Efficient Detection Method Research Articles

Application of stealth nanobeacon for traceability assurance in pharmaceutical tablets via surface-enhanced Raman scattering during the tablet coating process

Microtaggant technologies for on-dose authentication have garnered significant interest for use in the anti-counterfeit activities and traceability of pharmaceutical dosage forms. Previously, we proposed a stealth nanobeacon (NB) comprising self-assembled colloidal gold nanoparticles with reporter molecules that demonstrated characteristic surface-enhanced Raman scattering (SERS) activity. However, the integration of such microtaggants into standard production lines remains underexplored. In this study, we demonstrate the incorporation of NB into tablet coatings using a simple mixing method with conventional coating solutions. Rapid and discernible SERS responses from the NB-coated tablets were observed in response to laser excitation at 785 nm for 0.1s, implying that it is an advanced and efficient method for counterfeit detection. In addition, the SERS intensity of NB increased with coating time, suggesting that NB can be used as a tracer for the real-time monitoring of coating thickness. Furthermore, NB-coated tablets were indistinguishable from NB-free tablets, even during colorimetric analysis. These results suggest that the NB possesses stealth properties and can be easily incorporated into counterfeit detection products.

Optical Interaction of CdTe/ZnS Quantum Dots with Sodium Alginate Biopolymer PAA-Modified for Glucose Detection

In this paper, a novel and efficient method for glucose detection is designed and synthesized using a biopolymer-based nanocomposite covering zinc sulfide-coated cadmium telluride quantum dots. The obtained quantum dot is modified with polyacrylic acid (PAA) polymer attached to sodium alginate so that it can be used as a biosensor based on nanotechnology to detect glucose with a new method while taking advantage of the characteristics of fluorescent cadmium telluride-sulfide. The used method has advantages such as a safe preparation method and the use of cheap materials. The Fluorescence quenching method is used to systematically investigate the effect of quantum dots of cadmium telluride-zinc sulfide modified with biopolymer on glucose. One of the important advantages is the possibility of evaluating several parameters at the same time according to the multiple wavelengths of radiation of quantum dots of the sensor. The resulted experiments shows that quantum dots prepared from acrylic acid monomer with a concentration of 50 microliters and measured at temperatures lower than 25 °C at which the sensitivity is measured as 6.36 μg ml−1 at the lowest detection range whose characteristics can be evident from the glucose and absorption curve studies.

Efficient license plate recognition in unconstrained scenarios

Automatic license plate recognition (ALPR) is a critical technology for intelligent transportation systems. Most existing ALPR methods are focused on specific application scenarios. Although there are a few methods that focus on unconstrained scenarios, they are very time-consuming. In this work, we propose an efficient ALPR (EALPR) framework, where we can handle distorted license plates (LP) caused by perspective problems with high efficiency. We design a light LPD structure based on efficient object detection methods and use anchor-free strategies for LPD to alleviate the problem of expensive costs. Benefitting from these optimizations and a united framework structure, the proposed EALPR has real-time efficiency. We evaluate our method on five datasets and the results show that our method achieves state-of-the-art accuracy: 98.15% on OpenALPR(EU), 95.61% on OpenALPR(BR), 99.51% on AOLP(RP), 88.81% on SSIG, 79.41% on CD-HARD. Additionally, our method achieves an impressive speed of 74.9 FPS (Frames Per Second), outperforming existing approaches and demonstrating its efficiency. Our source code can be accessed at https://github.com/wechao18/Efficient-alpr-unconstrained.

