We perceive that some Brain-Computer Interface (BCI) researchers believe in totally different origins of invasive and non-invasive electrical BCI signals. Based on available literature we argue, however, that although...We perceive that some Brain-Computer Interface (BCI) researchers believe in totally different origins of invasive and non-invasive electrical BCI signals. Based on available literature we argue, however, that although invasive and non-invasive BCI signals are different, the underlying origin of electrical BCIs signals is the same.展开更多
BACKGROUND Despite the developments in the field of kidney transplantation,the already existing diagnostic techniques for patient monitoring are considered insufficient.Protein biomarkers that can be derived from mode...BACKGROUND Despite the developments in the field of kidney transplantation,the already existing diagnostic techniques for patient monitoring are considered insufficient.Protein biomarkers that can be derived from modern approaches of proteomic analysis of liquid biopsies(serum,urine)represent a promising innovation in the monitoring of kidney transplant recipients.AIM To investigate the diagnostic utility of protein biomarkers derived from proteomics approaches in renal allograft assessment.METHODS A systematic review was conducted in accordance with PRISMA guidelines,based on research results from the PubMed and Scopus databases.The primary focus was on evaluating the role of biomarkers in the non-invasive diagnosis of transplant-related com-plications.Eligibility criteria included protein biomarkers and urine and blood samples,while exclusion criteria were language other than English and the use of low resolution and sensitivity methods.The selected research articles,were categorized based on the biological sample,condition and methodology and the significantly and reproducibly differentiated proteins were manually selected and extracted.Functional and network analysis of the selected proteins was performed.RESULTS In 17 included studies,58 proteins were studied,with the cytokine CXCL10 being the most investigated.Biological pathways related to immune response and fibrosis have shown to be enriched.Applications of biomarkers for the assessment of renal damage as well as the prediction of short-term and long-term function of the graft were reported.Overall,all studies have shown satisfactory diagnostic accuracy of proteins alone or in combination with conventional methods,as far as renal graft assessment is concerned.CONCLUSION Our review suggests that protein biomarkers,evaluated in specific biological fluids,can make a significant contribution to the timely,valid and non-invasive assessment of kidney graft.展开更多
Gastric cancer(GC),a multifaceted and highly aggressive malignancy,represents challenging healthcare burdens globally,with a high incidence and mortality rate.Although endoscopy,combined with histological examination,...Gastric cancer(GC),a multifaceted and highly aggressive malignancy,represents challenging healthcare burdens globally,with a high incidence and mortality rate.Although endoscopy,combined with histological examination,is the gold stan-dard for GC diagnosis,its high cost,invasiveness,and specialized requirements hinder widespread use for screening.With the emergence of innovative techno-logies such as advanced imaging,liquid biopsy,and breath tests,the landscape of GC diagnosis is poised for radical transformation,becoming more accessible,less invasive,and more efficient.As the non-invasive diagnostic techniques continue to advance and undergo rigorous clinical validation,they hold the promise of sig-nificantly impacting patient outcomes,ultimately leading to better treatment results and improved quality of life for patients with GC.展开更多
This reviewpresents a comprehensive technical analysis of deep learning(DL)methodologies in biomedical signal processing,focusing on architectural innovations,experimental validation,and evaluation frameworks.We syste...This reviewpresents a comprehensive technical analysis of deep learning(DL)methodologies in biomedical signal processing,focusing on architectural innovations,experimental validation,and evaluation frameworks.We systematically evaluate key deep learning architectures including convolutional neural networks(CNNs),recurrent neural networks(RNNs),transformer-based models,and hybrid systems across critical tasks such as arrhythmia classification,seizure detection,and anomaly segmentation.The study dissects preprocessing techniques(e.g.,wavelet denoising,spectral normalization)and feature extraction strategies(time-frequency analysis,attention mechanisms),demonstrating their impact on model accuracy,noise robustness,and computational efficiency.Experimental results underscore the superiority of deep learning over traditional methods,particularly in automated feature extraction,real-time processing,cross-modal generalization,and achieving up to a 15%increase in classification accuracy and enhanced noise resilience across electrocardiogram(ECG),electroencephalogram(EEG),and electromyogram(EMG)signals.Performance is rigorously benchmarked using precision,recall,F1-scores,area under the receiver operating characteristic curve(AUC-ROC),and computational complexitymetrics,providing a unified framework for comparing model efficacy.Thesurvey addresses persistent challenges:synthetic data generationmitigates limited training samples,interpretability tools(e.g.,Gradient-weighted Class Activation Mapping(Grad-CAM),Shapley values)resolve model opacity,and federated learning ensures privacy-compliant deployments.Distinguished from prior reviews,this work offers a structured taxonomy of deep learning architectures,integrates emerging paradigms like transformers and domain-specific attention mechanisms,and evaluates preprocessing pipelines for spectral-temporal trade-offs.It advances the field by bridging technical advancements with clinical needs,such as scalability in real-world settings(e.g.,wearable devices)and regulatory alignment with theHealth Insurance Portability and Accountability Act(HIPAA)and General Data Protection Regulation(GDPR).By synthesizing technical rigor,ethical considerations,and actionable guidelines for model selection,this survey establishes a holistic reference for developing robust,interpretable biomedical artificial intelligence(AI)systems,accelerating their translation into personalized and equitable healthcare solutions.展开更多
Chronic kidney disease(CKD)is a degenerative disorder that affects millions of people throughout the world,causing considerable morbidity and healthcare burden.Frequent blood sampling is the current gold standard for ...Chronic kidney disease(CKD)is a degenerative disorder that affects millions of people throughout the world,causing considerable morbidity and healthcare burden.Frequent blood sampling is the current gold standard for monitoring CKD to evaluate biochemical and mineral indicators.However,there are draw-backs to frequent blood draws,such as pain for patients,the possibility of infe-ction,and higher medical expenses.Saliva-based diagnostics offer advantages such as ease of collection,reduced invasiveness,and improved patient compli-ance.A comprehensive literature review was conducted to analyze studies eva-luating the diagnostic utility of salivary creatinine,urea,calcium,and parathyroid hormone(PTH)in patients with CKD.Various saliva collection methods,inc-luding stimulated and unstimulated approaches,were investigated for efficiency and reliability,and a correlation was shown between serum and salivary crea-tinine,urea,PTH,and calcium levels,indicating their potential as CKD biomar-kers.Despite these promising findings,challenges such as standardization of collection methods,variability in salivary flow rates,and predictive value in association with blood parameters are addressed to ensure clinical applicability.This review explores the potential and challenges of saliva as a non-invasive alternative for CKD diagnostics.展开更多
