Detection Method Research Articles

Review of Methods for Automatic Plastic Detection in Water Areas Using Satellite Images and Machine Learning

Ocean plastic pollution is one of the global environmental problems of our time. “Rubbish islands” formed in the ocean are increasing every year, damaging the marine ecosystem. In order to effectively address this type of pollution, it is necessary to accurately and quickly identify the sources of plastic entering the ocean, identify where it is accumulating, and track the dynamics of waste movement. To this end, remote sensing methods using satellite imagery and aerial photographs from unmanned aerial vehicles are a reliable source of data. Modern machine learning technologies make it possible to automate the detection of floating plastics. This review presents the main projects and research aimed at solving the “plastic” problem. The main data acquisition techniques and the most effective deep learning algorithms are described, various limitations of working with space images are analyzed, and ways to eliminate such shortcomings are proposed.

Synthesis of novel polyethyleneimine-capped silver nanoclusters exhibiting ultraviolet-A fluorescence and their application in multiple sensing.

Cupric ions (Cu2+), pyrophosphate (PPi), and alkaline phosphatase (ALP) are involved in a variety of biochemical processes such as DNA replication, cellular metabolism and play an important role in human growth and development. It is of great significance to establish a method for the sensitive detection of Cu2+, PPi and ALP. In this work, polyethyleneimine-capped silver nanoclusters (PEI-AgNCs) were successfully synthesized by a one-pot method using hydrazine sulfate as reductant, exhibiting a unique strong fluorescence emission in the near-ultraviolet region at ∼339nm. Since the fluorescence of PEI-AgNCs can be quenched by Cu2+ through inner filtering effect (IFE), then recovered by competitive binding of pyrophosphate and Cu2+, and later weakened again by catalytic hydrolysis of alkaline phosphatase, a sensitive and selective strategy based on the changes of fluorescence "ON" or "OFF" was established to detect Cu2+, PPi and ALP. The LODs of these three analyteswere 36nM, 0.2μM, and 0.14 U L-1 at a S/N ratio of 3, respectively. A series of logic gate circuits for sensing cupric ions, pyrophosphate, and alkaline phosphatase were successfully constructed. The established methods have the potential for biosensing and environmental analysis and the specific UV-A fluorescence property of PEI-AgNCs may be helpful in photonic and optical areas.

A colorimetric biosensor with infrared sterilization based on CuSe nanoparticles for the detection of E. coli O157:H7 in food samples.

Escherichia coli O157:H7 is a major threat to public health. At present, the detection methods for E. coli O157:H7 mainly include traditional bacterial culture, immunology (enzyme-linked immune-sorbent assay) and molecular biology techniques (polymerase chain reaction). These methods have the limitations of professional operation, waste of time and energy, and high cost. Therefore, we have developed a simple, fast, bactericidal colorimetric biosensor to detect E. coli. O157:H7. The entire process was completed in 80 minutes. The method has been successfully applied to milk and mineral water samples with satisfactory results, proving that the method is an effective method for real-time detection and inactivation of bacteria.

A Novel Real-Time Detection and Classification Method for ECG Signal Images Based on Deep Learning

In this paper, a novel deep learning method Mamba-RAYOLO is presented, which can improve detection and classification in the processing and analysis of ECG images in real time by integrating three advanced modules. The feature extraction module in our work with a multi-branch structure during training can capture a wide range of features to ensure efficient inference and rich feature extraction. The attention mechanism module utilized in our proposed network can dynamically focus on the most relevant spatial and channel-wise features to improve detection accuracy and computational efficiency. Then, the extracted features can be refined for efficient spatial feature processing and robust feature fusion. Several sets of experiments have been carried out to test the validity of the proposed Mamba-RAYOLO and these indicate that our method has made significant improvements in the detection and classification of ECG images. The research offers a promising framework for more accurate and efficient medical ECG diagnostics.

Detection of Brain Tumor Employing Residual Network-based Optimized Deep Learning

Diagnosis and treatment planning play a very vital role in improving the survival of oncological patients. However, there is high variability in the shape, size, and structure of the tumor, making automatic segmentation difficult. The automatic and accurate detection and segmentation methods for Brain tumors are proposed in this paper. A modified ResNet50 model was used for tumor detection, and a ResUNetmodel-based convolutional neural network for segmentation is proposed in this paper. The detection and segmentation were performed on the same dataset consisting of pre-contrast, FLAIR, and postcontrast MRI images of 110 patients collected from the Cancer Imaging Archive. Due to the use of Residual Networks, the authors observed improvement in evaluation parameters, such as accuracy for tumor detection and dice similarity coefficient for tumor segmentation. The accuracy of tumor detection and Dice Similarity Coefficient achieved by the segmentation model were 96.77% and 0.893, respectively, for the TCIA dataset. The results were compared based on manual segmentation and existing segmentation techniques. The tumor mask was also individually compared to the ground truth using the SSIM value. The proposed detection and segmentation models were validated on BraTS2015 and BraTS2017 datasets, and the results were consensus. The use of residual networks in both the detection and the segmentation model resulted in improved accuracy and DSC score. DSC score was increased by 5.9% compared to the UNet model, and the accuracy of the model was increased from 92% to 96.77% for the test set.

