Actual Detection Research Articles

The luminescent Nb2C MXene nanosheet-based whispering gallery mode-enhanced ECL strategy for miRNA-214 detection

MiRNA-214 can regulate the expression of their downstream target genes after post-transcriptional and are involved in the biological processes of triple negative breast cancer (TNBC). In this work, the small-sized luminescent Nb2C nanosheet-based whispering gallery mode-enhanced electrochemiluminescence (ECL) strategy was successfully constructed to detect miRNA-214 in TNBC. Firstly, we have synthesized small-sized luminescent Nb2C nanosheets from Nb2AlC MXene. The Nb2C nanosheets not only exhibited more stable chemical properties and reduced the defects of the large sheet structures, but also possessed the quantum confinement effect with the discrete energy level. As a result, the prepared small-sized Nb2C nanosheets had unique luminescent and electrochemical properties. Furthermore, in order to improve the ECL performance of Nb2C nanosheets, SiO2 microspheres were self-assembled on the electrode surface by gas-liquid interface method to form whispering gallery mode structure. Because the light was continuously reflected at the interface of the microcavity in the whispering gallery mode, the ECL signal of Nb2C luminescent nanosheets was amplified largely. Finally, the whispering gallery mode-based ECL sensing platform was established. The results showed that the biosensor had a good linear correlation between the ECL intensity and the logarithm of concentration of miRNA-214 in the range of 10 fM to 100 nM with a limit of detection of 2.5 fM. The actual detection of miRNA-214 content in clinical TNBC tissue samples was realized successfully.

Predictive Analysis of a Building’s Power Consumption Based on Digital Twin Platforms

Colleges and universities are large consumers of energy, with a huge potential for building energy efficiency, and need to reduce energy consumption to build a low-carbon, energy-saving campus. Predicting the energy consumption of campus buildings can help to accurately manage the electricity consumption of buildings and reduce the energy consumption of buildings. However, the electricity consumption of a building’s operation is affected by many factors, and it is difficult to establish a model for analysis and prediction. Therefore, in this study, the training building of the BIM education center on campus was selected as the research object, and a digital twin O&M platform was established by integrating IoT, digital twin technology (DDT), smart meter monitoring devices, and indoor environment monitoring devices. The O&M management platform can monitor real-time changes in indoor power consumption data and environmental parameters, and organize data on multiple influencing factors and power consumption. Following training, validation, and testing, the machine learning models (back propagation neural network, support vector model, and multiple linear regression model) were assessed and compared for accuracy. Following the multiple linear regression and support vector models, the backpropagation neural network model exhibited the highest accuracy. Consistent with the actual power consumption detection results in the BIM education center, the backpropagation neural network model produced results. Consequently, the BP model created in this study demonstrated its dependability and ability to forecast campus building power usage, assisting the university in organizing its energy supply and creating a campus that prioritizes conservation.

Construction of a sensor array using TiO2 NRs/CuO/rMIP with imprinted sites regulation as light-activated nanozyme to colorimetric detect fluoroquinolones

Fluoroquinolone antibiotics are widely used in daily life, but their abuse has caused serious problems, so it is very important to detect fluoroquinolones accurately and conveniently. In this study, a light-activated nanozyme sensor array (TiO2 NRs/CuO/rMIP) was prepared, and principal component analysis (PCA) was employed for qualitative and quantitative determination of four fluoroquinolones in water environments. The photoactive sites of TiO2 were determined by photo-deposition of CuO, and the molecularly imprinted sites was anchored to the photoactive site by the composite action between antibiotic templates and CuO, which led to the consistent adjustment of the imprinted sites and photoactive sites, thus enhancing the enzyme-like activity of TiO2 NRs/CuO/rMIP. In Addition, by adjusting the types and proportions of antibiotic templates during the preparation of MIP, TiO2 NRs/CuO/rMIP sensors with different response signals to four fluoroquinolones were prepared, and a sensor array was established based on them. Through an RGB analysis system of smart phone and PCA analysis method, qualitative and quantitative detection of the four fluoroquinolones was realized. The detection range of the four antibiotics was 0.1–3000 μM, which showed a good prospect for the actual detection of antibiotics.

