Average Euclidean Distance Research Articles

An improved YOLOv8n-IRP model for natural rubber tree tapping surface detection and tapping key point positioning.

Aiming at the problem that lightweight algorithm models are difficult to accurately detect and locate tapping surfaces and tapping key points in complex rubber forest environments, this paper proposes an improved YOLOv8n-IRP model based on the YOLOv8n-Pose. First, the receptive field attention mechanism is introduced into the backbone network to enhance the feature extraction ability of the tapping surface. Secondly, the AFPN structure is used to reduce the loss and degradation of the low-level and high-level feature information. Finally, this paper designs a dual-branch key point detection head to improve the screening ability of key point features in the tapping surface. In the detection performance comparison experiment, the YOLOv8n-IRP improves the D_mAP50 and P_mAP50 by 1.4% and 2.3%, respectively, over the original model while achieving an average detection success rate of 87% in the variable illumination test, which demonstrates enhanced robustness. In the positioning performance comparison experiment, the YOLOv8n-IRP achieves an overall better localization performance than YOLOv8n-Pose and YOLOv5n-Pose, realizing an average Euclidean distance error of less than 40 pixels. In summary, YOLOv8n-IRP shows excellent detection and positioning performance, which not only provides a new method for the key point localization of the rubber-tapping robot but also provides technical support for the unmanned rubber-tapping operation of the intelligent rubber-tapping robot.

FMORT: The Meta-Heuristic routing method by integrating index parameters to optimize energy consumption and real execution time using FANET

Decreasing energy consumption in Unmanned Aerial Vehicles (UAVs) while simultaneously enhancing their reliability and processing capabilities is considered a fundamental challenge. The routing mechanisms employed in Flying Ad Hoc Networks (FANETs) are more complex compared to those in Mobile Ad Hoc Networks (MANETs) and Vehicular Ad Hoc Networks (VANETs), a challenge addressed by the FMORT method. To tackle these complex routing challenges, clustering techniques that utilize hybrid Meta-heuristic algorithms can be applied. Data analysis within the FMORT framework identified factors influencing service integration, leading to a reduction in redundant request transmissions and overall redundancy in the proposed method. The identification of food sources in the hybrid Meta-heuristic algorithm of the FMORT method is achieved through the integration of the Sparrow and Dragonfly algorithms. These algorithms work simultaneously to increase energy efficiency and increase network lifetime. This strategy optimizes information exchange by selecting an intelligent threshold detector and categorizing inputs, thereby minimizing node mobility. As a result, it improves performance metrics and decreases delivery costs, energy consumption, and delays. In the proposed method, a balanced performance is achieved by comparing existing methods in terms of transmission delay, Packet Delivery Ratio( PDR), throughput, and energy consumption. Simulation results show that the FMORT approach provides effective and stable outcomes in terms of reliability, decreased delays, and improved packet delivery rates. The FMORT framework includes principles for neighbor selection, determining suitable cluster heads, and scoring based on the average Euclidean distance. Additionally, it manages topology access, ensures proper distribution, guarantees data connectivity, and accurately categorizes inputs. By optimizing the sensitivity rate, this method minimizes the average delays and meekly values input data through effective load balancing. Key parameters considered for real time optimization of overall performance include the number of cluster heads during re-clustering, the ratio of request-to-acknowledgment packet transmission, node, and network lifetime, end-to-end delay, and energy consumption. Ultimately, the simulation results show that compared to the MWCRSF algorithm, the average optimization of index parameters,% 0.73 decrease in energy consumption,% 2.23 network lifetime, 1.35 re-cluster construction time and also% 0.11 re-cluster lifetime has increased.