Flip-flow screens offer unique advantages in grading fine-grained materials.To address inaccuracies caused by sensor vibra-tions in traditional contact measurement methods,we constructed a non-invasive measurement sys...Flip-flow screens offer unique advantages in grading fine-grained materials.To address inaccuracies caused by sensor vibra-tions in traditional contact measurement methods,we constructed a non-invasive measurement system based on electrical and optical sig-nals.A trajectory tracking algorithm for the screen-body was developed to visually measure the kinematics.Employing the principle oflaser reflection for distance measurement,optical techniques were performed to capture the kinematic information of the screen-plate.Ad-ditionally,by using Wi-Fi and Bluetooth transmission of electrical signals,tracer particle tracking technology was implemented to elec-trically measure the kinematic information of mineral particles.Consequently,intelligent fusion and perception of the kinematic informa-tion for the screen-body,screen-plate,and particles in the screening system have been achieved.展开更多
Ca^(2+)signaling plays crucial roles in plant stress responses,including defense against insects.To counteract insect feeding,different parts of a plant deploy systemic signaling to communicate and coordinate defense ...Ca^(2+)signaling plays crucial roles in plant stress responses,including defense against insects.To counteract insect feeding,different parts of a plant deploy systemic signaling to communicate and coordinate defense responses,but little is known about the underlying mechanisms.In this study,micrografting,in vivo imaging of Ca^(2+)and reactive oxygen species(ROS),quantification of jasmonic acid(JA)and defensive metabolites,and bioassay were used to study how Arabidopsis seedlings regulate systemic responses in leaves after hypocotyls are wounded.We show that wounding hypocotyls rapidly activated both Ca^(2+)and ROS signals in leaves.RBOHD,which functions to produce ROS,along with two glutamate receptors GLR3.3 and GLR3.6,but not individually RBOHD or GLR3.3 and GLR3.6,in hypocotyls regulate the dynamics of systemic Ca^(2+)signals in leaves.In line with the systemic Ca^(2+)signals,after wounding hypocotyl,RBOHD,GLR3.3,and GLR3.6 in hypocotyl also cooperatively regulate the transcriptome,hormone jasmonic acid,and defensive secondary metabolites in leaves of Arabidopsis seedlings,thus controlling the systemic resistance to insects.Unlike leaf-to-leaf systemic signaling,this study reveals the unique regulation of wounding-induced hypocotyl-to-leaf systemic signaling and sheds new light on how different plant organs use complex signaling pathways to modulate defense responses.展开更多
Non-cooperative communication detection is a key technology for locating radio interfer-ence sources and conducting reconnaissance on adversary radiation sources.To meet the require-ments of wide-area monitoring,a sin...Non-cooperative communication detection is a key technology for locating radio interfer-ence sources and conducting reconnaissance on adversary radiation sources.To meet the require-ments of wide-area monitoring,a single interception channel often contains mixed multi-source sig-nals and interference,resulting in generally low signal-to-noise ratio(SNR)of the received signals;meanwhile,improving detection quality urgently requires either high frequency resolution or high-time resolution,which poses severe challenges to detection techniques based on time-frequency rep-resentations(TFR).To address this issue,this paper proposes a fixed-frame-structure signal detec-tion algorithm that integrates image enhancement and multi-scale template matching:first,the Otsu-Sauvola hybrid thresholding algorithm is employed to enhance TFR features,suppress noise interference,and extract time-frequency parameters of potential target signals(such as bandwidth and occurrence time);then,by exploiting the inherent time-frequency characteristics of the fixed-frame structure,the signal is subjected to multi-scale transformation(with either high-frequency resolution or high-time resolution),and accurate detection is achieved through the corresponding multi-scale template matching.Experimental results demonstrate that under 0 dB SNR conditions,the proposed algorithm achieves a detection rate greater than 87%,representing a significant improvement over traditional methods.展开更多
Hyaline Membrane Disease(HMD)in newborns,also known as neonatal respiratory distress syndrome,is a common critical illness in premature infants,with an incidence inversely correlated with gestational age,posing a seri...Hyaline Membrane Disease(HMD)in newborns,also known as neonatal respiratory distress syndrome,is a common critical illness in premature infants,with an incidence inversely correlated with gestational age,posing a serious threat to the life and health of newborns.This paper systematically reviews the core pathogenesis of HMD,focusing on the abnormal metabolism of pulmonary surfactant(PS),genetic factors,immature lung development,and the synergistic effects of inflammatory oxidative stress.It highlights the advances in non-invasive ventilation(NIV)therapy for HMD,including the mechanisms of action,clinical application effects,and optimization strategies of mainstream modalities such as nasal continuous positive airway pressure ventilation(NCPAP),nasal intermittent positive pressure ventilation(NIPPV),and heated humidified high-flow nasal cannula ventilation(HHHFNC).The aim is to provide references for standardized clinical treatment.展开更多
Metabolic dysfunction-associated steatotic liver disease(MASLD)requires accurate liver fibrosis assessment for management.While liver biopsy remains the gold standard,its invasiveness drives demand for non-invasive bi...Metabolic dysfunction-associated steatotic liver disease(MASLD)requires accurate liver fibrosis assessment for management.While liver biopsy remains the gold standard,its invasiveness drives demand for non-invasive biomarkers.This review evaluates blood biomarkers for MASLD fibrosis staging.Established scores(fibrosis-4,non-alcoholic fatty liver disease fibrosis score)offer accessible screening but exhibit variable performance influenced by age,obesity,and comorbidities.Patented panels(e.g.,enhanced liver fibrosis test,FibroMeter)improve accuracy by integrating extracellular matrix or metabolic markers,though context-specific thresholds are essential.Emerging biomarkers like propeptide of type 3 collagen,Mac-2 binding protein glycosylation isomer,epigenetic markers(proliferator-activated receptor-γmethylation),and angiopoietin-like proteins a family of eight glycoproteins show promise but require large-scale validation.Genetic risk scores and multi-omics approaches face generalizability challenges.Integration strategies,such as combining serum biomarkers with liver stiffness measurement via Agile scores,enhance diagnostic precision and reduce indeterminate classifications.Current tools aid risk stratification,but no single biomarker replicates biopsy-level precision.Future efforts must prioritize MASLD-specific diagnostic frameworks,standardized protocols,and multi-modal integration to enhance clinical utility and address MASLD’s growing burden.展开更多
