Fluorescent Double Network Hydrogels with Ionic Responsiveness and High Mechanical Properties for Visual Detection

We developed a fluorescent double network hydrogel with ionic responsiveness and high mechanical properties for visual detection.The nanocomposite hydrogel of laponite and polyacrylamide serves as the first network,while the ionic cross-linked hydrogel of terbium ions and sodium alginate serves as the second network.The double-network structure,the introduction of nanoparticles and the reversible ionic crosslinked interactions confer high mechanical properties to the hydrogel.Terbium ions are not only used as the ionic cross-linked points,but also used as green emitters to endow hydrogels with fluorescent properties.On the basis of the “antenna effect” of terbium ions and the ion exchange interaction,the fluorescence of the hydrogels can make selective responses to various ions(such as organic acid radical ions,transition metal ions) in aqueous solutions,which enables a convenient strategy for visual detection toward ions.Consequently,the fluorescent double network hydrogel fabricated in this study is promising for use in the field of visual sensor detection.

Automatic Sensing and Detection for Subway Tunnel Pathologies

Subway tunnels often suffer from surface pathologies such as cracks,corrosion,fractures,peeling,water and sand infiltration,and sudden hazards caused by foreign object intrusions.Installing a mobile visual pathology sensing system at the front end of operating trains is a critical measure to ensure subway safety.Taking leakage as the typical pathology,a tunnel pathology automatic visual detection method based on Deeplabv3+(ASTPDS)was proposed to achieve automatic and high-precision detection and pixel-level morphology extraction of pathologies.Compared with similar methods,this approach showed significant advantages and achieved a detection accuracy of 93.12%,surpassing FCN and U-Net.Moreover,it also exceeded the recall rates for detecting leaks of FCN and U-Net by 8.33%and 8.19%,respectively.

Integration system research and development for three-dimensional laser scanning information visualization in goaf

An integration processing system of three-dimensional laser scanning information visualization in goaf was developed. It is provided with multiple functions, such as laser scanning information management for goaf, cloud data de-noising optimization, construction, display and operation of three-dimensional model, model editing, profile generation, calculation of goaf volume and roof area, Boolean calculation among models and interaction with the third party soft ware. Concerning this system with a concise interface, plentiful data input/output interfaces, it is featured with high integration, simple and convenient operations of applications. According to practice, in addition to being well-adapted, this system is favorably reliable and stable.

Visual Detection of Vibrio parahaemolyticus using Combined CRISPR/Cas12a and Recombinase Polymerase Amplification

Objective To establish an ultra-sensitive,ultra-fast,visible detection method for Vibrio parahaemolyticus(VP).Methods We established a new method for detecting the tdh and trh genes of VP using clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 12a(CRISPR/Cas12a)combined with recombinase polymerase amplification and visual detection(CRISPR/Cas12a-VD).Results CRISPR/Cas12a-VD accurately detected target DNA at concentrations as low as 10^(-18)M(single molecule detection)within 30 min without cross-reactivity against other bacteria.When detecting pure cultures of VP,the consistency of results reached 100%compared with real-time PCR.The method accurately analysed pure cultures and spiked shrimp samples at concentrations as low as 10^(2)CFU/g.Conclusion The novel CRISPR/Cas12a-VD method for detecting VP performed better than traditional detection methods,such as real-time PCR,and has great potential for preventing the spread of pathogens.

