricular dysfunction inreal data with a classiffcation error of 9.44%. Conclusions: Although representing a limited subset of thevast data available on the Pediatric Heart Network, generative adversarial networks can create syntheticdata that mimics the probability distribution of real Fontan echocardiographic measures. Clinicians can usethese synthetic data to create models that predict health outcomes for Fontan patients.展开更多
Precise experimental control and characterization of electron wave packet dynamics driven by external optical fields remain a fundamental challenge,particularly at ultrafast temporal and sub-microscopic spatial scales...Precise experimental control and characterization of electron wave packet dynamics driven by external optical fields remain a fundamental challenge,particularly at ultrafast temporal and sub-microscopic spatial scales.To overcome these challenges,we introduce a photon-based simulation platform employing a traveling-wave electrooptic phase-modulated waveguide.In our setup,the incident electromagnetic pulse serves as an analog to the electron wave packet,while the traveling-wave modulation simulates the external optical driving field.Our experimental study systematically explores pulse evolution under three distinct regimes defined by the relation between the pulse duration(Δt)and the modulation period(T).When the pulse duration is significantly shorter than the modulation period,we observe a uniform spectral shift analogous to electron acceleration in dielectric laser accelerators,where spectral phase gradients represent electron momentum accumulation.Conversely,when the pulse duration greatly exceeds the modulation period,discrete diffraction patterns emerge,closely resembling the discrete sideband features of electron-photon coupling observed in photon-induced near-field electron microscopy.Notably,in the intermediate regime(T/4<Δt<T/2),the pulse spectrum exhibits Airy-function-type characteristics with self-healing effects.These experimental results provide critical insights into electron-wave interactions under external optical fields and establish a robust,programmable framework for further investigation.展开更多
The US market for women's synthetic trou-sers is characterised by intense competition,with Asian countries playing a dominant role in exports.This analysis delves into the market performance of leading exporters,a...The US market for women's synthetic trou-sers is characterised by intense competition,with Asian countries playing a dominant role in exports.This analysis delves into the market performance of leading exporters,assessing key indicators such as export values,revealed comparative advantage(RCA).unit value realisation(UVR),and the effect of tariff rates.展开更多
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Category B,XDB1090000).
文摘Plant synthetic biology has emerged as a transformative field in agriculture,offering innovative solutions to enhance food security,provide resilience to climate change,and transition to sustainable farming practices.By integrating advanced genetic tools,computational modeling,and systems biology,researchers can precisely modify plant genomes to enhance traits such as yield,stress tolerance,and nutrient use efficiency.The ability to design plants with specific characteristics tailored to diverse environmental conditions and agricultural needs holds great potential to address global food security challenges.Here,we highlight recent advancements and applications of plant synthetic biology in agriculture,focusing on key areas such as photosynthetic efficiency,nitrogen fixation,drought tolerance,pathogen resistance,nutrient use efficiency,biofortification,climate resilience,microbiology engineering,synthetic plant genomes,and the integration of artificial intelligence with synthetic biology.These innovations aim to maximize resource use efficiency,reduce reliance on external inputs,and mitigate environmental impacts associated with conventional agricultural practices.Despite challenges related to regulatory approval and public acceptance,the integration of synthetic biology in agriculture holds immense promise for creating more resilient and sustainable agricultural systems,contributing to global food security and environmental sustainability.Rigorous multi-field testing of these approaches will undoubtedly be required to ensure reproducibility.
文摘An efficient and practical synthetic process for Daprodustat was developed.Starting with N,N'-dicyclohexylcarbodiimide(DCC)and malonic acid,the key intermediate 1,3-dicyclohexylpyrimidine-2,4,6(1H,3H,5H)-trione was synthesized via condensation reaction with 91%yield.Subsequent activation of this intermediate by 1,1'-carbonyldiimidazole(CDI),followed by a one-pot reaction with glycine ethyl ester hydrochloride,directly afforded Daprodustat in 92%yield with>99.8%HPLC purity.The process achieved an overall yield of 84%upon validation at 62-gram scale.Structural confirmation of the key intermediate was accomplished through nuclear magnetic resonance(NMR)spectroscopy and high-resolution mass spectrometry(HRMS).Compared with existing methods,this streamlined protocol demonstrates advantages including simplified operation,reduced reaction time,and lower production costs,offering significant potential for industrial-scale synthesis of Daprodustat.
