Manual Method Research Articles

Enhancing Medical Device Management with IoT Powerstrip.

Managing medical devices efficiently was a challenge, especially with manual methods proving error-prone amid staff shortages. To overcome these issues, we developed a novel IoT powerstrip device for tracking usage and location. This helps monitor devices in real-time, empowering more efficient management and utilization. We introduced it in four hospitals, connecting 192 medical devices to the strips and quantitatively confirmed its effectiveness.

Evaluation of automated photograph-cephalogram image integration using artificial intelligence models.

To develop and evaluate an automated method for combining a digital photograph with a lateral cephalogram. A total of 985 digital photographs were collected and soft tissue landmarks were manually detected. Then 2500 lateral cephalograms were collected, and corresponding soft tissue landmarks were manually detected. Using the images and landmark identification information, two different artificial intelligence (AI) models-one for detecting soft tissue on photographs and the other for identifying soft tissue on cephalograms-were developed using different deep-learning algorithms. The digital photographs were rotated, scaled, and shifted to minimize the squared sum of distances between the soft tissue landmarks identified by the two different AI models. As a validation process, eight soft tissue landmarks were selected on digital photographs and lateral cephalometric radiographs from 100 additionally collected validation subjects. Paired t-tests were used to compare the accuracy of measures obtained between the automated and manual image integration methods. The validation results showed statistically significant differences between the automated and manual methods on the upper lip and soft tissue B point. Otherwise, no statistically significant difference was found. Automated photograph-cephalogram image integration using AI models seemed to be as reliable as manual superimposition procedures.

Percutaneous Extraction of Transvenous Permanent Pacemaker/Defibrillator Leads-A Single-Center Experience.

Background and Objectives: The number of cardiac pacemakers being used has increased in recent decades, and this increase has led to a rise in device-related complications, requiring percutaneous device extraction. Our aim was to present our single-center clinical experience in percutaneous lead extractions. Materials and Methods: We analyzed an observational retrospective cohort study of 93 patients for the transvenous removal of a total of 163 endocardial leads. We evaluated the device details, indications, lead characteristics, extraction methods, complications, reimplantation procedure, follow-up data, effectiveness, and safety. Results: Patients' mean age was 68.6 ± 11.6 years. Lead extraction indications were pocket infection in 33 (35.5%), lead dysfunction in 33 (35.5%), and system upgrade in 21 (23%) cases, and lead endocarditis in 6 (6%) cases. The duration from implantation to extraction time was a detected median of 43 (24-87) months. The most common retracted lead type was the RV defibrillator lead (62%), and the lead fixation type was active for one hundred (61%) patients. A new device was inserted in 74 (80%) patients, and the device type was most commonly a CRT-D (61%). Patients were followed up at a median of 17 (8-36) months, and 18 patients (19%) died at follow-up. Complete procedural success was obtained in 78 (84%) patients, and clinical procedural success was obtained in 83 (89%) patients. Procedural failure was detected in 15 (16%) patients. Major and minor complications were detected in 10 (11%) and 6 (6.5%) patients, respectively. The most common minor complication was pocket hematoma. Conclusions: Our experience suggests that transvenous lead extraction has a high success rate with an acceptable risk of procedural complications. The simple manual traction method has a high rate of procedural success, despite a high dwell time of the lead.

Enhanced interfacial and mechanical properties of basalt fiber/epoxy composites via synergistic surface modification

In this work, the in-situ growth of silicon nanowires (SiNW) on basalt fiber (BF) surfaces was achieved by chemical vapor deposition (CVD), followed by surface coating of waterborne polyurethane (WPU) on the BF to synergistically enhance the interfacial interaction between BF and epoxy resin (EP). Subsequently, BF/EP composites were prepared by a manual layering method. The results showed that the tensile strength of the modified BF multifilment increased by 48.1 %. The flexural modulus, flexural strength of the modified BF/EP composite material increased by 23.4 %, 29.6 % respectively compared to the unmodified composite. The results indicate that the synergistic modification of the composite by SiNW and WPU promotes stress transfer between the matrix and fibers, thereby improving mechanical properties.