An Adjustment Strategy for Tilted Moiré Fringes via Deep Q-Network

Overlay accuracy, one of the three fundamental indicators of lithography, is directly influenced by alignment precision. During the alignment process based on the Moiré fringe method, a slight angular misalignment between the mask and wafer will cause the Moiré fringes to tilt, thereby affecting the alignment accuracy. This paper proposes a leveling strategy based on the DQN (Deep Q-Network) algorithm. This strategy involves using four consecutive frames of wafer tilt images as the input values for a convolutional neural network (CNN), which serves as the environment model. The environment model is divided into two groups: the horizontal plane tilt environment model and the vertical plane tilt environment model. After convolution through the CNN and training with the pooling operation, the Q-value consisting of n discrete actions is output. In the DQN algorithm, the main contributions of this paper lie in three points: the adaptive application of environmental model input, parameter optimization of the loss function, and the possibility of application in the actual environment to provide some ideas. The environment model input interface can be applied to different tilt models and more complex scenes. The optimization of the loss function can match the leveling of different tilt models. Considering the application of this strategy in actual scenarios, motion calibration and detection between the mask and the wafer provide some ideas. To verify the reliability of the algorithm, simulations were conducted to generate tilted Moiré fringes resulting from tilt angles of the wafer plate, and the phase of the tilted Moiré fringes was subsequently calculated. The angle of the wafer was automatically adjusted using the DQN algorithm, and then various angles were measured. Repeated measurements were also conducted at the same angle. The angle deviation accuracy of the horizontal plane tilt environment model reached 0.0011 degrees, and the accuracy of repeated measurements reached 0.00025 degrees. The angle deviation accuracy of the vertical plane tilt environment model reached 0.0043 degrees, and repeated measurements achieved a precision of 0.00027 degrees. Moreover, in practical applications, it also provides corresponding ideas to ensure the determination of the relative position between the mask and wafer and the detection of movement, offering the potential for its application in the industry.

Label-Free and DNAzyme-Mediated Biosensor with a High Signal-to-Noise Ratio for a Lumpy Skin Disease Virus Assay.

Lumpy skin disease virus (LSDV) is a severe and highly contagious form of cowpox. As LSDV continues to mutate and there is no vaccine and treatment in nonendemic countries, early detection of LSDV becomes an important basis for epidemic prevention and control, especially for detection of conserved sequences. A new label-free and sensitive fluorescence method was developed based on a light-up RNA aptamer for detecting LSDV. The method integrated recombinase polymerase amplification (RPA), CRISPR/Cas12a, 10-23 DNAzyme, and Baby Spinach RNA aptamer for triple cascade signal amplification. Based on highly sensitive and specific RPA and CRISPR/Cas12a, DNAzyme achieved a third signal amplification. Additionally, the Baby Spinach RNA aptamer had stronger fluorescence signals and higher quantum yields. The label-free method had ultrahigh sensitivity with the actual detection limit as 1.29 copies·μL-1. The method was 100-fold more sensitive compared to RPA with Cas12a. Moreover, it had no cross-reactivity with viruses belonging to the Capripoxvirus, such as sheep pox virus and goat pox virus with genetic homology as 97%. Furthermore, the method displayed 100% accuracy in 50 actual samples. Therefore, the method based on RPA, Cas12a, and 10-23 DNAzyme had advantages in LSDV detection and provided a new solution for LSD prevention and control.

Research on new method of transformer winding deformation detection

As one of the most important electrical equipment in power system, the stable operation of transformer is the key factor to ensure the safety of power grid system. Winding deformation is the most common fault of transformer, which will seriously affect the reliable operation of the whole power system. Therefore, the detection of transformer winding deformation is of great significance to prevent transformer accidents. In order to further improve the reliability of transformer winding deformation detection, this paper proposes a transformer winding deformation detection method based on oscillation wave method. Firstly, the equivalent circuit model of transformer winding is established, and the transformer winding fault is simulated by changing the inductance and capacitance parameters in the model, and the law between the change of oscillation wave curve and the change of related performance indexes and equivalent parameters is obtained. Finally, the field test verification is carried out on a 10 kV transformer in the laboratory. The results show that the oscillation wave method can diagnose the transformer winding deformation and can be used for the actual transformer winding deformation detection.