文摘The use of synthetic biology technology to develop cosmetic ingredients is attracting widespread attention due to its effectiveness,safety,and environmental friendliness.This article explains the concept of synthetic biology and its key technologies and current status in the production of cosmetic ingredients.It also briefly analyzes the regulatory approaches to synthetic biology-based cosmetic ingredients in different countries and regions,providing guidance for the management of this field in China.The goal is to ensure product safety,enhance consumer trust,and promote the healthy development of the industry.
文摘There is a growing body of clinical research on the utility of synthetic data derivatives,an emerging research tool in medicine.In nephrology,clinicians can use machine learning and artificial intelligence as powerful aids in their clinical decision-making while also preserving patient privacy.This is especially important given the epidemiology of chronic kidney disease,renal oncology,and hypertension worldwide.However,there remains a need to create a framework for guidance regarding how to better utilize synthetic data as a practical application in this research.
文摘Imbalanced multiclass datasets pose challenges for machine learning algorithms.They often contain minority classes that are important for accurate predictions.However,when the data is sparsely distributed and overlaps with data points fromother classes,it introduces noise.As a result,existing resamplingmethods may fail to preserve the original data patterns,further disrupting data quality and reducingmodel performance.This paper introduces Neighbor Displacement-based Enhanced Synthetic Oversampling(NDESO),a hybridmethod that integrates a data displacement strategy with a resampling technique to achieve data balance.It begins by computing the average distance of noisy data points to their neighbors and adjusting their positions toward the center before applying random oversampling.Extensive evaluations compare 14 alternatives on nine classifiers across synthetic and 20 real-world datasetswith varying imbalance ratios.This evaluation was structured into two distinct test groups.First,the effects of k-neighbor variations and distance metrics are evaluated,followed by a comparison of resampled data distributions against alternatives,and finally,determining the most suitable oversampling technique for data balancing.Second,the overall performance of the NDESO algorithm was assessed,focusing on G-mean and statistical significance.The results demonstrate that our method is robust to a wide range of variations in these parameters and the overall performance achieves an average G-mean score of 0.90,which is among the highest.Additionally,it attains the lowest mean rank of 2.88,indicating statistically significant improvements over existing approaches.This advantage underscores its potential for effectively handling data imbalance in practical scenarios.
文摘The immune system is the body's main cancer surveillance system.Unlike surgery,radiation,and chemotherapy,which are typically nonspecific,cancer immunotherapy holds tremendous promise as it harnesses the high specificity of a person's immune system to kill cancer cells selectively.This promising approach includes checkpoint inhibitors,chimeric antigen receptor (CAR)-T cell therapy,cancer vaccines,cytokines,and monoclonal antibodies,among others.Cancer immunotherapy has progressed tremendously,resulting from basic science discoveries in the molecular and cellular biology of T cells.
