High Detection Efficiency Research Articles

Deep learning method to automatically diagnose periodontal bone loss and periodontitis stage in dental panoramic radiograph.

Artificial intelligence (AI) could be used as an automatically diagnosis method for dental disease due to its accuracy and efficiency. This research proposed a novel convolutional neural network (CNN)-based deep learning (DL) ensemble model for tooth position detection, tooth outline segmentation, tooth tissue segmentation, periodontal bone loss and periodontitis stage prediction using dental panoramic radiographs. The dental panoramic radiographs of 320 patients during the period January 2020 to December 2023 were collected in our dataset. All images were de-identified without private information. In total, 8462 teeth were included. The algorithms that DL ensemble model adopted include YOLOv8, Mask R-CNN, and TransUNet. The prediction results of DL method were compared with diagnosis of periodontists. The periodontal bone loss degree deviation between the DL method and ground truth drawn by the three periodontists was 5.28%. The overall PCC value of the DL method and the periodontists' diagnoses was 0.832 (P <​ 0.001). ​The ICC value was 0.806 (P <​ 0.001). The total diagnostic accuracy of the DL method was 89.45%. The proposed DL ensemble model in this study shows high accuracy and efficiency in radiographic detection and a valuable adjunct to periodontal diagnosis. The method has strong potential to enhance clinical professional performance and build more efficient dental health services. The DL method not only could help dentists for rapid and accurate auxiliary diagnosis and prevent medical negligence, but also could be used as a useful learning resource for inexperienced dentists and dental students.

Research on Automatic Detection System of Drawing Defects based on Machine Vision

Background: For a long time, product packaging has been used as an instruction manual to connect consumers and factories. Product packaging is an important column in product image display and information presentation. However, missing prints, misprints, and surface stains during the manufacture of packaging bags will cause consumers to misunderstand product information. Based on machine vision, image processing technology, and Python language, this paper designs an automatic detection system for paper defects. Through the preprocessing of the image of the paper to be tested, after the paper area is extracted and compared with the standard template paper, the defective parts of the paper to be tested relative to the standard template paper can be quickly and accurately obtained. The system has a single drawing detection time of 2~3 seconds, and the measurement accuracy rate reaches 100%. The results show that the system has high measurement accuracy, high measurement precision, fast measurement speed, strong adaptability to the environment, and can meet the requirements of detecting defective paper. Objective: The purpose of this study is to develop an automatic detection system for packaging paper, which can detect all defective parts of defective paper compared with standard paper templates. This study aims to reduce the misprints or stains that may occur when producing high-volume bags. The system optimizes and controls the detection accuracy, detection time, detection accuracy and detection environment to ensure that the system can meet the real detection requirements. Method: First, the accompanying software of this system is used to import the standard template of the inspection paper and use the industrial camera to obtain the original image of the inspection drawing. Then, a series of necessary processing is performed on the image: grayscale, Gaussian filter, median filter, binarization, edge detection, contour detection, and the paper area covered with the image is extracted through inverse perspective transformation. Secondly, divide the picture into several blocks and measure the translation matrix of each block to achieve translation fine-tuning to achieve higher detection accuracy. Then, the defect mask is obtained by comparing it with the standard template, and the mask is fine-tuned and processed by the strong noise reduction algorithm. After median filtering, binarization, erosion, marking and other operations are performed to realize the final defect area finding and marking. Finally, all defective areas will be displayed in the designated area of the included software. Results: The detection accuracy rate of this system for the defect area reaches 100%, the minimum range of the recognition area reaches 1mm (2 pixels), the light intensity of the detection environment can adapt to 50 gray levels compared with the template, and the detection of a single drawing only takes 2 ~3 seconds, indicating the high detection efficiency of the system. A patent application for the system has already begun. Conclusion: The system has strong adaptability to the light intensity range of the testing environment, and the minimum testing area can meet the requirements of most production drawings. The accuracy of identifying the defect area of the testing drawings shows that the system can complete the testing task well when the testing environment is suitable.

MitoSort: Robust Demultiplexing of Pooled Single-cell Genomics Data Using Endogenous Mitochondrial Variants.

