Automated Research Articles

Deep learning based highly accurate transplanted bioengineered corneal equivalent thickness measurement using optical coherence tomography

Corneal transplantation is the primary treatment for irreversible corneal diseases, but due to limited donor availability, bioengineered corneal equivalents are being developed as a solution, with biocompatibility, structural integrity, and physical function considered key factors. Since conventional evaluation methods may not fully capture the complex properties of the cornea, there is a need for advanced imaging and assessment techniques. In this study, we proposed a deep learning-based automatic segmentation method for transplanted bioengineered corneal equivalents using optical coherence tomography to achieve a highly accurate evaluation of graft integrity and biocompatibility. Our method provides quantitative individual thickness values, detailed maps, and volume measurements of the bioengineered corneal equivalents, and has been validated through 14 days of monitoring. Based on the results, it is expected to have high clinical utility as a quantitative assessment method for human keratoplasties, including automatic opacity area segmentation and implanted graft part extraction, beyond animal studies.

Computer vision applications for the detection or analysis of tuberculosis using digitised human lung tissue images - a systematic review

ObjectiveTo conduct a systematic review of the computer vision applications that detect, diagnose, or analyse tuberculosis (TB) pathology or bacilli using digitised human lung tissue images either through automatic or semi-automatic methods. We categorised the computer vision platform into four technologies: image processing, object/pattern recognition, computer graphics, and deep learning. In this paper, the focus is on image processing and deep learning (DL) applications for either 2D or 3D digitised human lung tissue images. This review is useful for establishing a common practice in TB analysis using human lung tissue as well as identifying opportunities for further research in this space. The review brings attention to the state-of-art techniques for detecting TB, with emphasis on the challenges and limitations of the current techniques. The ultimate goal is to promote the development of more efficient and accurate algorithms for the detection or analysis of TB, and raise awareness about the importance of early detection.DesignWe searched five databases and Google Scholar for articles published between January 2017 and December 2022 that focus on Mycobacterium tuberculosis detection, or tuberculosis pathology using digitised human lung tissue images. Details regarding design, image processing and computer-aided techniques, deep learning models, and datasets were collected and summarised. Discussions, analysis, and comparisons of state-of-the-art methods are provided to help guide future research. Further, a brief update on the relevant techniques and their performance is provided.ResultsSeveral studies have been conducted to develop automated and AI-assisted methods for diagnosing Mtb and TB pathology from digitised human lung tissue images. Some studies presented a completely automated method of diagnosis, while other studies developed AI-assisted diagnostic methods. Low-level focus areas included the development of a novel μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT scanner for soft tissue image contract, and use of multiresolution computed tomography to analyse the 3D structure of the human lung. High-level focus areas included the investigation the effects of aging on the number and size of small airways in the lungs using CT and whole lung high-resolution μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT, and the 3D microanatomy characterisation of human tuberculosis lung using μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT in conjunction with histology and immunohistochemistry. Additionally, a novel method for acquiring high-resolution 3D images of human lung structure and topology is also presented.ConclusionThe literature indicates that post 1950s, TB was predominantly studied using animal models even though no animal model reflects the full spectrum of human pulmonary TB disease and does not reproducibly transmit Mtb infection to other animals (Hunter, 2011). This explains why there are very few studies that used human lung tissue for detection or analysis of Mtb. Nonetheless, we found 10 studies that used human tissues (predominately lung) of which five studies proposed machine learning (ML) models for the detection of bacilli and the other five used CT on human lung tissue scanned ex-vivo.

Automated assessment of non-technical skills by heart-rate data.

Observer-based scoring systems, or automatic methods, based on features or kinematic data analysis, are used to perform surgical skill assessments. These methods have several limitations, observer-based ones are subjective, and the automatic ones mainly focus on technical skills or use data strongly related to technical skills to assess non-technical skills. In this study, we are exploring the use of heart-rate data, a non-technical-related data, to predict values of an observer-based scoring system thanks to random forest regressors. Heart-rate data from 35 junior resident orthopedic surgeons were collected during the evaluation of a meniscectomy performed on a bench-top simulator. Each participant has been evaluated by two assessors using the Arthroscopic Surgical Skill Evaluation Tool (ASSET) score. A preprocessing stage on heart-rate data, composed of threshold filtering and a detrending method, was considered before extracting 41 features. Then a random forest regressor has been optimized thanks to a randomized search cross-validation strategy to predict each score component. The prediction of the partially non-technical-related components presents promising results, with the best result obtained for the safety component with a mean absolute error of 0.24, which represents a mean absolute percentage error of 5.76%. The analysis of feature important allowed us to determine which features are the more related to each ASSET component, and therefore determine the underlying impact of the sympathetic and parasympathetic nervous systems. In this preliminary work, a random forest regressor train on feature extract from heart-rate data could be used for automatic skill assessment and more especially for the partially non-technical-related components. Combined with more traditional data, such as kinematic data, it could help to perform accurate automatic skill assessment.