基金related to grant PM-31-22-626-414 from the Prime Minister's Research Fellows(PMRF)of the Indian Institute of Technology Roorkee。
文摘The earthquake early warning(EEW)system provides advance notice of potentially damaging ground shaking.In EEW,early estimation of magnitude is crucial for timely rescue operations.A set of thirty-four features is extracted using the primary wave earthquake precursor signal and site-specific information.In Japan's earthquake magnitude dataset,there is a chance of a high imbalance concerning the earthquakes above strong impact.This imbalance causes a high prediction error while training advanced machine learning or deep learning models.In this work,Conditional Tabular Generative Adversarial Networks(CTGAN),a deep machine learning tool,is utilized to learn the characteristics of the first arrival of earthquake P-waves and generate a synthetic dataset based on this information.The result obtained using actual and mixed(synthetic and actual)datasets will be used for training the stacked ensemble magnitude prediction model,MagPred,designed specifically for this study.There are 13295,3989,and1710 records designated for training,testing,and validation.The mean absolute error of the test dataset for single station magnitude detection using early three,four,and five seconds of P wave are 0.41,0.40,and 0.38 MJMA.The study demonstrates that the Generative Adversarial Networks(GANs)can provide a good result for single-station magnitude prediction.The study can be effective where less seismic data is available.The study shows that the machine learning method yields better magnitude detection results compared with the several regression models.The multi-station magnitude prediction study has been conducted on prominent Osaka,Off Fukushima,and Kumamoto earthquakes.Furthermore,to validate the performance of the model,an inter-region study has been performed on the earthquakes of the India or Nepal region.The study demonstrates that GANs can discover effective magnitude estimation compared with non-GAN-based methods.This has a high potential for wide application in earthquake early warning systems.
文摘Synthetic biology(SynBio)is an emerging field of study with great potential in designing,engineering,and constructing new microbial synthetic cells that do not pre-exist in nature or re-engineering existing cells to accomplish industrial purposes.Systems biology seeks to understand biology at multiple dimensions,beginning with the molecular and cellular level and progressing to the tissues and organismal level and characterizes cells as complex information-processing systems.SynBio,on the other hand,toggles further and strives to develop and create its systems from scratch.SynBio is now applied in the development of novel therapeutic drugs for the prevention of human diseases,scale up industrial processes,and accomplish previously unfeasible industrial outcomes.This is made possible through significant breakthroughs in DNA sequencing and synthesis technology,as well as insights gained from synthetic chemistry and systems biology.SynBio technologies have allowed for the introduction of improved and synthetic metabolic functionalities in microorganisms to enable the synthesis of a range of pharmacologically-relevant compounds for pharmaceutical exploration.SynBio applications range from finding new ways to making industrial chemical synthesis processes more sustainable as well as the microbial synthesis of improved therapeutic modalities.Hence,this study underpins several innovations,auspicious potentials,and future directions afforded by SynBio that proposes improved industrial microbial synthesis for pharmaceutical exploration.
基金supported by the National Key Research and Development Program of China(Nos.2021YFC3200801 and 2021YFC3200804).
文摘Synthetic phenolic antioxidants(SPAs)and parabens,particularly the high-molecularweight(HMW)SPAs and long alkyl chain(LAC)parabens with higher environmental persistence and toxicities,are widely produced and applied worldwide.However,their occurrence and potential risks in aquatic environments remain largely unknown.This work investigated 11 HMW SPAs and 3 LAC parabens in the river and treated industrial wastewater samples along the Yangtze River,the largest river and most important source water in China.For convenience of comparison,6 short alkyl chain(SAC)parabens were also included.In 15 industrial wastewater treatment plant effluents(effluent-IWTPs)and 34 surface water monitoring sections along the river,19 out of 20 target compounds were detected with total concentrations(TCs)from 152.5 to 1955.5 ng/L and 141.3 to 1364.9 ng/L in effluent-IWTPs and surface water,respectively.HMW SPAs 1,3,5-tris-[(3,5-di–tert–butyl–4-hydroxyphenyl)methyl]-1,3,5-triazinane-2,4,6-trione(AO3114)and octadecyl 3-(3,5-di–tert–butyl–4-hydroxyphenyl)propionate(AO1076)were the dominant ones.HMWSPAs bis(3–tert–butyl–4–hydroxy-5-methylphenyl)propionate(AO245)and 4,4'-sulfanediylbis(2–tert–butyl–5-methylphenol)(AO-TBM6)and SAC parabens propylparaben(PrP)and butylparaben(BuP)posed medium to high potential ecological risks in 27 surface water monitoring sections.Additionally,AO-TBM6 also exhibited potential health risks in 2 out of 11 drinking water sources.Though the ecotoxicity data are very limited,the ubiquitous presence of the LAC parabens including hexylparaben(HeP)and octylparaben(OcP)in surface water should be of concern since these compounds usually exhibit stronger estrogen potencies than the SAC ones.Finally,kernel density analysis revealed that regulation of industrial discharges is necessary to mitigate the HMW SPA and paraben contamination.
