Diagnostic Value of the Padua Score Combined with Thrombotic Biomarker Tissue Plasminogen Activator Inhibitor-1 (tPAI-1) Detection for the Risk of Deep Vein Thrombosis in Patients with Pulmonary Heart Disease

This study explores the diagnostic value of combining the Padua score with the thrombotic biomarker tissue plasminogen activator inhibitor-1(tPAI-1)for assessing the risk of deep vein thrombosis(DVT)in patients with pulmonary heart disease.These patients often exhibit symptoms similar to venous thrombosis,such as dyspnea and bilateral lower limb swelling,complicating differential diagnosis.The Padua Prediction Score assesses the risk of venous thromboembolism(VTE)in hospitalized patients,while tPAI-1,a key fibrinolytic system inhibitor,indicates a hypercoagulable state.Clinical data from hospitalized patients with cor pulmonale were retrospectively analyzed.ROC curves compared the diagnostic value of the Padua score,tPAI-1 levels,and their combined model for predicting DVT risk.Results showed that tPAI-1 levels were significantly higher in DVT patients compared to non-DVT patients.The Padua score demonstrated a sensitivity of 82.61%and a specificity of 55.26%at a cutoff value of 3.The combined model had a significantly higher AUC than the Padua score alone,indicating better discriminatory ability in diagnosing DVT risk.The combination of the Padua score and tPAI-1 detection significantly improves the accuracy of diagnosing DVT risk in patients with pulmonary heart disease,reducing missed and incorrect diagnoses.This study provides a comprehensive assessment tool for clinicians,enhancing the diagnosis and treatment of patients with cor pulmonale complicated by DVT.Future research should validate these findings in larger samples and explore additional thrombotic biomarkers to optimize the predictive model.

Pilot protection of hybrid MMC DC grid based on active detection

Considering the advantages and limitations of traditional identification method,combined with the strategy of active detection,the principle of DC grid pilot protection based on active detection is proposed to improve the sensitivity and reliability of hybrid MMC DC grid protection,and to ensure the accurate identification of fault areas in DC grid.By using the DC fault ride-through control strategy of the hybrid sub-module MMC,the fault current at the converter station DC terminal is limited.Based on the high controllability of hybrid MMC,sinusoidal fault detection signals with the same frequency are injected into the line at each converter station.Based on model recognition,the capacitance model condition is satisfied by the detected signals at both ends during external faults whereas not satisfied during internal faults.The Spearman correlation coefficients is then introduced,and the correlation discriminant of capacitance model is constructed to realize fault area discrimination of DC grid.The simulation results show that the active detection protection scheme proposed in this paper can accurately identify the fault area of DC grid,and is not affected by fault impedance and has low sampling rate requirement.

An echo detection algorithm for underwater continuous wave active detection

The model of linear frequency modulation continuous wave （LFMCW） applied in underwater detection and the method for the detection of echo signal and the estimation of target parameters were studied. By analyzing the heterodyne signal, an algorithm with the structure of heterodyne-Practional Fourier Transform （FRFT） was proposed. To reduce the computation of searching targets in a two-dimensional FRFT result, the heterodyne signal would be processed by FRFT at a specific order, after Radon-Ambiguity Transform （RAT） was applied to estimate the sweep rate of the signal. Simulations proved that the algorithm can eliminate the coupling phenomenon of distance and velocity of LFMCW, and estimate targets＇ parameters accurately. The lake trial results showed that the processing gain of LFMCW processed by the algorithm in this paper was 13 dB better than that of the LFM processed by matched filter. The research results indicated that the algorithm applied in LFMCW underwater detection was feasible and effective, and it could estimate targets＇ parameters accurately and obtain a good detection performance.