MFT: A novel memory flow transformer efficient intrusion detection method

Intrusion detection is a critical field in network security research that is devoted to detecting malicious traffic or attacks on networks. Even with the advances in today's Internet environment, a lot of intrusion detection techniques still fail to take into account the long-term characteristics present in network data, which results in a high false alarm rate. Some researchers have tried to address this problem by using the traditional transformer model; however, it is not very effective when dealing with complex relationships and the subtle classification requirements of large amounts of sequential data. This work presents a novel solution called the memory flow transformer (MFT) in response to the limitations of the conventional transformer model. By utilizing a carefully designed memory flow structure, MFT transcends traditional limitations and makes it possible to obtain complex long-term features from network traffic. This innovation enables the model to identify deep connections at a finer level between a wide variety of network traffic data. Extensive experiments were carried out on the complex CICIDS 2017 and NSL-KDD datasets to validate the effectiveness of the MFT model. The results were outstanding, demonstrating MFT's powerful detection abilities. With regard to performance metrics like accuracy, F1 score, false alarm rate, and training time, MFT is superior to current state-of-the-art approaches. Network security is greatly strengthened by MFT, which provides practitioners in the intrusion detection field with novel and effective techniques.

Airport runway FOD detection model based on improved YOLOv10m

Abstract Foreign Object Debris (FOD) poses a significant safety risk to aircraft operations, making efficient and accurate detection methods crucial. Existing detection techniques often struggle to meet the demands of busy airports due to limitations in real-time performance and detection accuracy. To address these challenges, we proposed a lightweight FOD detection model based on YOLOv10. By incorporating PConv and EMA attention mechanisms, our model enhances detection accuracy, achieving a 0.4% increase in mAP@50 and a 0.7% improvement in mAP@50-95 compared to the original YOLOv10m. Additionally, our model reduces the number of parameters by 19.8% and decreases computational complexity by 20.9%, thanks to the integration of Feature Pyramid Shared Convolution (FPSConv) and a lightweight shared detection head (Detect-LSCD). These improvements make our model more suitable for real-time FOD detection on airport runways, offering a more efficient and accurate solution than the original YOLOv10m.

The enhancement detection method based on the Fabry–Pérot cavity using terahertz frequency-domain spectroscopy

This article demonstrates a simple, efficient, and low-cost gas detection method for gases with absorption peaks in the terahertz range. A modes-adjustable Fabry–Pérot cavity is designed. By adjusting the length of the cavity, the center resonant frequency of the cavity can be coupled to the gas absorption peak. This kind of coupling can greatly enhance gas detection. To detect gas absorption peaks, we choose terahertz frequency domain spectroscopy (THz-FDS), whose frequency resolution can be up to the MHz level. Vapor is selected to verify the coupling phenomenon. The resonant frequency of the cavity is modified to couple to 0.56 THz, the absorption peak of vapor. Experiments are conducted at different humidity levels, the humidity is controlled by the supersaturated salt solution. Results indicate that at a humidity level of 15%, the coupling effect can enhance the detectability of vapor by approximately 167%, and this enhancement effect diminishes as humidity increases. We analyze the effect of different modes on the coupling and find that the high modes can make the coupling easier, but have little effect on the enhancement. Furthermore, the method is used to detect biomolecule-α-tyrosine to ensure it has wide applicability. This method can be used to detect substances with absorption peaks in the THz regime, especially for low-concentration gas.

Efficient anomaly detection method for offshore wind turbines

Time-series anomaly detection plays a crucial role in the operation of offshore wind turbines. Various wind turbine monitoring systems rely on time-series data to monitor and identify anomalies in real-time, as well as to initiate early warning processes. However, for offshore wind turbines with a high data density, conventional methods have high computational overhead in detecting anomalies while failing to accurately detect anomalies due to variations in data scales. To address this challenge, we propose an efficient anomaly detection method with contrastive learning, called Hawkeye. Hawkeye is based on residual clustering, an unsupervised anomaly detection method for multivariate time-series data. To ensure accurate anomaly detection, a trend-capturing prediction module is also combined with an automatic labeling module. As a result, the most common information can be learned from multivariate time-series data to reconstruct data trends. By evaluating Hawkeye on public datasets and real offshore wind turbine operation datasets, the results show that Hawkeye’s F1-score improves by an average of 14% compared with Isolation Forest, and its size shrinks by up to 11.5 times on the largest dataset compared with other methods. The proposed Hawkeye is potential to real-time monitoring and early warning systems for wind turbines, accelerating the development of intelligent operation and maintenance.