基金supported by the National Key Research and Development Program of China(No.2023YFC3706602)the National Natural Science Foundation of China(Nos.22225605 and 22193051)the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDB0750200).
文摘Synthetic phenolic antioxidants(SPAs)are widely used in diverse industries due to their exceptional antioxidant characteristics.However,human exposure to SPAs may cause health problems.In this study,226 dust samples were collected from 10 provinces in China,and six SPAs(three parent SPAs and their three transformation products)were analyzed.The concentrations of6SPAs(the sum of six target compounds)ranged from 15.4 to 3210 ng/g(geometric mean(GM):169 ng/g).The highest concentration of6SPAswas found in Sichuan Province(GM:349 ng/g),which was approximately 4 times higher than that in Hubei Province(81.6 ng/g)(p<0.05).The concentrations of butylated hydroxytoluene(BHT),2,2'-methylene bis(4-methyl-6–tert-butylphenol)(AO2246),2,6-di–tert–butyl–1,4-benzoquinone(BHT-Q),2,6-di–tert–butyl–4-(hydroxymethyl)phenol(BHT-OH),and ∑_(p)-SPAs were substantially higher in dust from urban areas than rural areas(p<0.05).AO2246 concentration in dust from homes(GM:0.400 ng/g)was about 4 times higher than that in workplaces(0.116 ng/g)(p<0.01).Significantly higherp-SPAs concentrations were found in dust from homes(GM:17.5 ng/g)than workplaces(11.4 ng/g)(p<0.01).The estimated daily intakes(EDIs)of ∑_(6)SPAs exposed through dust ingestion were 0.582,0.342,0.197,0.076,and 0.080 ng/kg bw/day in different age groups,and exposed through dermal contact was 0.358,0.252,0.174,0.167,and 0.177 ng/kg bw/day.EDIs showed that the exposure risks of SPAs decreased with age.This is the first work to determine SPAs in dust from10 provinces in China and investigate the spatial distribution of SPAs in those regions.
基金supported partly by the National Natural Science Foundation of China(NSFC)(62101568 and 62371460)the Scientific Research Program of the National University of Defense Technology(ZK21-06)the Taishan Scholars of Shandong Province(ts20190968)。
文摘Amplitude stripes imposed by ionospheric scintillation have been frequently observed in many of the equatorial nighttime acquisitions of the Advanced Land Observing Satellite(ALOS)Phased Array-type L-band Synthetic Aperture Radar(PALSAR).This type of ionospheric artifact impedes PALSAR interferometric and polarimetric applications,and its formation cause,morphology,and negative influence have been deeply investigated.However,this artifact can provide an alternative opportunity in a positive way for probing and measuring ionosphere scintillation.In this paper,a methodology for measuring ionospheric scintillation parameters from PALSAR images with amplitude stripes is proposed.Firstly,sublook processing is beneficial for recovering the scattered stripes from a single-look complex image;the amplitude stripe pattern is extracted via band-rejection filtering in the frequency domain of the sublook image.Secondly,the amplitude spectrum density function(SDF)is estimated from the amplitude stripe pattern.Thirdly,a fitting scheme for measuring the scintillation strength and spectrum index is conducted between the estimated and theoretical long-wavelength SDFs.In addition,another key parameter,the scintillation index,can be directly measured from the amplitude stripe pattern or indirectly derived from the scintillation strength and spectrum index.The proposed methodology is fully demonstrated on two groups of PALSAR acquisitions in the presence of amplitude stripes.Self-validation is conducted by comparing the measured and derived scintillation index and by comparing the measurements of range lines and azimuth lines.Cross-validation is performed by comparing the PALSAR measurements with in situ Global Position System(GPS)measurements.The processing results demonstrate a powerful capability to robustly measure ionospheric scintillation parameters from space with high spatial resolution.
