Enhanced Detection Efficiency Research Articles

Luminescent Multifunctional Nanomaterials: Capacitive Removal and Enhanced Detection Efficiency of Heavy Metals Ions for Advanced Water and Wastewater Treatment Application

AbstractIn recent years, heavy metal ions pollution in the industrialized environment of the societies threaten human health that flaunt ill‐sorted blueprints in freshwater resources obviously. The paradigm of designing luminescent multifunctional nanomaterials finds directions to the strategies of synthesizing cost‐effective, green, and versatile nanomaterials not only for detection, but also removal process of heavy metal ions in large scale applications. Among them, discovering the advances of luminescent multifunctional nanomaterials provides broad types of biomaterials, polymers and porous nanoparticles that grabs focal of investigations over the past several years due to their unique advantages such as enhanced detection efficiency with lowest limit of detection (LOD), minimum ions interference in versatile removal process, fast responsivity and selectivity as outstanding as unique physicochemical properties. This review paper tries to highlight the paradigm of principles for design, development, and utilization of luminescence nanomaterials for considering fundamental detection and removal mechanisms of heavy metal ions. In particular, these nanomaterials increase the remediation quality that are tackled in detail by focusing on opportunities and challenges in the field. Finally, design methods of these nanomaterials and concentrating on empowered detection and removal efficiency for heavy metals ions highlights novel prospective and strategies for largescale applications.

A comprehensive framework for trans-ancestry pathway analysis using GWAS summary data from diverse populations.

As more multi-ancestry GWAS summary data become available, we have developed a comprehensive trans-ancestry pathway analysis framework that effectively utilizes this diverse genetic information. Within this framework, we evaluated various strategies for integrating genetic data at different levels-SNP, gene, and pathway-from multiple ancestry groups. Through extensive simulation studies, we have identified robust strategies that demonstrate superior performance across diverse scenarios. Applying these methods, we analyzed 6,970 pathways for their association with schizophrenia, incorporating data from African, East Asian, and European populations. Our analysis identified over 200 pathways significantly associated with schizophrenia, even after excluding genes near genome-wide significant loci. This approach substantially enhances detection efficiency compared to traditional single-ancestry pathway analysis and the conventional approach that amalgamates single-ancestry pathway analysis results across different ancestry groups. Our framework provides a flexible and effective tool for leveraging the expanding pool of multi-ancestry GWAS summary data, thereby improving our ability to identify biologically relevant pathways that contribute to disease susceptibility.

Second harmonic generation based on graphene hyperstructure for higher resolution and performance on top of achieving fundamental wave detection in theory

In this paper, an innovative one-dimensional graphene hyperstructure (GHS) is proposed, allowing for the concurrent detection of multiple physical parameters in both the fundamental and second harmonic generation. The sensing characteristics of GHS pertaining to magnetic field strength (B), incident electromagnetic wave angle (θ), and graphene thickness (dgt) are systematically investigated. Moreover, through the incorporation of second harmonic generation alongside fundamental detection, higher resolution and performance are achieved. The findings indicate an expansion of the measurement range for B, θ, and dgt, from 0.3∼0.5 T, 35∼55°, and 1∼6 layers to 0.3∼1 T, 35∼65°, and 1∼10 layers, providing increased flexibility and adjustability. Additionally, by leveraging nonlinear effects and widening the Fabry-Perot cavity width, this structure effectively enhances the quality factor (Q) from 2.94 × 102 to 1.95 × 105, resulting in a substantial improvement in sensing performance. This development holds tremendous promise in surpassing the diffraction limit and addressing high-Q value sensing requirements. In comparison to conventional detectors, the GHS not only enhances detection efficiency but also harbors the potential for multiple physical quantities detection. This forward-looking research is pivotal in its successful resolution of detector performance limitations, ushering in novel possibilities across diverse domains.

