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The Chang'e-6 (CE-6) landing area on the far side of the Moon is located in the southern part of the Apollo basin within the South Pole–Aitken (SPA) basin. The statistical analysis of impact craters in this region is crucial for ensuring a safe landing and supporting geological research. Aiming at existing impact crater identification problems such as complex background, low identification accuracy, and high computational costs, an efficient impact crater automatic detection model named YOLOv8-LCNET (YOLOv8-Lunar Crater Net) based on the YOLOv8 network is proposed. The model first incorporated a Partial Self-Attention (PSA) mechanism at the end of the Backbone, allowing the model to enhance global perception and reduce missed detections with a low computational cost. Then, a Gather-and-Distribute mechanism (GD) was integrated into the Neck, enabling the model to fully fuse multi-level feature information and capture global information, enhancing the model's ability to detect impact craters of various sizes. The experimental results showed that the YOLOv8-LCNET model performs well in the impact crater detection task, achieving 87.7% Precision, 84.3% Recall, and 92% AP, which were 24.7%, 32.7%, and 37.3% higher than the original YOLOv8 model. The improved YOLOv8 model was then used for automatic crater detection in the CE-6 landing area (246 km × 135 km, with a DOM resolution of 3 m/pixel), resulting in a total of 770,671 craters, ranging from 13 m to 19,882 m in diameter. The analysis of this impact crater catalogue has provided critical support for landing site selection and characterization of the CE-6 mission and lays the foundation for future lunar geological studies. [ABSTRACT FROM AUTHOR]

Background: The number of focal liver lesions (FLLs) detected by imaging has increased worldwide, highlighting the need to develop a robust, objective system for automatically detecting FLLs. Purpose: To assess the performance of the deep learning–based artificial intelligence (AI) software in identifying and measuring lesions on contrast‐enhanced magnetic resonance imaging (MRI) images in patients with FLLs. Study Type: Retrospective. Subjects: 395 patients with 1149 FLLs. Field Strength/Sequence: The 1.5 T and 3 T scanners, including T1‐, T2‐, diffusion‐weighted imaging, in/out‐phase imaging, and dynamic contrast‐enhanced imaging. Assessment: The diagnostic performance of AI, radiologist, and their combination was compared. Using 20 mm as the cut‐off value, the lesions were divided into two groups, and then divided into four subgroups: <10, 10–20, 20–40, and ≥40 mm, to evaluate the sensitivity of radiologists and AI in the detection of lesions of different sizes. We compared the pathologic sizes of 122 surgically resected lesions with measurements obtained using AI and those made by radiologists. Statistical Tests: McNemar test, Bland–Altman analyses, Friedman test, Pearson's chi‐squared test, Fisher's exact test, Dice coefficient, and intraclass correlation coefficients. A P‐value <0.05 was considered statistically significant. Results: The average Dice coefficient of AI in segmentation of liver lesions was 0.62. The combination of AI and radiologist outperformed the radiologist alone, with a significantly higher detection rate (0.894 vs. 0.825) and sensitivity (0.883 vs. 0.806). The AI showed significantly sensitivity than radiologists in detecting all lesions <20 mm (0.848 vs. 0.788). Both AI and radiologists achieved excellent detection performance for lesions ≥20 mm (0.867 vs. 0.881, P = 0.671). A remarkable agreement existed in the average tumor sizes among the three measurements (P = 0.174). Data Conclusion: AI software based on deep learning exhibited practical value in automatically identifying and measuring liver lesions. Level of Evidence: 4. Technical Efficacy: Stage 2. [ABSTRACT FROM AUTHOR]

Active infrared thermography (IRT) in non-destructive testing is an attractive technique used to detect wide areas in real-time on site. Most of the objects inspected on site generally have rough surfaces and foreign substances, which significantly affects their detectability. To solve this problem, in this study, line scanning (LS)-based induction thermography was used to acquire thermal image data of a specimen containing foreign substances. The heat distribution caused by foreign substances was removed using the Gaussian filtering-based Fast Fourier Transform (FFT) algorithm. After that, the data augmentation was performed by analyzing the correlation, and crack detection for the images was performed using you only look once (YOLO) deep learning. This study presents a method for removing non-uniform heat sources using the FFT algorithm, securing virtual data augmentation, and a detection mechanism for moving inspection objects using AI deep learning. [ABSTRACT FROM AUTHOR]