A laser ultrasonic intelligent inspection method for metal surface defects based on digital twin model

To enhance the efficiency and intelligence of laser ultrasonic detection of metal surface defects, a detection method based on digital twins is proposed. Given the scarcity of defect samples and the difficulty of obtaining labeled data in practical applications, the finite element method is used to construct a laser ultrasonic metal defect detection model, effectively generating twin data for defect detection. With the help of generative adversarial networks, the distribution difference between twin data and real data is minimized. Additionally, by using continuous wavelet transform, one-dimensional ultrasonic signals are converted into two-dimensional time–frequency images for detection input. In terms of detection algorithm development, a residual network with an optimized global attention mechanism is introduced to ensure real-time synchronization and updates between the digital twin model and the actual laser ultrasonic detection equipment. Finally, experimental validation of the proposed method shows that it effectively addresses the issue of small sample sizes in defect detection and achieves high detection accuracy. Moreover, comparative experiments on crack defect migration tasks under different settings demonstrate the method’s good generalization ability.

A Cost-Effective and Sensitive Method for the Determination of Lincomycin in Foods of Animal Origin Using High-Performance Liquid Chromatography.

Lincomycin (LIN) is extensively used for treating diseases in livestock and promoting growth in food animal farming, and it is frequently found in both the environment and in food products. Currently, most of the methods for detecting lincomycin either lack sensitivity and precision or require the use of costly equipment such as mass spectrometers. In this study, we developed a reliable high-performance liquid chromatography-ultraviolet detection (HPLC-UVD) method and used it to detect LIN residue in 11 types of matrices (pig liver and muscle; chicken kidney and liver; cow fat, liver and milk; goat muscle, liver and milk; and eggs) for the first time. The tissue homogenates and liquid samples were extracted via liquid-liquid extraction, and subsequently purified and enriched via sorbent and solid phase extraction (SPE). After nitrogen drying, the products were derivatized with p-toluene sulfonyl isocyanic acid (PTSI) (100 µL) for 30 min at room temperature. Finally, the derivatized products were analyzed by HPLC at 227 nm. Under the optimized conditions, the method displayed impressive performance and demonstrated its reliability and practicability, with a limit of detection (LOD) and quantification (LOQ) of LIN in each matrix of 25-40 μg/kg and 40-60 μg/kg, respectively. The recovery ranged from 71.11% to 98.30%. The results showed that this method had great selectivity, high sensitivity, satisfactory recovery and cost-effectiveness-fulfilling the criteria in drug residue and actual detection requirements-and proved to have broad applicability in the field of detecting LIN in animal-derived foods.

Colorimetric detection of neomycin sulfate in serum based on ultra-small gold nanoparticles with peroxidase-like activity

Neomycin sulfate (NEO) is a kind of aminoglycoside antibiotics. Because of its strong ototoxicity, nephrotoxicity and other side effects, its content in the body should be strictly monitored during use. In this paper, a rapid colorimetric detection method for NEO based on ultrasmall polyvinylpyrrolidone modified gold nanoparticles (PVP/Au NPs) with peroxidase-like activity was developed. Firstly, ultra small PVP/Au NPs with weak peroxidase-like activity were synthetized. When they were mixed with NEO, strong hydrogen bonds were formed between NEO and PVP, resulting in the aggregation of PVP/Au NPs, and the aggregated PVP/Au NPs showed stronger peroxidase-like activity. Therefore, rapid colorimetric detection of NEO was achieved by utilizing the enhanced peroxidase-like activity mechanism caused by the aggregation of ultra small PVP/Au NPs. The naked eye detection limit of this method is 50 nM. Within the range of 1 nM-300 nM, there was a good linear relationship between NEO concentration and the change in absorbance intensity of PVP/Au NPs-H2O2-TMB solution at 652 nm, with the regression curve of y = 0.0045x + 0.0525 (R2 = 0.998), and the detection limit is 1 nM. In addition, this method was successfully applied to the detection of NEO in mouse serum. The recoveries were 104.4 % −107.6 % compared with HPLC assay results, indicating that this method for NEO detection based on PVP/Au NPs has great potential in actual detection of NEO in serum.