Deep Learning Based Method for Dynamic Tracking of Bubbles in Microfluidic Gas-Driven Water Experiments

Using microfluidic chips for gas displacement experiments is an important approach for studying CO2 displacement mechanisms and gas-liquid exchange processes. Analyzing the micro-scale distribution of gas and water during gas displacement enables the optimization of injection strategies, offering valuable guidance for enhancing oil and gas recovery rates. Traditional image processing techniques often face challenges in effectively handle the complex gas-liquid flow dynamics and irregular bubble shapes encountered in gas displacement experiments. To address this challenge, this paper proposes a gas bubble detection and tracking method for gas-driven water microfluidic experiments based on domain adaptation in deep learning and improved YOLOv8. Using the CycleGAN style transfer network to generate numerous simulated bubble training samples significantly reduces the manual labeling workload for bubbles. Additionally, the SE attention mechanism is integrated into the YOLOv8 backbone network to enhance the feature extraction capability of bubbles, and the Fast-PConv Head structure is incorporated into the detection head to enhance detection efficiency. Experimental results demonstrate that using just 20 annotated experimental images, the bubble detection accuracy of the proposed method can reach 94%. Lastly, DeepSORT is employed to track bubbles and visualize the dynamic trends of bubble changes in the form of visual charts. The deep learning approach proposed in this paper provides a new perspective for intelligent analysis of bubbles in microfluidic chip experiments, and future work aiming to its application to the complex multiphase flow field in porous media.

Vision-based mixed color detection of plastic particles.

In the production process of high-end PP-R pipes, mixing different colored raw material particles can result in uneven color in the final product, affecting its appearance quality. in addition, color mixing can reduce the physical properties of the pipes, impacting their durability and safety. To address this issue, we propose a visual, non-destructive inspection solution based on image processing technology. The solution aims to enhance detection efficiency and accuracy by reducing background interference and enabling adaptive adjustments in various environments. Initially, the K-Means image segmentation algorithm is employed to eliminate complex background factors from the original image, significantly improving image segmentation accuracy. Subsequently, the Gaussian mixture model algorithm is utilized to automatically extract the color threshold of the foreground image after background removal, facilitating adaptive algorithm adjustments. Finally, the mean value algorithm is introduced to swiftly and accurately identify plastic particles of different colors using the automatically obtained color thresholds. Experimental results demonstrate that this method can quickly and accurately identify different color particles and effectively support the rejection of impurity particles. Through this approach, the algorithm achieves an average detection accuracy of 99.3%.

Identification of Pine Wilt-Diseased Trees Using UAV Remote Sensing Imagery and Improved PWD-YOLOv8n Algorithm

Pine wilt disease (PWD) is one of the most destructive diseases for pine trees, causing a significant effect on ecological resources. The identification of PWD-infected trees is an effective approach for disease control. However, the effects of complex environments and the multi-scale features of PWD trees hinder detection performance. To address these issues, this study proposes a detection model based on PWD-YOLOv8 by utilizing aerial images. In particular, the coordinate attention (CA) and convolutional block attention module (CBAM) mechanisms are combined with YOLOv8 to enhance feature extraction. The bidirectional feature pyramid network (BiFPN) structure is used to strengthen feature fusion and recognition capability for small-scale diseased trees. Meanwhile, the lightweight FasterBlock structure and efficient multi-scale attention (EMA) mechanism are employed to optimize the C2f module. In addition, the Inner-SIoU loss function is introduced to seamlessly improve model accuracy and reduce missing rates. The experiment showed that the proposed PWD-YOLOv8n algorithm outperformed conventional target-detection models on the validation set (mAP@0.5 = 94.3%, precision = 87.9%, recall = 87.0%, missing rate = 6.6%; model size = 4.8 MB). Therefore, the proposed PWD-YOLOv8n model demonstrates significant superiority in diseased-tree detection. It not only enhances detection efficiency and accuracy but also provides important technical support for forest disease control and prevention.

Development of DNA Insertion-specific Markers Based on the Intergenic Region of Oplegnathus punctatus Cdkn1/srsf3 for Sex Identification.