Here, we aimed to clarify the factors that cause individual differences in manual spindle detection during sleep by comparing it with automatic detection and to show the limitations of manual detection. Polysomnography (PSG) signals were recorded from ten young male participants, and sleep stages were classified based on these signals. Using time-frequency analysis, we detected sleep spindles from the single-channel electroencephalography (EEG) of C4-A1 within the same PSG data. Our results show a detailed accuracy evaluation by comparing the two skilled scorers' outputs of automatic and manual sleep spindle detection and differences between the number of sleep spindle detections and spindle time length. Additionally, based on automatic detection, the distribution of Cohen's kappa for each scorer quantitatively showed that individual scorers had detection thresholds based on EEG amplitude. Conventionally, automatic detection has been validated using manual detection outputs as the criterion. However, using automatic detection as the standard and analyzing the manual detection outputs, we quantitatively showcased the differences in individual scorers. Therefore, our method offers a quantitative approach to examining factors contributing to discrepancies in sleep spindle detection. However, individual differences cannot be avoided when using manual detection, and automatic detection is preferable when analyzing data to a certain standard. [ABSTRACT FROM AUTHOR]

In traditional pest monitoring, specimens are manually inspected, identified, and counted. These techniques can lead to poor data quality and hinder effective pest management decisions due to operational and economic limitations. This study aimed to develop an automatic detection and early warning system using the European Pepper Moth, Duponchelia fovealis (Lepidoptera: Crambidae), as a study model. A prototype water trap equipped with an infrared digital camera controlled using a microprocessor served as the attraction and capture device. Images captured by the system in the laboratory were processed to detect objects. Subsequently, these objects were labeled, and size and shape features were extracted. A machine learning model was then trained to identify the number of insects present in the trap. The model achieved 99% accuracy in identifying target insects during validation with 30% of the data. Finally, the prototype with the trained model was deployed in the field for result confirmation. [ABSTRACT FROM AUTHOR]

Sports performance initiation is of significant interest in sports sciences, particularly in beach volleyball (BV), where players usually combine indoor and BV disciplines in the formative stages. This research aimed to apply an electronic performance tracking system to quantify the physical-conditional performance of young male BV players during competition, considering age group (U15 or U19), sport specialisation (indoor or beach) and the set outcome (winner or loser). Thirty-two young male players, categorised by age and sport specialisation, were analysed during 40 matches using electronic performance tracking systems (Wimu PROTM). Data collected were the set duration, total and relative distances covered, and number and maximum values in acceleration and deceleration actions. U19 players and BV specialists, compared to their younger and indoor counterparts, covered more distance (719.25 m/set vs. 597.85 m/set; 719.25 m/set vs. 613.15 m/set) and exhibited higher intensity in terms of maximum values in acceleration (4.09 m/s2 vs. 3.45 m/s2; 3.99 m/s2 vs. 3.65 m/s2) and deceleration (−5.05 m/s2 vs. −4.41 m/s2). More accelerations (557.50 n/set vs. 584.50 n/set) and decelerations (561.50 n/set vs. 589.00 n/set) were found in indoor players. Additionally, no significant differences were found in variables regarding the set outcome. These findings suggest that both age and specialisation play crucial roles in determining a great physical-conditional performance in young players, displaying a higher volume and intensity in external load metrics, whereas indoor players seem to need more accelerations and decelerations in a BV adaptation context. These insights highlight the age development and sport specialisation in young volleyball and BV athletes. [ABSTRACT FROM AUTHOR]

The kidneys play an energetic role in eliminating excess products and fluids from the body, by a complex mechanism which is crucial for upholding a stable balance of body chemicals. Chronic kidney disease (CKD) is considered by an unhurried weakening in renal function that may eventually result in kidney injury or failure. The difficulty of diagnosing the illness rises as it worsens. However, using data from normal medical visits to evaluate the various phases of CKD could help with early detection and prompt care. Researchers suggest a classification strategy for CKD along with optimization strategies used in the learning process. The incorporation of artificial intelligence offers promise because it may often astonish with its skills and enable seemingly difficult undertakings. Modern machine learning techniques have been developed to detect renal illness in light of this. In the current study, a new deep learning model for CKD initial recognition and prediction is introduced. The main objective of the project is to build a strong deep neural network (DNN) and estimate its result outcomes in comparison to other leading-edge machine learning techniques. The outcomes demonstrate that the proposed strategy outperforms current approaches and has promise as a useful tool for CKD detection. [ABSTRACT FROM AUTHOR]

Copyright of Steel Pipe is the property of Steel Pipe Magazine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