A high-precision automatic extraction method for shedding diseases of painted cultural relics based on three-dimensional fine color model

In recent years, with the development of 3D digitization of cultural relics, most cultural sites contain a large number of fine 3D data of cultural relics, especially complex geometric objects such as painted cultural relics. At present, how to automatically extract surface damage information from the fine 3D color model of painted cultural relics and avoid the loss of accuracy caused by reducing the dimension using conventional methods is an urgentproblem. In view of the above issues, this paper proposes an automatic and high-precision extraction method for cultural relics surface shedding diseases based on 3D fine data. First, this paper designs a 2D and 3D integrated data conversion model based on OpenSceneGraph, a 3D engine, which performs mutual conversion between 3D color model textures and 2D images. Second, this paper proposes a simple linear iterative clustering segmentation algorithm with an adaptive k value, which solves the problem of setting the superpixel k value and improves the accuracy of image segmentation. Finally, through the 2D and 3D integrated models, the disease is statistically analyzed and labeled on the 3D model. Experiments show that for painted plastic objects with complex surfaces, the disease extraction method based on the 3D fine model proposed in this paper has improved geometric accuracy compared with the current popular orthophoto extraction method, and the disease investigation is more comprehensive. Compared with the current 3D manual extraction method in commercial software, this method greatly improves the efficiency of disease extraction while ensuring extraction accuracy. The research method of this paper activates many existing 3D fine data of cultural protection units and converts conventional 2D data mining and analysis into 3D, which is more in line with the scientific utilization of data in terms of accuracy and efficiency and has certain scientific research value, leading value and practical significance.

Evaluation of Open-Source Ciliary Analysis Software in Primary Ciliary Dyskinesia: A Comparative Assessment.

Primary Ciliary Dyskinesia (PCD) is a rare genetic disorder characterized by alterations in motile cilia function. The diagnosis of PCD is challenging due to the lack of standardized methods in clinical practice. High-speed video microscopy analysis (HSVA) directly evaluates ciliary beat frequency (CBF) in PCD. Recently, open-source ciliary analysis software applications have shown promise in measuring CBF accurately. However, there is limited knowledge about the performance of different software applications, creating a gap in understanding their comparative effectiveness in measuring CBF in PCD. We compared two open-source software applications, CiliarMove (v219) and Cilialyzer (v1.2.1-b3098cb), against the manual count method. We used high-speed videos of nasal ciliary brush samples from PCD RSPH4A-positive (PCD (RSPH4A)) patients and healthy controls. All three methods showed lower median CBF values for patients with PCD (RSPH4A) than in healthy controls. CiliarMove and Cilialyzer identified lower CBF in patients with PCD (RSPH4A), similarly to the manual count. Cilialyzer, CiliarMove, and manual count methods demonstrated statistical significance (p-value < 0.0001) in the difference of median CBF values between patients with PCD (RSPH4A) and healthy controls. Correlation coefficients between the manual count values against both software methods demonstrated positive linear relationships. These findings support the utility of open-source software-based analysis tools. Further studies are needed to validate these findings with other genetic variants and identify the optimal software for accurate CBF measurement in patients with PCD.

