Sandwich-like CuNPs@AgNPs@PSB SERS substrates for sensitive detection of R6G and Forchlorfenuron.

The development of a highly sensitive, synthetically simple and economical SERS substrate is technically very important. A fast, economical, sensitive and reproducible CuNPs@AgNPs@ Porous silicon Bragg reflector (PSB) SERS substrate was prepared by electrochemical etching and in situ reduction method. The developed CuNPs@AgNPs@PSB has a large specific surface area and abundant "hot spot" region, which makes the SERS performance excellent. Meanwhile, the successful synthesis of CuNPs@AgNPs can not only modulate the plasmon resonance properties of nanoparticles, but also effectively prolong the time stability of Cu nanoparticles. The basic performance of the substrate was evaluated using rhodamine 6G (R6G). (Detection limit reached 10-15 M, R2 = 0.9882, RSD = 5.3 %) The detection limit of Forchlorfenuron was 10 µg/L. The standard curve with a regression coefficient of 0.979 was established in the low concentration range of 10 µg/L -100 µg/L. This indicates that the prepared substrates can accomplish the detection of pesticide residues in the low concentration range. The prepared high-performance and high-sensitivity SERS substrate have a very promising application in detection technology.

Surface defect detection of industrial components based on vision.

Early and effective surface defect detection in industrial components can avoid the occurrence of serious safety hazards. Since most industrial component surfaces have tiny defects with high similarity to the detection background, there are often issues of missed or false detections when defects are detected, leading to low detection accuracy. To deal with the aforementioned issue, this essay suggests a high-precision detection model for surface defects in industrial components based on the YOLOv5 algorithm. First, the original spatial pyramid pooling (SPPF) is innovated by proposing the SPPFKCSPC module, which improves the network's capacity for feature extraction from targets at different scales and fuses multiscale features better. Then, C3 is combined with SPPFKCSPC and replaces the C3 module of the backbone network, which improves feature expression and enhances the receptive field of the network. Finally, the coordinate attention mechanism (CA) has been embedded into the YOLOv5 neck network, and the bounding box regression loss function of the algorithm is improved to EIOU, not only improving the precision of the target localization and recognition model but also enhancing the overall network performance. Based on the public datasets NEU-DET and PV-Multi-Defect, multiple sets of experiments were conducted using innovative algorithms. On the NEU-DET dataset, we got a mean average accuracy (mAP) of 88.3%, which is 7.2% greater than the original approach. On the PV-Multi-Defect dataset, the mAP value reached 97.5%, an improvement of 1.5%. As shown by the experimental data, the detection results significantly improved.

MS-Net: a novel lightweight and precise model for plant disease identification.

The rapid development of image processing technology and the improvement of computing power in recent years have made deep learning one of the main methods for plant disease identification. Currently, many neural network models have shown better performance in plant disease identification. Typically, the performance improvement of the model needs to be achieved by increasing the depth of the network. However, this also increases the computational complexity, memory requirements, and training time, which will be detrimental to the deployment of the model on mobile devices. To address this problem, a novel lightweight convolutional neural network has been proposed for plant disease detection. Skip connections are introduced into the conventional MobileNetV3 network to enrich the input features of the deep network, and the feature fusion weight parameters in the skip connections are optimized using an improved whale optimization algorithm to achieve higher classification accuracy. In addition, the bias loss substitutes the conventional cross-entropy loss to reduce the interference caused by redundant data during the learning process. The proposed model is pre-trained on the plant classification task dataset instead of using the classical ImageNet for pre-training, which further enhances the performance and robustness of the model. The constructed network achieved high performance with fewer parameters, reaching an accuracy of 99.8% on the PlantVillage dataset. Encouragingly, it also achieved a prediction accuracy of 97.8% on an apple leaf disease dataset with a complex outdoor background. The experimental results show that compared with existing advanced plant disease diagnosis models, the proposed model has fewer parameters, higher recognition accuracy, and lower complexity.