Spotted knifejaw (Oplegnathus punctatus) is a marine economic fish with high food and ecological value, and its growth process has obvious male and female sexual dimorphism, with males growing significantly faster than females. However, the current sex identification technology is not yet mature, which will limit the growth rate of O. punctatus aquaculture and the efficiency of separate sex breeding, so the development of efficient sex molecular markers is imperative. This study identified a 926bp DNA insertion fragment in the cdkn1/srsf3 intergenic region of O. punctatus males through whole-genome scanning, comparative genomics, and structural variant analysis. A pair of primers was designed based on the insertion information of the Y chromosome intergenic region in male individuals. Agarose gel electrophoresis revealed the amplification of two DNA fragments, 1118bp and 192bp, in male O. punctatus individuals. The 926bp fragment was identified as the insertion in the intergenic region of cdkn1/srsf3 in males, while only a single 192bp DNA fragment was amplified in females. The biological sex of the individuals identified in this manner was consistent with their known phenotypic sex.In this study, we developed a method to detect DNA insertion variants in the intergenic region of O. punctatus. Additionally, we introduced a new DNA marker for the rapid identification of the sex of O. punctatus, which enhances detection efficiency. The text has important reference significance and application value in sex identification, all-male breeding, and lineage selection. It provides new insights into the regulation of variation in the intergenic region of cdkn1/srsf3 genes and the study of RNA shearing.

Performance testing of a novel short axis photomultiplier tube for the HUNT project

Photomultiplier tubes (PMTs) with large-area cathodes are increasingly being used in cosmic-ray experiments to enhance detection efficiency. The optical modules (OMs) of the High-Energy Underwater Neutrino Telescope (HUNT) have employed a brand new N6205 20-inch microchannel plate photomultiplier tube (MCP-PMT) developed by the North Night Vision Science & Technology (Nanjing) Research Institute Co. Ltd. (NNVT). In order to make the 20-inch PMT fit into the 23-inch diameter pressure-resistant glass sphere, NNVT improved the internal structure of PMT and shortened the height of PMT by more than 10 cm. The first batch of these PMTs has been delivered for preliminary research work. This paper describes a specific PMT testing platform built for the first batch of 15 MCP-PMTs, and some performance parameters of PMT, such as peak-to-valley ratio, TTS and nonliniearity, are measured. The measurement results show that the new PMT still has good performance and can meet the requirements of HUNT project.

Direction of Arrival Joint Prediction of Underwater Acoustic Communication Signals Using Faster R-CNN and Frequency–Azimuth Spectrum

Utilizing hydrophone arrays for detecting underwater acoustic communication (UWAC) signals leverages spatial information to enhance detection efficiency and expand the perceptual range. This study redefines the task of UWAC signal detection as an object detection problem within the frequency–azimuth (FRAZ) spectrum. Employing Faster R-CNN as a signal detector, the proposed method facilitates the joint prediction of UWAC signals, including estimates of the number of sources, modulation type, frequency band, and direction of arrival (DOA). The proposed method extracts precise frequency and DOA features of the signals without requiring prior knowledge of the number of signals or frequency bands. Instead, it extracts these features jointly during training and applies them to perform joint predictions during testing. Numerical studies demonstrate that the proposed method consistently outperforms existing techniques across all signal-to-noise ratios (SNRs), particularly excelling in low SNRs. It achieves a detection F1 score of 0.96 at an SNR of −15 dB. We further verified its performance under varying modulation types, numbers of sources, grating lobe interference, strong signal interference, and array structure parameters. Furthermore, the practicality and robustness of our approach were evaluated in lake-based UWAC experiments, and the model trained solely on simulated signals performed competitively in the trials.

Investigation of the Seroprevalence of Brucella Antibodies and Characterization of Field Strains in Immunized Dairy Cows by B. abortus A19.