DB-FCN: An end-to-end dual-branch fully convolutional nucleus detection model

Detecting cells or nuclei in histopathological image analysis is a crucial prerequisite for subsequent cancer diagnosis. By precisely locating cell nuclei, pathologists can quantitatively analyze the morphology of each cell nucleus. This enables accurate cancer grading, allowing for the implementation of tailored treatment plans based on distinct cancer stages. Manual nucleus detection methods are labor-intensive, making the development of automatic cell nucleus detection algorithms highly necessary. However, due to the small size of the nucleus and increased adhesions between cells, tissue staining may be uneven, leading to a higher occurrence of false positives in the results of the current automatic nucleus detection algorithm. This paper introduces an end-to-end dual-branch-based fully convolutional neural network (DB-FCN) that effectively addresses the aforementioned challenges, thereby enhancing the accuracy of automatic nucleus detection. This algorithm introduces for the first time the use of two detection branches, namely the coarse detection branch and the fine detection branch, to accomplish the detection task. The role of the coarse detection branch is to identify all cell regions in the pathological image as comprehensively as possible and then transmit the detection results as prior information to the fine detection branch. The fine detection branch is necessary solely to conduct more precise detection based on the coarse detection results. Given that the coarse detection branch has already eliminated interference from many background regions, the fine detection branch can focus on detecting the nucleus region, thereby significantly enhancing the efficiency and accuracy of model detection. The detection model proposed in this paper was evaluated on three classic datasets and compared with many existing detection algorithms. Compared with other algorithms, the detection algorithm proposed in this paper has made significant progress in detecting cell nuclei in histopathological images.

Identification of defects on the inner wall of water supply pipes based on the optimized residual network

ABSTRACT The urban water supply network is part of the infrastructure that sustains the economic and social functions of cities and regions. Timely inspection and maintenance of the network can effectively reduce resource wastage and prevent accidents. Traditional manual detection methods are inefficient and can be based on subjective judgments. A classification model based on an improved ResNet34 network has been proposed to classify and detect various types of corrosion on the inner walls of pipes under challenging conditions. The introduction of the attention mechanism and multi-scale feature fusion modules further improved the model's effectiveness in classifying defects within the inner walls of pipes. The model can detect various types of pipeline corrosion with a detection accuracy of 98.61%. This accuracy is significantly superior to that achieved with traditional models such as ResNet34, AlexNet, MobileNet, and VGGNet.

Soybean seed pest damage detection method based on spatial frequency domain imaging combined with RL-SVM

Soybean seeds are susceptible to damage from the Riptortus pedestris, which is a significant factor affecting the quality of soybean seeds. Currently, manual screening methods for soybean seeds are limited to visual inspection, making it difficult to identify seeds that are phenotypically defect-free but have been punctured by stink bugs on the sub-surface. To facilitate the convenient and efficient identification of healthy soybean seeds, this paper proposes a soybean seed pest detection method based on spatial frequency domain imaging combined with RL-SVM. Firstly, soybean optical data is obtained using single integration sphere technique, and the vigor index of soybean seeds is obtained through germination experiments. Then, based on the above two data items using feature extraction algorithms (the successive projections algorithm and the competitive adaptive reweighted sampling algorithm), the characteristic wavelengths of soybeans are identified. Subsequently, the spatial frequency domain imaging technique is used to obtain the sub-surface images of soybean seeds in a forward manner, and the optical coefficients such as the reduced scattering coefficient μ′s\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\mu }{\\prime}}_{s}$$\\end{document} and absorption coefficient μa\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mu }_{a}$$\\end{document} of soybean seeds are inverted. Finally, RL-MLR, RL-GRNN, and RL-SVM prediction models are established based on the ratio of the area of insect-damaged sub-surface to the entire seed, soybean varieties, and μa\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mu }_{a}$$\\end{document} at three wavelengths (502 nm, 813 nm, and 712 nm) for predicting and identifying soybean the stinging and sucking pest damage levels of soybean seeds. The experimental results show that the spatial frequency domain imaging technique yields small errors in the optical coefficients of soybean seeds, with errors of less than 15% for μa\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mu }_{a}$$\\end{document} and less than 10% for μ′s\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\mu }{\\prime}}_{s}$$\\end{document}. After parameter adjustment through reinforcement learning, the Macro-Recall metrics of each model have improved by 10%-15%, and the RL-SVM model achieves a high Macro-Recall value of 0.9635 for classifying the pest damage levels of soybean seeds.