Salient Object Detection Based on Optimization of Feature Computation by Neutrosophic Set Theory.

In recent saliency detection research, too many or too few image features are used in the algorithm, and the processing of saliency map details is not satisfactory, resulting in significant degradation of the salient object detection result. To overcome the above deficiencies and achieve better object detection results, we propose a salient object detection method based on feature optimization by neutrosophic set (NS) theory in this paper. First, prior object knowledge is built using foreground and background models, which include pixel-wise and super-pixel cues. Simultaneously, the feature maps are selected and extracted for feature computation, allowing the object and background features of the image to be separated as much as possible. Second, the salient object is obtained by fusing the features decomposed by the low-rank matrix recovery model with the object prior knowledge. Finally, for salient object detection, we present a novel mathematical description of neutrosophic set theory. To reduce the uncertainty of the obtained saliency map and then obtain good saliency detection results, the new NS theory is proposed. Extensive experiments on five public datasets demonstrate that the results are competitive and superior to previous state-of-the-art methods.

Novel SERS biosensor for rapid detection of breast cancer based on Ag2O-Ag-PSi nanochips.

Ag2O-Ag-PSi (porous silicon) surface-enhanced Raman scattering (SERS) chip was successfully synthesized by electrochemical corrosion, in situ reduction and heat treatment technology. The influence of different heat treatment temperature on SERS performance of the chip is studied. The results show that the chip treated at 300 °C has the best SERS performance. The chip was composed of Ag2O-Ag nano core shell with a diameter of 40-60 nm and porous silicon substrate. Then, the optimized chip was used to perform SERS test on serum samples from 30 healthy volunteers and 30 early breast cancer patients, and the baseline was corrected by LabSpec6 software. Finally, the data were analyzed by principal component analysis combined with t-distributed Stochastic Neighbor Embedding (PCA-t-SNE). The results showed that the accuracy of the improved substrate combined with multivariate statistical method was 98%. The shelf life of the chips exceeded six months due to the presence of the Ag2O shell. This study provides a basis for developing a low-cost rapid and sensitive early screening technology for breast cancer.

Multi-Focus Image Fusion via Distance-Weighted Regional Energy and Structure Tensor in NSCT Domain.

In this paper, a multi-focus image fusion algorithm via the distance-weighted regional energy and structure tensor in non-subsampled contourlet transform domain is introduced. The distance-weighted regional energy-based fusion rule was used to deal with low-frequency components, and the structure tensor-based fusion rule was used to process high-frequency components; fused sub-bands were integrated with the inverse non-subsampled contourlet transform, and a fused multi-focus image was generated. We conducted a series of simulations and experiments on the multi-focus image public dataset Lytro; the experimental results of 20 sets of data show that our algorithm has significant advantages compared to advanced algorithms and that it can produce clearer and more informative multi-focus fusion images.

Functionalized magnetic nanobeads for SERS-based detection of Staphylococcus aureus.

In this study, we introduced a Raman detection technique based on a combination of functionalized magnetic beads and surface-enhanced Raman scattering (SERS) tags to develop a rapid and sensitive strategy for the detection of Staphylococcus aureus (S. aureus), a typical foodborne pathogen. Polyethylene glycol (PEG) and bovine serum albumin (BSA) dual-mediated teicoplanin functionalized magnetic beads (TEI-BPBs) were prepared for separation of target bacteria. SERS tags were used to immobilize antibodies on gold surfaces with bifunctional linker proteins to ensure specific recognition of S. aureus. Under optimal conditions, the combination of TEI-BPBs and SERS tags showed reliable performance, exhibiting good capture efficiency even in the presence of 106 CFU mL-1 of non-target bacteria. The SERS tag provided an effective hot spot for subsequent Raman detection, presenting good linearity in the range of 102-107 CFU mL-1. Good performance has also been shown in detecting target bacteria in milk samples, where it has a recovery of 95.5-101.3%. Thus, the highly sensitive Raman detection technique combined with TEI-BPBs capture probes and SERS tags is a promising method for the detection of foodborne pathogens in food or clinical samples.