One of the main challenges for city development is to ensure a sustainable water resource management for the water supply system. A clear identification of the urban water consumption patterns supports policy and decision makers in managing the water resources, satisfying the total demand and, at the same time, reducing losses and identifying potential leakages or other issues in the distribution network. High resolution smart meters have widely shown to be an efficient tool to measure in-pipe water consumption. The collected data can be used to identify water demand patterns at different temporal and spatial scales, reaching the end-uses level. Water consumption patterns at building level can be influenced by multiple factors, such as socio-demographic aspects, seasonality, and house characteristics. The presence of a garden that requires summer irrigation strongly alters the daily consumption pattern. In this framework, we present an innovative approach to automatically detect the presence of garden irrigation, identifying daily average water consumption patterns with and without it. The proposed methodology was tested in a residential area in Northen Italy, where 23 smart meters recorded data at 1-minute resolution for two years. Results show very good performances in distinguishing between days with and without garden irrigation. The derived average normalized water consumption patterns for both scenarios can help decision makers and water managers to regulate the pressure regimes in the distribution network correctly. Highlights: High resolution smart meter data have been used to identify water consumption patterns; Automatic detection criteria to classify days with and without garden irrigation are designed; Normalized average water consumption patterns with and without irrigation are proposed. [ABSTRACT FROM AUTHOR]

Abstract InSAR monitoring technology is widely used in investigating landslide hazards. Leveraging object detection algorithms to quickly extract landslide information from Wide-Area InSAR measurements is of great significance. Our InSAR-YOLOv8, an algorithm that automatically detects landslides from InSAR measurements, addresses the low accuracy and suboptimal detection performance of existing network models. In this method, we first design and add a detection head specifically targeting small-scale objects. This improvement enhances the model’s ability to extract features across different scales and strengthens its capability to detect landslides of varying sizes. We also replace the original C2f module with the lighter C2f_Faster module to process information more efficiently, making the model lighter and more efficient. Finally, the SIoU loss function replaces the CIoU loss function to improve the bounding box regression and enhance detection accuracy. Our results show that the proposed algorithm achieves a 97.41% mAP50, a 66.47% mAP50:95, and a 92.06% F1 score on the InSAR landslide dataset, while reducing the number of parameters by 25%. Compared with YOLOv8 and other advanced models (YOLOvX, Faster R-CNN, etc.), our model exhibits distinct advantages and possesses a wider range of potential applications in InSAR measurement for landslide detection.

The creation of effective systems for filtering media texts is due to the need to develop artificial intelligence systems, which is a large language model that should be trained using “correct” text samples that do not contain signs of disinformation, infodemic and unreliability. The article presents the results of automatic detection of high-quality media texts, as well as text samples with infodemic features carried out using a trained natural language model based on a manually labeled corpus. Manual marking of the corpus was carried out by experts based on the parameterization of the text content. The goal of our work is to build a model of the language of media messages, assess the quality and identify detection errors caused by the linguistic characteristics of texts. Creating a model of the language of media messages is a condition for increasing the efficiency and quality of artificial intelligence systems. It has been established that the test use of a trained natural language model allows filtering media texts with fairly high accuracy. The support vector machine method proved to be most effective. The share of incorrectly recognized informative texts that meet the criteria of reliability and novelty is low and amounts to 6.2 percent. The percentage of incorrectly recognized uninformative texts is approximately 3.9 percent, which indicates a fairly high efficiency of the developed model. The errors in the detection of informative texts are associated with the use of proper names (anthroponyms, toponyms) and numerals in the headings. Linguistic features of misclassified texts containing signs of fake and misinformation comprise text samples using statements with speech verbs that are often used in informative texts.

Introduction. At present, automatic capture and tracking of moving objects in video data obtained by a video camera mounted on a mobile carrier represents a relevant research task. Its successful solution is challenged by such factors, as a non-uniform background, object overlapping between one another and the background, significant and rapid changes in the size of the object of interest, abrupt changes in the movement trajectory of the mobile carrier.Aim. To develop an automatic method for detecting moving objects followed by their tracking in video data obtained under difficult observation conditions. An additional requirement imposed on the tracking stage consists in the restriction of computing resources.Materials and methods. The method is based on a convolutional neural network with a YOLO architecture. Due to the restriction of computing resources, object tracking is implemented without neural network solutions. In order to ensure stable tracking, several detectors are used simultaneously with the subsequent analysis of the data obtained. The tracking stage involves a detector based on histograms of oriented gradients (HOG), supplemented by a detector based on correlation filtering and motion trajectory prediction based on the Kalman filter.Results. At the automatic detection stage, the TPR, averaged over all video files participating in the experiments, was equal to 0.81, with the FPR corresponding to 0.10. At the tracking stage, the failure rate (tracking failures) was 6·10 –5.Conclusion. The proposed method can be successfully used to detect and track objects at a distance of 1500 m with an object projection size on the frame of 5 × 5 pixels under the conditions of global motion, a non-uniform background, and significant changes in the properties of the object of interest.