Classification of movements of collegiate female soccer players using inertial measurement units

This study aimed to classify the movements of female soccer players during matches using raw data measured by inertial measurement units (IMUs). Twelve collegiate female soccer players were equipped with IMUs (100 Hz), and raw triaxial acceleration data from eight official matches were analyzed. The measurement data were separated every 3 s, and a Fast Fourier Transform (FFT) was performed. After FFT, the Euclidean distances between the data when the players were in the stationary state and other states were calculated to classify the movements of the players using the k-means method. The data of the clustering numbers classified as the stationary state were eliminated after analyzing the movements of players by video filming the matches. After classification, the average Euclidean distances between the stationary state and other movements were calculated. Consequently, the results showed that the upward and downward directions of the raw data affected the classification. Using the methods of this study, it was also shown that the distribution of Euclidean distances differed from player to player. Our findings indicate that the method used in this study can be used to classify and characterize the movements of female collegiate soccer players.

Effect of impact kinematic filters on brain strain responses in contact sports.

Impact kinematics are widely employed to investigate mechanisms of traumatic brain injury (TBI). However, they are susceptible to noise and artefacts; thus, require data filtering. Few studies have focused on how data filtering affects brain strain most relevant to TBI. Here, we report that impact-induced brain strains are much less sensitive to data filtering than kinematics based on three filtering methods: CFC180, lowpass 200Hz, and a new method called Head Exposure to Acceleration Database in Sport (HEADSport). Using mouthguard-measured head impacts in elite rugby (N=5694), average Euclidean distances between the three filtered angular velocity profiles and their unfiltered counterparts are used to identify three groups of impacts with large variations: 90-95th, 95-99th, and >99th percentile. From each group, 20 impacts are randomly selected for simulation using the anisotropic Worcester Head Injury Model (WHIM) V1.0. HEADSport and CFC180 are the most and least effective, respectively, in suppressing "unphysical artefacts" shown as sharp spikes with a rather short impulse duration (e.g., <3 ms) in angular velocity. However, maximum principal strain (MPS), especially that in the corpus callosum, is much less sensitive to data filtering compared to kinematic peaks (e.g., reduction of 3% vs. 47% and 90% for peak angular velocity and acceleration with HEADSport for impacts of >99th percentile). These findings confirm that the brain acts as a low-pass filter, itself, to suppress high frequency noise in impact kinematics. Therefore, brain strain can serve as a common metric for TBI biomechanical studies to maximize relevance to the injury, as it is not sensitive to kinematic filters.

Various distance between generalized Diophantine fuzzy sets using multiple criteria decision making and their real life applications

This paper describes a generalized Diophantine fuzzy sets, which can be seen as a generalization of both Diophantine fuzzy sets and Pythagorean fuzzy sets. We define the basic properties of generalized Diophantine fuzzy set, as well as their relationships and distances. We compare Diophantine fuzzy sets with other Diophantine Pythagorean fuzzy sets to demonstrate their importance in the literature. We introduce new operators including necessity, possibility, accuracy function and score function. Furthermore, we discuss the new distance between normalized Euclidean distance and normalized Hamming distance. For a generalized Diophantine fuzzy relation, image and inverse image functions are defined. Numerous real-world applications can be found for the prevalent ideas of intuitionistic fuzzy sets, Pythagorean fuzzy sets, Diophantine fuzzy sets and q-rung orthopair fuzzy sets. Regretfully, these theories about the membership and non-membership grades have their own limits. We provide a new idea the generalized Diophantine fuzzy set that eliminates these limitations by including reference parameters. Compared to other kinds of fuzzy sets, there are more applications for generalized Diophantine fuzzy sets. We offer practical examples that show how different enhanced distances might be used in everyday situations. Additionally, to demonstrate the effectiveness of the suggested approach, flowchart based multi-criteria decision-making is provided and used to a numerical example. The outcomes are assessed for various parameter values. Furthermore, a comparative analysis developed to demonstrate the superiority of the suggested technique over current methodologies.

A Novel Algorithm for Forensic Identification Using Geometric Cranial Patterns in Digital Lateral Cephalometric Radiographs in Forensic Dentistry.