Intelligent Detection of Tunnel Leakage Based on Improved Mask R-CNN

The instance segmentation model based on deep learning has addressed the challenges in intelligently detecting water leakage in shield tunneling. Due to the limited generalization ability of the baseline model, occurrences of missed detections, false detections, and repeated detections are encountered during the actual detection of tunnel water leakage. This paper adopts Mask R-CNN as the baseline model and introduces a mask cascade strategy to enhance the quality of positive samples. Additionally, the backbone network in the model is replaced with RegNetX to enlarge the model’s receptive field, and MDConv is introduced to enhance the model’s feature extraction capability in the edge receptive field region. Building upon these improvements, the proposed model is named Cascade-MRegNetX. The backbone network MRegNetX features a symmetrical block structure, which, when combined with deformable convolutions, greatly assists in extracting edge features from corresponding regions. During the dataset preprocessing stage, we augment the dataset through image rotation and classification, thereby improving both the quality and quantity of samples. Finally, by leveraging pre-trained models through transfer learning, we enhance the robustness of the target model. This model can effectively extract features from water leakage areas of different scales or deformations. Through instance segmentation experiments conducted on a dataset comprising 766 images of tunnel water leakage, the experimental results demonstrate that the improved model achieves higher precision in tunnel water leakage mask detection. Through these enhancements, the detection effectiveness, feature extraction capability, and generalization ability of the baseline model are improved. The improved Cascade-MRegNetX model achieves respective improvements of 7.7%, 2.8%, and 10.4% in terms of AP, AP0.5, and AP0.75 compared to the existing Cascade Mask R-CNN model.

Progress of MEMS acoustic emission sensor: a review

PurposeBased on the micro-electro-mechanical system (MEMS) technology, acoustic emission sensors have gained popularity owing to their small size, consistency, affordability and easy integration. This study aims to provide direction for the advancement of MEMS acoustic emission sensors and predict their future potential for structural health detection of microprecision instruments.Design/methodology/approachThis paper summarizes the recent research progress of three MEMS acoustic emission sensors, compares their individual strengths and weaknesses, analyzes their research focus and predicts their development trend in the future.FindingsPiezoresistive, piezoelectric and capacitive MEMS acoustic emission sensors are the three main streams of MEMS acoustic emission sensors, which have their own advantages and disadvantages. The existing research has not been applied in practice, and MEMS acoustic emission sensor still needs further research in the aspects of wide frequency/high sensitivity, good robustness and integration with complementary metal oxide semiconductor. MEMS acoustic emission sensor has great development potential.Originality/valueIn this paper, the existing research achievements of MEMS acoustic emission sensors are described systematically, and the further development direction of MEMS acoustic emission sensors in the future research field is pointed out. It provides an important reference value for the actual weak acoustic emission signal detection in narrow structures.

Construction of neural network model for exercise load monitoring based on yoga training data and rehabilitation therapy

The Internet of Things is based on the traditional Internet and its purpose is to achieve information exchange between users and devices, as well as between devices. The rapid development of sensor technology, communication network technology, and computer technology has enriched the coverage of the Internet of Things, including a wide range of intelligent applications such as healthcare, smart cities, and smart homes. The development of high-performance computing and machine learning technologies has promoted the wide application of intelligent auxiliary systems in sports medicine. With the rapid development of yoga in the field of sports, athletes can play the various functions of yoga, improve their physical strength and quality, and improve their strength, flexibility, etc., cultivate positive, optimistic, and healthy emotions, and these are conducive to rehabilitation treatment after sports injuries. Therefore, it is feasible and feasible to introduce yoga training into the monitoring of the exercise load of athletes. In this paper, neural network technology was used to break the traditional training method based on experience. Based on yoga training data, through experimental exercise research, it could explore a new effective way to monitor exercise load and rehabilitation treatment, and build an exercise load monitoring model of the Ant Colony Optimization (ACO) neural network. By sorting out the data, statistics and analysis of the data, this article confirmed the effect of yoga training on reducing fatigue after exercise. The experimental results showed that the prediction value obtained by the ACO neural network model was 9.106, and the error was only −0.003 compared to the actual detection value of 9.109. This result showed that the ACO neural network model can perfectly fit the functional relationship between yoga training level and exercise load and has high prediction accuracy. This also marked that the development of high-performance computing systems has entered a new journey in the field of sports and health.