Abstract Ovarian cancer is a common gynecological tumor, with a high mortality rate and difficult clinical treatment. Early detection of ovarian cancer has significant diagnostic value. In response to the problem of poor diagnostic performance of traditional early diagnosis methods, this article designed an automated early ovarian cancer detection system to improve the detection of early ovarian cancer. The conventional early diagnosis methods include serum CA125 (carbohydrate antigen 125) detection and positron emission tomography/computed tomography (PET/CT) imaging. This article combined serum CA125 detection and PET/CT imaging to detect the CA125 level and maximum standardized uptake value (SUV) in patient’s serum. When the CA125 level exceeded 35U/ml and the maximum SUV value exceeded 2.5, the test was considered positive. This article selected 200 patients from Jingzhou Hospital for the experiment and compared the three detection methods. The average specificity of single serum CA125 detection, single PET/CT imaging, and automated detection in patients under 50 were 61.24%, 79.57%, and 97.79%, respectively. The automated early ovarian cancer detection system designed in this article can significantly improve the specificity of early ovarian cancer detection and has excellent application value for early ovarian cancer detection.

Objective The detection of foreign objects in platform door gap is critical to metro operational safety. Therefore, it is essential to develop a new anti-clamping detection system for metro platform door, enhancing the safety and efficiency of future FAO (fully automatic operation) systems. Method Based on the video and LiDAR algorithm fusion technology, a dual-criterion AI detection strategy that combines video image recognition with LiDAR point cloud data is proposed. PointNet algorithm framework is innovatively adopted for the detection of foreign objects in metro platform door gap, implementing a camera video assisted LiDAR working mode. In the event of foreign object detection in door gap, the system triggers an alarm-video synergistic operation and initiates video capture of the incident site immediately. The use of multi-dimensional deep learning techniques reduces the probability of false alarms. Result & Conclusion In system design, a cross-stacking layered sensor installation method is proposed, enabling the redundant detection function of foreign objects in platform door gaps. The cross-verification mechanism significantly enhances the redundancy and reliability of the detection device, and using 2D sensors to achieve 3D detection effects. The developed system provides safety interlocking signals to metro signaling system, sends alarm information to the integrated monitoring system, pushing wristband alerts to on-site operation personnel. This system ensures more accurate and reliable detection of foreign objects in platform door gaps, offering safety support for FAO.

Background/Objectives: The role of artificial intelligence (AI) in dentistry is becoming increasingly significant, particularly in diagnosis and treatment planning. This study aimed to assess the sensitivity, specificity, accuracy, and precision of AI-driven software in analyzing dental panoramic radiographs (DPRs) in patients with permanent dentition. Methods: Out of 638 DPRs, 600 fulfilled the inclusion criteria. The radiographs were analyzed by AI software and two researchers. The following variables were assessed: (1) missing tooth, (2) root canal filling, (3) endodontic lesion, (4) implant, (5) abutment, (6) pontic, (7) crown, (8) and sound tooth. Results: The study revealed very high performance metrics for the AI algorithm in detecting missing teeth, root canal fillings, and implant abutment crowns, all greater than 90%. However, it demonstrated moderate sensitivity and precision in identifying endodontic lesions and the lowest precision (65.30%) in detecting crowns. Conclusions: AI software can be a valuable tool in clinical practice for diagnosis and treatment planning but may require additional verification by clinicians, especially for identifying endodontic lesions and crowns. Due to some limitations of the study, further research is recommended. [ABSTRACT FROM AUTHOR]

Bladder urothelial carcinoma is the most common malignant tumor disease in urinary system, and its incidence rate ranks ninth in the world. In recent years, the continuous development of hyperspectral imaging technology has provided a new tool for the auxiliary diagnosis of bladder cancer. In this study, based on microscopic hyperspectral data, an automatic detection algorithm of bladder tumor cells combining color features and shape features is proposed. Support vector machine (SVM) is used to build classification models and compare the classification performance of spectral feature, spectral and shape fusion feature, and the fusion feature proposed in this paper on the same classifier. The results show that the sensitivity, specificity, and accuracy of our classification algorithm based on shape and color fusion features are 0.952, 0.897, and 0.920, respectively, which are better than the classification algorithm only using spectral features. Therefore, this study can effectively extract the cell features of bladder urothelial carcinoma smear, thus achieving automatic, real-time, and noninvasive detection of bladder tumor cells, and then helping doctors improve the efficiency of pathological diagnosis of bladder urothelial cancer, and providing a reliable basis for doctors to choose treatment plans and judge the prognosis of the disease. [ABSTRACT FROM AUTHOR]