Unsupervised Anomaly Detection via Nonlinear Manifold Learning

Abstract Anomalies are samples that significantly deviate from the rest of the data and their detection plays a major role in building machine learning models that can be reliably used in applications such as data-driven design and novelty detection. The majority of existing anomaly detection methods either are exclusively developed for (semi) supervised settings, or provide poor performance in unsupervised applications where there are no training data with labeled anomalous samples. To bridge this research gap, we introduce a robust, efficient, and interpretable methodology based on nonlinear manifold learning to detect anomalies in unsupervised settings. The essence of our approach is to learn a low-dimensional and interpretable latent representation (aka manifold) for all the data points such that normal samples are automatically clustered together and hence can be easily and robustly identified. We learn this low-dimensional manifold by designing a learning algorithm that leverages either a latent map Gaussian process (LMGP) or a deep autoencoder (AE). Our LMGP-based approach, in particular, provides a probabilistic perspective on the learning task and is ideal for high-dimensional applications with scarce data. We demonstrate the superior performance of our approach over existing technologies via multiple analytic examples and real-world datasets.

Development of a multienzyme isothermal and lateral flow dipstick combination assay for the rapid detection of goose astrovirus II

IntroductionGoose astrovirus (GAstV) is a newly emerging pathogen that is currently widespread among geese, causing visceral gout and leading to substantial gosling mortalities, posing a severe threat to the waterfowl industry. GAstV II is the predominant epidemic strain, characterized by its high morbidity and mortality rate. Consequently, there is an urgent necessity to develop an effective diagnostic approach to control the dissemination of GAstV II, particularly in clinical farms with limited laboratory resources.MethodsIn this study, a novel multi-enzyme isothermal rapid amplification (MIRA) and lateral flow dipstick (LFD) combined assay was developed. Different primers designed specific targeting a highly conserved region within the viral RdRp gene for the detection of GAstV II. Primers optimized and MIRA-LFD assay analyzed its performance regarding limits of detection, specificity, and efficiency of detection.ResultsThe developed MIRA amplification is conducted at a constant temperature and accomplished within 10 minutes. Subsequent naked-eye observation of the LFD strips merely takes 5 minutes. The established MIRA-LFD method exhibits high specificity, with no cross-reaction with other pathogens and attains a detection sensitivity of 1 copy/μl, which is consistent with the reverse transcription quantitative PCR (RT-qPCR) assay. Further evaluation with clinical samples indicates that the accuracy of this MIRA-LFD method correlates well with RT-qPCR for the detection of GAstV II. ConclusionIn summary, the convenience, sensitivity, and rapidity of this newly developed detection method offer a significant advantage for on-site diagnosis of GAstV II.

Ransomware Detection Using Machine Learning Techniques

The proliferation of ransomware attacks is a critical cybersecurity threat that organizations globally face. This situation necessitates effective prevention and mitigation strategies. These malicious programs encrypt data and extort payments, impacting various industries. They highlight the urgent need for robust defense mechanisms. Despite advancements in machine learning for ransomware detection, there is a notable gap in the comparative analysis of individual algorithms such as Decision Tree (DT), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). This study aims to fill the gap by providing a comparative analysis of these algorithms. It focuses on using the UG Ransome dataset and key metrics including accuracy, precision, recall, and F-measure. The experiments were conducted using Python. The results demonstrate that the Decision Tree outperforms SVM and MLP across all metrics. It achieves an accuracy of 98.83%, precision of 99.41%, recall of 99.41%, and F-measure of 99.41%. SVM and MLP, on the other hand, achieved lower scores. These results highlight the Decision Tree's superior performance in capturing non-linear data relationships, which is crucial for ransomware detection. The major contribution of this study is the identification of the Decision Tree as a highly effective model for ransomware detection. It significantly outperforms other models. The findings suggest that the Decision Tree's ability to model complexities within the data makes it a robust and reliable tool for safeguarding systems against ransomware attacks. Future research should explore the Decision Tree's performance across diverse ransomware datasets, integrate ensemble learning, investigate adversarial machine learning techniques, and enhance real-time detection methods. These efforts will improve the robustness and applicability of machine learning-based ransomware detection systems.