Automatic Landmark Detection for Preoperative Planning of High Tibial Osteotomy Using Traditional Feature Extraction and Deep Learning Methods.

Automatic High Tibial Osteotomy (HTO) landmark detection methods promise to improve the effectiveness and standardisation of HTO preoperative planning. Unfortunately, due to the limited number of HTO datasets, existing methods are less robust when dealing with patients with varied deformities than traditional manual planning, severely limiting their clinical viability and application in practical surgical settings. Here, we present a new HTO landmark detection framework using an integration of optimised heatmap-offset aggregation method and traditional feature extraction. Subjective and objective approaches were employed to reflect the final clinical acceptance of our model. Average Mean Absolute Error of prediction results compared to the surgeon's gold standard was 0.35° for the hip-knee-ankle angle. The objective score rated by surgeons reached 4.4 on a scale of 5. The study demonstrated that the automatic detection method has great potential serving as an alternative to manual radiological analysis in practical surgical pre-operative planning.

Semiautomatic volumetry of the temporal lobes of the brain and correlation with electroencephalography results in dogs with assumed idiopathic epilepsy.

Lesions causing refractory epilepsy, often associated with temporal lobe epilepsy (TLE), can be undetectable on standard magnetic resonance imaging (MRI) in dogs. Automated brain volumetry, widely used in human medicine, can now be applied in veterinary medicine because of the availability of brain atlases. This study aimed to develop an automatic volumetry method, translate the outcomes into the assessment of temporal lobe volumes in dogs with idiopathic epilepsy, and correlate the results with the electroencephalography (EEG) data of epileptiform discharges (EDs). Thirty-one dogs of various breeds with dominant temporal lobe discharge. Retrospective, observational study. The MRI and EEG examination results of dogs referred for neurological diagnosis data between 2016 and 2021 were retrospectively analyzed. An automated volumetry method was developed, which allowed the evaluation of temporal lobe volumes of the dogs. The asymmetric ratio (AR) was then estimated, and the results were correlated with the EEG EDs. 12/31 (38%; 95% CI: 21.8%-57.8%) dogs had an asymmetric ratio >6%. Among them, reduction in temporal lobe volumes correlated with the side of the EEG EDs in 7 cases. There was no statistical correlation between temporal lobe volume changes and ED location. Preliminary volumetric analysis of the temporal lobes indicates the presence of volume differences between the lobes in some dogs with idiopathic epilepsy. Diagnosis of TLE in dogs based on MRI volumetry in correlation with EEG examination, especially for dogs with drug-resistant epilepsy, can influence the development of new therapeutic options, such as surgery.

Automatic Detection of Trees using Airborne LiDAR Data Based on Geometric Characteristics

Abstract. One of the essential factors in analyzing urban environments is the presence of trees. Thus, the development of automatic or semi-automatic tree detection strategies is important for monitoring and providing data for municipal authorities’ planning efforts. In this context, we propose an automatic method for detecting trees using LiDAR data collected by airborne platforms. The proposed strategy uses the omnivariance as a key attribute, which is estimated locally from eigenvalues. Additionally, it utilizes an adaptive process to determine the optimal radius, followed by successive filtering based on the majority filter and mathematical morphology operators. The effectiveness of the proposed approach was evaluated on six study areas from two distinct datasets (Presidente Prudente/Brazil and Palmerston/New Zealand). In general, the results indicate a completeness rate around 99% and a correctness rate around 91%, resulting in an average Fscore of 95%. These findings suggest that the proposed approach has potential to detect trees in urban regions using airborne LiDAR data. Compared to related works, the proposed strategy tends to have a better result in terms of completeness.