ABSTRACT Objective Materials and Methods Results Conclusions Trial Registration Oral diseases, specifically malignant lesions, are serious global health concerns requiring early diagnosis for effective treatment. In recent years, deep learning (DL) has emerged as a powerful tool for the automated detection and classification of oral lesions. This research, by conducting a scoping review and meta‐analysis, aims to provide an overview of the progress and achievements in the field of automated detection of oral lesions using DL.A scoping review was conducted to identify relevant studies published in the last 5 years (2018–2023). A comprehensive search was conducted using several electronic databases, including PubMed, Web of Science, and Scopus.Two reviewers independently assessed the studies for eligibility and extracted data using a standardized form, and a meta‐analysis was conducted to synthesize the findings.Fourteen studies utilizing various DL algorithms were identified and included for the detection and classification of oral lesions from clinical images. Among these, three were included in the meta‐analysis. The estimated pooled sensitivity and specificity were 0.86 (95% confidence interval [CI] = 0.80–0.91) and 0.67 (95% CI = 0.58–0.75), respectively.The results of meta‐analysis indicate that DL algorithms improve the diagnosis of oral lesions. Future research should develop validated algorithms for automated diagnosis.Open Science Framework (https://osf.io/4n8sm) [ABSTRACT FROM AUTHOR]

As cracks are a precursor landslide deformation feature, they can provide forecasting information that is useful for the early identification of landslides and determining motion instability characteristics. However, it is difficult to solve the size effect and noise-filtering problems associated with the currently available automatic crack detection methods under complex conditions using single remote sensing data sources. This article uses multidimensional target scene images obtained by UAV photogrammetry as the data source. Firstly, under the premise of fully considering the multidimensional image characteristics of different crack types, this article accomplishes the initial identification of landslide cracks by using six algorithm models with indicators including the roughness, slope, eigenvalue rate of the point cloud and pixel gradient, gray value, and RGB value of the images. Secondly, the initial extraction results are processed through a morphological repair task using three filtering algorithms (calculating the crack orientation, length, and frequency) to address background noise. Finally, this article proposes a multi-dimensional information fusion method, the Bayesian probability of minimum risk methods, to fuse the identification results derived from different models at the decision level. The results show that the six tested algorithm models can be used to effectively extract landslide cracks, providing Area Under the Curve (AUC) values between 0.6 and 0.85. After the repairing and filtering steps, the proposed method removes complex noise and minimizes the loss of real cracks, thus increasing the accuracy of each model by 7.5–55.3%. Multidimensional data fusion methods solve issues associated with the spatial scale effect during crack identification, and the F-score of the fusion model is 0.901. [ABSTRACT FROM AUTHOR]

ABSTRACT Recent years have seen a dramatic rise in the use of passive acoustic monitoring (PAM) for biological and ecological applications, and a corresponding increase in the volume of data generated. However, data sets are often becoming so sizable that analysing them manually is increasingly burdensome and unrealistic. Fortunately, we have also seen a corresponding rise in computing power and the capability of machine learning algorithms, which offer the possibility of performing some of the analysis required for PAM automatically. Nonetheless, the field of automatic detection of acoustic events is still in its infancy in biology and ecology. In this review, we examine the trends in bioacoustic PAM applications, and their implications for the burgeoning amount of data that needs to be analysed. We explore the different methods of machine learning and other tools for scanning, analysing, and extracting acoustic events automatically from large volumes of recordings. We then provide a step‐by‐step practical guide for using automatic detection in bioacoustics. One of the biggest challenges for the greater use of automatic detection in bioacoustics is that there is often a gulf in expertise between the biological sciences and the field of machine learning and computer science. Therefore, this review first presents an overview of the requirements for automatic detection in bioacoustics, intended to familiarise those from a computer science background with the needs of the bioacoustics community, followed by an introduction to the key elements of machine learning and artificial intelligence that a biologist needs to understand to incorporate automatic detection into their research. We then provide a practical guide to building an automatic detection pipeline for bioacoustic data, and conclude with a discussion of possible future directions in this field. [ABSTRACT FROM AUTHOR]

The rapid adoption of Unmanned Aerial Vehicles (UAVs) in the construction industry has revolutionized safety, surveying, quality monitoring, and maintenance assessment. UAVs are increasingly used to prevent accidents caused by falls from heights or being struck by falling objects by ensuring workers comply with safety protocols. This study focuses on leveraging UAV technology to enhance labor safety by monitoring the use of personal protective equipment, particularly helmets, among construction workers. The developed UAV system utilizes the tensorflow technique and an alert system to detect and identify workers not wearing helmets. Employing the high-precision, high-speed, and widely applicable Faster R-CNN method, the UAV can accurately detect construction workers with and without helmets in real-time across various site conditions. This proactive approach ensures immediate feedback and intervention, significantly reducing the risk of injuries and fatalities. Additionally, the implementation of UAVs minimizes the workload of site supervisors by automating safety inspections and monitoring, allowing for more efficient and continuous oversight. The experimental results indicate that the UAV system's high precision, recall, and processing capabilities make it a reliable and cost-effective solution for improving construction site safety. The precision, mAP, and FPS of the developed system with the R-CNN are 93.1%, 58.45%, and 27 FPS. This study demonstrates the potential of UAV technology to enhance safety compliance, protect workers, and improve the overall quality of safety management in the construction industry. [ABSTRACT FROM AUTHOR]