(1) Background: One method of eradicating brucellosis is to cull cattle that test positive for antibodies 12 months after being vaccinated with the 19-strain vaccine. Variations in immunization regimens and feeding practices may contribute to differences in the rate of persistent antibodies. We conducted this study to investigate the real positive rate of Brucella antibody in field strains of Brucella spp. after immunization over 12 months in dairy cows. This research aims to provide data to support the development of strategies for preventing, controlling, and eradicating brucellosis. (2) Method: We employed the baseline sampling method to collect samples from cows immunized with the A19 vaccine for over 12 months in Lingwu City from 2021 to 2023. Serological detection was conducted using the RBPT method. An established PCR method that could distinguish between 19 and non-19 strains of Brucella was utilized to investigate the field strains of Brucella on 10 dairy farms based on six samples mixed into one using the Mathematical Expectation strategy. (3) Results: We analyzed the rates of individual seropositivity and herd seropositive rates in dairy cattle in Lingwu City from 2021 to 2023 and revealed that antibodies induced by the Brucella abortus strain A19 vaccine persist in dairy herds for more than 12 months. We established a PCR method for identifying both Brucella A19 and non-A19 strains, resulting in the detection of 10 field strains of Brucella abortus from 1537 dairy cows. By employing a Mathematical Expectation strategy, we completed testing of 1537 samples after conducting only 306 tests, thereby reducing the workload by 80.1%. (4) Conclusions: There was a certain proportion of cows with a persistent antibody titer, but there was no evidence that all of these cattle were naturally infected with Brucella. The established PCR method for distinguishing between Brucella abortus strain 19 and non-19 strains can be specifically utilized for detecting natural Brucella infection in immunized cattle. We propose that relying solely on the detection of antibodies in cattle immunized with the A19 vaccine more than 12 months previously should not be solely relied upon as a diagnostic basis for brucellosis, and it is essential to complement this approach with PCR analysis to specifically identify field Brucella spp. Brucella abortus was the predominant strain identified in the field during this study. Detection based on the Mathematical Expectation strategy can significantly enhance detection efficiency.

Pixel-level automatic detection and quantification of running bands on rail surfaces

The running band on the rail surface serves as a crucial indicator of the wheel-rail contact relationship, making quantitative detection of the running band vital for railroad inspection and maintenance. However, current rail running band detection methods remain manual, and leveraging state-of-the-art deep learning technology can significantly enhance detection efficiency and minimize the reliance on human labor. This paper presents an improved version of ShuttleNet, called ShuttleNetM2T, for rail surface running band detection, emphasizing high stability. Efficient self-attention in ShuttleNetM2T captures global information, maintaining efficiency and rectifying issues like misrecognition and incomplete edges. Experimental results show ShuttleNetM2T achieves 0.9823 average F-measure and 0.9653 average Intersection-Over-Union on test images. For automatic running band width detection, we propose a pixel-to-physical distance conversion-based calculation, yielding just 0.625% relative error. Overall, the findings in this paper facilitate accurate detection and automatic calculation of the running band width, with promising applications in intelligent rail surface detection.

Fluorescent labelling combined with confocal differential Raman spectroscopy to detect microplastics in seawater

As an emerging marine pollutant, microplastics represent a focal point in global monitoring and management efforts. With seawater accounts for 97 % of the total global water resources, scientific assessments of microplastics in seawater are crucial for pollution control and management of marine environments. This study focuses on investigating microplastics in near-shore seawater and proposes a rapid and accurate detection method using a constructed confocal Raman spectroscopy detection system. By optimizing the pretreatment process of seawater microplastic samples, the efficient removal of organic matter interference in microplastic detection is achieved. Employing fluorescent labeling addresses the issues of prolonged detection time and high false positive rates associated with traditional methods, enabling rapid differentiation between microplastics and other substances and significantly enhancing detection efficiency and accuracy. Additionally, the use of differential Raman spectroscopy effectively mitigates fluorescence signal interference, thus improving the signal-to-noise ratio of the spectra. By employing dual-wavelength laser excitation at 784 nm/785 nm, microplastics such as polyethylene (PE), polypropylene (PP), polystyrene (PS) ranging in size from 60 to 500 μm are successfully detected in seawater. The results demonstrate that the proposed pretreatment method for seawater microplastics and novel detection techniques enable rapid screening and comprehensive non-destructive detection of microplastics in seawater, thereby facilitating the characterization of marine microplastics and providing scientific support for enhancing the management of microplastic pollution and ecological risk control.