Patient factors that lead to presentation delay in breast cancer among Sri Lankan women: A cross-sectional study

Background: Breast cancer (BC) poses a significant public health concern as it is the most common cancer detected among women worldwide. Early detection and intervention are associated with better prognosis and survival. A primary reason for the rising mortality and morbidity in developing countries is the lack of patient education and practice early detection programs and presentation. Aims and Objectives: The main objective of this study is to evaluate the prevalence of delayed presentation and the factors lead to delay among BC patients in Sri Lanka. Materials and Methods: A descriptive cross-sectional study was conducted from January 01, 2023, to June 30, 2023 among women diagnosed with BC attending two selected state hospitals in Sri Lanka. All women with histological diagnosis of early, locally advanced, or metastatic BC attending for treatment were included and women with recurrent BC and concomitant other cancers were excluded. Presentation delay was defined as ≥3 months. The calculated sample size was 396. Results: About 47.5% of our study population (n=188) had a presentation delay of more than 3 months. The main reasons for the delay in presentation were, being unaware that BC is a major problem in Sri Lanka, difficulty in accessing healthcare, and not being able to find time due to family and social commitments. Women who had not been practicing self-breast examination had a significant association with presentation delay (OR=3.26, 95% CI - 0.090–0.362, P=0.001). The participants who were having a presentation delay were also more likely to be diagnosed with an advanced stage of cancer (OR=2.86, 95% CI - 2.573–3.153, P=0.000). Conclusion: Our study population depicts a good knowledge related to BC symptoms, risk factors, and early detection methods. However, very few are practicing them. There is also a significant delay of presentation due to various factors. Awareness and good practices would lead to early detection and diagnosis and improve overall survival.

A novel framework for low-contrast and random multi-scale blade casting defect detection by an adaptive global dynamic detection transformer

The radiographic inspection plays a crucial role in ensuring the casting quality for improving the service life under harsh environments. However, due to the low-contrast between the defects and the image background, the random spatial position distribution, random shapes and aspect ratios of the defects, the development of an accurate defect automatic detection system is still challenging. To address these issues, this paper proposes a novel framework for low-contrast and random multi-scale casting defect detection, which is referred to as adaptive global dynamic detection transformer (AGD-DETR). A novel defect-aware data augmentation method is first proposed to adaptively highlight the feature of the low-contrast defect boundary. A multi-attentional pyramid feature refinement (MPFR) module is then established to refine and fuse the multi-scale defect features of random sizes. Afterwards, a novel global dynamic receptive fusion-transformer (GDRF-Transformer) detection scheme is designed to perform the global perception and feature dynamic extraction of complex internal casting defects. It includes 4D-anchor query and cross-layer box update strategy, query rectification by prior information of defect aspect ratio, and global adaptive-feed forward network (GA-FFN). A dataset comprising turbine blade casting defect radiographic (TBCDR) images, is used to demonstrate the high efficiency of the proposed AGD-DETR. The obtained results show that the proposed method can accurately capture the spatial position distributions and complex defect shapes. Furthermore, it outperforms existing state-of-the-art defect detection methods.

Improved echo signal detection for pseudo-random single-photon counting laser ranging

In this paper, a new echo signal detection method, to the best of our knowledge, for pseudo-random single-photon counting ranging (PSPCR) LiDAR systems is proposed, which is applied for long distances, low repetition rates, and system cost reduction. First, in order to achieve a comparable temporal resolution as that in time-correlated single-photon counting (TCSPC) systems, we extend the pseudo-random code to discriminate the minimal time slot in time correlation. Second, we use the full width at half maxima (FWHM) in the duration of each pseudo-random code for correlation to reduce the impact of pulse width variation and timing jitters on ranging accuracy. Third, we study the bias and errors caused by using synchronous signals as the “START” signal, and propose to use the time of flight (ToF) at half energy to reduce the walk error. Simulation results show that, compared with existing PSPCR methods, the proposed method improves ranging accuracy with a lower repetition rate and lower peak and average power—centimeter-level ranging accuracy over tens of kilometers can be achieved using a laser with a repetition rate of 400 kHz, peak power of up to 1 kW, and average power of up to 1 W.