Enhancing patient safety: detection of in-hospital hazards and effect of training on detection (by training in a low-fidelity simulation Room of Improvement based on hospital-specific CIRS cases)

ImportanceAdequate situational awareness in patient care increases patient safety and quality of care. To improve situational awareness, an innovative, low-fidelity simulation method referred to as Room of Improvement, has proven...

Highly sensitive electrochemical quantification of carbendazim via synergistic enhancement of ring-opening metathesis polymerization and polyethyleneimine modified graphene oxide.

Anovel aptamer-based sensor was developed using the signal amplification strategy of ring-opening metathesis polymerization (ROMP) and polyethyleneimine modified graphene oxide to achieve trace detection of carbendazim (CBZ). The dual identification of aptamer and antibody was used to avoid false positive results and improve the selectivity. Polyethyleneimine modified graphene oxide (GO-PEI), as a substrate material with excellent conductivity, was modified on the surface of aglassy carbon electrode (GCE) to increase the grafting amount of aptamer on the electrode surface. Moreover, a large number of cyclopentenyl ferrocene (CFc) was aggregated to form long polymer chains through ring-opening metathesis polymerization (ROMP), so as to significantly improve the detection sensitivity of the biosensor. The linear range of this sensor was 1 pg/mL-100 ng/mL with a detection limit as low as 7.80fg/mL. The sensorexhibited excellent reproducibility and stability, and also achieved satisfactory results in actual sample detection. The design principle of such a sensor could provide innovative ideas for sensors in the detection of other types of targets.

Fabrication of porous copper oxide nanostructure on nickel foam electrode by a co-precipitation method for non-enzymatic glucose detection

A novel porous copper oxide nanostructure on nickel foam (CuO/NF) electrode has been successfully prepared through a co-precipitation way. The porous CuO structure and nickel foam can be integrated into a three-dimensional ordered and hierarchal CuO electrode sensor. The porous CuO structure contains many loosely packed nanoparticles and the nanochannels, which are beneficial for providing more active sites for electrocatalytic oxidation–reduction reactions. The Cu(III)/Cu(II) pair plays an essential role, which completes the charge transfer during the electrochemically catalyzed reaction. Two Cu(III) ions obtain two electrons from one molecule of glucose, which completes two electron transfers to produce one molecule of gluconic acid lactone. The results of the actual detection of Fetal Bovine Serum indicate that the CuO/NF electrode produced could be utilized for the determination of actual samples.

Investigation on the induced electrical wakes generated by underwater vehicles

The induced electrical wake generated by underwater vehicles in the conductive seawater subject to the geomagnetic field can offer significant information for non-acoustic detection. However, the crucial demand in actual detection requires the sophisticated detection technology to capture the variations in the wake, which has not been experimentally explored. In this paper, an underwater measurement platform, which is capable of accurately capturing the weak electrical signature, is developed to verify the detectability of the induced electric field in the wake. Besides, a towing system is designed and established to generate the wake driving the motion of the conducting seawater in the current experiment. The results indicate that the induced electric field distributions in the wake demonstrate prominent peak characteristics. The detectable intensity of the measured electric signatures in the wake can range from 1 to 3 μV/m, primarily concentrated within the extremely low frequency band below 1 Hz. Additionally, the induced electric field intensity is positively correlated with the towing velocity and demonstrates a similar evolutionary trend. Subsequently, the numerical simulations are performed to not only cross-validate the measurement results but also further demonstrate the detailed distribution of the electrical wake evolution. These findings illustrate the feasibility of detection based on the induced electric field disturbance signal in the wake, which holds potential for non-acoustic detection and tracking of underwater targets.