Multiplexing across donors has emerged as a popular strategy to increase throughput, reduce costs, overcome technical batch effects, and improve doublet detection in single-cell genomic studies. To eliminate additional experimental steps, endogenous nuclear genome variants are used for demultiplexing pooled single-cell RNA sequencing (scRNA-seq) data by several computational tools. However, these tools have limitations when applied to single-cell sequencing methods that do not cover nuclear genomic regions well, such as single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq). Here, we demonstrate that mitochondrial germline variants are an alternative, robust, and computationally efficient endogenous barcode for sample demultiplexing. We propose MitoSort, a tool that uses mitochondrial germline variants to assign cells to their donor of origin and identify cross-genotype doublets in single-cell genomics datasets. We evaluate its performance by using in silico pooled mitochondrial scATAC-seq (mtscATAC-seq) libraries and experimentally multiplexed data with cell hashtags. MitoSort achieves high accuracy and efficiency in genotype clustering and doublet detection for mtscATAC-seq data, addressing the limitations of current computational techniques tailored for scRNA-seq data. Moreover, MitoSort exhibits versatility and can be applied to various single-cell sequencing approaches beyond mtscATAC-seq, provided the mitochondrial variants are reliably detected. Furthermore, we demonstrate the application of MitoSort in a case study where B cells from eight donors were pooled and assayed by single-cell multi-omics sequencing. Altogether, our results demonstrate the accuracy and efficiency of MitoSort, which enables reliable sample demultiplexing in various single-cell genomic applications. MitoSort is available at https://github.com/tangzhj/MitoSort.

Development of a multi-layer high-efficiency GEM-based neutron detector for spallation sources

Neutron detection is nowadays mostly based on 3He gas detectors, but its shortage and the continuous upgrades of the neutron facilities require new devices to perform experiments with maximum performances. This work presents a new detector based on the Gas Electron Multiplier (GEM) combined with several boron layers. This detector combines the features of GEM technology with the properties of boron as a neutron converter and the device is produced to sustain high neutron fluxes with high detection efficiency. The detector has been characterised at the ISIS Pulsed Neutron and Muon Source (UK). Based on the analysis of our results, the detector has shown a good response to thermal and epithermal neutrons reaching a detection efficiency of 16% at 1.8 Å (25 meV). The good detection efficiency (even increasable with the addition of further boron GEM foils) and the good time resolution, make the detector a unique device for the neutron techniques. In particular, its use can easily be envisaged in techniques involving neutron transmission measurements, that require high fluxes impinging on the detectors, with the added bonus of a 2D-resolved capability due to the padded anode.

A Deep Learning Inversion Method for Airborne Time-Domain Electromagnetic Data Using Convolutional Neural Network

Due to the high detection efficiency of the airborne time-domain electromagnetic method, it can quickly collect electromagnetic response data for large area-wide regions, but it also brings great challenges to the inversion interpretation of the data because there are numerous survey data that need to be inverted. Conventional optimal inversion and fast imaging methods still take a long time to obtain conductivity and depth information, which affect the efficiency of real-time data interpretation. In this paper, we present a deep learning inversion method that can be used to solve the fast inversion problem of airborne time-domain electromagnetic data; the method uses a one-dimensional convolutional neural network. The network structure consists of two parts containing different numbers of convolutional and pooling layers. The training sample dataset was generated via two ways of constructing geoelectric models through forward modelling. To check the effectiveness of our deep learning inversion strategy, we tested it on synthetic data and two types of survey data. The experimental results show that this inversion method is effective and that it can be applied to airborne time-domain electromagnetic data collected using different observation systems. The proposed inversion method can obtain better inversion results for both simple and complex stratigraphic structures and requires significantly less computation time compared to conventional optimal inversion methods.

Development of a large NaI(Tl) detection system

A large NaI(Tl) detection system was developed to address the increasing demand for high detection efficiency and high energy resolution detectors across various applications. Through Geant4 simulation and experimental research, critical characteristics of the detector prototype were examined, encompassing energy response, energy nonlinearity, and energy resolution. Given the substantial impact of variations in Tl concentration and the complex process of photon propagation on the detection performance of large NaI(Tl) crystals, the detection efficiency and energy resolution with the radioactive source placed at different incident spots were studied to evaluate its spatial response uniformity. The results demonstrate that the detector's energy response range spans from 50 keV to 3 MeV with an energy nonlinearity of 1.5%. The developed large NaI(Tl) detection system achieved an overall energy resolution of 7.93%, along with excellent uniformity in its resolution and detection efficiency, satisfying the application requirements for large dimensions and high detection efficiency.