A Cognitive Memory-Augmented Network for Visual Anomaly Detection

With the rapid development of automated visual analysis,visual analysis systems have become a popular research topic in the field of computer vision and automated analysis.Visual analysis systems can assist humans to detect anomalous events(e.g.,fighting,walking alone on the grass,etc).In general,the existing methods for visual anomaly detection are usually based on an autoencoder architecture,i.e.,reconstructing the current frame or predicting the future frame.Then,the reconstruction error is adopted as the evaluation metric to identify whether an input is abnormal or not.The flaws of the existing methods are that abnormal samples can also be reconstructed well.In this paper,inspired by the human memory ability,we propose a novel deep neural network(DNN)based model termed cognitive memory-augmented network(CMAN)for the visual anomaly detection problem.The proposed CMAN model assumes that the visual analysis system imitates humans to remember normal samples and then distinguishes abnormal events from the collected videos.Specifically,in the proposed CMAN model,we introduce a memory module that is able to simulate the memory capacity of humans and a density estimation network that can learn the data distribution.The reconstruction errors and the novelty scores are used to distinguish abnormal events from videos.In addition,we develop a two-step scheme to train the proposed model so that the proposed memory module and the density estimation network can cooperate to improve performance.Comprehensive experiments evaluated on various popular benchmarks show the superiority and effectiveness of the proposed CMAN model for visual anomaly detection comparing with the state-of-the-arts methods.The implementation code of our CMAN method can be accessed at https://github.com/CMANcode/CMAN_pytorch.

Sequential Pattern Technology for Visual Fire Detection

Visual fire detection technologies can detect fire and alarm warnings earlier than conventional fire detectors. This study proposes an effective visual fire detection method that combines the statistical fire color model and sequential pattern mining technology to detect fire in an image. Furthermore, the proposed method also supports real-time fire detection by integrating adaptive background subtraction technologies. Experimental results show that the proposed method can effectively detect fire in test images and videos. The detection accuracy of the proposed hybrid method is better than that of Celik＇s method.

Vehicle Detection Based on Visual Saliency and Deep Sparse Convolution Hierarchical Model

Traditional vehicle detection algorithms use traverse search based vehicle candidate generation and hand crafted based classifier training for vehicle candidate verification.These types of methods generally have high processing times and low vehicle detection performance.To address this issue,a visual saliency and deep sparse convolution hierarchical model based vehicle detection algorithm is proposed.A visual saliency calculation is firstly used to generate a small vehicle candidate area.The vehicle candidate sub images are then loaded into a sparse deep convolution hierarchical model with an SVM-based classifier to perform the final detection.The experimental results demonstrate that the proposed method is with 94.81% correct rate and 0.78% false detection rate on the existing datasets and the real road pictures captured by our group,which outperforms the existing state-of-the-art algorithms.More importantly,high discriminative multi-scale features are generated by deep sparse convolution network which has broad application prospects in target recognition in the field of intelligent vehicle.

Visual positioning of rectangular lead components based on Harris corners and Zernike moments

With the increasing necessities for reliable printed circuit board(PCB) product, there has been a considerable demand for high speed and high precision vision positioning system. To locate a rectangular lead component with high accuracy and reliability, a new visual positioning method was introduced. Considering the limitations of Ghosal sub-pixel edge detection algorithm, an improved algorithm was proposed, in which Harris corner features were used to coarsely detect the edge points and Zernike moments were adopted to accurately detect the edge points. Besides, two formulas were developed to determine the edge intersections whose sub-pixel coordinates were calculated with bilinear interpolation and conjugate gradient method. The last experimental results show that the proposed method can detect the deflection and offset, and the detection errors are less than 0.04° and 0.02 pixels.

Visualization Detection of Solid-Liquid Two-Phase Flow in Filling Pipeline by Electrical Capacitance Tomography Technology

During mine filling,the caking in the pipeline and the waste rock in the filling slurry may cause serious safety accidents such as pipe blocking or explosion.Therefore,the visualization of the innermine filling of the solid-liquid two-phase flow in the pipeline is important.This paper proposes a method based on capacitance tomography for the visualization of the solid-liquid distribution on the section of a filling pipe.A feedback network is used for electrical capacitance tomography reconstruction.This reconstruction method uses radial basis function neural network fitting to determine the relationship between the capacitance vector and medium distribution error.In the reconstruction process,the error in the linear back projection is removed;thus,the reconstruction problem becomes an accurate linear problem.The simulation results showthat the reconstruction accuracy of this algorithm is better than that of many traditional algorithms;furthermore,the reconstructed image artifacts are fewer,and the phase distribution boundary is clearer.This method can help determine the location and size of the caking and waste rock in the cross section of the pipeline more accurately and has great application prospects in the visualization of filling pipelines in mines.