Intelligent detection method of microparticle virus in silkworm based on YOLOv8 improved algorithm

The presence of microparticle viruses significantly impacts the quality of silkworm seeds for domestic sericulture, making their exclusion from detection in silkworm seed production crucial. Traditional methods for detecting microparticle viruses in silkworms, such as manual microscopic observation, molecular biology, and immunological approaches, are cumbersome and unable to achieve intelligent, batch real-time detection. To address this challenge, we employ the YOLOv8 algorithm in this paper. Firstly, NAM attention is introduced in the original algorithm’s Backbone component, allowing the model to extract more generic feature information. Secondly, ODConv replaces Conv in the Head component of the original algorithm, enhancing the model’s ability to identify microparticle viruses. Finally, NWD-LOSS modifies the CIoU loss of the original algorithm to obtain a more accurate prediction box. Experimental results demonstrate that the NN-YOLOv8 model outperforms mainstream detection algorithms in detecting silkworm microparticle diseases. With an average detection time of 22.6 milliseconds per image, the model shows promising prospects for future applications. This model improvement enhances detection efficiency and reduces human resource costs, effectively realizing detection intelligence.

Carbon dots-functionalized metal−organic framework for the dual detection of tetracycline and norfloxacin

Tetracycline and norfloxacin are two important antibiotics known for their effective treatment of inflammation. However, their excessive use inevitably leads to serious environmental hazards. Rapid and highly sensitive detection of their trace amounts is crucial for early prevention of related diseases. While there are numerous reported fluorescent probes capable of highly sensitive detection of tetracycline and norfloxacin individually, very few can simultaneously detect both. Developing a probe capable of detecting both antibiotics simultaneously is particularly necessary to enhance detection efficiency. In this study, carbon quantum dots derived from fragrans were functionalized with Metal-Organic Framework (MOF) materials. This successful approach enabled simultaneous detection of both tetracycline and norfloxacin, with detection limits of 0.15 μM and 0.87 μM, respectively. Furthermore, successful detections were achieved in water, milk and soil samples, yielding excellent results.

Multiple RNA Rapid In Situ Imaging Based on Cas9 Code Key System.

Existing RNA in situ imaging strategies mostly utilize parallel repetitive nucleic acid self-assembly to achieve multiple analysis, with limitations of complicated systems and cumbersome steps. Here, a Cas9 code key system with key probe (KP) encoder and CRISPR/Cas9 signal exporter is developed. This system triggers T-protospacer adjacent motif (T-PAMstructural transitions of multiple KP encoders to form coding products with uniform single-guide RNA (sgRNA) target sequences as tandem nodes. Only single sgRNA/Cas9 complex is required to cleave multiple coding products, enabling efficient "many-to-one" tandem signaling, and non-collateral cleavage activity-dependent automatic signaling output through active introduction of mismatched bases. Compared with conventional parallel multiple signaling analysis model, the proposed system greatly simplifies reaction process and enhances detection efficiency. Further, a rapid multiple RNA in situ imaging system is developed by combining the Cas9 code key system with a T-strand displacement amplification (T-SDA) signal amplifier. The constructed system is applied to tumor cells and clinicopathology slices, generating clear multi-mRNA imaging profiles in less than an hour with just one step. Therefore, this work provides reliable technical support for clinical tumor typing and molecular mechanism investigation.

Correction to “Enhanced detection efficiency in reaction time‐based concealed information test through response preparation interference”

Correction to “Enhanced detection efficiency in reaction time‐based concealed information test through response preparation interference”

Unified weakly and semi-supervised crack segmentation framework using limited coarse labels

Obtaining extensive, high-quality datasets for crack segmentation with pixel-level labels is expensive and labor-intensive. The Unified Weakly and Semi-supervised Crack Segmentation (UWSCS) framework addresses this challenge by leveraging a limited number of images with coarse labels and a larger set of unlabeled images. Two label correction modules, based on super-pixel segmentation and a shrink module, are incorporated in the model training to improve crack label accuracy and optimize edge refinement. UWSCS employs a dual-encoder fusion network, combining transformers and convolutional neural networks, to enhance crack segmentation in complex backgrounds. An enhanced algorithm using the medial axis transform is proposed for accurately quantifying crack length and width. Extensive experiments were conducted on both synthetic and real crack datasets to validate the superior performance of UWSCS. The results underscore the significant impact of label quality and quantity used in training on model prediction accuracy. Trained on a concrete crack dataset with limited coarse labels, UWSCS achieves an Intersection of Union (IoU) of 77.53%, surpassing the fully supervised model using the same number of coarse labels by 28.64%. It closely approaches the performance of a fully supervised model with the same number of fine labels (IoU of 80.21%). UWSCS outperforms other advanced networks and semi-supervised/weakly supervised algorithms when trained with a limited set of more cost-effective manually labeled coarse labels. Integrated with the crack segmentation network, super-pixel segmentation, and shrink modules during training, UWSCS with limited coarse labels performs similarly to a fully supervised model using fine labels, thereby reducing manual labeling costs by over 90% and enhancing detection efficiency in practical engineering.