Ovarian cancer is a common gynecological tumor, with a high mortality rate and difficult clinical treatment. Early detection of ovarian cancer has significant diagnostic value. In response to the problem of poor diagnostic performance of traditional early diagnosis methods, this article designed an automated early ovarian cancer detection system to improve the detection of early ovarian cancer. The conventional early diagnosis methods include serum CA125 (carbohydrate antigen 125) detection and positron emission tomography/computed tomography (PET/CT) imaging. This article combined serum CA125 detection and PET/CT imaging to detect the CA125 level and maximum standardized uptake value (SUV) in patient's serum. When the CA125 level exceeded 35U/ml and the maximum SUV value exceeded 2.5, the test was considered positive. This article selected 200 patients from Jingzhou Hospital for the experiment and compared the three detection methods. The average specificity of single serum CA125 detection, single PET/CT imaging, and automated detection in patients under 50 were 61.24%, 79.57%, and 97.79%, respectively. The automated early ovarian cancer detection system designed in this article can significantly improve the specificity of early ovarian cancer detection and has excellent application value for early ovarian cancer detection. [ABSTRACT FROM AUTHOR]

[Objective] The detection of foreign objects in platform door gap is critical to metro operational safety. There- fore, it is essential to develop a new anti-clamping detection system for metro platform door, enhancing the safety and effi- ciency of future FAO (fully automatic operation) systems. [Method] Based on the video and LiDAR algorithm fusion technology, a dual-criterion Al detection strategy that combines video image recognition with LiDAR point cloud data is pro- posed. PointNet algorithm framework is innovatively adopted for the detection of foreign objects in metro platform door gap, implementing a camera video assisted LiDAR working mode. In the event of foreign object detection in door gap, the system triggers an alarm-video synergistic operation and initiates video capture of the incident site immediately. The use of multi-di- mensional deep learning techniques reduces the probability of false alarms. [Result & Conclusion] In system design, a cross-stacking layered sensor installation method is proposed, enabling the redundant detection function of foreign objects in platform door gaps. The cross-verification mechanism signifi- cantly enhances the redundancy and reliability of the detection device, and using 2D sensors to achieve 3D detection effects. The developed system provides safety interlocking signals to metro signaling system, sends alarm information to the inte- grated monitoring system, pushing wristband alerts to on-site operation personnel. This system ensures more accurate and re- liable detection of foreign objects in platform door gaps, offer- ing safety support for FAO. [ABSTRACT FROM AUTHOR]

Moving through a dynamic world, humans need to intermittently stabilize gaze targets on their retina to process visual information. Overt attention being thus split into discrete intervals, the automatic detection of such fixation events is paramount to downstream analysis in many eye-tracking studies. Standard algorithms tackle this challenge in the limiting case of little to no head motion. In this static scenario, which is approximately realized for most remote eye-tracking systems, it amounts to detecting periods of relative eye stillness. In contrast, head-mounted eye trackers allow for experiments with subjects moving naturally in everyday environments. Detecting fixations in these dynamic scenarios is more challenging, since gaze-stabilizing eye movements need to be reliably distinguished from non-fixational gaze shifts. Here, we propose several strategies for enhancing existing algorithms developed for fixation detection in the static case to allow for robust fixation detection in dynamic real-world scenarios recorded with head-mounted eye trackers. Specifically, we consider (i) an optic-flow-based compensation stage explicitly accounting for stabilizing eye movements during head motion, (ii) an adaptive adjustment of algorithm sensitivity according to head-motion intensity, and (iii) a coherent tuning of all algorithm parameters. Introducing a new hand-labeled dataset, recorded with the Pupil Invisible glasses by Pupil Labs, we investigate their individual contributions. The dataset comprises both static and dynamic scenarios and is made publicly available. We show that a combination of all proposed strategies improves standard thresholding algorithms and outperforms previous approaches to fixation detection in head-mounted eye tracking. [ABSTRACT FROM AUTHOR]

Studies show that the common cuckoo exhibits a strong vocal response to rival playback. In this study, we aimed to assess if males would more eagerly vocally defend their home ranges a day after a simulated rival encounter. At 48 sites in Poland, we conducted a playback experiment where we played 20 calls repeated 5 times with a 5-min break between each series. Using the automatic analysis software Kaleidoscope Pro, we detected cuckoo calls in two datasets, directly after the experiment and the day after. Our results show that even though cuckoos respond to rival calls, this response is short-lived. [ABSTRACT FROM AUTHOR]