文摘We identified the antimony species present in a wide variety of plastic samples by X ray absorption spectroscopy(XAS)at the Sb L_(3)-edge.The samples contained different concentrations of antimony(Sb),ranging from PET bottles in which Sb compounds are used as catalysts,with concentrations around 300 mg/kg,to electrical equipment in which the element is used as a flame retardant,with concentrations of several tens of thousands of mg/kg.Although the shape of the spectra at the L_(3)-edge is quite similar for all Sb reference materials,we were able to identify antimony glycolate or acetate in PET bottles,bound organic Sb in c-PET trays and senarmontite in electrical materials as themain Sb components.In samples with high Ca content(e.g.,electrical objects,some c-PET food trays and textiles)the Ca Ka emission line interferes with the Sb La line by introducing a high background which reduces the signal-to-noise ratio in the Sb XAS spectrum,resulting in noisy and distorted spectra.The element-resolved map on a PET bottle sample revealed both Sb and Ca hot spots of around 10-20 microns in size,with no correlation.
基金supported by the National Nat-ural Science Foundation of China(No.42376174)the Natural Science Foundation of Shanghai(No.23ZR 1426900).
文摘Synthetic aperture radar(SAR)aboard SEASAT was first launched in 1978.At the beginning of the 21st century,the Chinese remote sensing community recognized the urgent need to develop domestic SAR capabilities.Unlike scatterometers and al-timeters,space-borne SAR offers high-resolution images of the ocean,regardless of weather conditions or time of day.SAR imagery provides rich information about the sea surface,capturing complicated dynamic processes in the upper layers of the ocean,particular-ly in relation to tropical cyclones.Over the past four decades,the advantages of SAR have been increasingly recognized,leading to notable marine applications,especially in the development of algorithms for retrieving wind and wave data from SAR images.This study reviews the history,progress,and future outlook of SAR-based monitoring of sea surface wind and waves.In particular,the ap-plicability of various SAR wind and wave algorithms is systematically investigated,with a particular focus on their performance un-der extreme sea conditions.
基金supported by project ZR2022MF330 supported by Shandong Provincial Natural Science Foundationthe National Natural Science Foundation of China under Grant No.61701286.
文摘Synthetic speech detection is an essential task in the field of voice security,aimed at identifying deceptive voice attacks generated by text-to-speech(TTS)systems or voice conversion(VC)systems.In this paper,we propose a synthetic speech detection model called TFTransformer,which integrates both local and global features to enhance detection capabilities by effectively modeling local and global dependencies.Structurally,the model is divided into two main components:a front-end and a back-end.The front-end of the model uses a combination of SincLayer and two-dimensional(2D)convolution to extract high-level feature maps(HFM)containing local dependency of the input speech signals.The back-end uses time-frequency Transformer module to process these feature maps and further capture global dependency.Furthermore,we propose TFTransformer-SE,which incorporates a channel attention mechanism within the 2D convolutional blocks.This enhancement aims to more effectively capture local dependencies,thereby improving the model’s performance.The experiments were conducted on the ASVspoof 2021 LA dataset,and the results showed that the model achieved an equal error rate(EER)of 3.37%without data augmentation.Additionally,we evaluated the model using the ASVspoof 2019 LA dataset,achieving an EER of 0.84%,also without data augmentation.This demonstrates that combining local and global dependencies in the time-frequency domain can significantly improve detection accuracy.
基金supported by the National Natural Science Foundation of China(Nos.U2341202,12402333).