Gambaran Jumlah Leukosit pada Penderita Demam Berdarah Dengue di Klinik DR.Trisna Garut

Dengue hemorrhagic fever (DHF) is a public health problem in Indonesia whose number of cases continues to increase and its spread is increasingly widespread.Tests used to diagnose dengue fever include a white blood cell count test.White blood cells are cells that play a role in the body's defense system. The reason is, white blood cells help fight infections in the body, such as viruses, bacteria, parasites and fungi. In patients infected with dengue fever, the white blood cell count is usually normal but then decreases (leukopenia) and neutropenia becomes dominant. Thisresearch was conducted from March to April 2023.The aim of this study was to measure the number of white blood cells in dengue fever patients.This type of research is descriptive. The research was carried out using an automatic analysis method (blood analysis) in the laboratory ofKlinik Dr.Trisna Garut, using samples from 30 dengue fever patients who were tested. In this study, the number of white blood cells in dengue fever patients at Klinik Dr.Trisna Garut was 14 (46.7%).Wich is Normal And the white blood cell count results of dengue fever patients at Klinik Dr.Tisna Garut General Hospital decreased by 16 (54.3%).

Development of a method for estimating asari clam distribution by combining three-dimensional acoustic coring system and deep neural network

Developing non-contact, non-destructive monitoring methods for marine life is crucial for sustainable resource management. Recent monitoring technologies and machine learning analysis advancements have enhanced underwater image and acoustic data acquisition. Systems to obtain 3D acoustic data from beneath the seafloor are being developed; however, manual analysis of large 3D datasets is challenging. Therefore, an automatic method for analyzing benthic resource distribution is needed. This study developed a system to estimate benthic resource distribution non-destructively by combining high-precision habitat data acquisition using high-frequency ultrasonic waves and prediction models based on a 3D convolutional neural network (3D-CNN). The system estimated the distribution of asari clams (Ruditapes philippinarum) in Lake Hamana, Japan. Clam presence and count were successfully estimated in a voxel with an ROC-AUC of 0.9 and a macro-average ROC-AUC of 0.8, respectively. This system visualized clam distribution and estimated numbers, demonstrating its effectiveness for quantifying marine resources beneath the seafloor.

A study of automatic output power control methods for stand-alone photovoltaic power generation systems

Abstract The conventional power generation system’s output power automatic control structure is generally unidirectional, and the control coverage is small, leading to the increase in the random error obtained in the final test. Therefore, the design and research of this article are proposed. According to the current control demand, the generation system shall be stabilized first, and the power compensation shall be calculated. The multi-step mode shall be adopted to improve the control coverage and design the multi-step automatic control structure. According to this, it is constructed, and the automatic control processing is realized using jump balance. The test results show that compared with the traditional DFIG-DRWT system power output automatic control method and the traditional three-phase asynchronous generation system power output automatic control method, the random error finally obtained by the designed independent photovoltaic power station output power automatic control method is relatively effective. This indicates that the application effect and coverage of the designed power automatic control method are high, that controllability has been significantly improved, and that control flexibility has also been strengthened.

Ligand Identification in CryoEM and X-ray Maps Using Deep Learning.

Accurately identifying ligands plays a crucial role in the process of structure-guided drug design. Based on density maps from X-ray diffraction or cryogenic-sample electron microscopy (cryoEM), scientists verify whether small-molecule ligands bind to active sites of interest. However, the interpretation of density maps is challenging, and cognitive bias can sometimes mislead investigators into modeling fictitious compounds. Ligand identification can be aided by automatic methods, but existing approaches are available only for X-ray diffraction and are based on iterative fitting or feature-engineered machine learning rather than end-to-end deep learning. Here, we propose to identify ligands using a deep learning approach that treats density maps as 3D point clouds. We show that the proposed model is on par with existing machine learning methods for X-ray crystallography while also being applicable to cryoEM density maps. Our study demonstrates that electron density map fragments can aid the training of models that can later be applied to cryoEM structures but also highlights challenges associated with the standardization of electron microscopy maps and the quality assessment of cryoEM ligands.

Breast cancer survival prediction using an automated mitosis detection pipeline.

