Development of an Integrated Disposable Device for SARSCoV-2 Nucleic Acid Extraction and Detection

Objective To develop a highly sensitive and rapid nucleic acid detection method for the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).Methods We designed,developed,and manufactured an integrated disposable device for SARS-CoV-2 nucleic acid extraction and detection.The precision of the liquid transfer and temperature control was tested.A comparison between our device and a commercial kit for SARS-Cov-2 nucleic acid extraction was performed using real-time fluorescence reverse transcription polymerase chain reaction(RT-PCR).The entire process,from SARS-CoV-2 nucleic acid extraction to amplification,was evaluated.Results The precision of the syringe transfer volume was 19.2±1.9μL(set value was 20),32.2±1.6(set value was 30),and 57.2±3.5(set value was 60).Temperature control in the amplification tube was measured at 60.0±0.0℃(set value was 60)and 95.1±0.2℃(set value was 95)respectively.SARS-Cov-2 nucleic acid extraction yield through the device was 7.10×10^(6) copies/mL,while a commercial kit yielded 2.98×10^(6) copies/mL.The mean time to complete the entire assay,from SARS-CoV-2 nucleic acid extraction to amplification detection,was 36 min and 45 s.The detection limit for SARS-CoV-2 nucleic acid was 250 copies/mL.Conclusion The integrated disposable devices may be used for SARS-CoV-2 Point-of-Care test(POCT).

Research on Fall Detection System Based on Commercial Wi-Fi Devices

Falls are a major cause of disability and even death in the elderly,and fall detection can effectively reduce the damage.Compared with cameras and wearable sensors,Wi-Fi devices can protect user privacy and are inexpensive and easy to deploy.Wi-Fi devices sense user activity by analyzing the channel state information(CSI)of the received signal,which makes fall detection possible.We propose a fall detection system based on commercial Wi-Fi devices which achieves good performance.In the feature extraction stage,we select the discrete wavelet transform(DWT)spectrum as the feature for activity classification,which can balance the temporal and spatial resolution.In the feature classification stage,we design a deep learning model based on convolutional neural networks,which has better performance compared with other traditional machine learning models.Experimental results show our work achieves a false alarm rate of 4.8%and a missed alarm rate of 1.9%.

A research on the effect of plasma spectrum collection device on LIBS spectral intensity

Only a small amount of spectral information is collected because the collection solid angle of the optical fiber probe and lens is very limited when collecting spectral information.To overcome this limitation,this study presents a novel method for acquiring plasma spectral information from various spatial directions.A parabolic-shaped plasma spectral collection device(PSCD)is employed to effectively collect more spectral information into the spectrometer,thereby enhancing the overall spectral intensity.The research objects in this study were soil samples containing different concentrations of heavy metals Pb,Cr,and Cd.The results indicate that the PSCD significantly enhances the spectral signal,with an enhancement rate of up to 45%.Moreover,the signal-to-noise ratio also increases by as much as 36%.Simultaneously,when compared to the absence of a device,it is found that there is no significant variation in plasma temperature when the PSCD is utilized.This observation eliminates the impact of the spatial effect caused by the PSCD on the spectral intensity.Consequently,a concentrationspectral intensity relationship curve is established under the PSCD.The results revealed that the linear fitting R^(2)for Pb,Cr,and Cd increased by 0.011,0.001,and 0.054,respectively.Additionally,the limit of detection(LOD)decreased by 0.361 ppm,0.901 ppm,and 0.602 ppm,respectively.These findings indicate that the spectral enhancement rate elevates with the increase in heavy metal concentration.Hence,the PSCD can effectively enhance the spectral intensity and reduce the detection limit of heavy metals in soil.