BACKGROUND Internet gaming disorder(IGD)is a growing concern among adolescents and adults,necessitating effective treatment strategies beyond pharmacological interventions.AIM To evaluated the effectiveness of non-inv...BACKGROUND Internet gaming disorder(IGD)is a growing concern among adolescents and adults,necessitating effective treatment strategies beyond pharmacological interventions.AIM To evaluated the effectiveness of non-invasive interventions for treating IGD among adolescents and adults.METHODS A total of 11 randomized controlled trials published between 2020 and 2025 were included in this meta-analysis,encompassing 1208 participants from diverse geographic and cultural contexts.The interventions examined included cognitive behavioral therapy(CBT),internet-based CBT,neurofeedback,virtual reality therapy,abstinence-based programs,and school-based prevention.The primary outcomes assessed were reductions in gaming time and IGD severity.Secondary outcomes included improvements in mood,anxiety,and psychosocial functioning(e.g.,stronger peer relationships,better academic or work performance,and healthier daily-life role fulfillment).RESULTS The pooled standardized mean difference for IGD symptom reduction significantly favored non-invasive interventions(Hedges’g=0.56,95%CI:0.38-0.74,P<0.001),with moderate heterogeneity observed(I2=47%).Subgroup analyses indicated that CBT-based programs,both in-person and online,yielded the strongest effects,particularly when caregiver involvement or self-monitoring was incorporated.Funnel plot asymmetry was minimal,suggesting a low risk of publication bias.CONCLUSION These findings support the efficacy of scalable,low-risk non-invasive interventions as first-line treatment options for IGD,particularly in youth populations.Future studies should prioritize investigating long-term outcomes,comparing the effectiveness of different non-invasive modalities,and developing culturally adaptive delivery methods.展开更多
In this article,we comment on the article by Peta et al.This study evaluates the diagnostic performance of FibroTest-Actitest,transient elastography,and the fibrosis-4 index against a histological reference.Using the ...In this article,we comment on the article by Peta et al.This study evaluates the diagnostic performance of FibroTest-Actitest,transient elastography,and the fibrosis-4 index against a histological reference.Using the Obuchowski measure,the authors demonstrate that FibroTest and vibration-controlled transient elastography outperform the fibrosis-4 index in detecting fibrosis.Additionally,Actitest offers superior estimation of inflammatory activity compared to conventional biomarkers.Assessing liver fibrosis is crucial for managing autoimmune hepatitis(AIH),yet reliance on invasive liver biopsy remains higher than in other liver diseases.This is partly due to more complex diagnostic criteria for AIH,the lack of standardized scoring for non-invasive testing,and the presence of inflammation,which can lead to falsely elevated results with non-invasive tests.A Bayesian latent class model further supports the reliability of these non-invasive tests,highlighting their potential to complement biopsy,particularly for longterm disease monitoring.These findings underscore the importance of noninvasive diagnostics in optimizing AIH management.展开更多
Patients with acute exacerbation of chronic obstructive pulmonary disease(COPD)often suffer from respiratory failure and require respiratory support therapy.High-flow nasal cannula oxygen therapy(HFNC)and non-invasive...Patients with acute exacerbation of chronic obstructive pulmonary disease(COPD)often suffer from respiratory failure and require respiratory support therapy.High-flow nasal cannula oxygen therapy(HFNC)and non-invasive positive pressure ventilation(NIPPV)are commonly used non-invasive respiratory support methods.HFNC can provide precisely heated and humidified high-flow oxygen,reducing dead space and increasing alveolar ventilation.NIPPV can supply stable high-concentration oxygen and improve gas exchange.This article reviews the application of HFNC and NIPPV in the acute exacerbation stage of COPD,aiming to provide references for reasonable clinical selection.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
The gravitational memory effect manifests gravitational nonlinearity,degenerate vacua,and asymptotic symmetries;its detection is considered challenging.We propose using a space-borne interferometer to detect memory si...The gravitational memory effect manifests gravitational nonlinearity,degenerate vacua,and asymptotic symmetries;its detection is considered challenging.We propose using a space-borne interferometer to detect memory signals from stellar-mass binary black holes(BBHs),typically targeted by ground-based detectors.We use DECIGO detector as an example.Over 5 years,DECIGO is estimated to detect approximately 2,036 memory signals(SNRs>3)from stellar-mass BBHs.Simulations used frequency-domain memory waveforms for direct SNR estimation.Predictions utilized a GWTC-3 constrained BBH population model(Power law+Peak mass,DEFAULT spin,Madau-Dickinson merger rate).The analysis used conservative lower merger rate limits and considered orbital eccentricity.The high detection rate stems from strong memory signals within DECIGO’s bandwidth and the abundance of stellar-mass BBHs.This substantial and conservative detection count enables statistical use of the memory effect for fundamental physics and astrophysics.DECIGO exemplifies that space interferometers may better detect memory signals from smaller mass binaries than their typical targets.Detectors in lower frequency bands are expected to find strong memory signals from∼10^(4)M⊙binaries.展开更多
This study sought to investigate adverse drug event(ADE)signals associated with eltrombopag use in pediatric patients aged 0–18 years,utilizing data from the U.S.Food and Drug Administration Adverse Event Reporting S...This study sought to investigate adverse drug event(ADE)signals associated with eltrombopag use in pediatric patients aged 0–18 years,utilizing data from the U.S.Food and Drug Administration Adverse Event Reporting System(FAERS).By analyzing this extensive pharmacovigilance database,the study aimed to offer meaningful insights for improving the clinical safety of eltrombopag in children.Data covering eltrombopag-related ADEs from Q12004 to Q42023 were extracted from FAERS,and signal detection was conducted using both the reporting odds ratio(ROR)and proportional reporting ratio(PRR)methods.ADEs were categorized based on the System Organ Class(SOC)classification in MedDRA version 25.0.A total of 582 reports involving pediatric patients receiving eltrombopag were identified,encompassing 21 SOC categories.The analysis revealed that,in addition to the known ADEs listed in the drug label,clinicians should remain vigilant for potential off-label ADE signals.These included abnormal platelet counts,thrombocytosis,antiphospholipid syndrome,myelofibrosis,reduced serum iron levels,myelodysplastic syndrome,hepatic infections,and other related conditions.Given these findings,it is strongly recommended that serum iron and ferritin levels should be routinely monitored in pediatric patients undergoing eltrombopag therapy,particularly during long-term treatment.Such proactive surveillance may help prevent the onset of iron deficiency anemia and enhance overall treatment safety.展开更多