Multifunctional BiOCl: Sm3+ nanophosphors: A luminescent platform for solid-state lighting applications, optical thermometry, photocatalytic and forensic applications

Rare earth-activated phosphor materials have been widely used as active probes in optical materials for multidimensional applications. Here, we report a series of ultrahigh purity orange-red emitting BiOCl: xSm3+ (x= 0.5- 2.5 mol %) nanophosphors synthesized via conventional solid-state method at relatively low temperatures for various photonic and forensic applications. XRD and Rietveld refinement results confirmed the tetragonal crystal structure of the synthesized phosphor materials with space group P4/nmm. Fourier transform infrared (FT-IR) and Raman spectroscopy were used to perform the functional group analysis. The UV-Vis-NIR spectra were analyzed to determine the Judd-Ofelt (JO) intensity parameters (Ω2, Ω4, Ω6) and to identify the bonding structure and spontaneous emission properties of the samples. The prepared phosphors exhibit intense orange-red emission under 408 nm excitation and beyond 1 mol% doping of Sm3+ ions, the emission intensity decreases due to concentration quenching mechanism via, dipole-dipole interaction between the optically active Sm3+ ions in the phosphor sample. The synthesized BiOCl: Sm3+ nanophosphors possess low correlated color temperature (CCT), admirable high color purity (99.4%) and outstanding internal quantum efficiency (IQE) of 95.27%. The optical thermometry behavior of the optimized sample has been investigated in detail. Moreover, the optimized nanophosphor stains on porous and non-porous surfaces show clear ridge patterns with high sensitivity, efficiency, and minimal background hindrance for latent fingerprints and lip print detection in forensic applications and are used as security ink in anti-counterfeiting applications. Overall, the results revealed the potential prospects of BiOCl: Sm3+ nanophosphors in the field of display applications, optical thermometry, and forensic sciences.

Feasibility study of YSO/SiPM based detectors for virtual monochromatic image synthesis.

The development of photon-counting CT systems has focused on semiconductor detectors like cadmium zinc telluride (CZT) and cadmium telluride (CdTe). However, these detectors face high costs and charge-sharing issues, distorting the energy spectrum. Indirect detection using Yttrium Orthosilicate (YSO) scintillators with silicon photomultiplier (SiPM) offers a cost-effective alternative with high detection efficiency, low dark count rate, and high sensor gain. This work aims to demonstrate the feasibility of the YSO/SiPM detector (DexScanner L103) based on the Multi-Voltage Threshold (MVT) sampling method as a photon-counting CT detector by evaluating the synthesis error of virtual monochromatic images. In this study, we developed a proof-of-concept benchtop photon-counting CT system, and employed a direct method for empirical virtual monochromatic image synthesis (EVMIS) by polynomial fitting under the principle of least square deviation without X-ray spectral information. The accuracy of the empirical energy calibration techniques was evaluated by comparing the reconstructed and actual attenuation coefficients of calibration and test materials using mean relative error (MRE) and mean square error (MSE). In dual-material imaging experiments, the overall average synthesis error for three monoenergetic images of distinct materials is 2.53% ±2.43%. Similarly, in K-edge imaging experiments encompassing four materials, the overall average synthesis error for three monoenergetic images is 4.04% ±2.63%. In rat biological soft-tissue imaging experiments, we further predicted the densities of various rat tissues as follows: bone density is 1.41±0.07 g/cm3, adipose tissue density is 0.91±0.06 g/cm3, heart tissue density is 1.09±0.04 g/cm3, and lung tissue density is 0.32±0.07 g/cm3. Those results showed that the reconstructed virtual monochromatic images had good conformance for each material. This study indicates the SiPM-based photon-counting detector could be used for monochromatic image synthesis and is a promising method for developing spectral computed tomography systems.