Active User and Data Detection for Uplink Grant-free NOMA Systems

This paper proposes some low complexity algorithms for active user detection(AUD),channel estimation(CE)and multi-user detection(MUD)in uplink non-orthogonal multiple access(NOMA)systems,including single-carrier and multi-carrier cases.In particular,we first propose a novel algorithm to estimate the active users and the channels for single-carrier based on complex alternating direction method of multipliers(ADMM),where fast decaying feature of non-zero components in sparse signal is considered.More importantly,the reliable estimated information is used for AUD,and the unreliable information will be further handled based on estimated symbol energy and total accurate or approximate number of active users.Then,the proposed algorithm for AUD in single-carrier model can be extended to multi-carrier case by exploiting the block sparse structure.Besides,we propose a low complexity MUD detection algorithm based on alternating minimization to estimate the active users’data,which avoids the Hessian matrix inverse.The convergence and the complexity of proposed algorithms are analyzed and discussed finally.Simulation results show that the proposed algorithms have better performance in terms of AUD,CE and MUD.Moreover,we can detect active users perfectly for multi-carrier NOMA system.

Geospatial Analytics for COVID-19 Active Case Detection

Ever since the COVID-19 pandemic started in Wuhan,China,much research work has been focusing on the clinical aspect of SARS-CoV-2.Researchers have been leveraging on various Artificial Intelligence techniques as an alternative to medical approach in understanding the virus.Limited studies have,however,reported on COVID-19 transmission pattern analysis,and using geography features for prediction of potential outbreak sites.Predicting the next most probable outbreak site is crucial,particularly for optimizing the planning of medical personnel and supply resources.To tackle the challenge,this work proposed distance-based similarity measures to predict the next most probable outbreak site together with its magnitude,when would the outbreak likely to happen and the duration of the outbreak.The work began with preprocessing of 1365 patient records from six districts in the most populated state named Selangor in Malaysia.The dataset was then aggregated with population density information and human elicited geography features that might promote the transmission of COVID-19.Empirical findings indicated that the proposed unified decision-making approach outperformed individual distance metric in predicting the total cases,next outbreak location,and the time interval between start dates of two similar sites.Such findings provided valuable insights for policymakers to perform Active Case Detection.

Multi-Panel Extra-Large Scale MIMO Based Joint Activity Detection and Channel Estimation for Near-Field Massive IoT Access

The extra-large scale multiple-input multiple-output(XL-MIMO)for the beyond fifth/sixth generation mobile communications is a promising technology to provide Tbps data transmission and stable access service.However,the extremely large antenna array aperture arouses the channel near-field effect,resulting in the deteriorated data rate and other challenges in the practice communication systems.Meanwhile,multi-panel MIMO technology has attracted extensive attention due to its flexible configuration,low hardware cost,and wider coverage.By combining the XL-MIMO and multi-panel array structure,we construct multi-panel XL-MIMO and apply it to massive Internet of Things(IoT)access.First,we model the multi-panel XL-MIMO-based near-field channels for massive IoT access scenarios,where the electromagnetic waves corresponding to different panels have different angles of arrival/departure(AoAs/AoDs).Then,by exploiting the sparsity of the near-field massive IoT access channels,we formulate a compressed sensing based joint active user detection(AUD)and channel estimation(CE)problem which is solved by AMP-EM-MMV algorithm.The simulation results exhibit the superiority of the AMP-EM-MMV based joint AUD and CE scheme over the baseline algorithms.

Improved Active Islanding Detection Technique for Multi-Inverter Power System

Due to the increased penetration of multi-inverter distributed generation(DG)systems,different DG technologies,inverter control methods,and other inverter functions are challenging the capabilities of islanding detection.In addition,multi-inverter networks connecting the distribution system point of common coupling(PCC)create islanding at paralleling inverters,which adds the vulnerability of islanding detection.Furthermore,available islanding detection must overcome more challenges from non-detection zones(NDZs)under reduced power mismatches.Therefore,in this study,a new method called parameter self-adapting active islanding detection was utilized to minimize the dilution effect and reduce NDZs in multi-inverter power systems.The method utilizes an active frequency drift(AFD)method and applies a positive feedback gain of adoption parameters,which significantly minimizes NDZs at parallel inverters.The simulation and experimental outcomes indicate that the proposed method can effectively weaken the dilution effect in multi-inverter networks connecting the distribution system PCC.