Tumor stroma,or tumor microenvironment(TME),has been in the spotlight during recent years for its role in tumor development,growth,and metastasis.It consists of a myriad of elements,including tumor-associated macropha...Tumor stroma,or tumor microenvironment(TME),has been in the spotlight during recent years for its role in tumor development,growth,and metastasis.It consists of a myriad of elements,including tumor-associated macrophages,cancer-associated fibroblasts,a deregulated extracellular matrix,endothelial cells,and vascular vessels.The release of proinflammatory molecules,due to the inflamed microenvironment,such as cytokines and chemokines is found to play a pivotal role in progression of cancer and response to therapy.This review discusses the major key players and important chemical inflammatory signals released in the TME.Furthermore,the latest breakthroughs in cytokine-mediated crosstalk between immune cells and cancer cells have been highlighted.In addition,recent updates on alterations in cytokine signaling between chronic inflammation and malignant TME have also been reviewed.展开更多
There are all kinds of unknown and known signals in the actual electromagnetic environment,which hinders the development of practical cognitive radio applications.However,most existing signal recognition models are di...There are all kinds of unknown and known signals in the actual electromagnetic environment,which hinders the development of practical cognitive radio applications.However,most existing signal recognition models are difficult to discover unknown signals while recognizing known ones.In this paper,a compact manifold mixup feature-based open-set recognition approach(OR-CMMF)is proposed to address the above problem.First,the proposed approach utilizes the center loss to constrain decision boundaries so that it obtains the compact latent signal feature representations and extends the low-confidence feature space.Second,the latent signal feature representations are used to construct synthetic representations as substitutes for unknown categories of signals.Then,these constructed representations can occupy the extended low-confidence space.Finally,the proposed approach applies the distillation loss to adjust the decision boundaries between the known categories signals and the constructed unknown categories substitutes so that it accurately discovers unknown signals.The OR-CMMF approach outperformed other state-of-the-art open-set recognition methods in comprehensive recognition performance and running time,as demonstrated by simulation experiments on two public datasets RML2016.10a and ORACLE.展开更多
Terahertz(THz) and millimeter Wave(mmWave) have been considered as potential frequency bands for 6G cellular systems to meet the need of ultra-high data rates. However, indoor communications could be blocked in THz/mm...Terahertz(THz) and millimeter Wave(mmWave) have been considered as potential frequency bands for 6G cellular systems to meet the need of ultra-high data rates. However, indoor communications could be blocked in THz/mmW cellular systems due to the high free-space propagation loss. Deploying a large number of small base stations indoors has been considered as a promising solution for solving indoor coverage problems. However, base station dense deployment leads to a significant increase in system energy consumption. In this paper, we develop a novel ultra-efficient energy-saving mechanism with the aim of reducing energy consumption in 6G distributed indoor base station scenarios. Unlike the existing relevant protocol framework of 3GPP, which operates the cellular system based on constant system signaling messages(including cell ID, cell reselection information, etc.), the proposed mechanism eliminates the need for system messages. The intuition comes from the observation that the probability of having no users within the coverage area of an indoor base station is high, hence continuously sending system messages to guarantee the quality of service is unnecessary in indoor scenarios. Specifically, we design a dedicated beacon signal to detect whether there are users in the coverage area of the base station and switch off the main communication module when there are no active users for energy saving. The beacon frame structure is carefully designed based on the existing 3GPP specifications with minimal protocol modifications, and the protocol parameters involved are optimized. Simulation results show that the proposed mechanism can reduce the system energy from the order of tens of watts to the order of hundreds of milliwatts. Compared to traditional energy-saving schemes, the proposed mechanism achieves an average energy-saving gain of 58%, with a peak energy-saving gain of 90%.展开更多
Neutron time-of-flight(ToF)measurement is a highly accurate method for obtaining the kinetic energy of a neutron by measuring its velocity,but requires precise acquisition of the neutron signal arrival time.However,th...Neutron time-of-flight(ToF)measurement is a highly accurate method for obtaining the kinetic energy of a neutron by measuring its velocity,but requires precise acquisition of the neutron signal arrival time.However,the high hardware costs and data burden associated with the acquisition of neutron ToF signals pose significant challenges.Higher sampling rates increase the data volume,data processing,and storage hardware costs.Compressed sampling can address these challenges,but it faces issues regarding optimal sampling efficiency and high-quality reconstructed signals.This paper proposes a revolutionary deep learning-based compressed sampling(DL-CS)algorithm for reconstructing neutron ToF signals that outperform traditional compressed sampling methods.This approach comprises four modules:random projection,rising dimensions,initial reconstruction,and final reconstruction.Initially,the technique adaptively compresses neutron ToF signals sequentially using three convolutional layers,replacing random measurement matrices in traditional compressed sampling theory.Subsequently,the signals are reconstructed using a modified inception module,long short-term memory,and self-attention.The performance of this deep compressed sampling method was quantified using the percentage root-mean-square difference,correlation coefficient,and reconstruction time.Experimental results showed that our proposed DL-CS approach can significantly enhance signal quality compared with other compressed sampling methods.This is evidenced by a percentage root-mean-square difference,correlation coefficient,and reconstruction time results of 5%,0.9988,and 0.0108 s,respectively,obtained for sampling rates below 10%for the neutron ToF signal generated using an electron-beam-driven photoneutron source.The results showed that the proposed DL-CS approach significantly improves the signal quality compared with other compressed sampling methods,exhibiting excellent reconstruction accuracy and speed.展开更多
文摘We perceive that some Brain-Computer Interface (BCI) researchers believe in totally different origins of invasive and non-invasive electrical BCI signals. Based on available literature we argue, however, that although invasive and non-invasive BCI signals are different, the underlying origin of electrical BCIs signals is the same.