Determination of Polyphenols in Cigar Tobacco by HPLC

A high performance liquid chromatography (HPLC) method was developed for the determination of neochlorogenic acid, cryptochlorogenic acid and caffeic acid in cigar tobacco. Firstly, the extraction method, extraction liquid concentration and dosage, column temperature and mobile phase flow rate of polyphenols in cigar tobacco were discussed. Then the detection limit, recovery rate, precision and standard curve of three polyphenols were tested. Finally, tobacco samples were tested. The results showed that the optimal extraction conditions were: ultrasonic extraction, extraction time was 30 min, methanol volume concentration was 50%, dosage was 20 mL, the optimal detection conditions were: column temperature was 30 ℃, flow rate was 1.0 mL/min, detection wavelength was 340 nm. The contents of neochlorogenic acid, cryptochlorogenic acid and caffeic acid were in the range of 0~0.202 mg/g, 0~0.331 mg/g and 0.050~0.073 mg/g, respectively, and the linear correlation coefficients of the three polyphenols were ≥0.9991, indicating that the linear correlation of the method was good, and the detection limit was ≤0.066 mg/L. The precision was ≤0.427%, and the recovery was 95.13~96.39%. The results showed that the method had good precision, high accuracy, short analysis time and high detection efficiency, and could be used as an effective method for the detection of polyphenols in cigar tobacco leaves.

The nitrogen heterocyclic dithione rare earth complexes as a multi-response high-efficiency detection sensor for the detection of Cu2+, Co2+, Cr3+, Fe3+, Bi3+ cations and its magnetic study

In this paper, eight novel rare earth complexes Ln(H3L2)(DMA)3(H2O), (Ln = Sm (1), Tb(2), Dy(3), Gd(4), Ho(5), Tm(6), Er(H3L2)(DMA)2(H2O)2 (7), [(μ3-OH)(μ2OH2)3Eu3(H4/3L1)3]n (8)), have been successfully synthesized by solvothermal method. Herein, H4L1 is 1,4,1′,4′-Tetrahydro-[3,3′]bi[[1,2,4] triazolyl]-5,5′-dithione, H6L2 is 5-((5-thioxo-4,5-dihydro-1H,4′H-[3,3′-bi(1,2,4-triazol)]-5′-yl)thio)-1′,4′- dihydro-1H,5′H-[3,3′-bi(1,2,4-triazole)]-5′-thione which was synthesized through in situ condensation reaction of two H4L1. The complexes 1–7 are mononuclear zero-dimensional structures of SmIII, TbIII, DyIII, GdIII, HoIII, TmIII, and ErIII, respectively, and complex 8 is a trinuclear EuIII coordinational polymer. Complexes 1–8 were characterized by the single-crystal X-ray diffraction, powder XRD, infrared spectroscopy and elemental analysis. The luminescence sensing and basic magnetic properties will be studied further. It was found that complexes 2 and 3 could specifically recognize Cu2+, Fe3+, Co2+, Cr3+, and Bi3+ in DMF solution. In 2, the limit of detection (LOD) are 2.33 × 10–5 M, 1.10 × 10–5 M, 2.14 × 10–5 M, 2.15 × 10–5 M for Cu2+, Fe3+, Co2+, Cr3+, respectively, while in 3, the LOD is 7.98 × 10–6 M for Bi3+. Magnetic studies revealed that 3 and 5 exhibit dominant antiferromagnetic interactions.

Ratiometric Fluorescent Probes for Copper Ion Detection and Cuproptosis Studies

Abstract: This study reviews the latest progress of ratiometric fluorescent probes of copper ions from 2022 till now. As a key trace element in biological systems, copper ions play an important role in a variety of biological processes. However, the abnormal change in copper ion concentration is closely related to a variety of diseases, so the development of highly sensitive and highly selective copper ion detection methods is of great significance for scientific research and clinical diagnosis. In this paper, we first introduced the classification of copper ion fluorescence probes, including molecular sensors based on five-membered azocycles, six-membered azocycles, imide groups (R2C=NR'), oxygen and nitrogen functional groups (such as ethers, aldehydes, ketones, esters, carboxylic acids and hydroxyl groups) and group ⅥA elements. Subsequently, the application of nanosensors in the detection of copper ions is further discussed, especially the nanosensors in which copper is used as a reactant, catalyst or alternative reaction. Finally, the current research status of copper ion fluorescence probes is summarized, and the future development direction is prospected in order to provide reference and inspiration for the design of new high-efficiency copper ion detection tools.