IoT Smart Devices Risk Assessment Model Using Fuzzy Logic and PSO

Increasing Internet of Things(IoT)device connectivity makes botnet attacks more dangerous,carrying catastrophic hazards.As IoT botnets evolve,their dynamic and multifaceted nature hampers conventional detection methods.This paper proposes a risk assessment framework based on fuzzy logic and Particle Swarm Optimization(PSO)to address the risks associated with IoT botnets.Fuzzy logic addresses IoT threat uncertainties and ambiguities methodically.Fuzzy component settings are optimized using PSO to improve accuracy.The methodology allows for more complex thinking by transitioning from binary to continuous assessment.Instead of expert inputs,PSO data-driven tunes rules and membership functions.This study presents a complete IoT botnet risk assessment system.The methodology helps security teams allocate resources by categorizing threats as high,medium,or low severity.This study shows how CICIoT2023 can assess cyber risks.Our research has implications beyond detection,as it provides a proactive approach to risk management and promotes the development of more secure IoT environments.

Research on Detection Device for Broken Wires of Coal Mine-Hoist Cable

In order to overcome the flaws of present domestic devices for detecting faulty wires such as low precision,low sensitivity and instability,a new instrument for detecting and processing the signal of flux leakage caused by bro-ken wires of coal mine-hoist cables is investigated. The principle of strong magnetic detection was adopted in the equipment. Wires were magnetized by a pre-magnetic head to reach magnetization saturation. Our special feature is that the number of flux-gates installed along the circle direction on the wall of sensors is twice as large as the number of strands in the wire cable. Neighboring components are connected in series and the interference on the surface of the wire cable,produced by leakage from the flux field of the wire strands,is efficiently filtered. The sampled signal se-quence produced by broken wires,which is characterized by a three-dimensional distribution of the flux-leakage field on the surface of the wire cable,can be dimensionally condensed and characteristically extracted. A model of a BP neu-ral network is built and the algorithm of the BP neural network is then used to identify the number of broken wires quantitatively. In our research,we used a 6×37+FC,Φ24 mm wire cable as our test object. Randomly several wires were artificially broken and damaged to different degrees. The experiments were carried out 100 times to obtain data for 100 groups from our samples. The data were then entered into the BP neural network and trained. The network was then used to identify a total 16 wires,broken at five different locations. The test data proves that our new device can enhance the precision in detecting broken and damaged wires.

A method and device for online magnetic resonance multiphase flow detection

Most multiphase flow separation detection methods used commonly in oilfields are low in efficiency and accuracy,and have data delay.An online multiphase flow detection method is proposed based on magnetic resonance technology,and its supporting device has been made and tested in lab and field.The detection technology works in two parts:measure phase holdup in static state and measure flow rate in flowing state.Oil-water ratio is first measured and then gas holdup.The device is composed of a segmented magnet structure and a dual antenna structure for measuring flowing fluid.A highly compact magnetic resonance spectrometer system and intelligent software are developed.Lab experiments and field application show that the online detection system has the following merits:it can measure flow rate and phase holdup only based on magnetic resonance technology;it can detect in-place transient fluid production at high frequency and thus monitor transient fluid production in real time;it can detect oil,gas and water in a full range at high precision,the detection isn’t affected by salinity and emulsification.It is a green,safe and energy-saving system.

Detection of Cracks in Aerospace Turbine Disks Using an Ultrasonic Phased Array C-scan Device

Crack detection in an aerospace turbine disk is essential for aircraft-quality detection.With the unique circular stepped structure and superalloy material properties of aerospace turbine disk,it is difficult for the traditional ultrasonic testing method to perform efficient and accurate testing.In this study,ultrasound phased array detection technology was applied to the non-destructive testing of aviation turbine disks:(i)A phased array ultrasonic c-scan device for detecting aerospace turbine disk cracks(PAUDA)was developed which consists of phased array ultrasonic,transducers,a computer,a displacement encoder,and a rotating scanner;(ii)The influence of the detection parameters include frequency,wave-type,and elements number of the ultrasonic phased array probe on the detection results on the near-surface and the far surface of the aerospace turbine disk is analyzed;(iii)Specimens with flat-bottom-hole(FBH)defects were scanned by the developed PAUDA and the results were analyzed and compared with the conventional single probe ultrasonic water immersion testing.The experiment shows that by using the ultrasonic phased array c-scan to scan the turbine disk the accuracy of the detection can be significantly improved which is of greater accuracy and higher efficiency than traditional immersion testing.