Adaptive and augmented active anomaly detection on dynamic network traffic streams

Active anomaly detection queries labels of sampled instances and uses them to incrementally update the detection model,and has been widely adopted in detecting network attacks.However,existing methods cannot achieve desirable performance on dynamic network traffic streams because(1)their query strategies cannot sample informative instances to make the detection model adapt to the evolving stream and(2)their model updating relies on limited query instances only and fails to leverage the enormous unlabeled instances on streams.To address these issues,we propose an active tree based model,adaptive and augmented active prior-knowledge forest(A3PF),for anomaly detection on network trafic streams.A prior-knowledge forest is constructed using prior knowledge of network attacks to find feature subspaces that better distinguish network anomalies from normal traffic.On one hand,to make the model adapt to the evolving stream,a novel adaptive query strategy is designed to sample informative instances from two aspects:the changes in dynamic data distribution and the uncertainty of anomalies.On the other hand,based on the similarity of instances in the neighborhood,we devise an augmented update method to generate pseudo labels for the unlabeled neighbors of query instances,which enables usage of the enormous unlabeled instances during model updating.Extensive experiments on two benchmarks,CIC-IDS2017 and UNSW-NB15,demonstrate that A3PF achieves significant improvements over previous active methods in terms of the area under the receiver operating characteristic curve(AUC-ROC)(20.9%and 21.5%)and the area under the precision-recall curve(AUC-PR)(44.6%and 64.1%).

Artificial neural network-based method for discriminating Compton scattering events in high-purity germaniumγ-ray spectrometer

To detect radioactive substances with low activity levels,an anticoincidence detector and a high-purity germanium(HPGe)detector are typically used simultaneously to suppress Compton scattering background,thereby resulting in an extremely low detection limit and improving the measurement accuracy.However,the complex and expensive hardware required does not facilitate the application or promotion of this method.Thus,a method is proposed in this study to discriminate the digital waveform of pulse signals output using an HPGe detector,whereby Compton scattering background is suppressed and a low minimum detectable activity(MDA)is achieved without using an expensive and complex anticoincidence detector and device.The electric-field-strength and energy-deposition distributions of the detector are simulated to determine the relationship between pulse shape and energy-deposition location,as well as the characteristics of energy-deposition distributions for fulland partial-energy deposition events.This relationship is used to develop a pulse-shape-discrimination algorithm based on an artificial neural network for pulse-feature identification.To accurately determine the relationship between the deposited energy of gamma(γ)rays in the detector and the deposition location,we extract four shape parameters from the pulse signals output by the detector.Machine learning is used to input the four shape parameters into the detector.Subsequently,the pulse signals are identified and classified to discriminate between partial-and full-energy deposition events.Some partial-energy deposition events are removed to suppress Compton scattering.The proposed method effectively decreases the MDA of an HPGeγ-energy dispersive spectrometer.Test results show that the Compton suppression factors for energy spectra obtained from measurements on ^(152)Eu,^(137)Cs,and ^(60)Co radioactive sources are 1.13(344 keV),1.11(662 keV),and 1.08(1332 keV),respectively,and that the corresponding MDAs are 1.4%,5.3%,and 21.6%lower,respectively.

Combined Extracting Process and Activity Detection of Porcine Blood Superoxide Dismutase

[Objective] TO study the combined extracting process of porcine blood superoxide dismutase （SOD） and other bioactive substances and thus to provide technical basis for making full use of blood resources and large-scale production of SOD. [Method] Fibronectin, immunoglobulin, and hemoglobin were isolated from porcine blood, and SOD was extracted. Trace pyrogallol self-oxidation method to determine SOD activity was modified by optimizing the volume of pyrogallol and SOD samples, reaction temperature, and buffer pH. The specific activity of SOD was determined with the optimized extraction conditions. [ Result] The improved experimental conditions of SOD activity detection were as follows： 7 pyrogallol （50 mmol/L）, 3 ml Tris-HCI （50 mmol/L, pH 8.2）, reactive temperature at 25（3, and 10 pl SOD sample solution. The specific activity of extracted SOD was 5 056 U/mg protein. [ Conclusion] Four kinds of bioactive substance can be isolated from porcine blood by modern biological engi- neering integration technology, and the extracted SOD has better activity.