文摘Dual Synthetic Jets (DSJ) can directly affect the development of spray through the complex vortex structure. The mechanism of flow control on spray and its thermal management application are studied by combining experiment and simulation. The spray characteristics under different injection angles are studied, and the results show that the angle should be controlled in the range of 45°–60°, so that sufficient momentum transfer can be obtained, and meanwhile spray impingement area narrowing can be avoided. The spray characteristics under flow control of DSJ with different Reynolds numbers are studied, and the results show that Reynolds number should be controlled in the range of 2859–3574, so that strong particle streamwise acceleration and wall film disturbing can be achieved. In addition, the DSJ kinetic energy is utilized more efficiently. On the basis of previous research, this paper proposes a novel active heat pipe based on spray controlled by DSJ. The space occupancy has been reduced by more than 60%. Even in a sealed state, the active heat pipe is able to cool a hot surface with heat flux of 22.2 kW/m^(2) from 111℃ to 57℃ only in 20 s. The noise of DSJ is reduced from 85 dB to 60 dB, which is expected to promote the practical application of DSJ in thermal management.
基金supported by the National Natural Science Foundation of China(Nos.92271110,12072352)the Major National Science and Technology Project,China(No.J2019-Ⅲ-0010-0054)。
文摘To delay the vortex breakdown position of the slender delta wing,this study innovativelyproposes the application of control near the Leading-Edge Vortex(LEV)core sweeping path,whichis called Coupled Core Rotation Dual Synthetic Jets(CCR-DSJ)control.The results show that thevortex breakdown points at each angle of attack are moved backward after control,and the max-imum delayed displacement is 32.4%of the root chord at 30°.Besides,there is a linear relationshipbetween the breakdown position and the angle of attack after control,indicating that CCR-DSJcontrol has a significant effect on the pressure gradient of the vortex axis.Furthermore,the lift coef-ficient C_(L)is enhanced after control,with a maximum CLincrement of 0.078 at 27°,and an effectiveincrement interval of[25°,32°].This interval is different from most previous studies,which isdirectly related to the position of the actuators.According to the lift change mechanism,the anglesof attack are divided into three stages:Stage 1(a=15°–25°),Stage 2(a=25°–32°),and Stage 3(a=32°–40°).In conclusion,CCR-DSJ control can significantly change the pressure distribution,thereby offering promising prospects for the flight stage of the slender delta wing.
文摘This work focuses on enhancing low frequency seismic data using a convolutional neural network trained on synthetic data.Traditional seismic data often lack both high and low frequencies,which are essential for detailed geological interpretation and various geophysical applications.Low frequency data is particularly valuable for reducing wavelet sidelobes and improving full waveform inversion(FWI).Conventional methods for bandwidth extension include seismic deconvolution and sparse inversion,which have limitations in recovering low frequencies.The study explores the potential of the U-net,which has been successful in other geophysical applications such as noise attenuation and seismic resolution enhancement.The novelty in our approach is that we do not rely on computationally expensive finite difference modelling to create training data.Instead,our synthetic training data is created from individual randomly perturbed events with variations in bandwidth,making it more adaptable to different data sets compared to previous deep learning methods.The method was tested on both synthetic and real seismic data,demonstrating effective low frequency reconstruction and sidelobe reduction.With a synthetic full waveform inversion to recover a velocity model and a seismic amplitude inversion to estimate acoustic impedance we demonstrate the validity and benefit of the proposed method.Overall,the study presents a robust approach to seismic bandwidth extension using deep learning,emphasizing the importance of diverse and well-designed but computationally inexpensive synthetic training data.