High-efficiency surface defect detection based on laser ultrasonic state space embedding and compressive sensing

Abstract The laser nonlinear wave modulation spectroscopy(LNWMS) technique has gained considerable attention due to its high sensitivity in detecting small surface defects and its ultra-fast scanning speed. This paper proposes a novel method for synthesizing intact wavefield reference, significantly enhancing the accuracy of surface defect imaging. Moreover, considering the potential for parallel processing of the nonlinearity calculation of ultrasonic signals at scanning points, we incorporate compressive sensing technology to accelerate this process. This innovative approach reduces the computational load to 10% of the original, thereby substantially increasing the imaging speed. The paper validates the method’s superior accuracy and efficiency in defect detection through conducting experiments using a high-speed laser ultrasonic scanning system on aluminum plates and turbine blade, and by comparing with local wavenumber estimation, demonstrating the promising potential of this technology for surface defect analysis.

Entropy Bounds for Device-Independent Quantum Key Distribution with Local Bell Test.

One of the main challenges in device-independent quantum key distribution (DIQKD) is achieving the required Bell violation over long distances, as the channel losses result in low overall detection efficiencies. Recent works have explored the concept of certifying nonlocal correlations over extended distances through the use of a local Bell test. Here, an additional quantum device is placed in close proximity to one party, using short-distance correlations to verify nonlocal behavior at long distances. However, existing works have either not resolved the question of DIQKD security against active attackers in this setup, or used methods that do not yield tight bounds on the key rates. In this work, we introduce a general formulation of the key rate computation task in this setup that can be combined with recently developed methods for analyzing standard DIQKD. Using this method, we show that if the short-distance devices exhibit sufficiently high detection efficiencies, positive key rates can be achieved in the long-distance branch with lower detection efficiencies as compared to standard DIQKD setups. This highlights the potential for improved performance of DIQKD over extended distances in scenarios where short-distance correlations are leveraged to validate quantum correlations.

Highly sensitive detection of carbendazim in agricultural products using colorimetric and photothermal lateral flow immunoassay based on plasmonic gold nanostars

The wide use and high toxicity of carbendazim (CBD) in agriculture pose unprecedented demands for convenient, sensitive, and cost-effective on-site monitoring. Herein, we propose a novel colorimetric and photothermal dual-mode lateral flow immunoassay (LFIA) based on plasmonic gold nanostars (AuNSs) for CBD detection in agricultural products. The AuNSs were synthesized via a rapid seed-mediated growth method (with growth time of ∼5 s). A stable immunoprobe was formed by adsorbing CBD antibodies onto AuNSs. This immunoprobe exhibited high conversion efficiency and sensitivity in photothermal detection with a low limit of detection (LOD) of 0.28 ng mL−1. The LOD of the colorimetric mode was higher (0.48 ng mL−1). The results of CBD detection in various agricultural products aligned well with ultra-performance liquid chromatography tandem mass spectrometry. Overall, our LFIA shows excellent sensitivity, specificity, reproducibility, and rapidness in CBD detection, and thus is a highly potential on-site platform in resource-limited environments.

Enhancing Disease Detection in Electronic Medical Records: Integrating Human Expertise and Large Language Models with Application to Diabetes, Hypertension, and Acute Myocardial Infarction