Rapid identification of milk powder adulteration based on surface-enhanced Raman spectroscopy

In recent years, milk powder adulteration has emerged as a matter of great concern. In this study, a rapid, accurate, and efficient detection method based on surface-enhanced Raman spectroscopy (SERS) combined with principal component analysis (PCA) was established to detect milk powder adulteration. The “coffee ring” effect-based gold nanoparticles (Au NPs) as the SERS-enhancing substrate were coupled with a portable Raman spectrometer, which enabled the differentiation of various brands of milk powder and the detection of melamine in milk powder. The substrate exhibited good SERS enhancement ability with an enhancement factor of 104. Furthermore, a strong linear correlation with a correlation coefficient (R2) of 0.9903 was observed between the melamine Raman intensity and concentration from 0.5 to 5.0 mg/kg. The calculated limit of detection of melamine (LOD) was 0.15 mg/kg, while the limit of quantitation (LOQ) was 0.5 mg/kg. In addition, when the method was applied to the detection of melamine in milk powder samples, this method achieved the recovery rates of melamine in milk powder samples ranged from 92.83% to 98.86% with relative standard deviations between 0.84% and 1.14%. In summary, the established method offers the advantages of cost-effectiveness, less sample requirement, and shorter detection time, meeting the needs for milk powder classification and rapid melamine detection.

A mixed Ce/Eu metal-organic framework for ratiometric detection of Al3+ ion.

As a heavy metal ion, excessive aluminum ions pose a serious threat to human health and the ecological environment. Developing a simple, efficient, and fast detection method to detect the content of aluminum ions is of great significance, especially for ensuring human health and ecological safety. Herein, the mixed rare earth metal-organic framework (Ce0.74Eu0.26TPTC and Ce0.62Eu0.38TPTC) were prepared based on simple ligand 1,1':4',1″-Terphenyl-2',4,4″,5'-tetracarboxylic acid (H4TPTC). The Ce0.74Eu0.26TPTC and Ce0.62Eu0.38TPTC have dual luminescence centers, which can be used as ratio fluorescent probes to detect Al3+ ions, making the detection results more accurate and reliable. Therefore, this work can promote the further development of rare earth-based MOFs in the detection of heavy metal ions.

FindCSV: a long-read based method for detecting complex structural variations.

Structural variations play a significant role in genetic diseases and evolutionary mechanisms. Extensive research has been conducted over the past decade to detect simple structural variations, leading to the development of well-established detection methods. However, recent studies have highlighted the potentially greater impact of complex structural variations on individuals compared to simple structural variations. Despite this, the field still lacks precise detection methods specifically designed for complex structural variations. Therefore, the development of a highly efficient and accurate detection method is of utmost importance. In response to this need, we propose a novel method called FindCSV, which leverages deep learning techniques and consensus sequences to enhance the detection of SVs using long-read sequencing data. Compared to current methods, FindCSV performs better in detecting complex and simple structural variations. FindCSV is a new method to detect complex and simple structural variations with reasonable accuracy in real and simulated data. The source code for the program is available at https://github.com/nwpuzhengyan/FindCSV .

Computer vision based distributed denial of service attack detection for resource-limited devices

The growing adoption of Internet of Things (IoT) has rendered them a desirable target for cyber-attacks. One of the biggest threats to these systems is the distributed denial of service (DDoS) attack, which is a botnet-based attack. The reason for the increasing usage of machine learning and deep learning-based intrusion detection systems in IoT network security is their ability to recognize DDoS attack. Recent studies, however, shows how susceptible IoT networks are to these kinds of attacks and detection accuracy can be greatly lowered. While a majority of studies has concentrated on DDoS attack detection for deep learning, little attention has been paid to computer vision, especially image-based artificial intelligence technologies like convolutional neural network (CNN). In this study, we use an image-based dataset to evaluate the effectiveness of CNN, an effective computer vision approach, for DDoS attack detection in IoT contexts. Owing to the small size of the selected dataset and in order to improve the CNN model’s detection efficiency, we implement various data augmentation techniques prior to the model’s training, including scaling, rotation, and vertical and horizontal flipping. Next, we introduce an efficient CNN-based method for detection of DDoS attacks in IoT settings. Ultimately, we came to the conclusion that the statistical significance testing showed that there is a significance difference among the five models employed during the study, and the VGG19 which has higher accuracy (99.74%) and less computing cost (6020.80 s), which enables IoT devices to perform DDoS attack detection with cost-effectiveness.