Lateral cephalometric radiographs are crucial in dentistry and orthodontics for diagnosis and treatment planning. However, their use in forensic identification, especially with burned bodies or in mass disasters, is challenging. AM (antemortem) and PM (postmortem) radiographs can be compared for identification. This study introduces and evaluates a novel algorithm for extracting cranial patterns from digital lateral cephalometric radiographs for identification purposes. Due to the unavailability of AM cephalograms from deceased individuals, the algorithm was tested using pre- and post-treatment cephalograms of living individuals from an orthodontic archive, considered as AM and PM data. The proposed algorithm encodes cranial patterns into a database for future identification. It matches PM cephalograms with AM records, accurately identifying individuals by comparing cranial features. The algorithm achieved an accuracy of 97.5%, a sensitivity of 97.7%, and a specificity of 95.2%, correctly identifying 350 out of 358 cases. The mean similarity score improved from 91.02% to 98.10% after applying the Automatic Error Reduction (AER) function. Intra-observer error analysis showed an average Euclidean distance of 3.07 pixels (SD = 0.73) for repeated landmark selections. The proposed algorithm shows promise for identity recognition based on cranial patterns and could be enhanced with artificial intelligence (AI) algorithms in future studies.

A spatial–temporal data-driven deep learning framework for enhancing ultra-short-term prediction of distributed photovoltaic power generation

Effective utilization of spatial–temporal information can improve the accuracy of ultra-short-term prediction of power generation from distributed photovoltaic (PV) stations in the region. This paper introduces an ultra-short-term spatial–temporal prediction model for distributed PV power generation, blending data-driven methodology with deep learning technique. The model integrates a self-attention mechanism (SA), a Bi-directional Convolutional Gated Recurrent Unit (BiConvGRU), and an encoder-decoder structure, called ABCGRU. The spatial–temporal attributes of PV power generation can be effectively utilized to accurately predict the output of PV power stations at different locations. Firstly, this paper proposes a 2D distributed PV measurement frame approach considering the spatial–temporal properties of PV power. The combination of Pearson correlation coefficient, the normalized Euclidean distance, the Shape-based distance (SBD) analysis based on cross-correlation and geographic distance reduces the input dimensionality. Secondly, to better capture the spatial–temporal patterns within the 2D distributed PV measurement frame, this paper proposes the ABCGRU model. Finally, the predictive performance of the model is verified through experiments. On the Birmingham dataset, the relative absolute error (RAE) for single-step (15 min) prediction is 0.13, and the average RAE for multi-step (30–60 min) prediction is about four times higher than ConvGRU. The single-step prediction RAE of Little Rock and New Orleans datasets is about 3–4 times higher than ConvGRU. In the comparison between the same series of models, the 4-layer ABCGRU has the highest accuracy. Moreover, the effectiveness of data dimensionality reduction was verified through experimental comparison. The RAE for single-step prediction on the Datong dataset is 0.0048.

Achromatic CMOS-Integrated Four-Bit Orbital Angular Momentum Mode Detector at Three Wavelengths.

The simultaneous detection of the orbital angular momentum (OAM) and wavelength offers new opportunities for optical multiplexing. However, because of the dispersion of lens functions for Fourier transformation, the mode conversions at distinct wavelengths cannot be achieved in the same plane. Here we propose an ultracompact achromatic complementary metal oxide semiconductor (CMOS)-integrated OAM mode detector. Specifically, a spatial multiplexed scheme, randomly interleaving the phase distributions for distributing the superposed OAM modes into preset positions at distinct wavelengths, is presented. In addition, such a nanoprinted achromatic OAM detector featuring a microscale size and a short focal length can be integrated onto a CMOS chip. Consequently, the four-bit incident light beams at three discrete wavelengths (633, 532, and 488 nm) can be distinguished with a high degree of accuracy evaluated by the average standardized Euclidean distance of ∼0.75 between the analytical and target results. Our results showcase a miniaturized platform for achieving high-capacity information processing.