文摘BACKGROUND Despite the developments in the field of kidney transplantation,the already existing diagnostic techniques for patient monitoring are considered insufficient.Protein biomarkers that can be derived from modern approaches of proteomic analysis of liquid biopsies(serum,urine)represent a promising innovation in the monitoring of kidney transplant recipients.AIM To investigate the diagnostic utility of protein biomarkers derived from proteomics approaches in renal allograft assessment.METHODS A systematic review was conducted in accordance with PRISMA guidelines,based on research results from the PubMed and Scopus databases.The primary focus was on evaluating the role of biomarkers in the non-invasive diagnosis of transplant-related com-plications.Eligibility criteria included protein biomarkers and urine and blood samples,while exclusion criteria were language other than English and the use of low resolution and sensitivity methods.The selected research articles,were categorized based on the biological sample,condition and methodology and the significantly and reproducibly differentiated proteins were manually selected and extracted.Functional and network analysis of the selected proteins was performed.RESULTS In 17 included studies,58 proteins were studied,with the cytokine CXCL10 being the most investigated.Biological pathways related to immune response and fibrosis have shown to be enriched.Applications of biomarkers for the assessment of renal damage as well as the prediction of short-term and long-term function of the graft were reported.Overall,all studies have shown satisfactory diagnostic accuracy of proteins alone or in combination with conventional methods,as far as renal graft assessment is concerned.CONCLUSION Our review suggests that protein biomarkers,evaluated in specific biological fluids,can make a significant contribution to the timely,valid and non-invasive assessment of kidney graft.
基金Supported by National Natural Science Foundation of China,No.82300451Research Foundation of Wuhan Union Hospital,No.2022xhyn050.
文摘Gastric cancer(GC),a multifaceted and highly aggressive malignancy,represents challenging healthcare burdens globally,with a high incidence and mortality rate.Although endoscopy,combined with histological examination,is the gold stan-dard for GC diagnosis,its high cost,invasiveness,and specialized requirements hinder widespread use for screening.With the emergence of innovative techno-logies such as advanced imaging,liquid biopsy,and breath tests,the landscape of GC diagnosis is poised for radical transformation,becoming more accessible,less invasive,and more efficient.As the non-invasive diagnostic techniques continue to advance and undergo rigorous clinical validation,they hold the promise of sig-nificantly impacting patient outcomes,ultimately leading to better treatment results and improved quality of life for patients with GC.
基金The Natural Sciences and Engineering Research Council of Canada(NSERC)funded this review study.
文摘This reviewpresents a comprehensive technical analysis of deep learning(DL)methodologies in biomedical signal processing,focusing on architectural innovations,experimental validation,and evaluation frameworks.We systematically evaluate key deep learning architectures including convolutional neural networks(CNNs),recurrent neural networks(RNNs),transformer-based models,and hybrid systems across critical tasks such as arrhythmia classification,seizure detection,and anomaly segmentation.The study dissects preprocessing techniques(e.g.,wavelet denoising,spectral normalization)and feature extraction strategies(time-frequency analysis,attention mechanisms),demonstrating their impact on model accuracy,noise robustness,and computational efficiency.Experimental results underscore the superiority of deep learning over traditional methods,particularly in automated feature extraction,real-time processing,cross-modal generalization,and achieving up to a 15%increase in classification accuracy and enhanced noise resilience across electrocardiogram(ECG),electroencephalogram(EEG),and electromyogram(EMG)signals.Performance is rigorously benchmarked using precision,recall,F1-scores,area under the receiver operating characteristic curve(AUC-ROC),and computational complexitymetrics,providing a unified framework for comparing model efficacy.Thesurvey addresses persistent challenges:synthetic data generationmitigates limited training samples,interpretability tools(e.g.,Gradient-weighted Class Activation Mapping(Grad-CAM),Shapley values)resolve model opacity,and federated learning ensures privacy-compliant deployments.Distinguished from prior reviews,this work offers a structured taxonomy of deep learning architectures,integrates emerging paradigms like transformers and domain-specific attention mechanisms,and evaluates preprocessing pipelines for spectral-temporal trade-offs.It advances the field by bridging technical advancements with clinical needs,such as scalability in real-world settings(e.g.,wearable devices)and regulatory alignment with theHealth Insurance Portability and Accountability Act(HIPAA)and General Data Protection Regulation(GDPR).By synthesizing technical rigor,ethical considerations,and actionable guidelines for model selection,this survey establishes a holistic reference for developing robust,interpretable biomedical artificial intelligence(AI)systems,accelerating their translation into personalized and equitable healthcare solutions.