A Comparative Study of Deep Learning and Manual Methods for Identifying Anatomical Landmarks through Cephalometry and Cone-Beam Computed Tomography: A Systematic Review and Meta-Analysis

Background: Researchers have noted that the advent of artificial intelligence (AI) heralds a promising era, with potential to significantly enhance diagnostic and predictive abilities in clinical settings. The aim of this meta-analysis is to evaluate the discrepancies in identifying anatomical landmarks between AI and manual approaches. Methods: A comprehensive search strategy was employed, incorporating controlled vocabulary (MeSH) and free-text terms. This search was conducted by two reviewers to identify published systematic reviews. Three major electronic databases, namely, Medline via PubMed, the Cochrane database, and Embase, were searched up to May 2024. Results: Initially, 369 articles were identified. After conducting a comprehensive search and applying strict inclusion criteria, a total of ten studies were deemed eligible for inclusion in the meta-analysis. The results showed that the average difference in detecting anatomical landmarks between artificial intelligence and manual approaches was 0.35, with a 95% confidence interval (CI) ranging from −0.09 to 0.78. Additionally, the overall effect between the two groups was found to be insignificant. Upon further analysis of the subgroup of cephalometric radiographs, it was determined that there were no significant differences between the two groups in terms of detecting anatomical landmarks. Similarly, the subgroup of cone-beam computed tomography (CBCT) revealed no significant differences between the groups. Conclusions: In summary, the study concluded that the use of artificial intelligence is just as effective as the manual approach when it comes to detecting anatomical landmarks, both in general and in specific contexts such as cephalometric radiographs and CBCT evaluations.

Utilizing phpMyAdmin for System Design in Enterprise Administration

In today's digital landscape, effective data management is essential for organizations, particularly small and medium-sized enterprises (SMEs) that often struggle with traditional manual methods, leading to inefficiencies and data inaccuracies. This research aims to investigate the implementation of phpMyAdmin, a web-based database management tool, to enhance administrative systems within SMEs. The study employs a mixed-methods approach, integrating qualitative case studies and quantitative surveys to gather comprehensive insights into user experiences and operational performance. The findings reveal that the adoption of phpMyAdmin significantly improves data management efficiency, with 75% of respondents expressing satisfaction with its user-friendly interface. However, challenges such as security vulnerabilities and the necessity for user training were also identified, indicating that while phpMyAdmin offers substantial benefits, organizations must address these issues to fully leverage their capabilities. The implications of this research suggest that SMEs should prioritize investing in user training and implementing robust security measures to mitigate risks associated with data management. By doing so, organizations can enhance their operational efficiency and decision-making processes. Future research should focus on the long-term impacts of phpMyAdmin and explore its integration with other management systems to further optimize organizational performance.

An in-vitro evaluation of residual dentin retained after using novel enzymatic-based chemomechanical caries removal agents

To assess the biochemical, mechanical and structural characteristics of retained dentin after applying three novel bromelain-contained chemomechanical caries removal (CMCR) formulations in comparison to the conventional excavation methods (hand and rotary) and a commercial papain-contained gel (Brix 3000). Seventy-two extracted permanent molars with natural occlusal carious lesions (score > 4 following the International Caries Detection and Assessment System (ICDAS-II)) were randomly allocated into six groups (n = 12) according to the excavation methods: hand excavation, rotary excavation, Brix 3000, bromelain-contained gel (F1), bromelain-chloramine-T (F2), and bromelain-chlorhexidine gel (F3). The superficial and deeper layers of residual dentin were examined by Raman microspectroscopy and Vickers microhardness, while the surface morphology was assessed by the scanning electron microscope (SEM). A multivariate analysis of variance followed by Tukey’s test (p > 0.05) was performed for data analysis. The novel formulations showed an ability to preserve the partially demineralized dentin that showed a reduced phosphate content with a higher organic matrix. This was associated with lower Vickers microhardness values in comparison to sound dentin and rotary excavation. The collagen integration ratio in all methods was close to sound dentin (0.9–1.0) at the deeper dentin layer. The bromelain-chloramine-T gel (F2) produced the smoothest smear-free dentin surface with a higher number of opened dentinal tubules. In contrast, dense smearing covering the remaining dentin was observed in the manual and rotary methods with obstructed dentin tubule orifices. The bromelain-contained formulations can be considered a new minimally invasive approach for selectively removing caries in deep cavitated dentin lesions.