Electrochemically reduced graphene oxide with enhanced electrocatalytic activity toward tetracycline detection

An electrochemically reduced graphene oxide sample, ERGO_0.8v, was prepared by electrochemical reduction of graphene oxide （GO） at -0.8 V, which shows unique electrocatalytic activity toward tetracycline （TTC） detection compared to the ERGO-12v （GO applied to a negative potential of-1.2 V）, GO, chemically reduced GO （CRGO）-modified glassy carbon electrode （GC） and bare GC electrodes. The redox peaks of TTC on an ERGO-0.8v-modifled glass carbon electrode （GC/ERGO-0.8v） were within 0-0.5 V in a pH 3.0 buffer solution with the oxidation peak current correlating well with TTC concentration over a wide range from 0.1 to 160 mg/L Physical characterizations with Fourier transform infrared （FT-IR）, Raman, and X-ray photoelectron spectroscopies （XPS） demonstrated that the oxygen-containing functional groups on GO diminished after the electrochemical reduction at -0.8 V, yet still existed in large amounts, and the defect density changed as new sp2 domains were formed. These changes demonstrated that this adjustment in the number of oxygen-containing groups might be the main factor affecting the electrocatalytic behavior of ERGO. Additionally, the defect density and sp2 domains also exert a profound influence on this behavior. A possible mechanism for the TTC redox reaction at the GC/ERGO-0.8v electrode is also presented. This work suggests that the electrochemical reduction is an effective method to establish new catalytic activities of GO by setting appropriate parameters.

Voice activity detection based on deep belief networks using likelihood ratio

A novel technique is proposed to improve the performance of voice activity detection(VAD) by using deep belief networks(DBN) with a likelihood ratio(LR). The likelihood ratio is derived from the speech and noise spectral components that are assumed to follow the Gaussian probability density function(PDF). The proposed algorithm employs DBN learning in order to classify voice activity by using the input signal to calculate the likelihood ratio. Experiments show that the proposed algorithm yields improved results in various noise environments, compared to the conventional VAD algorithms. Furthermore, the DBN based algorithm decreases the detection probability of error with [0.7, 2.6] compared to the support vector machine based algorithm.

Speech enhancement through voice activity detection using speech absence probability based on Teager energy

In this work, a novel voice activity detection （VAD） algorithm that uses speech absence probability （SAP） based on Teager energy （TE） was proposed for speech enhancement. The proposed method employs local SAP （LSAP） based on the TE of noisy speech as a feature parameter for voice activity detection （VAD） in each frequency subband, rather than conventional LSAP. Results show that the TE operator can enhance the abiTity to discriminate speech and noise and further suppress noise components. Therefore, TE-based LSAP provides a better representation of LSAP, resulting in improved VAD for estimating noise power in a speech enhancement algorithm. In addition, the presented method utilizes TE-based global SAP （GSAP） derived in each frame as the weighting parameter for modifying the adopted TE operator and improving its performance. The proposed algorithm was evaluated by objective and subjective quality tests under various environments, and was shown to produce better results than the conventional method.

Speech detection method based on a multi-window analysis

Aiming at the poor performance of speech signal detection at low signal-to-noise ratio(SNR),a method is proposed to detect active speech frames based on multi-window time-frequency(T-F)diagrams.First,the T-F diagram of the signal is calculated based on a multi-window T-F analysis,and a speech test statistic is constructed based on the characteristic difference between the signal and background noise.Second,the dynamic double-threshold processing is used for preliminary detection,and then the global double-threshold value is obtained using K-means clustering.Finally,the detection results are obtained by sequential decision.The experimental results show that the overall performance of the method is better than that of traditional methods under various SNR conditions and background noises.This method also has the advantages of low complexity,strong robustness,and adaptability to multi-national languages.

VOICE ACTIVITY DETECTION UNDER RAYLEIGH DISTRIBUTION

This paper presents an improved Voice Activity Detection （VAD） algorithm which uses the Signal-to-Noise Ratio （SNR） measure. We assume that noise Power Spectral Density （PSD） in each spectral bin follows a Rayleigh distribution. Rayleigh distributions with its asymmetric tail characteristics give a better description of the noise PSD distribution than Gaussian distribution. Under this asstlmption, a new threshold updating expression is derived. Since the analytical integral of the false alarm probability, the threshold updating expression can be represented without the inverse complementary error function and low computational complexity is achieved in our system. Experimental results show that the proposed VAD outperforms or at least is comparable with the VAD scheme presented by Davis under several noise environments and has a lower computational complexity.