Paper-based biosensors based on multiple recognition modes for visual detection of microbially contaminated food

Microbially contaminated food can cause serious health hazards and economic losses,therefore sensitive,rapid,and highly specific visual detection is called for.Traditional detection of microorganisms is complex and time-consuming,which cannot meet current testing demands.The emergence of paper-based biosensors provided an effective method for efficient and visual detection of microorganisms,due to its high speed,all-in-one device,low cost,and convenience.This review focused on 5 biomarkers,namely nucleic acids,proteins,lipopolysaccharides.metabolites,and the whole microorganism of microorganisms.Besides,the recognition methods were summed up in 5 forms,including immunological recognition,aptamer recognition,nucleic acid amplification-mediated recognition.DNAzyme recognition and clustered regularly interspaced short palindromic repeats mediated recognition.In addition,we summarized the applications of paper-based biosensors in the detection of microorganisms thoroughly.Through the exploration of different biomarkers,identification methods,and applications,we hope to provide a reference for the development of paper-based biosensors and their application in safeguarding the food chain.

A fast detection algorithm for ceramic ball surface defects based on fringe reflection

A ceramic ball is a basic part widely used in precision bearings.There is no perfect testing equipment for ceramic ball surface defects at present.A fast visual detection algorithm for ceramic ball surface defects based on fringe reflection is designed.By means of image preprocessing,grayscale value accumulative differential positioning,edge detection,pixel-value row difference and template matching,the algorithm can locate feature points and judge whether the spherical surface has defects by the number of points.Taking black silicon nitride ceramic balls with a diameter of 6.35 mm as an example,the defect detection time for a single gray scale image is 0.78 s,and the detection limit is 16.5μm.

Fast Image Segmentation Algorithm Based on Salient Features Model and Spatial-frequency Domain Adaptive Kernel

A fast image segmentation algorithm based on salient features model and spatial-frequency domain adaptive kernel is proposed to solve the accurate discriminate objects problem of online visual detection in such scenes of variable sample morphological characteristics,low contrast and complex background texture.Firstly,by analyzing the spectral component distribution and spatial contour feature of the image,a salient feature model is established in spatial-frequency domain.Then,the salient object detection method based on Gaussian band-pass filter and the design criterion of adaptive convolution kernel are proposed to extract the salient contour feature of the target in spatial and frequency domain.Finally,the selection and growth rules of seed points are improved by integrating the gray level and contour features of the target,and the target is segmented by seeded region growing.Experiments have been performed on Berkeley Segmentation Data Set,as well as sample images of online detection,to verify the effectiveness of the algorithm.The experimental results show that the Jaccard Similarity Coefficient of the segmentation is more than 90%,which indicates that the proposed algorithm can availably extract the target feature information,suppress the background texture and resist noise interference.Besides,the Hausdorff Distance of the segmentation is less than 10,which infers that the proposed algorithm obtains a high evaluation on the target contour preservation.The experimental results also show that the proposed algorithm significantly improves the operation efficiency while obtaining comparable segmentation performance over other algorithms.

Multifunctional Semiconducting Fibers for Visual Detection of Sarin Gas

There is a growing need for protective instruments that can be used in extreme environments,including those encountered during exoplanet exploration,anti-terrorism activities,and in chemical plants.These instruments should have the ability to detect external threats visually and monitor internal physiological signals in real time for maximum safety.To address this need,multifunctional semiconducting fibers with visual detection ranging from yellow to red and near-field communication(NFC)capabilities have been developed for use in personal protective clothing.A composite conductive yarn with semiconducting fluorescent probe molecules is embroidered on the clothing,forming an NFC coil that allows for the visual monitoring of atmospheric safety through color changes.The fluorescence detection system was able to selectively detect diethyl chlorophosphate(DCP),a substitute for the toxic gas sarin,with a detection limit of 6.08 ppb,which is lower than the life-threatening concentration of sarin gas.Furthermore,an intelligent protective suit with the abovementioned dual functions was fabricated with good mechanical cycle stability and repeatability.Real-time physiological signals such as the temperature and humidity of the wearer could be read through the NFC conveniently.Such intelligent protective suits can quickly provide an early warning to the identified low-dose DCP and evaluate the health of wearer according to the changes in physiological signals.This study offers a smart,low-cost strategy for designing intelligent protective devices for extreme environments.