Smartphone-enabled colorimetric immunoassay for deoxynivalenol based on Mn2+-mediated aggregation of AuNPs

Deoxynivalenol (DON) is a common mycotoxin in food that mainly pollutes grain crops and feeds, such as barley, wheat and corn. DON has caused widespread concern in the field of food and feed safety. In this study, a colorimetric immunoassay was proposed based on the aggregation of gold nanoparticles (AuNPs) due to the decomposition of Mn2+ from gold-coated manganese dioxide (AuNP@MnO2) nanosheets. In this study, 2-(dihydrogen phosphate)-l-ascorbic acid (AAP) was hydrolyzed by alkaline phosphatase (ALP) and converted to ascorbic acid (AA). Then, AuNP@MnO2 was reduced to Mn2+ and AuNPs aggregation occurred. Using the unique optical characteristics of AuNPs and AuNP@MnO2, visible color changes realized simple detection of DON with high sensitivity and portability. With increasing DON content, the color changed more obviously. To quantitatively detect DON, pictures can be taken and the blue value can be read by a smartphone. The detection limit (Ic10) of this method was 0.098 ng mL−1, which was 326 times higher than that of traditional competitive ELISA, and the detection range was 0.177–6.073 ng mL−1. This method exhibited high specificity with no cross-reaction in other structural analogs. The average recovery rate of DON in corn flour samples was 89.1 %–110.2 %, demonstrating the high accuracy and stability of this assay in actual sample detection. Therefore, the colorimetric immunoassay can be used for DON-related food safety monitoring.

Health Risk Prediction of Operational Subsea Tunnel Structure Based on Bayesian Network

Recently, subsea tunnel construction has developed rapidly in China. The traffic volume of subsea metro tunnels is large. Once a safety accident occurs, economic losses and social impacts will be extremely serious. To eliminate accidents in operational subsea metro tunnel structures, a health risk prediction method is proposed based on a discrete Bayesian network. Detecting and monitoring data of the tunnel structures in operation were used to evaluate the health risk by employing the proposed method. This method establishes a Bayesian network model for the health risk prediction of the shield tunnel structure through the dependency relationship between the health risk of the operational tunnel structure and 13 risk factors in five aspects: the mechanical condition, material performance, integrity state, environmental state, and deformation state. By utilizing actual detection and monitoring data of various risk factors for the health risk of the operational subsea metro shield tunnel structure, this method reflects the actual state of the tunnel structure and improves the accuracy of health risk predictions. The validity of the proposed method is verified through expert knowledge and the subsea shield tunnel structure of the Dalian Subway Line 5. The results demonstrate that the health risk prediction outcomes effectively reflect the actual service state of the shield tunnel structure, thus providing decision support for the control of health risks in the subsea metro shield tunnel.

Generating Synthetic Health Sensor Data for Privacy-Preserving Wearable Stress Detection.

Smartwatch health sensor data are increasingly utilized in smart health applications and patient monitoring, including stress detection. However, such medical data often comprise sensitive personal information and are resource-intensive to acquire for research purposes. In response to this challenge, we introduce the privacy-aware synthetization of multi-sensor smartwatch health readings related to moments of stress, employing Generative Adversarial Networks (GANs) and Differential Privacy (DP) safeguards. Our method not only protects patient information but also enhances data availability for research. To ensure its usefulness, we test synthetic data from multiple GANs and employ different data enhancement strategies on an actual stress detection task. Our GAN-based augmentation methods demonstrate significant improvements in model performance, with private DP training scenarios observing an 11.90-15.48% increase in F1-score, while non-private training scenarios still see a 0.45% boost. These results underline the potential of differentially private synthetic data in optimizing utility-privacy trade-offs, especially with the limited availability of real training samples. Through rigorous quality assessments, we confirm the integrity and plausibility of our synthetic data, which, however, are significantly impacted when increasing privacy requirements.

Smart urban management identification by integrating optical flow characteristics and HSV space

n smart cities, sanitation workers are the key to urban construction. Accurate targeting of sanitation workers can help managers better monitor and manage them. Pedestrian detection is the core and key component of object detection technology. The difficulty of pedestrian detection in the actual feature recognition is still how to quickly and accurately identify the identity in the complex video scene. To realize the effective detection of sanitation workers, the study designs an identity identification scheme conducive to the friendly management of smart urban management. Since the optical flow feature extraction method and HSV color space extraction method can not meet the actual detection efficiency, this study innovatively integrates the two methods to improve the detection accuracy of the mode. Meanwhile, the study also introduces PCA algorithm to identify the specific identity of sanitation workers. In the actual detection of sanitation workers, the identification rate of two sanitation workers is high, and the similarity is 98.61%. This technology greatly reduces the false detection rate of actual detection and improves the detection accuracy.