BDS time spoofing detection method based on the dynamic time warping

BDS time spoofing detection method based on the dynamic time warping

A novel recombinase polymerase amplification approach with propidium iodide reporter for rapid detection of porcine adulteration in food

The adulteration of food products with porcine components is among the most widespread concerns in the global food industry. Apart from health and religious reasons, being able to detect porcine adulteration is crucial to warrant fair trading and legal compliance. Current detection methods are often constrained by their low sensitivity and expensive equipment. In this work, a recombinase polymerase amplification (RPA) assay for the rapid and sensitive detection of porcine is constructed, with propidium iodide (PI) dye as the reporter molecule. Under the optimized conditions, the RPA-PI assay achieved a wide linear range of 0.005–100 ng/μL and a detection limit of 0.005 ng/μL for porcine DNA with no cross-reactivity. Furthermore, the application of the assay to actual food samples demonstrated a 100 % consistency with conventional PCR results. This developed assay not only provides a novel porcine adulteration detection platform with simpler and faster protocols, but it also holds the potential to be developed further for point-of-care authenticity testing.

Spectrofluorimetric and smartphone-based detection methods for determination of gentamicin

AbstractThis paper presents the development of spectrofluorimetric and smartphone-based detection methods for gentamicin determination using fluorescamine as a reagent. The research included selecting excitation (415 nm) and emission (489 nm) wavelengths, reaction time, and conditions like reagent concentration, and pH of the reaction medium. Moreover, the optimal operating parameters of the smartphone camera, like ISO, white balance, camera shutter, and RGB model channel were selected. Analytical parameters of the developed spectrofluorimetric and smartphone-based methods were estimated including the linear range: 0.04–15.00 mg dm−3 and 0.18–1.20 mg dm−3, respectively, limits of detection and quantification: 0.01 and 0.04 mg dm−3, and 0.06 and 0.18 mg dm−3, respectively, and precision (CV, n = 6): 5.2% and 2.8%, respectively. The proposed approaches were successfully applied to determine gentamicin in pharmaceutical samples. The obtained results were consistent with values declared by manufacturers and satisfactory recovery values, 93.2–113.6% were obtained for both spectrofluorimetric and smartphone-based methods. The developed fluorimetric method with smartphone-based detection provides a low limit of detection specific to spectrofluorimetric methods whereas the measurement system is a simple, easily accessible, compact, and low-cost device. Hence, it can become a competitive alternative to other gentamicin determination methods. Graphical abstract

Label-free fluorescence turn-on detection of alkaline phosphatase activity using the calcein-Ce3+ complex.

This study introduces an innovative approach for the real-time and efficient detection of alkaline phosphatase (ALP) activity, using a calcein fluorescence probe and leveraging the static quenching properties of calcein fluorescence by Ce3+ metal ions. In this method, calcein serves as the signal element, with its fluorescence effectively preserved through energy transfer or charge transfer when coordinated with Ce3+. Conversely, ALP catalyzes the phosphopeptide substrate to generate a substantial amount of Pi, preventing calcein fluorescence quenching due to the higher affinity between Pi and Ce3+ compared with that between calcein and Ce3+. The fluorescence intensity ratio (F-F0/F0) exhibited excellent linearity, facilitating sensitive ALP detection. The proposed ALP detection method covers a range from 0 to 1.4mU/mL (R2 = 0.9942), with the limit of detection at 0.069mU/mL (S/N = 3). Additionally, this method was successfully applied for detecting ALP in serum samples and studying its inhibitors. This research introduces a novel clinical diagnosis approach for ALP sensing while broadening the potential applications of calcein.

A lightweight and efficient model for grape bunch detection and biophysical anomaly assessment in complex environments based on YOLOv8s

As one of the most important economic crops, grapes have attracted considerable attention due to their high yield, rich nutritional value, and various health benefits. Identifying grape bunches is crucial for maintaining the quality and quantity of grapes, as well as managing pests and diseases. In recent years, the combination of automated equipment with object detection technology has been instrumental in achieving this. However, existing lightweight object detection algorithms often sacrifice detection precision for processing speed, which may pose obstacles in practical applications. Therefore, this thesis proposes a lightweight detection method named YOLOv8s-grape, which incorporates several effective improvement points, including modified efficient channel attention (MECA), slim-neck, new spatial pyramid pooling fast (NSPPF), dynamic upsampler (DySample), and intersection over union with minimum point distance (MPDIoU). In the proposed method, MECA and NSPPF enhance the feature extraction capability of the backbone, enabling it to better capture crucial information. Slim-neck reduces redundant features, lowers computational complexity, and effectively reuses shallow features to obtain more detailed information, further improving detection precision. DySample achieves excellent performance while maintaining lower computational costs, thus demonstrating high practicality and rapid detection capability. MPDIoU enhances detection precision through faster convergence and more precise regression results. Experimental results show that compared to other methods, this approach performs better in the grapevine bunch detection dataset and grapevine bunch condition detection dataset, with mean average precision (mAP50–95) increasing by 2.4% and 2.6% compared to YOLOv8s, respectively. Meanwhile, the computational complexity and parameters of the method are also reduced, with a decrease of 2.3 Giga floating-point operations per second and 1.5 million parameters. Therefore, it can be concluded that the proposed method, which integrates these improvements, achieves lightweight and high-precision detection, demonstrating its effectiveness in identifying grape bunches and assessing biophysical anomalies.