Rapid and equipment-free identification of papaya mealybug Paracoccus marginatus based on RPA-CRISPR/Cas12a.

Paracoccus marginatus has invaded many countries, spreading rapidly and causing significant economic losses to crops. Accurate detection during the monitoring process is critical to prevent its expansion into new areas, therefore it is necessary to develop efficient and reliable detection methods. Traditional detection methods are time-consuming and instrument-dependent owing to the morphological similarities and small sizes of P. marginatus and other mealybugs, therefore establishing an efficient, rapid, and sensitive method for field detection in resource-limited settings is critical. A sensitive and rapid detection system was developed to detect P. marginatus using recombinase polymerase amplification (RPA) combined with clustered regularly interspaced short palindromic repeats (CRISPR)/Cas12a. The RPA-CRISPR/Cas12a assay distinguished P. marginatus from 10 other mealybugs. The entire process can be completed in approximately an hour, and the identification results can be determined by the naked eye using lateral flow strips or a portable mini-UV torch. A method was developed to extract DNA from P. marginatus within 5 min. This method was combined with the RPA-CRISPR/Cas12a assay to achieve rapid and simple detection. In addition, two portable thermos cups with temperature displays were used to maintain the reagents and assay reactions in the field. This assay represents the first application of portable and easily available items (mini-UV torch and thermos cup) based on the combination of RPA and CRISPR/Cas12a for rapid pest detection. This method is rapid, highly specific, and instrument-flexible, allowing for the early monitoring of P. marginatus in the field. This study provides guidance for the development of suitable management strategies. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

YOLO‐RSFM: An efficient road small object detection method

YOLO‐RSFM: An efficient road small object detection method

Emerging Diagnostic Modalities in Oral Cancer Detection and Management

Oral cancer remains a significant global health concern, yet it is often detected at an advanced stage. The limitations of traditional diagnostic techniques have prompted increased research efforts towards the development of more efficient and early detection methods. Recent advancements in oral cancer diagnosis include the use of salivary biomarkers, optical imaging, liquid biopsy, advanced imaging, and AI algorithms. These non-invasive and painless sampling methods have shown high sensitivity and specificity, particularly in the case of salivary biomarkers. Clinical trials and cooperation are necessary to demonstrate the effectiveness of these technologies and gain approval from relevant authorities and acceptance among clinicians. This review highlights the potential of these new modalities in transforming the approach to oral cancer diagnosis, leading to early detection, accurate diagnosis, and quality treatment for patients, ultimately reducing the global burden of this disease.

Microfluidics Relevant Approaches in Drug Delivery System Treatment of Cancer – A Review

Microfluidics technology is a promising method for creating advanced drug delivery systems, particularly in cancer detection and treatment. These systems provide accurate, efficient, and user-friendly methods for cancer detection and treatment by examining small samples. Microfluidic devices can produce nanoparticles for medication administration and identify cancer-diagnostic variables from biological fluids. Due to their high sensitivity, high throughput, and low cost, microfluidics may be useful in cancer study. While not currently used in clinical settings, microfluidic systems are expected to replace current technologies as the primary means of cancer diagnosis and treatment. Microfluidic lab-on-a-chip platforms have shown potential in designing novel procedures for cancer detection, therapy, and disease follow-up, as well as developing new drug delivery systems for cancer treatment. They are also being considered a rising method in natural disease studies due to their small volume and ability to be used in clinical settings.[1,2.3] Keywords: Microfluidics, detection, treatment