FF3D: A Rapid and Accurate 3D Fruit Detector for Robotic Harvesting.

This study presents the Fast Fruit 3D Detector (FF3D), a novel framework that contains a 3D neural network for fruit detection and an anisotropic Gaussian-based next-best view estimator. The proposed one-stage 3D detector, which utilizes an end-to-end 3D detection network, shows superior accuracy and robustness compared to traditional 2D methods. The core of the FF3D is a 3D object detection network based on a 3D convolutional neural network (3D CNN) followed by an anisotropic Gaussian-based next-best view estimation module. The innovative architecture combines point cloud feature extraction and object detection tasks, achieving accurate real-time fruit localization. The model is trained on a large-scale 3D fruit dataset and contains data collected from an apple orchard. Additionally, the proposed next-best view estimator improves accuracy and lowers the collision risk for grasping. Thorough assessments on the test set and in a simulated environment validate the efficacy of our FF3D. The experimental results show an AP of 76.3%, an AR of 92.3%, and an average Euclidean distance error of less than 6.2 mm, highlighting the framework's potential to overcome challenges in orchard environments.

A novel approach to craniofacial analysis using automated 3D landmarking of the skull

Automatic dense 3D surface registration is a powerful technique for comprehensive 3D shape analysis that has found a successful application in human craniofacial morphology research, particularly within the mandibular and cranial vault regions. However, a notable gap exists when exploring the frontal aspect of the human skull, largely due to the intricate and unique nature of its cranial anatomy. To better examine this region, this study introduces a simplified single-surface craniofacial bone mask comprising of 6707 quasi-landmarks, which can aid in the classification and quantification of variation over human facial bone surfaces. Automatic craniofacial bone phenotyping was conducted on a dataset of 31 skull scans obtained through cone-beam computed tomography (CBCT) imaging. The MeshMonk framework facilitated the non-rigid alignment of the constructed craniofacial bone mask with each individual target mesh. To gauge the accuracy and reliability of this automated process, 20 anatomical facial landmarks were manually placed three times by three independent observers on the same set of images. Intra- and inter-observer error assessments were performed using root mean square (RMS) distances, revealing consistently low scores. Subsequently, the corresponding automatic landmarks were computed and juxtaposed with the manually placed landmarks. The average Euclidean distance between these two landmark sets was 1.5 mm, while centroid sizes exhibited noteworthy similarity. Intraclass coefficients (ICC) demonstrated a high level of concordance (> 0.988), with automatic landmarking showing significantly lower errors and variation. These results underscore the utility of this newly developed single-surface craniofacial bone mask, in conjunction with the MeshMonk framework, as a highly accurate and reliable method for automated phenotyping of the facial region of human skulls from CBCT and CT imagery. This craniofacial template bone mask expansion of the MeshMonk toolbox not only enhances our capacity to study craniofacial bone variation but also holds significant potential for shedding light on the genetic, developmental, and evolutionary underpinnings of the overall human craniofacial structure.

Construction of multi-component finite element model to predict biomechanical behaviour of breasts during running and quantification of the stiffness impact of internal structure

This study aims to investigate the biomechanical behaviour and the stiffness impact of the breast internal components during running. To achieve this, a novel nonlinear multi-component dynamic finite element method (FEM) has been established, which uses experimental data obtained via 4D scanning technology and a motion capture system. The data are used to construct a geometric model that comprises the rigid body, layers of soft tissues, skin, pectoralis major muscle, fat, ligaments and glandular tissues. The traditional point-to-point method has a relative mean absolute error of less than 7.92% while the latest surface-to-surface method has an average Euclidean distance (d) of 7.05 mm, validating the simulated results. After simulating the motion of the different components of the breasts, the displacement analysis confirms that when the motion reaches the moment of largest displacement, the displacement of the breast components is proportional to their distance from the chest wall. A biomechanical analysis indicates that the stress sustained by the breast components in ascending order is the glandular tissues, pectoralis major muscle, adipose tissues, and ligaments. The ligaments provide the primary support during motion, followed by the pectoralis major muscle. In addition, specific stress points of the breast components are identified. The stiffness impact experiment indicates that compared with ligaments, the change of glandular tissue stiffness had a slightly more obvious effect on the breast surface. The findings serve as a valuable reference for the medical field and sports bra industry to enhance breast protection during motion.