To address the issues of low detection accuracy, slow speed, and large parameter size in detecting fresh table grapes in natural orchard environments, this study proposes an improved grape detection model based on YOLOv8n, termed YOLOGPnet. The model replaces the C2f module with a Squeeze-and-Excitation Network V2 (SENetV2) to enhance gradient flow through more branched cross-layer connections, thereby improving detection accuracy. Additionally, the Spatial Pyramid Pooling with Enhanced Local Attention Network (SPPELAN) substitutes the SPPF module, enhancing its ability to capture multi-scale information of the target fruits. The introduction of the Focaler-IoU loss function, along with different weight adjustment mechanisms, further improves the precision of bounding box regression in object detection. After comparing with multiple algorithms, the experimental results show that YOLOGPnet achieves an accuracy of 93.6% and mAP@0.5 of 96.8%, which represents an improvement of 3.5 and 1.6 percentage points over the baseline model YOLOv8n, respectively. The model's computational load, parameter count, and weight file size are 6.8 Gflops, 2.1 M, and 4.36 MB, respectively. The detection time per image is 12.5 ms, showing reductions of 21.84%, 33.13%, 30.79%, and 25.60% compared to YOLOv8n. Additionally, comparisons with YOLOv5n and YOLOv7-tiny in the same parameters reveal accuracy improvements of 0.7% and 1.9%, respectively, with other parameters also showing varying degrees of enhancement. This study offers a solution for accurate and rapid detection of table grapes in natural orchard environments for intelligent grape harvesting equipment. [ABSTRACT FROM AUTHOR]

Background: The automatic detection of activities of daily living (ADL) is necessary to improve long-term home-based monitoring of Parkinson's disease (PD) symptoms. While most body-worn sensor algorithms for ADL detection were developed using laboratory research systems covering full-body kinematics, it is now crucial to achieve ADL detection using a single body-worn sensor that remains commercially available and affordable for ecological use. Aim: to detect and segment Walking, Turning, Sitting-down, and Standing-up activities of patients with PD using a Smartwatch positioned at the ankle. Method: Twenty-two patients living with PD performed a Timed Up and Go (TUG) task three times before engaging in cleaning ADL in a simulated free-living environment during a 3 min trial. Accelerations and angular velocities of the right or left ankle were recorded in three dimensions using a Smartwatch. The TUG task was used to develop detection algorithms for Walking, Turning, Sitting-down, and Standing-up, while the 3 min trial in the free-living environment was used to test and validate these algorithms. Sensitivity, specificity, and F-scores were calculated based on a manual segmentation of ADL. Results: Sensitivity, specificity, and F-scores were 96.5%, 94.7%, and 96.0% for Walking; 90.0%, 93.6%, and 91.7% for Turning; 57.5%, 70.5%, and 52.3% for Sitting-down; and 57.5%, 72.9%, and 54.1% for Standing-up. The median of time difference between the manual and automatic segmentation was 1.31 s for Walking, 0.71 s for Turning, 2.75 s for Sitting-down, and 2.35 s for Standing-up. Conclusion: The results of this study demonstrate that segmenting ADL to characterize the mobility of people with PD based on a single Smartwatch can be comparable to manual segmentation while requiring significantly less time. While Walking and Turning were well detected, Sitting-down and Standing-up will require further investigation to develop better algorithms. Nonetheless, these achievements increase the odds of success in implementing wearable technologies for PD monitoring in ecological environments. [ABSTRACT FROM AUTHOR]

Copyright of Laser Technology is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The ionospheric quasi-periodic wave is a type of typical and common electromagnetic wave phenomenon occurring in extremely low-frequency (ELF) and very low-frequency ranges (VLF). These emissions propagate in a distinct whistler-wave mode, with varying periodic modulations of the wave intensity over time scales from several seconds to a few minutes. We developed an automatic detection model for the QP waves in the ELF band recorded by the China Seismo-Electromagnetic Satellite. Based on the 827 QP wave events, which were collected through visual screening from the electromagnetic field observations, an automatic detection model based on the Transformer architecture was built. This model, comprising 34.27 million parameters, was trained and evaluated. It achieved mean average precision of 92.3% on the validation dataset, operating at a frame rate of 39.3 frames per second. Notably, after incorporating the proton cyclotron frequency constraint, the model displayed promising performance. Its lightweight design facilitates easy deployment on satellite equipment, significantly enhancing the feasibility of on-board detection. [ABSTRACT FROM AUTHOR]