Three-view cotton flower counting through multi-object tracking and RGB-D imagery

Monitoring the number of cotton flowers can provide important information for breeders to assess the flowering time and the productivity of genotypes because flowering marks the transition from vegetative growth to reproductive growth and impacts the final yield. Traditional manual counting methods are time-consuming and impractical for large-scale fields. To count cotton flowers efficiently and accurately, a multi-view multi-object tracking approach was proposed by using both RGB and depth images collected by three RGB-D cameras fixed on a ground robotic platform. The tracking-by-detection algorithm was employed to track flowers from three views simultaneously and remove duplicated counting from single views. Specifically, an object detection model (YOLOv8) was trained to detect flowers in RGB images and a deep learning-based optical flow model Recurrent All-pairs Field Transforms (RAFT) was used to estimate motion between two adjacent frames. The intersection over union and distance costs were employed to associate flowers in the tracking algorithm. Additionally, tracked flowers were segmented in RGB images and the depth of each flower was obtained from the corresponding depth image. Those flowers tracked with known depth from two side views were then projected onto the middle image coordinate using camera calibration parameters. Finally, a constrained hierarchy clustering algorithm clustered all flowers in the middle image coordinate to remove duplicated counting from three views. The results showed that the mean average precision of trained YOLOv8x was 96.4%. The counting results of the developed method were highly correlated with those counted manually with a coefficient of determination of 0.92. Besides, the mean absolute percentage error of all 25 testing videos was 6.22%. The predicted cumulative flower number of Pima cotton flowers is higher than that of Acala Maxxa, which is consistent with what breeders have observed. Furthermore, the developed method can also obtain the flower number distributions of different genotypes without laborious manual counting in the field. Overall, the three-view approach provides an efficient and effective approach to count cotton flowers from multiple views. By collecting the video data continuously, this method is beneficial for breeders to dissect genetic mechanisms of flowering time with unprecedented spatial and temporal resolution, also providing a means to discern genetic differences in fecundity, the number of flowers that result in harvestable bolls. The code and datasets used in this paper can be accessed on GitHub: https://github.com/UGA-BSAIL/Multi-view_flower_counting.

Research on sports image classification method based on SE-RES-CNN model

As computer image processing and digital technologies advance, creating an efficient method for classifying sports images is crucial for the rapid retrieval and management of large image datasets. Traditional manual methods for classifying sports images are impractical for large-scale data and often inaccurate when distinguishing similar images. This paper introduces an SE module that adaptively adjusts the weights of input feature mapping channels, and a Res module that excels in deep feature extraction, preventing gradient vanishing, multi-scale processing, and enhancing generalization in image recognition. Through extensive experimentation on network structure adjustments, the SE-RES-CNN neural network model is applied to sports image classification. The model is trained on a sports image classification dataset from Kaggle, alongside VGG-16 and ResNet50 models. Training results show that the proposed SE-RES-CNN model improves classification accuracy by approximately 5% compared to VGG-16 and ResNet50 models. Testing revealed that the SE-RES-CNN model classifies 100 out of 500 sports images in 6 s, achieving an accuracy rate of up to 98% and a single prediction time of 0.012 s. This validates the model's accuracy and effectiveness, significantly enhancing sports image retrieval and classification efficiency. This validates the model's accuracy and effectiveness, significantly enhancing sports image retrieval and classification efficiency.