Efficient matrix profile computation with Euclidean distance using Eigen transformation: Performance evaluation based on beat-to-beat interval (BBI) data.

The matrix profile serves as a fundamental tool to provide insights into similar patterns within time series. Existing matrix profile algorithms have been primarily developed for the normalized Euclidean distance, which may not be a proper distance measure in many settings. The methodology work of this paper was motivated by statistical analysis of beat-to-beat interval (BBI) data collected from smartwatches to monitor e-cigarette users' heart rate change patterns for which the original Euclidean distance ( -norm) would be a more suitable choice. Yet, incorporating the Euclidean distance into existing matrix profile algorithms turned out to be computationally challenging, especially when the time series is long with extended query sequences. We propose a novel methodology to efficiently compute matrix profile for long time series data based on the Euclidean distance. This methodology involves four key steps including (1) projection of the time series onto eigenspace; (2) enhancing singular value decomposition (SVD) computation; (3) early abandon strategy; and (4) determining lower bounds based on the first left singular vector. Simulation studies based on BBI data from the motivating example have demonstrated remarkable reductions in computational time, ranging from one-fourth to one-twentieth of the time required by the conventional method. Unlike the conventional method of which the performance deteriorates sharply as the time series length or the query sequence length increases, the proposed method consistently performs well across a wide range of the time series length or the query sequence length.

Three-dimensional infrared scanning: an enhanced approach for spatial registration of probes for neuroimaging.

Accurate spatial registration of probes (e.g., optodes and electrodes) for measurement of brain activity is a crucial aspect in many neuroimaging modalities. It may increase measurement precision and enable the transition from channel-based calculations to volumetric representations. This technical note evaluates the efficacy of a commercially available infrared three-dimensional (3D) scanner under actual experimental (or clinical) conditions and provides guidelines for its use. We registered probe positions using an infrared 3D scanner and validated them against magnetic resonance imaging (MRI) scans on five volunteer participants. Our analysis showed that with standard cap fixation, the average Euclidean distance of probe position among subjects could reach up to 43mm, with an average distance of 15.25mm [standard deviation (SD) = 8.0]. By contrast, the average distance between the infrared 3D scanner and the MRI-acquired positions was 5.69mm (SD = 1.73), while the average difference between consecutive infrared 3D scans was 3.43mm (SD = 1.62). The inter-optode distance, which was fixed at 30mm, was measured as 29.28mm (SD = 1.12) on the MRI and 29.43mm (SD = 1.96) on infrared 3D scans. Our results demonstrate the high accuracy and reproducibility of the proposed spatial registration method, making it suitable for both functional near-infrared spectroscopy and electroencephalogram studies. The 3D infrared scanning technique for spatial registration of probes provides economic efficiency, simplicity, practicality, repeatability, and high accuracy, with potential benefits for a range of neuroimaging applications. We provide practical guidance on anonymization, labeling, and post-processing of acquired scans.