Research on Phase Detection of Liquid Crystal Optical Device

The technology for phase detection of liquid crystal optical device is a difficult research in current domestic and overseas. However, for the existing liquid crystal optical device, aiming at the poor anti-vibration capability and poor versatile of phase detection, the complexity of phase retrieval algorithm, we propose a new phase measurement principle and experimental methods of liquid crystal optical device. It is a phase measurement method based on the combination of phase- shifting interferometer and phase conjugation technology. The deflection characteristics of the liquid crystal device means the device can implement phase modulation to only one direction of polarized light while is completely transparent to orthogonal polarized light. We put forward the phase shift of the orthogonal polarization phase shift interferometer method, using phase shifting interference as well as the combination of phase conjugate means to achieve its phase measurement. So we can retrieves devices modulation phase simply and efficiently combines with phase- shifting interferometer technology.

Research on the Design and Implementation of Detection System of Nutz Framework Meteorological Metrical Device

In people’s daily life, the role of weatherforecast is self-evident. However, the accuracy offorecasting is based on the accuracy and reliability ofmeteorological data which depends on the sensitivityof meteorological device. Therefore, an importantduty of the detection institution of meteorologicalmetrical device is to have the effective detectionof meteorological device, so as to ensure a highsensitivity of the device. However, the meteorologicaldevice used by some meteorological bureaus is nottechnologically advanced and the device detectionmode is too old, which cannot meet the new regulationsissued by the China Meteorological Administration.So it is necessary for the meteorological bureau todevelop a set of devices that can easily meet the newmeteorological measurement requirements, which is ofgreat significance to ensure the accurate measurementof meteorological data.

Convolutional Neural Network Model for Fire Detection in Real-Time Environment

Disasters such as conflagration,toxic smoke,harmful gas or chemical leakage,and many other catastrophes in the industrial environment caused by hazardous distance from the peril are frequent.The calamities are causing massive fiscal and human life casualties.However,Wireless Sensors Network-based adroit monitoring and early warning of these dangerous incidents will hamper fiscal and social fiasco.The authors have proposed an early fire detection system uses machine and/or deep learning algorithms.The article presents an Intelligent Industrial Monitoring System(IIMS)and introduces an Industrial Smart Social Agent(ISSA)in the Industrial SIoT(ISIoT)paradigm.The proffered ISSA empowers smart surveillance objects to communicate autonomously with other devices.Every Industrial IoT(IIoT)entity gets authorization from the ISSA to interact and work together to improve surveillance in any industrial context.The ISSA uses machine and deep learning algorithms for fire-related incident detection in the industrial environment.The authors have modeled a Convolutional Neural Network(CNN)and compared it with the four existing models named,FireNet,Deep FireNet,Deep FireNet V2,and Efficient Net for identifying the fire.To train our model,we used fire images and smoke sensor datasets.The image dataset contains fire,smoke,and no fire images.For evaluation,the proposed and existing models have been tested on the same.According to the comparative analysis,our CNN model outperforms other state-of-the-art models significantly.

Ocular surface heat effects on ocular hemodynamics detected by real-time measuring device

AIM: To investigate the ocular hemodynamic effects of applying a hot compress to the eye.METHODS: The right eyes of five New Zealand white rabbits, both male and female, were hot-compressed for 18 min. An independently designed novel ocular contacttype temperature measuring device was used to measure the ocular surface temperature before and after the heating. Relevant retrobulbar hemodynamic parameters such as peak systolic velocity(PSV), end diastolic velocity(EDV), and resistance index(RI) of each of the central retinal artery(CRA), long posterior ciliary artery(LPCA), and ophthalmic artery(OA), as well as the mean velocity(V_m) of the central retinal vein(CRV), were measured using a color Doppler flow imaging(CDFI) technique and expressed as mean values with standard deviation(mean±SD). A statistical analysis was conducted based on a paired t-test and the Wilcoxon signed-rank test. RESULTS: The employed real-time temperature measuring device was able to accurately measure ocular surface temperature during the hot-compress process. The temperature increased after the hot compress was applied. Analysis showed that the PSV and EDV values of the CRA and LPCA significantly increased after the application of the hot compress, as did the V_m of the CRV. There were no significant changes in the EDV of the OA nor the RI of each artery. CONCLUSION: This experiment, which is the first of its kind, confirms that the retrobulbar blood flow velocities can increase upon heating the ocular surface. This simple method may be useful in the future.