ObjectiveElectronic medical records (EMR) are widely available to complement administrative data-based disease surveillance and healthcare performance evaluation. Defining conditions from EMR is labour-intensive, requiring advanced medical informatics knowledge, and is challenging without effective data extraction tools. This study developed a high-throughput pipeline to detect diseases in EMRs. MethodsWe developed a pipeline that leverages a generative large language model (LLM) to analyze, understand, and interpret EMR notes by following clinical experts’ designed prompts. The pipeline was applied to detect diabetes, hypertension, and acute myocardial infarction (AMI) from the EMRs for a cardiac patient cohort in Calgary, Canada. The performance was compared against clinician-validated diagnoses as the reference standard. ResultsThe cohort consisted of 3,413 patients with 551,095 clinical notes. The prevalence was 27.8%, 66.3%, and 54.3% for diabetes, hypertension, and AMI, respectively. The performance for detecting conditions varied: diabetes had 90.5% sensitivity, 83% specificity, and 67% positive predictive value (PPV); hypertension had 94.2% sensitivity, 30.2% specificity, and 73.8% PPV; and AMI had 86.4% sensitivity, 61% specificity, and 75.3% PPV. The monthly prevalence trends between the detected cases and reference standard showed similar patterns. ConclusionThe proposed pipeline demonstrated reasonable accuracy and high efficiency in disease detection without manually curated labels, indicating the potential for automated real-time disease surveillance using EMRs. ImplicationVariations of documentation practices in clinical note can impact the detection performance of different diseases. Hence, an automated pipeline integrating LLMs with expert knowledge may improve detection accuracy with reduced labour costs while indicating documentation quality.

Fault Diagnosis of Distributed Energy Distribution Network Based on PSO-BP

With the increasing scale of distribution network at distribution time, its complexity grows geometrically, and its fault diagnosis becomes more and more difficult. Aiming at the slow convergence and low accuracy of traditional backpropagation neural network in dealing with single-phase ground faults, the study proposes a backpropagation neural network based on improved particle swarm optimization. The model optimizes the weights and acceleration constants of the particle swarm algorithm by introducing dynamic coefficients to enhance its global and local optimization seeking ability. It is also applied in optimizing the parameters of backpropagation neural network and constructing the routing model and ranging model for fault diagnosis about distributed energy distribution network. The simulation results revealed that the maximum absolute error of the improved method is 0.08. While the maximum absolute errors of the traditional backpropagation neural network and the particle swarm optimized backpropagation neural network were 0.65 and 0.10, respectively. The fluctuation of the relative errors of the research method was small under different ranges of measurements. At 8.0 km, the minimum relative error was 0.39% and the maximum relative error was 2.81%. The results show that the improved method proposed in the study significantly improves the accuracy and stability of fault diagnosis and localization in distribution networks and is applicable to complex distribution network environments. The method has high training efficiency and fault detection capability and provides an effective tool for distribution network fault management.

A Minor Groove Binder with Significant Cytotoxicity on Human Lung Cancer Cells: The Potential of Hesperetin Functionalised Silver Nanoparticles.

Natural drug functionalised silver (Ag) nanoparticles (NPs) have gained significant interest in pharmacology related applications due to their therapeutic efficiency. We have synthesised silver nanoparticle using hesperetin as a reducing and capping agent. This work aims to discuss the relevance of the hesperetin functionalised silver nanoparticles (H-AgNPs) in the field of nano-medicine. The article primarily investigates the anticancer activity of H-AgNPs and then their interactions with calf thymus DNA (ctDNA) through spectroscopic and thermodynamic techniques. The green synthesised H-AgNPs are stable, spherical in shape and size of 10 ± 3nm average diameter. The complex formation of H-AgNPs with ctDNA was established by UV-Visible absorption, fluorescent dye displacement assay, isothermal calorimetry and viscosity measurements. The binding constants obtained from these experiments were consistently in the order of 104 Mol-1. The melting temperature analysis and FTIR measurements confirmed that the structural alterations of ctDNA by the presence of H-AgNPs are minimal. All the thermodynamic variables and the endothermic binding nature were acquired from ITC experiments. All these experimental outcomes reveal the formation of H-AgNPs-ctDNA complex, and the results consistently verify the minor groove binding mode of H-AgNPs. The binding constant and limit of detection of 1.8μM found from the interaction studies imply the DNA detection efficiency of H-AgNPs. The cytotoxicity of H-AgNPs against A549 and L929 cell lines were determined by in vitro MTT cell viability assay and lactate dehydrogenase (LDH) assay. The cell viability and LDH enzyme release are confirmed that the H-AgNPs has high anticancer activity. Moreover, the calculated LD50 value for H-AgNPs against lung cancer cells is 118.49µl/ml, which is a low value comparing with the value for fibroblast cells (269.35µl/ml). In short, the results of in vitro cytotoxicity assays revealed that the synthesised nanoparticles can be considered in applications related to cancer treatments. Also, we have found that, H-AgNPs is a minor groove binder, and having high DNA detection efficiency.