This study presents laboratory measurements of P and S wave velocities of two carbonate rocks (porous limestone and yellow cemented limestone). The experimental results were validated and compared with the numerical simulation outputs using the 3D Fast Lagrangian Analysis of Continua software (FLAC3D). The main aim of this study is to evaluate the effect of frequency and mode of emission on ultrasonic pulse velocity (UPV) by applying an automatic method for the determination of P and S wave velocities. Based on the results, automatic detection of UPV can provide reliable outputs. The difference between numerical simulation results and laboratory measurement in terms of P and S wave velocities was, on average, around 7%, suggesting the applicability of the automatic detection method. Our study implies less noise in the perfect shear (PS) mode than in the single zone (SZ) emission mode. In summary, higher frequencies and the PS mode of emission are recommended.Highlights: The applicability of the numerical method in ultrasonic wave propagation was proven. Automatic detection of UPV needs to be adjusted based on the type of transducer. Increasing transducer frequency causes a more straightforward form of wave emission with a lower amount of noise. The perfect shear (PS) emission mode yielded more precise results than the single zone (SZ) emission mode and decreased the extra noise propagation. [ABSTRACT FROM AUTHOR]

• Seizure onset zone localization accuracy based on HFO rate was comparable for visual and automated HFO detection. • Optimizing automated HFO detection settings based on visual markings did not increase seizure onset zone localization accuracy. • For many patients, changing detection settings could improve SOZ localization accuracy, but new optimization methods are needed. High frequency oscillations (HFOs) are a biomarker of the seizure onset zone (SOZ) and can be visually or automatically detected. In theory, one can optimize an automated algorithm's parameters to maximize SOZ localization accuracy; however, there is no consensus on whether or how this should be done. Therefore, we optimized an automated detector using visually identified HFOs and evaluated the impact on SOZ localization accuracy. We detected HFOs in intracranial EEG from 20 patients with refractory epilepsy from two centers using (1) unoptimized automated detection, (2) visual identification, and (3) automated detection optimized to match visually detected HFOs. SOZ localization accuracy based on HFO rate was not significantly different between the three methods. Across patients, visually optimized detector settings varied, and no single set of settings produced universally accurate SOZ localization. Exploratory analysis suggests that, for many patients, detection settings exist that would improve SOZ localization. SOZ localization accuracy was similar for all three methods, was not improved by visually optimizing detector settings, and may benefit from patient-specific parameter optimization. Visual HFO marking is laborious, and optimizing automated detection using visual markings does not improve localization accuracy. New patient-specific detector optimization methods are needed. [ABSTRACT FROM AUTHOR]

Background/Objectives: The aim of this study was to assess the diagnostic accuracy of the AI-driven platform Diagnocat for evaluating endodontic treatment outcomes using cone beam computed tomography (CBCT) images. Methods: A total of 55 consecutive patients (15 males and 40 females, aged 12–70 years) referred for CBCT imaging were included. CBCT images were analyzed using Diagnocat's AI platform, which assessed parameters such as the probability of filling, adequate obturation, adequate density, overfilling, voids in filling, short filling, and root canal number. The images were also evaluated by two experienced human readers. Diagnostic accuracy metrics (accuracy, precision, recall, and F1 score) were assessed and compared to the readers' consensus, which served as the reference standard. Results: The AI platform demonstrated high diagnostic accuracy for most parameters, with perfect scores for the probability of filling (accuracy, precision, recall, F1 = 100%). Adequate obturation showed moderate performance (accuracy = 84.1%, precision = 66.7%, recall = 92.3%, and F1 = 77.4%). Adequate density (accuracy = 95.5%, precision, recall, and F1 = 97.2%), overfilling (accuracy = 95.5%, precision = 86.7%, recall = 100%, and F1 = 92.9%), and short fillings (accuracy = 95.5%, precision = 100%, recall = 86.7%, and F1 = 92.9%) also exhibited strong performance. The performance of AI for voids in filling detection (accuracy = 88.6%, precision = 88.9%, recall = 66.7%, and F1 = 76.2%) highlighted areas for improvement. Conclusions: The AI platform Diagnocat showed high diagnostic accuracy in evaluating endodontic treatment outcomes using CBCT images, indicating its potential as a valuable tool in dental radiology. [ABSTRACT FROM AUTHOR]

Epilepsy, as a serious neurological disorder, can be detected by analyzing the brain signals produced by neurons. Electroencephalogram (EEG) signals are the most important data source for monitoring these brain signals. However, these complex, noisy, nonlinear and nonstationary signals make detecting seizures become a challenging task. Feature-based seizure detection algorithms have become a dominant approach for automatic seizure detection. This study presents an algorithm for automatic seizure detection based on novel features with clinical and statistical significance. Our algorithms achieved the best results on two benchmark datasets, outperforming traditional feature-based methods and state-of-the-art deep learning algorithms. Accuracy exceeded 99.99% on both benchmark public datasets, with the 100% correct detection of all seizures on the second one. Due to the interpretability and robustness of our algorithm, combined with its minimal computational resource requirements and time consumption, it exhibited substantial potential value in the realm of clinical application. The coefficients of variation of datasets proposed by us makes the algorithm data-specific and can give theoretical guidance on the selection of appropriate random spectral features for different datasets. This will broaden the applicability scenario of our feature-based approach. [ABSTRACT FROM AUTHOR]