Coupling annealed silver nanoparticles with a porous silicon Bragg mirror SERS substrate and machine learning for rapid non-invasive disease diagnosis.

Ag2O-Ag-porous silicon Bragg mirror (PSB) composite SERS substrates were successfully synthesized by using a combination of electrochemical and thermochemical methods. Test results showed that the SERS signal increased and decreased as the annealing temperature used for the substrate increased, where the most intense SERS signal was obtained using a substrate annealed at 300 °C. Stability test results showed substantial enhancement of the SERS signal intensity of the Ag2O-Ag-PSB composite one month after preparation compared with that of conventional Ag-PSB. We conclude that Ag2O nanoshells play an essential role in SERS signal enhancement. Ag2O prevents natural oxidation of Ag nanoparticles (AgNPs) and has a solid localized surface plasmon resonance (LSPR). SERS signal enhancement was tested using this substrate for serum from patients with Sjögren's syndrome (SS) and Diabetic nephropathy (DN), as well as from healthy controls (HC). SERS feature extraction was performed using principal component analysis (PCA). The extracted features were analyzed by a support vector machine (SVM) algorithm. Finally, a rapid screening model for SS and HC, as well as DN and HC, was developed and used to perform controlled experiments. The results showed that the diagnostic accuracy, sensitivity and selectivity for SERS technology combined with machine learning algorithms reached 90.7%, 93.4% and 86.7% for SS/HC and 89.3%, 95.6% and 80% for DN/HC, respectively. The results of this study show that the composite substrate has excellent potential to be developed into a commercially available SERS chip for medical testing.

A Lightweight Feature Distillation and Enhancement Network for Super-Resolution Remote Sensing Images.

Super-resolution (SR) images based on deep networks have achieved great accomplishments in recent years, but the large number of parameters that come with them are not conducive to use in equipment with limited capabilities in real life. Therefore, we propose a lightweight feature distillation and enhancement network (FDENet). Specifically, we propose a feature distillation and enhancement block (FDEB), which contains two parts: a feature-distillation part and a feature-enhancement part. Firstly, the feature-distillation part uses the stepwise distillation operation to extract the layered feature, and here we use the proposed stepwise fusion mechanism (SFM) to fuse the retained features after stepwise distillation to promote information flow and use the shallow pixel attention block (SRAB) to extract information. Secondly, we use the feature-enhancement part to enhance the extracted features. The feature-enhancement part is composed of well-designed bilateral bands. The upper sideband is used to enhance the features, and the lower sideband is used to extract the complex background information of remote sensing images. Finally, we fuse the features of the upper and lower sidebands to enhance the expression ability of the features. A large number of experiments show that the proposed FDENet both produces less parameters and performs better than most existing advanced models.

Sparse Representation-Based Multi-Focus Image Fusion Method via Local Energy in Shearlet Domain.

Multi-focus image fusion plays an important role in the application of computer vision. In the process of image fusion, there may be blurring and information loss, so it is our goal to obtain high-definition and information-rich fusion images. In this paper, a novel multi-focus image fusion method via local energy and sparse representation in the shearlet domain is proposed. The source images are decomposed into low- and high-frequency sub-bands according to the shearlet transform. The low-frequency sub-bands are fused by sparse representation, and the high-frequency sub-bands are fused by local energy. The inverse shearlet transform is used to reconstruct the fused image. The Lytro dataset with 20 pairs of images is used to verify the proposed method, and 8 state-of-the-art fusion methods and 8 metrics are used for comparison. According to the experimental results, our method can generate good performance for multi-focus image fusion.

An efficient sharding consensus algorithm for consortium chains.