Establishment of RT-LAMP Assay for Infectious Pancreatic Necrosis Virus

The purpose of this study was to establish a method for the rapid detection of infectious pancreatic necrosis virus(IPNV,Jasper serotype)using reverse transcription loop-mediated isothermal amplification(RT-LAMP).Four groups of specific primers were designed,according to the genome sequence of a Chinese IPNV isolate ChRtm213.The results showed that primer set B2 had the best amplification effect.When the final concentration of Mg2+was 6 mmol·L-1,dNTPs were 1 mmol·L-1 and betaine was 0.4 mol·L-1,the reaction could be completed in a 63℃water bath within 60 min.This RT-LAMP assay for the detection of IPNV had no cross-reactivity with infectious hematopoietic necrosis virus,viral hemorrhagic septicemia virus,grass carp reovirus and spring viremia of carp virus.The detection limit was 3.2×10-12 ng·μL-1.The sensitivity of this method was 10-fold higher than that of a previously published RT-LAMP assay for detecting the Spajarup(Sp)serotype of IPNV.This method,aimed at detecting IPNV isolates that were currently prevalent in China,possessed the characteristics of strong specificity,high sensitivity and direct interpretation by the naked eyes.The IPNV RT-LAMP was successfully applied to determine the clinical samples,which indicated the IPNV RT-LAMP assay was suitable for the rapid and large-scale detections of IPNV in China.

Biogenic amines detection in meat and meat products:the mechanisms,applications,and future trends

Biogenic amine is one of the main categories of hazardous compounds in meat and meat products,making its detection methods vital for the assurance of edible safety.In this sense,many biogenic amine detection techniques such as chromatographic,electrophoretic,and electrochemical methods have been developed,which play an irreplaceable role in ensuring the safety of meat and meat products.Due to the increasing demand for fast and on-site detection techniques,visual detection methods have been gradually developed compared with non-visual methods such as chromatography and electrophoresis.Herein,we comprehensively review the mechanism and the latest progress of these biogenic amine detection methods;besides,we put forward the prospects for the future development of biogenic amine detection techniques,with a view to providing support for the establishment of more accurate and efficient detection,prevention and control strategies of biogenic amines.

Bioinspired bioassay platforms derived from colloidal crystals with topological shapes

Colloidal crystals are materials self-assembled from the colloidal nanoparticles.Due to the ordered microstructure,they exhibit significant optical properties and have shown huge potential in the field of biosensing.Besides,the unique macroscopic shapes can also play a critical role in the sensing process.Here,we present a comprehensive discussion on the colloidal crystal-based biosensors with different topological shapes,including the development strategies of currently reported colloidal crystal particles,films,and fibers,and their recent progress in biosensing.In addition,the faced challenges and the possible solutions are also concluded and discussed.We expect this review can enrich the knowledge and encourage the communication of interdisciplinary researchers,thus promoting the further development and practical applications of colloidal crystal-based biosensors.

Detection and removal of excess materials in aircraft wings using continuum robot end-effectors

Excess materials are left inside aircraft wings due to manual operation errors,and the removal of excess materials is very crucial.To increase removal efficiency,a continuum robot(CR)with a removal end-effector and a stereo camera is used to remove excess objects.The size and weight characteristics of excess materials in aircraft wings are analyzed.A novel negative pressure end-effector and a two-finger gripper are designed based on the CR.The negative pressure end-effector aims to remove nuts,small rivets,and small volumes of aluminum shavings.A two-finger gripper is designed to remove large volumes of aluminum shavings.A stereo camera is used to achieve automatic detection and localization of excess materials.Due to poor lighting conditions in the aircraft wing compartment,supplementary lighting devices are used to improve environmental lighting.Then,You Only Look Once(YOLO)v5 is used to classify and detect excess objects,and two training data sets of excess objects in two wings are constructed.Due to the limited texture features inside the aircraft wings,this paper adopts an image-matching method based on the results of YOLO v5 detection.This matching method avoids the performance instability problem based on Oriented Fast and Rotated BRIEF feature point matching.Experimental verification reveals that the detection accuracy of each type of excess exceeds 90%,and the visual localization error is less than 2 mm for four types of excess objects.Results show the two end-effectors can work well for the task of removing excess material from the aircraft wings using a CR.