A Review on the High-Efficiency Detection and Precision Positioning Technology Application of Agricultural Robots

The advancement of agricultural technology has increasingly positioned robotic detection and localization techniques at the forefront, ensuring critical support for agricultural development through their accuracy and reliability. This paper provides an in-depth analysis of various methods used in detection and localization, including UWB, deep learning, SLAM, and multi-sensor fusion. In the domain of detection, the application of deep algorithms in assessing crop maturity and pest analysis is discussed. For localization, the accuracy of different methods in target positioning is examined. Additionally, the integration of convolutional neural networks and multi-sensor fusion with deep algorithms in agriculture is reviewed. The current methodologies effectively mitigate environmental interference, significantly enhancing the accuracy and reliability of agricultural robots. This study offers directional insights into the development of robotic detection and localization in agriculture, clarifying the future trajectory of this field and promoting the advancement of related technologies.

Research on Nondestructive Testing Technology for Drilling Risers Based on Magnetic Memory and Deep Learning

Drilling risers play a crucial role in deepwater oil and gas development, and any compromise in their integrity can severely hinder the progress of drilling operations. In light of this, efficient and accurate nondestructive testing of drilling risers is paramount. However, existing inspection equipment falls short in both efficiency and accuracy, posing challenges to the sustainability of deepwater oil and gas exploration and development. To effectively assess the damage conditions of deepwater drilling risers, this study developed an inspection robot based on metal magnetic memory and researched intelligent defect recognition methods using computer vision. The robot can perform in situ inspections on drilling risers and has been successfully deployed for field application on a deepwater drilling platform. The application results demonstrate that this detection robot offers significant advantages regarding high reliability and detection efficiency. Utilizing data collected on-site, we constructed a dataset containing 1100 images that cover five typical types of defects in drilling risers, including pitting, groove corrosion, and wear. Based on this dataset, we proposed and trained a novel image classification model, SK-ConvNeXt-KAN. By deeply optimizing the ConvNeXt convolutional network incorporating the introduced SK attention module and replacing traditional linear classification layers with the KAN module, this model significantly enhanced its feature extraction capabilities and efficiency in handling complex nonlinear problems. Experimental results show that this model achieved an accuracy rate of 95.4% in identifying defects in drilling risers, which is significantly better than traditional methods. This achievement has dramatically improved the efficiency and accuracy of deepwater drilling riser inspections, providing robust technical support for deepwater oil and gas exploration and development sustainability.

Plasmon-enhanced photoelectrochemical-SERS simultaneous dual-read-out determination of enrofloxacin based on bifunctional Bi2S3 quantum dots/triangular Ag nanoparticles/Ti3C2 MXene heterojunction

The exploration of high-efficiency detection technologies of antibiotic residues plays vital roles for the evaluation of food quality and environmental safety risks owing to the adverse effects of antibiotics, which could accelerate the emergence of antibiotic-resistant genes. Herein, an integrated complementary dual-mode sensor was developed based on Bi2S3 quantum dots/triangular silver nanoparticles/Ti3C2 MXene (Bi2S3 QDs/T-Ag NPs/Ti3C2) heterojunction by in-situ coupling photoelectrochemical (PEC) analysis with surface-enhanced Raman spectroscopy (SERS) method. The bifunctional heterojunction could enhance light absorption, boost the generation of the electron-hole pairs, and facilitate the light-matter interactions, resulting in increased photoelectric conversion efficiency and enhanced Raman scattering for sensing applications. Moreover, the unique triangular shape and strong surface plasmon resonance effect of T-Ag NPs were conducted to further enhance the output signals of the dual-mode sensing platform. Such as-fabricated PEC/SERS detection system offered good complementarity and achieved mutually check via the combination of the inherent characteristics of two individual modes, which provided more accurate measurement results and more convincing information. Further, aptamer was adopted to recognize enrofloxacin molecules, which could achieve selective and sensitive detection of target. The detection limit of PEC and SERS was calculated as 0.61 fM (S/N = 3) and 0.21 fM (S/N = 3), respectively. Besides, the dual-mode aptasensor exhibited excellent stability and reproducibility, showing a promising prospect to detect enrofloxacin in real samples.