Infrastructure of Synchrotronic Biosensor Based on Semiconductor Device Fabrication for Tracking, Monitoring, Imaging, Measuring, Diagnosing and Detecting Cancer Cells

Copper Zinc Antimony Sulfide(CZAS)is derived from Copper Antimony Sulfide(CAS),a famatinite class of compound.In the current paper,the first step for using Copper,Zinc,Antimony and Sulfide as materials in manufacturing synchrotronic biosensor-namely increasing the sensitivity of biosensor through creating Copper Zinc Antimony Sulfide,CZAS(Cu1.18Zn0.40Sb1.90S7.2)semiconductor and using it instead of Copper Tin Sulfide,CTS(Cu2SnS3)for tracking,monitoring,imaging,measuring,diagnosing and detecting cancer cells,is evaluated.Further,optimization of tris(2,2'-bipyridyl)ruthenium(II)(Ru(bpy)32+)concentrations and Copper Zinc Antimony Sulfide,CZAS(Cu1.18Zn0.40Sb1.90S7.2)semiconductor as two main and effective materials in the intensity of synchrotron for tracking,monitoring,imaging,measuring,diagnosing and detecting cancer cells are considered so that the highest sensitivity obtains.In this regard,various concentrations of two materials were prepared and photon emission was investigated in the absence of cancer cells.On the other hand,ccancer diagnosis requires the analysis of images and attributes as well as collecting many clinical and mammography variables.In diagnosis of cancer,it is important to determine whether a tumor is benign or malignant.The information about cancer risk prediction along with the type of tumor are crucial for patients and effective medical decision making.An ideal diagnostic system could effectively distinguish between benign and malignant cells;however,such a system has not been created yet.In this study,a model is developed to improve the prediction probability of cancer.It is necessary to have such a prediction model as the survival probability of cancer is high when patients are diagnosed at early stages.

Research on detection of lane based on machine vision

To prevent a vehicle from departing the lane in assistant or automatic steering, real-time vision-based detection of lane is studied. The system architecture, detecting principle and lane model are described. Then the detecting algorithm of the lane image is discussed in detail. In this algorithm, several proper sub-windows in one image are first selected as the processing regions. To every sub-window, by means of such steps as appropriate pre-processing, edge detection and Hough transform, etc., the lane description features are extracted. Experimental results reveal that this detection method is of good real-time, high recognition reliability and strong robustness, etc., which can provide the decision-making foundation for the following automatic or assistant steering to some extent.

Improve the Accuracy of Fall Detection Based on Artificial Intelligence Algorithm

This work presents a fall detection system based on artificial intelligence.The system incorporates miniature wearable devices for fall detection.Fall detection is achieved by integrating a three-axis gyroscope and a threeaxis accelerometer.The system gathers the differential data collected by the gyroscope and accelerometer,applies artificial intelligence algorithms for model training and constructs an effective model for fall detection.To provide easywearing and effective position detection,it is designed as a small device attached to the user’swaist.Experiment results have shown that the accuracy of the proposed fall detection model is up to 98%,demonstrating the effectiveness of the model in real-life fall detection.

Design and Realization of Signal Processing Platform of Multi-Parameter Wearable Medical Devices

This paper presents a design of new type of multi-parameter wearable medical devices signal processing platform. The signal processing algorithm has a QRS-wave detection algorithm based on LADT, wavelet transformation and threshold detection with TMS320VC5509 DSP system. The DSP can greatly increase the speed of QRS-wave detection, and the results can be practical used for multi-parameter wearable device detection of abnormal ECG.