文摘Chronic kidney disease(CKD)is a degenerative disorder that affects millions of people throughout the world,causing considerable morbidity and healthcare burden.Frequent blood sampling is the current gold standard for monitoring CKD to evaluate biochemical and mineral indicators.However,there are draw-backs to frequent blood draws,such as pain for patients,the possibility of infe-ction,and higher medical expenses.Saliva-based diagnostics offer advantages such as ease of collection,reduced invasiveness,and improved patient compli-ance.A comprehensive literature review was conducted to analyze studies eva-luating the diagnostic utility of salivary creatinine,urea,calcium,and parathyroid hormone(PTH)in patients with CKD.Various saliva collection methods,inc-luding stimulated and unstimulated approaches,were investigated for efficiency and reliability,and a correlation was shown between serum and salivary crea-tinine,urea,PTH,and calcium levels,indicating their potential as CKD biomar-kers.Despite these promising findings,challenges such as standardization of collection methods,variability in salivary flow rates,and predictive value in association with blood parameters are addressed to ensure clinical applicability.This review explores the potential and challenges of saliva as a non-invasive alternative for CKD diagnostics.
基金financially supported by ChinaNational Funds for Distinguished Young Scientists(No.52125403)National Natural Science Foundation of China(Nos.52261135540 and 52404303)Science and Tech-nology Plan Special Fund Project of Jiangsu Province,China(No.BZ2024046)。
文摘Flip-flow screens offer unique advantages in grading fine-grained materials.To address inaccuracies caused by sensor vibra-tions in traditional contact measurement methods,we constructed a non-invasive measurement system based on electrical and optical sig-nals.A trajectory tracking algorithm for the screen-body was developed to visually measure the kinematics.Employing the principle oflaser reflection for distance measurement,optical techniques were performed to capture the kinematic information of the screen-plate.Ad-ditionally,by using Wi-Fi and Bluetooth transmission of electrical signals,tracer particle tracking technology was implemented to elec-trically measure the kinematic information of mineral particles.Consequently,intelligent fusion and perception of the kinematic informa-tion for the screen-body,screen-plate,and particles in the screening system have been achieved.
基金National Natural Science Foundation of China(U23A20199)Yunnan Revitalization Talent Support Program“Yunling Scholar”and Yunnan Fundamental Research Projects(202201AS070056)。
文摘Ca^(2+)signaling plays crucial roles in plant stress responses,including defense against insects.To counteract insect feeding,different parts of a plant deploy systemic signaling to communicate and coordinate defense responses,but little is known about the underlying mechanisms.In this study,micrografting,in vivo imaging of Ca^(2+)and reactive oxygen species(ROS),quantification of jasmonic acid(JA)and defensive metabolites,and bioassay were used to study how Arabidopsis seedlings regulate systemic responses in leaves after hypocotyls are wounded.We show that wounding hypocotyls rapidly activated both Ca^(2+)and ROS signals in leaves.RBOHD,which functions to produce ROS,along with two glutamate receptors GLR3.3 and GLR3.6,but not individually RBOHD or GLR3.3 and GLR3.6,in hypocotyls regulate the dynamics of systemic Ca^(2+)signals in leaves.In line with the systemic Ca^(2+)signals,after wounding hypocotyl,RBOHD,GLR3.3,and GLR3.6 in hypocotyl also cooperatively regulate the transcriptome,hormone jasmonic acid,and defensive secondary metabolites in leaves of Arabidopsis seedlings,thus controlling the systemic resistance to insects.Unlike leaf-to-leaf systemic signaling,this study reveals the unique regulation of wounding-induced hypocotyl-to-leaf systemic signaling and sheds new light on how different plant organs use complex signaling pathways to modulate defense responses.
文摘Non-cooperative communication detection is a key technology for locating radio interfer-ence sources and conducting reconnaissance on adversary radiation sources.To meet the require-ments of wide-area monitoring,a single interception channel often contains mixed multi-source sig-nals and interference,resulting in generally low signal-to-noise ratio(SNR)of the received signals;meanwhile,improving detection quality urgently requires either high frequency resolution or high-time resolution,which poses severe challenges to detection techniques based on time-frequency rep-resentations(TFR).To address this issue,this paper proposes a fixed-frame-structure signal detec-tion algorithm that integrates image enhancement and multi-scale template matching:first,the Otsu-Sauvola hybrid thresholding algorithm is employed to enhance TFR features,suppress noise interference,and extract time-frequency parameters of potential target signals(such as bandwidth and occurrence time);then,by exploiting the inherent time-frequency characteristics of the fixed-frame structure,the signal is subjected to multi-scale transformation(with either high-frequency resolution or high-time resolution),and accurate detection is achieved through the corresponding multi-scale template matching.Experimental results demonstrate that under 0 dB SNR conditions,the proposed algorithm achieves a detection rate greater than 87%,representing a significant improvement over traditional methods.
文摘Hyaline Membrane Disease(HMD)in newborns,also known as neonatal respiratory distress syndrome,is a common critical illness in premature infants,with an incidence inversely correlated with gestational age,posing a serious threat to the life and health of newborns.This paper systematically reviews the core pathogenesis of HMD,focusing on the abnormal metabolism of pulmonary surfactant(PS),genetic factors,immature lung development,and the synergistic effects of inflammatory oxidative stress.It highlights the advances in non-invasive ventilation(NIV)therapy for HMD,including the mechanisms of action,clinical application effects,and optimization strategies of mainstream modalities such as nasal continuous positive airway pressure ventilation(NCPAP),nasal intermittent positive pressure ventilation(NIPPV),and heated humidified high-flow nasal cannula ventilation(HHHFNC).The aim is to provide references for standardized clinical treatment.
基金Supported by the National Natural Science Foundation of China,No.82402719Sichuan Science and Technology Program,No.2025ZNSFSC1553.
文摘Metabolic dysfunction-associated steatotic liver disease(MASLD)requires accurate liver fibrosis assessment for management.While liver biopsy remains the gold standard,its invasiveness drives demand for non-invasive biomarkers.This review evaluates blood biomarkers for MASLD fibrosis staging.Established scores(fibrosis-4,non-alcoholic fatty liver disease fibrosis score)offer accessible screening but exhibit variable performance influenced by age,obesity,and comorbidities.Patented panels(e.g.,enhanced liver fibrosis test,FibroMeter)improve accuracy by integrating extracellular matrix or metabolic markers,though context-specific thresholds are essential.Emerging biomarkers like propeptide of type 3 collagen,Mac-2 binding protein glycosylation isomer,epigenetic markers(proliferator-activated receptor-γmethylation),and angiopoietin-like proteins a family of eight glycoproteins show promise but require large-scale validation.Genetic risk scores and multi-omics approaches face generalizability challenges.Integration strategies,such as combining serum biomarkers with liver stiffness measurement via Agile scores,enhance diagnostic precision and reduce indeterminate classifications.Current tools aid risk stratification,but no single biomarker replicates biopsy-level precision.Future efforts must prioritize MASLD-specific diagnostic frameworks,standardized protocols,and multi-modal integration to enhance clinical utility and address MASLD’s growing burden.