The consensus algorithm is very critical in any blockchain system, because it directly affects the performance and security of the blockchain system. At present, the classic Practical Byzantine Fault Tolerance Algorithm (PBFT), which is mainly used in the consortium chain, will lead to system communication congestion and reduced throughput when the number of nodes increases, so the PBFT algorithm is not suitable for large-scale consortium chains. In response to the above problems, this paper proposes a new clustering-based sharding consensus algorithm (KBFT), which aims to ensure that the consortium chain takes into account decentralization, security and scalability. The KBFT algorithm first uses the K-prototype clustering algorithm to shard the nodes in the network according to mixed attributes, and second, disjoint transactions are used to reach consensus in parallel in different shards. Concurrently, the KBFT algorithm introduces a supervision mechanism and a node credit mechanism, which is used to supervise and score the behavior of the nodes and select the proxy nodes, which improves security. We discuss the choice of shard size with the help of the binomial probability distribution and analyze the probability that the system can successfully form a global block under different node failure probabilities. Finally, the proposed algorithm is evaluated through theoretical analysis and simulation experiments. Results show that the proposed algorithm achieves a marked improvement in scalability and throughput along with a marked reduction in communication complexity compared with the classic baseline algorithm PBFT in this field of study, which improves the operating efficiency of the system and simultaneously guarantees the security and robustness of the system.

A high-performance fluorescent probe for detection of cysteine in plasma constructed by combining Cu(I) and 2,5-dimercapto-1,3,4-thiadiazole.

A high-performance fluorescent probe 2,5-dimercapto-1,3,4-thiadiazole copper nanoparticles (DMTD-CuNPs) was synthesized by hydrothermal method based on monovalent copper (Cu(I)) and 2,5-dimercapto-1,3,4-thiadiazole (DMTD), and it can effectively detect cysteine (Cys) in plasma. Experiments show that DMTD can reduces band gap of Cu(I) in DMTD-CuNPs, promote charge transfer transition from DMTD to Cu(I) and significantly enhance fluorescence intensity of DMTD-CuNPs at 515 nm. The large Stokes shift of DMTD-CuNPs is 315 nm, which can reduce the self-quenching of probe fluorescence and improves detection accuracy of the probe. In the presence of Cys, fluorescence of DMTD-CuNPs at 515 nm is significantly quenched because Cys reacts with Cu(I) in DMTD-CuNPs through Cu-S bond to form reduced charge transfer, which can be successfully used for the detection of Cys. Linear range and detection limit for Cys detection are 25-65 µM and 50 nM, respectively. Furthermore, feasibility of detecting Cys in plasma using DMTD-CuNPs probe was evaluated by standard addition method, and the absolute recovery is 96-99%. Such a DMTD-CuNPs probe shows high sensitivity, good selectivity and low detection limit for Cys, which is expected to be used for the practical analysis of Cys in plasma.

Spectral-Free Double Light Detection of DNA Based on a Porous Silicon Bragg Mirror.

To improve the detection sensitivity of a porous silicon optical biosensor in the real-time detection of biomolecules, a non-spectral porous silicon optical biosensor technology, based on dual-signal light detection, is proposed. Double-light detection is a combination of refractive index change detection and fluorescence change detection. It uses quantum dots to label probe molecules to detect target molecules. In the double-signal-light detection method, the first detection-signal light is the detection light that is reflected from the surface of the porous silicon Bragg mirror. The wavelength of the detection light is the same as the wavelength of the photonic band gap edge of the porous silicon Bragg mirror. CdSe/ZnS quantum dots are used to label the probe DNA and hybridize it with the target DNA molecules in the pores of porous silicon to improve its effective refractive index and enhance the detection-reflection light. The second detection-signal light is fluorescence, which is generated by the quantum dots in the reactant that are excited by light of a certain wavelength. The Bragg mirror structure further enhances the fluorescence signal. A digital microscope is used to simultaneously receive the digital image of two kinds of signal light superimposed on the surface of porous silicon, and the corresponding algorithm is used to calculate the change in the average grey value before and after the hybridization reaction to calculate the concentration of the DNA molecules. The detection limit of the DNA molecules was 0.42 pM. This method can not only detect target DNA by hybridization, but also detect antigen by immune reaction or parallel biochip detection for a porous silicon biosensor.