A dendrimer-based platform integrating surface-enhanced Raman scattering and class-incremental learning for rapidly detecting four pathogenic bacteria

Rapid monitoring of pathogens is crucial for preventing foodborne diseases, thus making it an urgent need to develop efficient, fast, and simple methods for on-site detection of multiple pathogens. With advances in SERS-based label-free biosensors and machine learning, progress is evident, yet challenges such as complex substrates and limited model interpretability persist. In this work, we reported a novel dendrimer-based platform that integrated surface-enhanced Raman scattering (SERS) with class-incremental learning (CIL) for quick and simultaneous detection of four pathogenic bacteria, namely Escherichia coli, Salmonella Paratyphi B, Pseudomonas aeruginosa, and Staphylococcus aureus. First, the poly(amidoamine) (PAMAM)-based gold nanoassemblies on silicon wafer (PGNAs/Si) was designed as a highly active SERS substrate with an easy fabrication process. Compared with naked gold nanoparticles, both sensitivity and repeatability were improved by introducing a controllable dendritic nanostructure with ultra-small internal nanogaps. The detection limits of four pathogens in water, food, and dietary supplement matrices were all on the order of 10 CFU/mL. Subsequently, to analyze the complex SERS spectra and distinguish samples with different pathogens, CIL models were established using the light gradient boosting machine (LightGBM) algorithm. Notably, the discriminative models demonstrated excellent classification performance with an accuracy of over 93.44 %. Further, the SHapley Additive exPlanations (SHAP) method was utilized to identify the key SERS features in accurately discriminating different pathogens. Together, these results demonstrate that the dendrimer-based platform, by integrating SERS with CIL, can rapidly detect four pathogenic bacteria, underscoring its potential for food safety testing or quality monitoring in medical supplies manufacturing.

Dual-emission rare-earth fluorescent nanomaterials for ratiometric and visual detection of N-acetylneuraminic acid and applications in information encryption and anti-counterfeiting

N-acetylneuraminic acid (NANA) can be used as a biomarker for many types of cancers. Currently, there are various methods for detecting NANA but showing some shortcomings that limit the real-time diagnosis of cancer. In contrast, fluorescence analysis has obvious advantages such as low cost, fast response time, and easy operation, and it also enables visual detection for real-time cancer monitoring. Therefore, the establishment of an efficient and rapid detection method is essential for the early prevention and treatment of the disease. Based on the properties of layered rare-earth hydroxide (LRH), we synthesized a dual-emission fluorescent material (NDC/SDS-LEuH), and further fabricated a fluorescent nanoprobe (ANP) for the detection of NANA. The probe has the advantages of high sensitivity (LOD = 32.9 μM) and high selectivity with fast response. During the sensing process, the dual emission of the probe shows opposite changes due to the photoinduced electron transfer (PET) effect and the interaction between NANA and the probe. The color changes of the system can be observed under UV irradiation. Therefore, a visual platform was developed to detect NANA with a LOD of 0.09 mM. In addition, a probe hydrogel was prepared, which can be applied in the anti-counterfeiting to improve the difficulty of counterfeiting and the security of anti-counterfeiting. The probe achieves ratiometric fluorescence detection of NANA, which reduces background interference and improves the accuracy of detection. A visual detection platform was fabricated to realize the real-time detection. In addition, the prepared probe hydrogel showed the potential applications in anti-counterfeiting, which provided new ideas for the design and application of anti-counterfeiting materials.