The Eye in the Sky—A Method to Obtain On-Field Locations of Australian Rules Football Athletes

The ability to overcome an opposition in team sports is reliant upon an understanding of the tactical behaviour of the opposing team members. Recent research is limited to a performance analysts’ own playing team members, as the required opposing team athletes’ geolocation (GPS) data are unavailable. However, in professional Australian rules Football (AF), animations of athlete GPS data from all teams are commercially available. The purpose of this technical study was to obtain the on-field location of AF athletes from animations of the 2019 Australian Football League season to enable the examination of the tactical behaviour of any team. The pre-trained object detection model YOLOv4 was fine-tuned to detect players, and a custom convolutional neural network was trained to track numbers in the animations. The object detection and the athlete tracking achieved an accuracy of 0.94 and 0.98, respectively. Subsequent scaling and translation coefficients were determined through solving an optimisation problem to transform the pixel coordinate positions of a tracked player number to field-relative Cartesian coordinates. The derived equations achieved an average Euclidean distance from the athletes’ raw GPS data of 2.63 m. The proposed athlete detection and tracking approach is a novel methodology to obtain the on-field positions of AF athletes in the absence of direct measures, which may be used for the analysis of opposition collective team behaviour and in the development of interactive play sketching AF tools.

A trajectory planning method for robotic arms based on improved dynamic motion primitives

PurposeTraditional robot arm trajectory planning methods have problems such as insufficient generalization performance and low adaptability. This paper aims to propose a method to plan the robot arm’s trajectory using the trajectory learning and generalization characteristics of dynamic motion primitives (DMPs).Design/methodology/approachThis study aligns multiple demonstration motion primitives using dynamic time warping; use the Gaussian mixture model and Gaussian mixture regression methods to obtain the ideal primitive trajectory actions. By establishing a system model that improves DMPs, the parameters of the nonlinear function are learned based on the ideal primitive trajectory actions of the robotic arm, and the robotic arm motion trajectory is reproduced and generalized.FindingsExperiments have proven that the robot arm motion trajectory learned by the method proposed in this article can not only learn to generalize and demonstrate the movement trend of the primitive trajectory, but also can better generate ideal motion trajectories and avoid obstacles when there are obstacles. The maximum Euclidean distance between the generated trajectory and the demonstration primitive trajectory is reduced by 29.9%, and the average Euclidean distance is reduced by 54.2%. This illustrates the feasibility of this method for robot arm trajectory planning.Originality/valueIt provides a new method for the trajectory planning of robotic arms in unstructured environments while improving the adaptability and generalization performance of robotic arms in trajectory planning.

Effectiveness of the online-eLearning program KeepCoool at improving the vaccine cold chain in general practices.

Protecting vaccines from freeze damage is a poorly addressed problem. We describe the effectiveness of the eLearning KeepCoool on cold chain maintenance in general practices. For this intervention study, temperatures of vaccine refrigerators were logged at one-minute intervals. Personnel from practices with cold chain breaches was offered the eLearning. The primary outcome was the intervention's effectiveness to achieve temperatures in the target range (2 to 8°C) in the sixth week (follow-up) compared to the first (baseline). Using continuous temperature data, a generalized additive model for location, scale and shape was estimated. The practice response rate was 38% (64 of 168). At baseline, 73% of the practices and 68% of the refrigerators (51 of 75) showed cold chain breaches. 47% of the practices (n = 22 with 24 refrigerators) participated in the eLearning (55 physicians and practice assistants). At follow-up, 17% of those refrigerators were in the target range continuously, 38% reached targets 95% of the time while always >0°C, and temperatures ≤0°C decreased by 63%. Based on 2 million temperature data, the average Euclidian distance based on regression showed a significant improvement (p<0.05). The eLearning KeepCoool improved the practices´ vaccine cold chain. It is freely available at https://keepcoool.ukbonn.de.