A deep learning based experimental framework for automatic staging of pressure ulcers from thermal images

ABSTRACT Pressure ulcers can be life-threatening, and accurate staging is crucial for proper treatment. Manual methods for ulcer detection and staging are subjective and prone to errors. Since the wound temperature holds valuable and informative data, we propose thermal imaging as a tool to detect the stages of pressure ulcers automatically. This paper implements a deep learning-based framework for the automatic detection and classification of pressure ulcer stages 2–4 using thermal images. 450 thermal images were used to train the classification model. The framework combines ‘R50-FPN’, retrained using transfer learning, for ulcer detection and cropping. An ensemble model is used for ulcer stage classification. The model was trained on a dataset consisting of 68 original images and their augmentations. The proposed method achieves a mAP for object detection of 0.70 and an ulcer stage classification accuracy of 0.9545 during evaluation. Class activation maps were also employed to study the model performance further, indicating that the trained model focuses on the wound periphery and the wound bed for classification. The results show a significant improvement in classification accuracy when compared with related works demonstrating the potential of deep learning-based thermal imaging as an objective method for the automated staging of pressure ulcers.

Melon ripeness detection by an improved object detection algorithm for resource constrained environments

BackgroundRipeness is a phenotype that significantly impacts the quality of fruits, constituting a crucial factor in the cultivation and harvesting processes. Manual detection methods and experimental analysis, however, are inefficient and costly.ResultsIn this study, we propose a lightweight and efficient melon ripeness detection method, MRD-YOLO, based on an improved object detection algorithm. The method combines a lightweight backbone network, MobileNetV3, a design paradigm Slim-neck, and a Coordinate Attention mechanism. Additionally, we have created a large-scale melon dataset sourced from a greenhouse based on ripeness. This dataset contains common complexities encountered in the field environment, such as occlusions, overlapping, and varying light intensities. MRD-YOLO achieves a mean Average Precision of 97.4% on this dataset, achieving accurate and reliable melon ripeness detection. Moreover, the method demands only 4.8 G FLOPs and 2.06 M parameters, representing 58.5% and 68.4% of the baseline YOLOv8n model, respectively. It comprehensively outperforms existing methods in terms of balanced accuracy and computational efficiency. Furthermore, it maintains real-time inference capability in GPU environments and demonstrates exceptional inference speed in CPU environments. The lightweight design of MRD-YOLO is anticipated to be deployed in various resource constrained mobile and edge devices, such as picking robots. Particularly noteworthy is its performance when tested on two melon datasets obtained from the Roboflow platform, achieving a mean Average Precision of 85.9%. This underscores its excellent generalization ability on untrained data.ConclusionsThis study presents an efficient method for melon ripeness detection, and the dataset utilized in this study, alongside the detection method, will provide a valuable reference for ripeness detection across various types of fruits.

Artistic sense of interior design and space planning based on human machine intelligent interaction

The rapid development of artificial intelligence technology is gradually penetrating into multiple fields such as interior design and spatial planning. The aim of this study is to integrate artificial intelligence with interior design, enhance design artistry and user experience, and address the interactive needs of interior space design choices. A set of indoor space design recognition system has been designed by introducing artificial intelligence networks and attention mechanisms. This study first optimizes the CenterNet algorithm based on attention mechanism and feature fusion to improve its accuracy in identifying complex components. Afterwards, the long short-term memory network and convolutional neural network are trained to complete the task of spatial layout feature recognition and design. The performance test results showed that after testing 100 images, the software could recognize indoor design space images and create corresponding vector format space maps in about 5 minutes, providing them to the 3D modeling interface to generate 3D scenes. Compared to the approximately 25 minutes required by manual methods, the design efficiency has been significantly improved. The research and design method has a fast convergence speed and low loss during the retraining process. In simulation testing, its mAP value reached 91.0%, higher than similar models. It performs better in detecting walls, doors and windows, bay windows, double doors, and two-way doors. Moreover, it has outstanding ability when facing structures such as short walls and door corners, and can recognize and create vector format spatial maps within 5 minutes, which is accurate and efficient. The system designed in this project has optimized the interaction between designers and clients in interior design, accurately capturing user intentions and assisting designers in improving work efficiency.