Activity Detection for Enhanced Configured-Grant:A Practical Perspective

We try to extend the current configuredgrant(CG)uplink scheme in 5G New Radio(NR)to support massive potential users and study activity detection under this scenario.Characteristics of the continuously varying channel and the multiple repetition scheme are utilized to improve the detection accuracy,which can be an enhancement to existing activity detection algorithms.Numerical results under 3 GPP TDL(Tapped Delay Line)fading channel show the superiority of our algorithm.And system-level simulation reveals that enhancements on activity detection can improve reliability and reduce latency.

Development of a Detection Method for Assaying Calf Intestine Alkaline Phosphatase Activity

[ Objective] This study was to develop a detection method for assaying calf intestine alkaline phasphatase activity in scientific research. [ Method ] By simulating the conditions and buffers for assaying calf intestine alkaline phosphatase used in the scientific research, the parameters influencing substmte concentration, reaction duration, eoloration time and buffer pH were optimized. [ Result] The activity of alkaline phosphatase detected varied hugely among different buffers, and potassium ferricyanide solution added with boracic acid achieved the stable coloration. The optimal water bath time was determined as 10 rain, and the substrate concentration was optimized as 0.04 moL/L. The increasing temperature did not have a large influence on low temperature enzymatic activity while did on high coneentration enzymatic activity. When the buffer pH was 7.0 - 8.0, the detection stability of alkaline phosphatase could be maintained well The ion intensity of buff- er had little effects on alkaline phasphatase activity. [ Conclusion] A detection method for assaying calf intestine alkaline phosphatase activity in scientific research was successfully developed in this study.

IMPROVING VOICE ACTIVITY DETECTION VIA WEIGHTING LIKELIHOOD AND DIMENSION REDUCTION

The performance of the traditional Voice Activity Detection （VAD） algorithms declines sharply in lower Signal-to-Noise Ratio （SNR） environments. In this paper, a feature weighting likelihood method is proposed for noise-robust VAD. The contribution of dynamic features to likelihood score can be increased via the method, which improves consequently the noise robustness of VAD. Divergence based dimension reduction method is proposed for saving computation, which reduces these feature dimensions with smaller divergence value at the cost of degrading the performance a little. Experimental results on Aurora Ⅱ database show that the detection performance in noise environments can remarkably be improved by the proposed method when the model trained in clean data is used to detect speech endpoints. Using weighting likelihood on the dimension-reduced features obtains comparable, even better, performance compared to original full-dimensional feature.

Abnormal activity detection for surveillance video synopsis

Video synopsis is an effective and innovative way to produce short video abstraction for huge video archives,while keeping the dynamic characteristic of activities in the original video.Abnormal activity,as the critical event,is always the main concern in video surveillance context.However,in traditional video synopsis,all the normal and abnormal activities are condensed together equally,which can make the synopsis video confused and worthless.In addition,the traditional video synopsis methods always neglect redundancy in the content domain.To solve the above-mentioned issues,a novel video synopsis method is proposed based on abnormal activity detection and key observation selection.In the proposed algorithm,activities are classified into normal and abnormal ones based on the sparse reconstruction cost from an atomically learned activity dictionary.And key observation selection using the minimum description length principle is conducted for eliminating content redundancy in normal activity.Experiments conducted in publicly available datasets demonstrate that the proposed approach can effectively generate satisfying synopsis videos.

Clinical Detection of Peripheral Blood Natural Killer Cell Activity

Natural Killer (NK) cells are specific immune cells in human immune system. They have a quick effect and can exert a cytotoxic function without prior sensitization, and they show great application potential in cell-based immunotherapy, anti-infection in vivo. NK cell activity in peripheral blood can be used as one of the biomarkers of immune function response. It has a great positive guiding significance for the clinical prognosis of tumor patients, the prevention of cancer and anti-aging. The clinical detection strategies of NK cell activity in circulation mainly grouped into five types: methyl thiazolyl tetrazolium colorimetric, lactate dehydrogenase release, radionuclide labeling, flow cytometry and NK Vue cytokine release method. It has played an important role in different stages of clinical application development. This paper will make a comparative review of the above-mentioned detection strategies for the NK cell activity.