基金Supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)Funded by the Ministry of Education,No.NRF-RS-2023-00237287.
文摘BACKGROUND Internet gaming disorder(IGD)is a growing concern among adolescents and adults,necessitating effective treatment strategies beyond pharmacological interventions.AIM To evaluated the effectiveness of non-invasive interventions for treating IGD among adolescents and adults.METHODS A total of 11 randomized controlled trials published between 2020 and 2025 were included in this meta-analysis,encompassing 1208 participants from diverse geographic and cultural contexts.The interventions examined included cognitive behavioral therapy(CBT),internet-based CBT,neurofeedback,virtual reality therapy,abstinence-based programs,and school-based prevention.The primary outcomes assessed were reductions in gaming time and IGD severity.Secondary outcomes included improvements in mood,anxiety,and psychosocial functioning(e.g.,stronger peer relationships,better academic or work performance,and healthier daily-life role fulfillment).RESULTS The pooled standardized mean difference for IGD symptom reduction significantly favored non-invasive interventions(Hedges’g=0.56,95%CI:0.38-0.74,P<0.001),with moderate heterogeneity observed(I2=47%).Subgroup analyses indicated that CBT-based programs,both in-person and online,yielded the strongest effects,particularly when caregiver involvement or self-monitoring was incorporated.Funnel plot asymmetry was minimal,suggesting a low risk of publication bias.CONCLUSION These findings support the efficacy of scalable,low-risk non-invasive interventions as first-line treatment options for IGD,particularly in youth populations.Future studies should prioritize investigating long-term outcomes,comparing the effectiveness of different non-invasive modalities,and developing culturally adaptive delivery methods.
文摘In this article,we comment on the article by Peta et al.This study evaluates the diagnostic performance of FibroTest-Actitest,transient elastography,and the fibrosis-4 index against a histological reference.Using the Obuchowski measure,the authors demonstrate that FibroTest and vibration-controlled transient elastography outperform the fibrosis-4 index in detecting fibrosis.Additionally,Actitest offers superior estimation of inflammatory activity compared to conventional biomarkers.Assessing liver fibrosis is crucial for managing autoimmune hepatitis(AIH),yet reliance on invasive liver biopsy remains higher than in other liver diseases.This is partly due to more complex diagnostic criteria for AIH,the lack of standardized scoring for non-invasive testing,and the presence of inflammation,which can lead to falsely elevated results with non-invasive tests.A Bayesian latent class model further supports the reliability of these non-invasive tests,highlighting their potential to complement biopsy,particularly for longterm disease monitoring.These findings underscore the importance of noninvasive diagnostics in optimizing AIH management.
基金2022 Inner Mongolia Autonomous Region Health and Wellness Science and Technology Program Project(Project No.:202201533)。
文摘Patients with acute exacerbation of chronic obstructive pulmonary disease(COPD)often suffer from respiratory failure and require respiratory support therapy.High-flow nasal cannula oxygen therapy(HFNC)and non-invasive positive pressure ventilation(NIPPV)are commonly used non-invasive respiratory support methods.HFNC can provide precisely heated and humidified high-flow oxygen,reducing dead space and increasing alveolar ventilation.NIPPV can supply stable high-concentration oxygen and improve gas exchange.This article reviews the application of HFNC and NIPPV in the acute exacerbation stage of COPD,aiming to provide references for reasonable clinical selection.
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supported by the National Natural Science Foundation of China(Grant Nos.11633001,11920101003,and 12205222 for S.H.)the Key Program of the National Natural Science Foundation of China(Grant No.12433001)+1 种基金the Strate-gic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB23000000)the National Key Research and Development Program of China(Grant No.2021YFC2203001 for Z.C.Z.).
文摘The gravitational memory effect manifests gravitational nonlinearity,degenerate vacua,and asymptotic symmetries;its detection is considered challenging.We propose using a space-borne interferometer to detect memory signals from stellar-mass binary black holes(BBHs),typically targeted by ground-based detectors.We use DECIGO detector as an example.Over 5 years,DECIGO is estimated to detect approximately 2,036 memory signals(SNRs>3)from stellar-mass BBHs.Simulations used frequency-domain memory waveforms for direct SNR estimation.Predictions utilized a GWTC-3 constrained BBH population model(Power law+Peak mass,DEFAULT spin,Madau-Dickinson merger rate).The analysis used conservative lower merger rate limits and considered orbital eccentricity.The high detection rate stems from strong memory signals within DECIGO’s bandwidth and the abundance of stellar-mass BBHs.This substantial and conservative detection count enables statistical use of the memory effect for fundamental physics and astrophysics.DECIGO exemplifies that space interferometers may better detect memory signals from smaller mass binaries than their typical targets.Detectors in lower frequency bands are expected to find strong memory signals from∼10^(4)M⊙binaries.
文摘This study sought to investigate adverse drug event(ADE)signals associated with eltrombopag use in pediatric patients aged 0–18 years,utilizing data from the U.S.Food and Drug Administration Adverse Event Reporting System(FAERS).By analyzing this extensive pharmacovigilance database,the study aimed to offer meaningful insights for improving the clinical safety of eltrombopag in children.Data covering eltrombopag-related ADEs from Q12004 to Q42023 were extracted from FAERS,and signal detection was conducted using both the reporting odds ratio(ROR)and proportional reporting ratio(PRR)methods.ADEs were categorized based on the System Organ Class(SOC)classification in MedDRA version 25.0.A total of 582 reports involving pediatric patients receiving eltrombopag were identified,encompassing 21 SOC categories.The analysis revealed that,in addition to the known ADEs listed in the drug label,clinicians should remain vigilant for potential off-label ADE signals.These included abnormal platelet counts,thrombocytosis,antiphospholipid syndrome,myelofibrosis,reduced serum iron levels,myelodysplastic syndrome,hepatic infections,and other related conditions.Given these findings,it is strongly recommended that serum iron and ferritin levels should be routinely monitored in pediatric patients undergoing eltrombopag therapy,particularly during long-term treatment.Such proactive surveillance may help prevent the onset of iron deficiency anemia and enhance overall treatment safety.