TPLAD: Template-Parsed Log Anomaly Detection for Electrical Database Systems

In recent years, with the increasing complexity of software systems, logs have become crucial for system maintenance. Log-based anomaly detection plays a vital role in automatically detecting system anomalies through log analysis. However, current log-based anomaly detection approaches encounter significant practical challenges. Supervised methods often require a large amount of manually labeled training data, which can be time-consuming and costly to obtain. On the other hand, unsupervised and semi-supervised approaches may suffer from subpar performance, as they do not leverage historical anomalies to improve their detection capabilities. These challenges underscore the necessity for the development of more efficient and effective log-based anomaly detection methods. Database anomaly access detection is critical for ensuring the stability and security of database systems. We present a survey of existing log anomaly detection models and propose a novel approach, Template-Parsed Log Anomaly Detection (TPLAD) model, for automated anomaly detection in massive database log files. The proposed model combines the original log template with template parsing using code and text semantic representation. Experimental results demonstrate the effectiveness of the proposed approach in detecting abnormal database access patterns, including runtime errors, unauthorized access, and data leaks. The findings indicate that TPLAD model shows promise in enhancing database security and stability in business systems.

Deep learning classification method for boar sperm morphology analysis.

Boar semen quality emphasizes three major criteria: sperm concentration, motility, and morphology. Methods to analyze concentration and motility quickly and objectively readily exist, but few exist for analyzing morphology outside of subjective manual counting. Other vital factors for fertilization, like acrosome health, lack efficient detection methods due to limitations in detection by the human eye and costly biomarker analysis, which is rarely used in semen diagnostics. To overcome these challenges, we propose a novel approach integrating deep-learning technology with high-throughput image-based flow cytometry (IBFC) for objective and accurate analysis of both morphology and label-free acrosome health of thousands of individual spermatozoa at once, as opposed to manually counting on a microscope slide. Images of 10,000 spermatozoa were captured using an IBFC and manually annotated based on the primary morphological defect or acrosome health status for the training of the convolutional neural network (CNN). The CNN used these images to train and then applied that training to unannotated images to predict the model accuracy. Using the CNNs, high F1 scores of 96.73%, 98.55%, and 99.31% for 20x, 40x, and 60x magnifications, respectively, for morphological classification were attained. Additionally, the model demonstrates an F1 score of 99.8% in detecting subtle acrosome health variations at the 60x magnification. We have established an integrated approach to rapidly collect and classify morphological defects and acrosome health status, without the use of manual counting or biomarker labeling. Our study underscores the potential of artificial intelligence in semen diagnostics, reducing technician variability, streamlining assays, and facilitating the development of additional label-free detection methods. This innovative approach addresses the barriers hindering biomarker adoption in semen analysis, offering a promising avenue for enhancing reproductive health assessments.

A dual-mode aptasensor based on rolling circle amplification enriched G-quadruplex for highly sensitive IFN-γ detection

BackgroundAptasensors have been extensively utilized in target detection due to their advantages of high sensitivity and fast response. However, the reliability of the detection results of the single-mode aptasensor cannot be verified in time. Developing efficient detection methods with cross-validation capability is beneficial to achieving highly reliable detection. This study aims to design a colorimetric and fluorescent dual-mode aptasensor by skillfully engineering G-quadruplex assembly and rolling circle amplification for highly reliable IFN-γ detection. ResultsThe complexes of anti–IFN–γ aptamers and complement sequences (cDNA) were modified on the magnetic beads. In the presence of IFN-γ, the preferential combination of aptamers with IFN-γ resulted in the release of cDNAs. The cDNAs were collected by magnetic separation and then used as primers to trigger rolling circle amplification reaction to generate enriched G-quadruplexes. The G-quadruplex could be utilized to combine with hemin to catalyze the oxidation of 3,3′,5,5′-tetramethylbenzidine for colormetric mode or to couple with the fluorogenic dye Thioflavin T for fluorescent mode. The developed dual-mode aptasensor displayed a linear range of 1–10000 pM with a detection limit of 0.406 pM for the colormetric mode and a range of 0.1–10000 pM with a detection limit of 0.037 pM for the fluorescent mode. Further, the designed aptasensor was applied to IFN-γ detection in serum samples and achieved satisfactory recoveries. SignificanceThis innovative dual-mode detection strategy skillfully leverages the effective target-binding ability of aptamer, dual-function of the G-quadruplex and the signal amplifying ability of rolling circle amplification. This approach not only provides a reliable testing tool for the detection of IFN-γ, but also promotes the development of multimode sensing platforms.