Data mining and safety analysis of dual orexin receptor antagonists (DORAs): a real-world pharmacovigilance study based on the FAERS database

ObjectiveUsing the Food and Drug Administration Adverse Event Reporting System (FAERS) database, four signal detection methods were applied to mine adverse drug events (ADEs) related to use of dual orexin receptor antagonists (DORAs) to provide reference for safe clinical use.Research design and MethodsData collected from Q3rd 2014 to Q4th 2023 were obtained from the FAERS database. According to the preferred terminology (PT) and systematic organ classification (SOC) of MedDRA v.26.0, the reporting odds ratio (ROR), proportional reporting ratio (PRR), multi-item gamma Poisson shrinker (MGPS), and Bayesian confidence propagation neural network (BCPNN) were used to detect ADE signals.ResultsA total of 11,857 DORAs-related adverse reactions were detected, reported with suvorexant, lemborexant, and daridorexant as the main suspected drugs was 8717584, and 2556, respectively. A higher proportion of females than males were reported (57.27% vs. 33.04%). The top 20 positive PT signals from three DORAs showed that “sleep paralysis” ranked first. “Brain fog” was stronger following daridorexant but was not detected for the other two drugs, and “sleep sex” and “dyssomnia” were stronger in suvorexant but not in the other two drugs. Additionally, some PTs occurred that were not included in drug instructions, such as “hangover” and “hypnagogic hallucination.”ConclusionIn this study, four algorithms (ROR, PRR, BCPNN, and MGPS) were used to mine the safety signals of DORAs. We identified some potential ADE signals that can promote the rational use of DORAs and improve their safety.

Recent Strategies for MicroRNA Detection: A Comprehensive Review of SERS-Based Nanobiosensors

With advances in technology, diagnostic techniques have become more sophisticated and efficient at detecting biomarkers rapidly. Biomarkers such as microRNA (miRNA), which exhibit exceptional specificity and sensitivity compared with other biomarkers, have garnered particular interest. Composed of 21–24 nucleotides, miRNAs constitute a noncoding RNA group that regulates gene expression, immune system activation, apoptosis, and other cellular processes; hence, they are frequently used as biomarkers for various diseases. This has sparked significant interest regarding the identification of the specific miRNAs implicated in many diseases. Presently, miRNA detection methods include northern blots, reverse transcription-quantitative polymerase chain reaction, and next-generation sequencing. While these methods are all sensitive, they are time-consuming, complex, and expensive, which renders them unsuitable for on-site detection. Surface-enhanced Raman scattering (SERS) can overcome these limitations to enable the sensitive and rapid detection of miRNA. This technique amplifies Raman signals, with signal enhancement levels changing sensitively depending on the distance between the target molecule and substrate. Therefore, this review covers the principle of SERS as a method for detecting miRNAs using nanomaterials, along with examples of nanomaterials and SERS applications. Based on the available literature, SERS is anticipated to enable the convenient, early diagnosis of various diseases, potentially lowering mortality rates. This review could therefore contribute significantly to the advancement of medical and diagnostic technologies.

Accurate Location Detection Method for Aluminum Profile Surface Defects Based on Improved YOLOX-S Algorithm

Accurate Location Detection Method for Aluminum Profile Surface Defects Based on Improved YOLOX-S Algorithm

Protein ligand binding site prediction using graph transformer neural network.

Ligand binding site prediction is a crucial initial step in structure-based drug discovery. Although several methods have been proposed previously, including those using geometry based and machine learning techniques, their accuracy is considered to be still insufficient. In this study, we introduce an approach that leverages a graph transformer neural network to rank the results of a geometry-based pocket detection method. We also created a larger training dataset compared to the conventionally used sc-PDB and investigated the correlation between the dataset size and prediction performance. Our findings indicate that utilizing a graph transformer-based method alongside a larger training dataset could enhance the performance of ligand binding site prediction.