文摘Tumor stroma,or tumor microenvironment(TME),has been in the spotlight during recent years for its role in tumor development,growth,and metastasis.It consists of a myriad of elements,including tumor-associated macrophages,cancer-associated fibroblasts,a deregulated extracellular matrix,endothelial cells,and vascular vessels.The release of proinflammatory molecules,due to the inflamed microenvironment,such as cytokines and chemokines is found to play a pivotal role in progression of cancer and response to therapy.This review discusses the major key players and important chemical inflammatory signals released in the TME.Furthermore,the latest breakthroughs in cytokine-mediated crosstalk between immune cells and cancer cells have been highlighted.In addition,recent updates on alterations in cytokine signaling between chronic inflammation and malignant TME have also been reviewed.
基金fully supported by National Natural Science Foundation of China(61871422)Natural Science Foundation of Sichuan Province(2023NSFSC1422)Central Universities of South west Minzu University(ZYN2022032)。
文摘There are all kinds of unknown and known signals in the actual electromagnetic environment,which hinders the development of practical cognitive radio applications.However,most existing signal recognition models are difficult to discover unknown signals while recognizing known ones.In this paper,a compact manifold mixup feature-based open-set recognition approach(OR-CMMF)is proposed to address the above problem.First,the proposed approach utilizes the center loss to constrain decision boundaries so that it obtains the compact latent signal feature representations and extends the low-confidence feature space.Second,the latent signal feature representations are used to construct synthetic representations as substitutes for unknown categories of signals.Then,these constructed representations can occupy the extended low-confidence space.Finally,the proposed approach applies the distillation loss to adjust the decision boundaries between the known categories signals and the constructed unknown categories substitutes so that it accurately discovers unknown signals.The OR-CMMF approach outperformed other state-of-the-art open-set recognition methods in comprehensive recognition performance and running time,as demonstrated by simulation experiments on two public datasets RML2016.10a and ORACLE.
基金supported by the National Natural Science Foundation of China under Grant No. 62201121the Fundamental Research Funds for Central Universities under Grant No. ZYGX2024XJ070.
文摘Terahertz(THz) and millimeter Wave(mmWave) have been considered as potential frequency bands for 6G cellular systems to meet the need of ultra-high data rates. However, indoor communications could be blocked in THz/mmW cellular systems due to the high free-space propagation loss. Deploying a large number of small base stations indoors has been considered as a promising solution for solving indoor coverage problems. However, base station dense deployment leads to a significant increase in system energy consumption. In this paper, we develop a novel ultra-efficient energy-saving mechanism with the aim of reducing energy consumption in 6G distributed indoor base station scenarios. Unlike the existing relevant protocol framework of 3GPP, which operates the cellular system based on constant system signaling messages(including cell ID, cell reselection information, etc.), the proposed mechanism eliminates the need for system messages. The intuition comes from the observation that the probability of having no users within the coverage area of an indoor base station is high, hence continuously sending system messages to guarantee the quality of service is unnecessary in indoor scenarios. Specifically, we design a dedicated beacon signal to detect whether there are users in the coverage area of the base station and switch off the main communication module when there are no active users for energy saving. The beacon frame structure is carefully designed based on the existing 3GPP specifications with minimal protocol modifications, and the protocol parameters involved are optimized. Simulation results show that the proposed mechanism can reduce the system energy from the order of tens of watts to the order of hundreds of milliwatts. Compared to traditional energy-saving schemes, the proposed mechanism achieves an average energy-saving gain of 58%, with a peak energy-saving gain of 90%.
基金supported by the National Defense Technology Foundation Program of China(No.JSJT2022209A001-3)Sichuan Science and Technology Program(No.2021JDRC0011)+1 种基金Nuclear Energy Development Research Program of China(Research on High Energy X-ray Imaging of Nuclear Fuel)Scientific Research and Innovation Team Program of Sichuan University of Science and Engineering(No.SUSE652A001).
文摘Neutron time-of-flight(ToF)measurement is a highly accurate method for obtaining the kinetic energy of a neutron by measuring its velocity,but requires precise acquisition of the neutron signal arrival time.However,the high hardware costs and data burden associated with the acquisition of neutron ToF signals pose significant challenges.Higher sampling rates increase the data volume,data processing,and storage hardware costs.Compressed sampling can address these challenges,but it faces issues regarding optimal sampling efficiency and high-quality reconstructed signals.This paper proposes a revolutionary deep learning-based compressed sampling(DL-CS)algorithm for reconstructing neutron ToF signals that outperform traditional compressed sampling methods.This approach comprises four modules:random projection,rising dimensions,initial reconstruction,and final reconstruction.Initially,the technique adaptively compresses neutron ToF signals sequentially using three convolutional layers,replacing random measurement matrices in traditional compressed sampling theory.Subsequently,the signals are reconstructed using a modified inception module,long short-term memory,and self-attention.The performance of this deep compressed sampling method was quantified using the percentage root-mean-square difference,correlation coefficient,and reconstruction time.Experimental results showed that our proposed DL-CS approach can significantly enhance signal quality compared with other compressed sampling methods.This is evidenced by a percentage root-mean-square difference,correlation coefficient,and reconstruction time results of 5%,0.9988,and 0.0108 s,respectively,obtained for sampling rates below 10%for the neutron ToF signal generated using an electron-beam-driven photoneutron source.The results showed that the proposed DL-CS approach significantly improves the signal quality compared with other compressed sampling methods,exhibiting excellent reconstruction accuracy and speed.