Visual Detection of COVID‑19 from Materials Aspect

In the recent COVID-19 pandemic,World Health Organization emphasized that early detection is an effective strategy to reduce the spread of SARS-CoV-2 viruses.Several diagnostic methods,such as reverse transcription-polymerase chain reaction(RT-PCR)and lateral flow immunoassay(LFIA),have been applied based on the mechanism of specific recognition and binding of the probes to viruses or viral antigens.Although the remarkable progress,these methods still suffer from inadequate cellular materials or errors in the detection and sampling procedure of nasopharyngeal/oropharyngeal swab collection.Therefore,developing accurate,ultrafast,and visualized detection calls for more advanced materials and technology urgently to fight against the epidemic.In this review,we first summarize the current methodologies for SARS-CoV-2 diagnosis.Then,recent representative examples are introduced based on various output signals(e.g.,colorimetric,fluorometric,electronic,acoustic).Finally,we discuss the limitations of the methods and provide our perspectives on priorities for future test development.

Chip-based visual detection of micro RNA using DNA-functionalized gold nanoparticles

In the present study, we developed a highly sensitive and convenient biosensor consisting of gold nanoparticle （AuNP） probes and a gene chip to detect microRNAs （miRNAs）. Specific oligonucleotides were attached to the glass surface as capture probes for the target miRNAs, which were then detected via hybridization to the AuNP probes. The signal was amplified via the re- duction of HAuCI4 by H202. The use of a single AuNP probe detected 10 pmol L-1 of target miRNA. The recovery rate for miR-126 from fetal bovine serum was 81.5%-109.1%. The biosensor detection of miR-126 in total RNA extracted from lung cancer tissues was consistent with the quantitative PCR （qPCR） results. The use of two AuNP probes further improved the de- tection sensitivity such that even 1 fmol L-t of target miR-125a-5p was detectable. This assay takes less than 1 h to complete and the results can be observed by the naked eye, The platform simultaneously detected lung cancer related miR-126 and miR-125a-5p. Therefore, this low cost, rapid, and convenient technology could be used for ultrasensitive and robust visual miRNA detection.

A ratiometric fluorescent nanoprobe based on ZIF-8@AuNCs-Tb for visual detection of 2,6-pyridinedicarboxylic acid

The rapid and sensitive detection of 2,6-pyridinedicarboxylic acid(DPA),one of the main biomarkers of Bacillus anthracis,is of great significance for the screening and diagnosing of anthrax.Herein,a ratiometric fluorescent nanoprobe based on zeolite imidazolate framework-8(ZIF-8)@AuNCs-Tb was constructed by embedding both gold nanoclusters(AuNCs)and terbium ions(Tb^(3+))into ZIF-8 for highresolution visual detection of DPA.Due to the aggregation induced emission enhancement(AIE)effect,AuNCs embedded in ZIF-8 emit a strong orange fluorescence.When Tb^(3+)is coordinated with DPA added to the nanoprobe,it will emit a strong green fluorescence owing to the antenna effect.The results reveal that ZIF-8@AuNCs-Tb nanoprobe can detect DPA effectively with a good linear relationship in the range of 40-200 and 200-1000μmol/L,the limit of detection(LOD)is estimated at 1.8μmol/L(3σ/k).The proposed nanoprobe shows a remarkable selectivity for DPA and is quite easy to realize visualization based on the fluorescent color changing from orange to green,which has potential application in clinical diagnosis.The feasibility of this method was verified by standard addition recovery experiments simulating the release of DPA from spores.