Image Reconstruction Based on Progressive Multistage Distillation Convolution Neural Network.

To address the problem that some current algorithms suffer from the loss of some important features due to rough feature distillation and the loss of key information in some channels due to compressed channel attention in the network, we propose a progressive multistage distillation network that gradually refines the features in stages to obtain the maximum amount of key feature information in them. In addition, to maximize the network performance, we propose a weight-sharing information lossless attention block to enhance the channel characteristics through a weight-sharing auxiliary path and, at the same time, use convolution layers to model the interchannel dependencies without compression, effectively avoiding the previous problem of information loss in channel attention. Extensive experiments on several benchmark data sets show that the algorithm in this paper achieves a good balance between network performance, the number of parameters, and computational complexity and achieves highly competitive performance in both objective metrics and subjective vision, which indicates the advantages of this paper's algorithm for image reconstruction. It can be seen that this gradual feature distillation from coarse to fine is effective in improving network performance. Our code is available at the following link: https://github.com/Cai631/PMDN.

Detection of ß-Lactoglobulin by a Porous Silicon Microcavity Biosensor Based on the Angle Spectrum.

In this paper, carbon quantum dot-labelled ß-lactoglobulin antibodies were used for refractive index magnification, and ß-lactoglobulin was detected by angle spectroscopy. In this method, the detection light is provided by a He-Ne laser whose central wavelength is the same as that of the porous silicon microcavity device, and the light source was changed to a parallel beam to illuminate the porous silicon microcavity' surface by collimating beam expansion, and the reflected light was received on the porous silicon microcavity' surface by a detector. The angle corresponding to the smallest luminous intensity before and after the onset of immune response was measured by a detector for different concentrations of ß-lactoglobulin antigen and carbon quantum dot-labelled ß-lactoglobulin antibodies, and the relationship between the variation in angle before and after the immune response was obtained for different concentrations of the ß-lactoglobulin antigen. The results of the experiment present that the angle variations changed linearly with increasing ß-lactoglobulin antigen concentration before and after the immune response. The limit of detection of ß-lactoglobulin by this method was 0.73 µg/L, indicating that the method can be used to detect ß-lactoglobulin quickly and conveniently at low cost.

Sand Dust Images Enhancement Based on Red and Blue Channels.

The scattering and absorption of light results in the degradation of image in sandstorm scenes, it is vulnerable to issues such as color casting, low contrast and lost details, resulting in poor visual quality. In such circumstances, traditional image restoration methods cannot fully restore images owing to the persistence of color casting problems and the poor estimation of scene transmission maps and atmospheric light. To effectively correct color casting and enhance visibility for such sand dust images, we proposed a sand dust image enhancement algorithm using the red and blue channels, which consists of two modules: the red channel-based correction function (RCC) and blue channel-based dust particle removal (BDPR), the RCC module is used to correct color casting errors, and the BDPR module removes sand dust particles. After the dust image is processed by these two modules, a clear and visible image can be produced. The experimental results were analyzed qualitatively and quantitatively, and the results show that this method can significantly improve the image quality under sandstorm weather and outperform the state-of-the-art restoration algorithms.

Dual signal light detection of beta-lactoglobulin based on a porous silicon bragg mirror.