A correction method for mitigating absorbance discrepancies between near-infrared spectrometers through the incorporation of blended carbon-titanium dioxide powder

In near-infrared spectroscopy analysis, ensuring the accurate transfer of models between different instruments relies on maintaining the accuracy of instrument wavelengths and absorbance. To mitigate absorbance drift at different wavelength points, this paper proposes a near-infrared spectroscopy point-by-point quadratic polynomial correction method based on carbon-titanium dioxide powder samples. The method establishes a quadratic polynomial relationship model for the absorbance of each wavelength point between the main instrument and slave instruments. The study utilized two S450 grating-based diffuse reflection near-infrared spectroscopy instruments, with one serving as the main instrument and the other as the slave instrument. The point-by-point quadratic polynomial was employed to correct wheat spectra collected by the slave instruments, and a crude protein content prediction model for wheat was established, comparing it with linear regression correction. After correction, the average Euclidean distance of wheat spectra decreased by 66.71%, from 0.0937 to 0.0321, and the average peak-valley Euclidean distance decreased by 72.28%, from 0.0203 to 0.0056. The standard deviation of the predicted results decreased by 90.69%, from 1.4372 to 0.1338. The correction effect of the method combined with traditional preprocessing methods was superior to using preprocessing methods alone. Overall, the near-infrared spectroscopy point-by-point quadratic polynomial correction method based on carbon-titanium dioxide powder samples significantly reduces spectral differences between different instruments, enhances spectral consistency, and diminishes prediction errors, achieving improved model sharing between instruments.

Monte Carlo simulation study of an in vivo four-dimensional tracking system with a diverging collimator for monitoring radiation source (Ir-192) location during brachytherapy: proof of concept and feasibility.

Introduction: The aim of this study was to demonstrate the potential of an in vivo four-dimensional (4D) tracking system to accurately localize the radiation source, Iridium-192 (Ir-192) in high-dose rate brachytherapy. Methods: To achieve time-dependent 3D positioning of the Ir-192 source, we devised a 4D tracking system employing multiple compact detectors. During the system's design phase, we conducted comprehensive optimization and analytical evaluations of the diverging collimator employed for detection purposes. Subsequently, we executed 3D reconstruction and positioning procedures based on the 2D images obtained by six detectors, each equipped with an optimized diverging collimator. All simulations for designing and evaluating the 4D tracking system were performed using the open-source GATE (v9.1) Monte Carlo platform based on the GEANT4 (v10.7) toolkit. In addition, to evaluate the accuracy of the proposed 4D tracking system, we conducted simulations and 3D positioning using a solid phantom and patient data. Finally, the error between the reconstructed position coordinates determined by the tracking system and the original coordinates of the Ir-192 radiation source was analyzed. Results: The parameters for the optimized diverging collimator were a septal thickness of 0.3mm and a collimator height of 30mm. A tracking system comprising 6 compact detectors was designed and implemented utilizing this collimator. Analysis of the accuracy of the proposed Ir-192 source tracking system found that the average of the absolute values of the error between the 3D reconstructed and original positions for the simulation with the solid phantom were 0.440mm for the x coordinate, 0.423mm for the y coordinate, and 0.764mm for the z coordinate, and the average Euclidean distance was 1.146mm. Finally, in a simulation based on data from a patient who underwent brachytherapy, the average Euclidean distance between the original and reconstructed source position was 0.586mm. Discussion: These results indicated that the newly designed in vivo 4D tracking system for monitoring the Ir-192 source during brachytherapy could determine the 3D position of the radiation source in real time during treatment. We conclude that the proposed positioning system has the potential to make brachytherapy more accurate and reliable.

Autoencoder‐based method to assess bridge health monitoring data quality

AbstractThe data quality determines the reliability of big data‐based bridge condition assessments. However, rapidly discerning data conditions and identifying low‐quality data segments pose considerable challenges. This study introduces a transformer‐based autoencoder neural network for rapid data quality assessment in bridge health monitoring. The average Euclidean distance was used to quantify the dispersion of multiple hidden variables, and the overall quality of the multiple data fragments was quantitatively evaluated. Moreover, a method was introduced to calculate the adaptive thresholds based on the Euclidean distance. The application to the acceleration data of a cable‐stayed bridge demonstrates that the proposed method can extract hidden variables from acceleration data segments of length 3000. The network achieves a high compression rate of 1/1500, and the extracted hidden variables retain pertinent information regarding data quality. The proposed approach is data‐driven and exhibits significant advantages in efficiency, accuracy, and user‐friendliness.