Low Cost,Low Power Magnetic IEDs Detection Method Using Biaxial Magnetometry Digital Sensors

The magnetic improvised explosive devices （IEDs）, also commonly known as a type of a sticky bomb, is simply constructed devices yet very lethal. This paper puts forward the idea of an electronic compass that is capable of sensing the change of a magnetic field generated by a magnet and translating it into interpretable data, which could act as the base for the further studies and assist in developing a greener automated system for detecting this device. The electronic compass is specifically chosen for reducing power consumption of systems in addition to the fact that it is available at a low cost.

Dot Size Thermal Objects Detection in Atmosphere of Infrared Range

The article deals with the experimental studies of atmosphere indistinct radiation structure. The information extraction background of dot size thermal object presence in atmosphere is reasonable. Indistinct generalization of experimental study regularities technique of space-time irregularity radiation structure in infrared wave range is offered. The approach to dot size thermal object detection in atmosphere is proved with a help of threshold method in the thermodynamic and turbulent process conditions, based on the indistinct statement return task solution.

Research on Known Vulnerability Detection Method Based on Firmware Analysis

At present,the network security situation is becoming more and more serious.Malicious network attacks such as computer viruses,Trojans and hacker attacks are becoming more and more rampant.National and group network attacks such as network information war and network terrorism have a serious damage to the production and life of the whole society.At the same time,with the rapid development of Internet of Things and the arrival of 5G era,IoT devices as an important part of industrial Internet system,have become an important target of infiltration attacks by hostile forces.This paper describes the challenges facing firmware vulnerability detection at this stage,and introduces four automatic detection and utilization technologies in detail:based on patch comparison,based on control flow,based on data flow and ROP attack against buffer vulnerabilities.On the basis of clarifying its core idea,main steps and experimental results,the limitations of its method are proposed.Finally,combined with four automatic detection methods,this paper summarizes the known vulnerability detection steps based on firmware analysis,and looks forward to the follow-up work.

A Bionic Degassing Device Inspired by Gills:Application on Underwater Oil and Gas Detection

Over the past decades,membrane-based separation processes have found numerous applications in various industries.Membrane contactor is an important part of the separation of dissolved gas in the early stage of gas detection.In this paper,to improve efficiency in the detection of the dissolved gas phase in seawater,a better flat membrane contactor is proposed to achieve efficient degassing,inspired by the way fish breathe underwater and the special structure of fish gills.The bioinspired flow channel structures in the flat membrane contactor are suggested along with the distribution of internal blood vessels in the gill platelet and the feature of the gill platelet surface.Using 3D printing,the special degassing devices are manufactured,and comparative analysis of relevant flow parameters is made using different flow channels,combined with the CFD simulation.The final result showed that the proposed flow channel in the degasser achieves a better degassing effect compared with conventional flow channel when the membrane contact area is limited,which can provide good conditions for subsequent gas detection.

融合GhostNet的YOLOv4轻量化网络设计与实现

由于存储资源和功耗等限制,在嵌入式设备上部署基于深度学习的目标检测算法存在困难,且效果不佳.基于YOLOv4算法,提出了一种改进的YOLOv4-Light轻量化网络模型,采用GhostNet网络结构替换CSPDarknet53作为主干提取网络,引入CBAM注意力机制关注通道和空间两个维度的特征信息,并利用感知量化方法对权重进行INT8量化处理,在保证精度的情况下降低网络模型规模和参数量.在PC端和NVIDIA Jetson Xavier NX上选用VisDrone无人机数据集分别对网络模型进行测试,结果表明YOLOv4-GhostNet-CBAM模型的尺寸是160M,比YOLOv4降低了34.43%;检测速率最高可达到34.6FPS,比YOLOv4提高了56.6%.YOLO-Light模型的尺寸是40.2M,比YOLOv4降低了83.5%;检测速率最高可达到78.6FPS,为YOLOv4的3.6倍,且交并比为0.5时的平均精度均值(mAP50)与YOLOv4相比仅下降了3%.YOLO-Light模型相较于原模型优势明显,能够在低功耗的嵌入式设备上完成实时目标检测.