Mitotic count (MC) is the most common measure to assess tumor proliferation in breast cancer patients and is highly predictive of patient outcomes. It is, however, subject to inter- and intraobserver variation and reproducibility challenges that may hamper its clinical utility. In past studies, artificial intelligence (AI)-supported MC has been shown to correlate well with traditional MC on glass slides. Considering the potential of AI to improve reproducibility of MC between pathologists, we undertook the next validation step by evaluating the prognostic value of a fully automatic method to detect and count mitoses on whole slide images using a deep learning model. The model was developed in the context of the Mitosis Domain Generalization Challenge 2021 (MIDOG21) grand challenge and was expanded by a novel automatic area selector method to find the optimal mitotic hotspot and calculate the MC per 2 mm2. We employed this method on a breast cancer cohort with long-term follow-up from the University Medical Centre Utrecht (N = 912) and compared predictive values for overall survival of AI-based MC and light-microscopic MC, previously assessed during routine diagnostics. The MIDOG21 model was prognostically comparable to the original MC from the pathology report in uni- and multivariate survival analysis. In conclusion, a fully automated MC AI algorithm was validated in a large cohort of breast cancer with regard to retained prognostic value compared with traditional light-microscopic MC.

Segmentation of Infarct Lesions and Prognosis Prediction for Acute Ischemic Stroke using Non-Contrast CT Scans

Background and PurposeIschemic stroke is the most common type of stroke and the second leading cause of global mortality. Prompt and accurate diagnosis is crucial for effective treatment. Non-contrast CT (NCCT) scans are commonly employed as the first-line imaging modality to identify the infarct lesion and affected brain areas, as well as to make prognostic predictions to guide the subsequent treatment planning. However, visual evaluation of infarct lesions in NCCT scans can be subjective and inconsistent due to reliance on expert experience. MethodsIn this study, we propose an automatic method using VB-Net with dual-channel inputs to segment acute infarct lesions (AIL) on NCCT scans and extract affected ASPECTS (Alberta Stroke Program Early CT Score) regions. Secondly, we establish a prediction model to distinguish reperfused patients from non-reperfused patients after treatment, based on multi-dimensional radiological features of baseline NCCT and stroke onset time. Thirdly, we create a prediction model estimating the infarct volume after a period of time, by combining NCCT infarct volume, radiological features, and surgical decision. ResultsThe median Dice coefficient of the AIL segmentation network is 0.76. Based on this, the patient triage model has an AUC of 0.837 (95% confidence interval [CI]: 0.734-0.941), sensitivity of 0.833 (95% CI: 0.626-0.953). The predicted follow-up infarct volume correlates strongly with the DWI ground truth, with a Pearson correlation coefficient of 0.931. ConclusionsOur proposed pipeline offers qualitative and quantitative assessment of infarct lesions based on NCCT scans, facilitating physicians in patient triage and prognosis prediction.

Amazonian manatee critical habitat revealed by artificial intelligence‐based passive acoustic techniques

AbstractFor many species at risk, monitoring challenges related to low visual detectability and elusive behavior limit the use of traditional visual surveys to collect critical information, hindering the development of sound conservation strategies. Passive acoustics can cost‐effectively acquire terrestrial and underwater long‐term data. However, to extract valuable information from large datasets, automatic methods need to be developed, tested and applied. Combining passive acoustics with deep learning models, we developed a method to monitor the secretive Amazonian manatee over two consecutive flooded seasons in the Brazilian Amazon floodplains. Subsequently, we investigated the vocal behavior parameters based on vocalization frequencies and temporal characteristics in the context of habitat use. A Convolutional Neural Network model successfully detected Amazonian manatee vocalizations with a 0.98 average precision on training data. Similar classification performance in terms of precision (range: 0.83–1.00) and recall (range: 0.97–1.00) was achieved for each year. Using this model, we evaluated manatee acoustic presence over a total of 226 days comprising recording periods in 2021 and 2022. Manatee vocalizations were consistently detected during both years, reaching 94% daily temporal occurrence in 2021, and up to 11 h a day with detections during peak presence. Manatee calls were characterized by a high emphasized frequency and high repetition rate, being mostly produced in rapid sequences. This vocal behavior strongly indicates an exchange between females and their calves. Combining passive acoustic monitoring with deep learning models, and extending temporal monitoring and increasing species detectability, we demonstrated that the approach can be used to identify manatee core habitats according to seasonality. The combined method represents a reliable, cost‐effective, scalable ecological monitoring technique that can be integrated into long‐term, standardized survey protocols of aquatic species. It can considerably benefit the monitoring of inaccessible regions, such as the Amazonian freshwater systems, which are facing immediate threats from increased hydropower construction.