Abstract 4980: Genomic and clinical characteristics of advanced pancreatobiliary cancer, stratified by KRAS mutation status

Abstract Mutations in KRAS are common in pancreatobiliary cancer, occurring in 90% of advanced pancreatic ductal adenocarcinomas (PDAC) and 20% of advanced cholangiocarcinomas (CCA). We analyzed clinico-genomic databases to evaluate the hypothesis that KRAS mutation status defines subsets of PDAC and CCA with similar genomic characteristics and clinical behavior. Using the genomic data of PDAC and CCA from AACR Project GENIE Cohort version 14.0-public, we analyzed the genomic landscape of four cohorts of pancreatobiliary cancer: KRAS mutated PDAC, KRAS wild-type PDAC, KRAS mutated CCA, and KRAS wild-type CCA. Excluding KRAS mutations, there were 53 gene alterations observed in &amp;gt;2.5% of cases in at least one of the four cohorts. The three most common gene alterations in each cohort were; TP53, SMAD4, and CDKN2A mutations (KRAS mutated PDAC), TP53, ARID1A, and SMAD4 mutations (KRAS wild-type PDAC), TP53 mutation, CDKN2A deletion and SMAD4 mutation (KRAS mutated CCA), and TP53, ARID1D and IDH1 mutations. (KRAS wild-type CCA) The similarity of gene alteration patterns was evaluated by principal component analysis (PCA) 3D plot and calculation of average Euclidean distance. The PCA plot suggested heterogeneous genomic profiles in KRAS wild-type CCA in contrast to relatively monotonous profiles in KRAS mutated PDAC. The average Euclidean distance between KRAS mutated CCA and KRAS wild-type CCA was significantly longer than that between KRAS mutated CCA and KRAS mutated PDAC (2.00 vs. 1.89, p &amp;lt; 0.001), suggesting that KRAS mutated CCA is more genomically related to KRAS mutated PDAC than KRAS wild-type CCA. The average Euclidean distance between KRAS mutated PDAC and KRAS wild-type PDAC was significantly shorter than that between KRAS wild-type CCA and KRAS wild-type PDAC. To compare the clinical characteristics of the four cohorts, we evaluated the data of patients with stage IV pancreatobiliary cancer, using cBioPortal. The number of cases in each cohort was 1544 (KRAS mutated PDAC), 121 (KRAS wild-type PDAC), 91 (KRAS mutated CCA), and 459 (KRAS wild-type CCA). Median overall survival (OS) was 14.7, 21.5, 10.7, and 15.4 months, respectively. Compared to KRAS wild-type CCA, KRAS mutated CCA had a significantly shorter OS (hazard ratio [HR] = 1.45, 95% confidence interval [CI] = 1.12 - 1.89). The OS of KRAS mutated CCA was shorter than KRAS mutated PDAC (HR 1.32, 95% CI 1.03 - 1.68). KRAS wild-type PDAC had a significantly longer OS compared to KRAS mutated PDAC. (HR 0.72, 95% CI 0.57 - 0.92) In conclusion, KRAS mutated CCA has a poor OS and a genomic landscape distinct from KRAS wild-type CCA; it is genomically more similar to KRAS mutated PDAC. KRAS wild-type PDAC has a better OS than KRAS mutated PDAC, though it was not genomically similar to KRAS wild-type CCA. Therapeutic approaches stratified by KRAS mutation status should be considered for pancreaticobiliary cancers. Citation Format: Hirotaka Miyashita, Parth S. Shah, Gabriel A. Brooks. Genomic and clinical characteristics of advanced pancreatobiliary cancer, stratified by KRAS mutation status [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4980.