A neural network framework based on ConvNeXt for side‐channel hardware Trojan detection

AbstractResearchers in the field of hardware security have been dedicated to the study of hardware Trojan detection. Among the various approaches, side‐channel detection methods are widely used because of their high detection accuracy and fewer constraints. However, most side‐channel detection methods cannot make full use of side‐channel information. In this paper, we propose a framework that utilizes the continuous wavelet transform to convert time‐series information and employs an improved ConvNeXt network to detect hardware Trojans. This detection framework first converts one‐dimensional time‐series information into a two‐dimensional time–frequency map using the continuous wavelet transform to leverage frequency information in electromagnetic side‐channel signals. Then, the two‐dimensional time–frequency map is fed into the improved ConvNeXt network, which increases the weight of the informative parts in the two‐dimensional time–frequency map and enhances detection efficiency. The results indicate that the method proposed in this paper significantly improves the accuracy of hardware Trojan detection.

Effect of extracorporeal membrane oxygenation combined with ulinastatin

This study aims to investigate the clinical efficacy of combining extracorporeal membrane oxygenation (ECMO) with ulinastatin in the treatment of severe pneumonia (SP) with respiratory failure, using an immuno-magnetic bead-based ELISA method. A total of 40 patients with SP complicated by respiratory failure who were admitted to the respiratory department of our hospital from January 2019 to December 2022, were selected. They were divided into the control group (n = 20) and the ECMO group (n = 20). The control group received conventional therapy such as mechanical ventilation, anti-infection, anticoagulation, and maintaining acid-base balance. The ECMO group established an ECMO system and injected ulinastatin intravenously based on conventional therapy. After 7 days of treatment, arterial blood was collected for blood gas analysis and pulmonary function index. The level of inflammatory factors was determined using the immunomagnetic bead-ELISA method. Flow cytometry was used to assess T lymphocyte subsets and clinical efficacy between the two groups. The ECMO group exhibited significantly shorter length of hospital stay and mechanical ventilation duration compared to the control group. Moreover, pulmonary function indexes (FEV1, FVC, PEF) and blood gas indexes were superior in the ECMO group than those in the control group. Additionally, the ECMO group exhibited significantly reduced levels of PCT, IL6, and CRP, and elevated CD3+% and CD4+% values compared to the control group. In conclusion, the combined therapy demonstrates efficacy in the treatment of patients with SP and respiratory failure, and the immunomagnetic bead-based ELISA method enhances detection efficiency and reduces errors.

基于改进YOLOv8s的实时葡萄病害检测模型

This research is dedicated to enhancing the accuracy and processing speed of grape disease recognition. As a result, a real-time grape disease detection model named MSCI-YOLOv8s, based on an improved YOLOv8s framework is proposed. The primary innovation of this model lies in replacing the backbone network of the original YOLOv8s with the more efficient MobileNetV3. This alteration not only strengthens the ability of the model to capture features of various disease manifestations in grape leaf images but also improves its generalization capabilities and stability. Additionally, the model incorporates the SPPFCSPC pyramid pooling structure, which maintains the stability of the receptive field while significantly enhancing processing speed. The integration of the CBAM attention mechanism further accentuates the ability of the model to identify key features, substantially increasing the accuracy of disease detection. Moreover, the model employs Inner-SIoU as the loss function, optimizing the precision of bounding box regression and accelerating model convergence, thereby further enhancing detection efficiency. Rigorous testing has shown that the MSCI-YOLOv8s model achieves an impressive average precision (mAP) of 97.7%, with an inference time of just 37.2 milliseconds and a memory footprint of 39.3 MB. These advancements render the MSCI-YOLOv8s not only highly efficient but also extremely practical for real-time grape disease detection, meeting the actual demands of grape orchard disease identification and demonstrating significant potential for application.