In this work, a new dual signal light detection method based on porous silicon Bragg mirror (PSBM) and biological labelling with quantum dots (QDs) is proposed for the detection of beta-lactoglobulin (ß-lg). The first signal light is a probe light emitted by a laser with wavelength of 633 nm, which enters the PSBM and is reflected from the surface. The wavelength of the probe light is located at the edge of the PSBM band gap, where it has the lowest reflectivity. ß-lg antibodies is labelled with CdSe/ZnS QDs and reacts with ß-lg molecules have been fixed to the inner wall of the porous silicon pores. Due to the specific binding of biomolecules in PSBM, the refractive index of the device increases, resulting in the enhancement of detection reflected light. The QDs play the role of refractive index amplification. The second signal light is the fluorescence of QDs in immune reactants. QDs produce fluorescence at 630 nm when excited by a short-wavelength laser. The fluorescence signal is further enhanced by PSBM. The superimposed images of two kinds of light on the surface of PSBM are obtained by digital microscope at the same time. By calculating the average grey value change of the image before and after biological reaction, ß-lg can be detected with high sensitivity. The detection limit of ß-lg was 0.12 ng/mL. The experimental results showed that the PSBM-based dual signal light method could be used to detect the content of cow milk adulterated in ß-lg free camel milk.

Denoising of Fluorescence Image on the Surface of Quantum Dot/Nanoporous Silicon Biosensors.

In the process of biological detection of porous silicon photonic crystals based on quantum dots, the concentration of target organisms can be indirectly measured via the change in the gray value of the fluorescence emitted from the quantum dots in the porous silicon pores before and after the biological reaction on the surface of the device. However, due to the disordered nanostructures in porous silicon and the roughness of the surface, the fluorescence images on the surface contain noise. This paper analyzes the type of noise and its influence on the gray value of fluorescent images. The change in the gray value caused by noise greatly reduces the detection sensitivity. To reduce the influence of noise on the gray value of quantum dot fluorescence images, this paper proposes a denoising method based on gray compression and nonlocal anisotropic diffusion filtering. We used the proposed method to denoise the quantum dot fluorescence image after DNA hybridization in a Bragg structure porous silicon device. The experimental results show that the sensitivity of digital image detection improved significantly after denoising.

Multiscale Geometric Analysis Fusion-Based Unsupervised Change Detection in Remote Sensing Images via FLICM Model.

Remote sensing image change detection is widely used in land use and natural disaster detection. In order to improve the accuracy of change detection, a robust change detection method based on nonsubsampled contourlet transform (NSCT) fusion and fuzzy local information C-means clustering (FLICM) model is introduced in this paper. Firstly, the log-ratio and mean-ratio operators are used to generate the difference image (DI), respectively; then, the NSCT fusion model is utilized to fuse the two difference images, and one new DI is obtained. The fused DI can not only reflect the real change trend but also suppress the background. The FLICM is performed on the new DI to obtain the final change detection map. Four groups of homogeneous remote sensing images are selected for simulation experiments, and the experimental results demonstrate that the proposed homogeneous change detection method has a superior performance than other state-of-the-art algorithms.

Cross-Attention Fusion Based Spatial-Temporal Multi-Graph Convolutional Network for Traffic Flow Prediction.

Accurate traffic flow prediction is essential to building a smart transportation city. Existing research mainly uses a given single-graph structure as a model, only considers local and static spatial dependencies, and ignores the impact of dynamic spatio-temporal data diversity. To fully capture the characteristics of spatio-temporal data diversity, this paper proposes a cross-Attention Fusion Based Spatial-Temporal Multi-Graph Convolutional Network (CAFMGCN) model for traffic flow prediction. First, introduce GCN to model the historical traffic data's three-time attributes (current, daily, and weekly) to extract time features. Second, consider the relationship between distance and traffic flow, constructing adjacency, connectivity, and regional similarity graphs to capture dynamic spatial topology information. To make full use of global information, a cross-attention mechanism is introduced to fuse temporal and spatial features separately to reduce prediction errors. Finally, the CAFMGCN model is evaluated, and the experimental results show that the prediction of this model is more accurate and effective than the baseline of other models.