Improving the Monitoring and Control of Egg Vitality of Lymantria dispar Linnaeus 1758 Using an Innovative Device and Procedure for Removing Egg Hairs

Spongy moth (Lymantria dispar Linnaeus 1758) populations have the potential to reach outbreak levels, causing disruptions to forest ecosystems across Eurasia and North America. Continuous monitoring of the size and health of the spongy moth population in the egg stage is important for managing population outbreaks. Current methods include counting eggs within egg masses using manual methods. This study introduces an innovative solution aimed at optimizing the prediction of biotic disturbances and preventing the potential risks associated with spongy moth population outbreaks. The challenges and constraints related to the process of hair removal from spongy moth eggs have been effectively addressed through the development of a device powered by a torque-generating unit. This study aims to (1) introduce a novel device designed for the removal of hairs from spongy moth (L. dispar) eggs; (2) introduce a new hair removal procedure; and (3) empirically demonstrate the benefits of the introduced innovations. The introduced device and the procedure enable a significantly expedited diagnosis of the potential for a population outbreak in the current year, with the potential for widespread utilization. This invention enhances our understanding of predicting biotic disorders and facilitates the rapid assessment of the risk of their occurrence.

A Comprehensive Survey on Visual Perception Methods for Intelligent Inspection of High Dam Hubs.

There are many high dam hubs in the world, and the regular inspection of high dams is a critical task for ensuring their safe operation. Traditional manual inspection methods pose challenges related to the complexity of the on-site environment, the heavy inspection workload, and the difficulty in manually observing inspection points, which often result in low efficiency and errors related to the influence of subjective factors. Therefore, the introduction of intelligent inspection technology in this context is urgently necessary. With the development of UAVs, computer vision, artificial intelligence, and other technologies, the intelligent inspection of high dams based on visual perception has become possible, and related research has received extensive attention. This article summarizes the contents of high dam safety inspections and reviews recent studies on visual perception techniques in the context of intelligent inspections. First, this article categorizes image enhancement methods into those based on histogram equalization, Retinex, and deep learning. Representative methods and their characteristics are elaborated for each category, and the associated development trends are analyzed. Second, this article systematically enumerates the principal achievements of defect and obstacle perception methods, focusing on those based on traditional image processing and machine learning approaches, and outlines the main techniques and characteristics. Additionally, this article analyzes the principal methods for damage quantification based on visual perception. Finally, the major issues related to applying visual perception techniques for the intelligent safety inspection of high dams are summarized and future research directions are proposed.

An Efficient and Accurate Quality Inspection Model for Steel Scraps Based on Dense Small-Target Detection

Scrap steel serves as the primary alternative raw material to iron ore, exerting a significant impact on production costs for steel enterprises. With the annual growth in scrap resources, concerns regarding traditional manual inspection methods, including issues of fairness and safety, gain increasing prominence. Enhancing scrap inspection processes through digital technology is imperative. In response to these concerns, we developed CNIL-Net, a scrap-quality inspection network model based on object detection, and trained and validated it using images obtained during the scrap inspection process. Initially, we deployed a multi-camera integrated system at a steel plant for acquiring scrap images of diverse types, which were subsequently annotated and employed for constructing an enhanced scrap dataset. Then, we enhanced the YOLOv5 model to improve the detection of small-target scraps in inspection scenarios. This was achieved by adding a small-object detection layer (P2) and streamlining the model through the removal of detection layer P5, resulting in the development of a novel three-layer detection network structure termed the Improved Layer (IL) model. A Coordinate Attention mechanism was incorporated into the network to dynamically learn feature weights from various positions, thereby improving the discernment of scrap features. Substituting the traditional non-maximum suppression algorithm (NMS) with Soft-NMS enhanced detection accuracy in dense and overlapping scrap scenarios, thereby mitigating instances of missed detections. Finally, the model underwent training and validation utilizing the augmented dataset of scraps. Throughout this phase, assessments encompassed metrics like mAP, number of network layers, parameters, and inference duration. Experimental findings illustrate that the developed CNIL-Net scrap-quality inspection network model boosted the average precision across all categories from 88.8% to 96.5%. Compared to manual inspection, it demonstrates notable advantages in accuracy and detection speed, rendering it well suited for real-world deployment and addressing issues in scrap inspection like real-time processing and fairness.