文摘Background: The population of Fontan patients, patients born with a single functioningventricle, is growing. There is a growing need to develop algorithms for this population that can predicthealth outcomes. Artiffcial intelligence models predicting short-term and long-term health outcomes forpatients with the Fontan circulation are needed. Generative adversarial networks (GANs) provide a solutionfor generating realistic and useful synthetic data that can be used to train such models. Methods: Despitetheir promise, GANs have not been widely adopted in the congenital heart disease research communitydue, in some part, to a lack of knowledge on how to employ them. In this research study, a GAN was usedto generate synthetic data from the Pediatric Heart Network Fontan I dataset. A subset of data consistingof the echocardiographic and BNP measures collected from Fontan patients was used to train the GAN.Two sets of synthetic data were created to understand the effect of data missingness on synthetic datageneration. Synthetic data was created from real data in which the missing values were imputed usingMultiple Imputation by Chained Equations (MICE) (referred to as synthetic from imputed real samples). Inaddition, synthetic data was created from real data in which the missing values were dropped (referred to assynthetic from dropped real samples). Both synthetic datasets were evaluated for ffdelity by using visualmethods which involved comparing histograms and principal component analysis (PCA) plots. Fidelitywas measured quantitatively by (1) comparing synthetic and real data using the Kolmogorov-Smirnovtest to evaluate the similarity between two distributions and (2) training a neural network to distinguishbetween real and synthetic samples. Both synthetic datasets were evaluated for utility by training aneural network with synthetic data and testing the neural network on its ability to classify patients thathave ventricular dysfunction using echocardiograph measures and serological measures. Results: Usinghistograms, associated probability density functions, and (PCA), both synthetic datasets showed visualresemblance in distribution and variance to real Fontan data. Quantitatively, synthetic data from droppedreal samples had higher similarity scores, as demonstrated by the Kolmogorov–Smirnov statistic, for all butone feature (age at Fontan) compared to synthetic data from imputed real samples, which demonstrateddissimilar scores for three features (Echo SV, Echo tda, and BNP). In addition, synthetic data from droppedreal samples resembled real data to a larger extent (49.3% classiffcation error) than synthetic data fromimputed real samples (65.28% classiffcation error). Classiffcation errors approximating 50% represent datasetsthat are indistinguishable. In terms of utility, synthetic data created from real data in which the missingvalues were imputed classiffed ventricular dysfunction in real data with a classiffcation error of 10.99%.Similarly, utility of the generated synthetic data by showing that a neural network trained on synthetic dataderived from real data in which the missing values were dropped could classify ventricular dysfunction inreal data with a classiffcation error of 9.44%. Conclusions: Although representing a limited subset of thevast data available on the Pediatric Heart Network, generative adversarial networks can create syntheticdata that mimics the probability distribution of real Fontan echocardiographic measures. Clinicians can usethese synthetic data to create models that predict health outcomes for Fontan patients.
基金supported by the National Natural Science Foundation of China(Grant No.12174260)the Shanghai Rising-Star Program(Grant No.21QA1406400)+1 种基金the Shanghai Science and Technology Development Fund(Grant Nos.21ZR1443500 and 21ZR1443600)supported by Research Grants Council,University Grants Committee(Grant Nos.STG3/E-704/23-N,CityU 11212721,and CityU 11204523).
文摘Precise experimental control and characterization of electron wave packet dynamics driven by external optical fields remain a fundamental challenge,particularly at ultrafast temporal and sub-microscopic spatial scales.To overcome these challenges,we introduce a photon-based simulation platform employing a traveling-wave electrooptic phase-modulated waveguide.In our setup,the incident electromagnetic pulse serves as an analog to the electron wave packet,while the traveling-wave modulation simulates the external optical driving field.Our experimental study systematically explores pulse evolution under three distinct regimes defined by the relation between the pulse duration(Δt)and the modulation period(T).When the pulse duration is significantly shorter than the modulation period,we observe a uniform spectral shift analogous to electron acceleration in dielectric laser accelerators,where spectral phase gradients represent electron momentum accumulation.Conversely,when the pulse duration greatly exceeds the modulation period,discrete diffraction patterns emerge,closely resembling the discrete sideband features of electron-photon coupling observed in photon-induced near-field electron microscopy.Notably,in the intermediate regime(T/4<Δt<T/2),the pulse spectrum exhibits Airy-function-type characteristics with self-healing effects.These experimental results provide critical insights into electron-wave interactions under external optical fields and establish a robust,programmable framework for further investigation.
文摘The US market for women's synthetic trou-sers is characterised by intense competition,with Asian countries playing a dominant role in exports.This analysis delves into the market performance of leading exporters,assessing key indicators such as export values,revealed comparative advantage(RCA).unit value realisation(UVR),and the effect of tariff rates.