Research on UK Unemployment Rate Forecast Based on ARIMA Model

The objective of this study is to predict the unemployment rate in the UK and the Eurozone for the years 2014 to 2024 using the time series analysis model known as ARIMA. It is essential to be informed of changes to employment rates in order to address them and changes in the economic environment including historical occurrences such as Brexit, COVID-19, and shifts in the labor market. The ARIMA model was used because it fits non-stationary time series, which is a feature present in the employment rates of both time series. The monthly unemployment rates for each state were seasonally adjusted and the ARIMA parameters were estimated by the automatic method. The study observed that while the interior portion of the trading cycle was effectively predicted by the ARIMA model, it failed to do so for the short-term UK unemployment in the post-2023 period because of exogenous shocks. However, the model was equally successful in predicting a marginal contraction in unemployment rates in the Euro zone after 2023. By performing the Ljung-Box test, it was established that the residuals of the models were random and therefore, the models were fit. Accordingly, the results suggest that the estimates derived from the ARIMA model are beneficial in economic predictions, which helps governments to design better social security programs and other economic plans to address the changing employment landscape.

An automatic hybrid mesh generation method for combustion fluid dynamics simulations

PurposeAutomatic mesh generation is still a challenge problem for combustion fluid dynamics simulations because of the high-quality requirement and complexity of geometries. This paper aims to find an efficient automatic analysis model creation and mesh generation method to save the time for pre-processing in numerical simulations.Design/methodology/approachBased on the previous work, we explore effective model processing and mesh generation methods from practical engineering applications. Considering the automation and high quality requirement, we construct an automatic mesh generation procedure for combustion fluid dynamics problems.FindingsThe numerical results show that the procedure we proposed in this paper can automatically generate high quality mesh for combustion fluid dynamics simulations. The strategy of fluid model construction is time-saving and with high-precision. The mesh generation method in our procedure is automatic and efficient.Practical implicationsThe procedure proposed in this paper is applicable to the practical engineering application model, such as aircraft simulation, aeroengine simulation and so on. The procedure has been integrated into an numerical simulation software.Originality/valueThe method proposed in this paper has very practical application value. It can be used in the practical application and saves a lot of manual processing time for numerical stimulation specialists.

Automatic Method for Detecting Deformation Cracks in Landslides Based on Multidimensional Information Fusion

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.

Ancient Yi Script Handwriting Sample Repository

The ancient Yi script has been used for over 8000 years, which can be ranked with Oracle,Sumerian,Egyptian,Mayan and Harappan,and is one of the six ancient scripts in the world. In this article, we collected 2922 handwritten single word samples of commonly used ancient Yi characters. Each character was written by 310 people respectively, with a total of 427,939 valid characters. We completed continuous handwritten text sampling, written by 250 people, with 5 texts per person, covering topics such as Yi astronomy, geography, rituals, and agriculture. In the process of data collection, we proposed an automatic sampling method for ancient Yi script, and completed the automatic cutting and labeling of handwritten samples. Furthermore, we tested the recognition performance of the sorted data set under different deep learning network models. The results show that ancient Yi script has diverse shape structures and rich writing styles, which can be used as a benchmark data set in related fields such as handwritten text recognition and handwritten text generation.

Single Vesicle Surface Protein Profiling and Machine Learning-Based Dual Image Analysis for Breast Cancer Detection

Single-vesicle molecular profiling of cancer-associated extracellular vesicles (EVs) is increasingly being recognized as a powerful tool for cancer detection and monitoring. Mask and target dual imaging is a facile method to quantify the fraction of the molecularly targeted population of EVs in biofluids at the single-vesicle level. However, accurate and efficient dual imaging vesicle analysis has been challenging due to the interference of false signals on the mask images and the need to analyze a large number of images in clinical samples. In this work, we report a fully automatic dual imaging analysis method based on machine learning and use it with dual imaging single-vesicle technology (DISVT) to detect breast cancer at different stages. The convolutional neural network Resnet34 was used along with transfer learning to produce a suitable machine learning model that could accurately identify areas of interest in experimental data. A combination of experimental and synthetic data were used to train the model. Using DISVT and our machine learning-assisted image analysis platform, we determined the fractions of EpCAM-positive EVs and CD24-positive EVs over captured plasma EVs with CD81 marker in the blood plasma of pilot HER2-positive breast cancer patients and compared to those from healthy donors. The amount of both EpCAM-positive and CD24-positive EVs was found negligible for both healthy donors and Stage I patients. The amount of EpCAM-positive EVs (also CD81-positive) increased from 18% to 29% as the cancer progressed from Stage II to III. No significant increase was found with further progression to Stage IV. A similar trend was found for the CD24-positive EVs. Statistical analysis showed that both EpCAM and CD24 markers can detect HER2-positive breast cancer at Stages II, III, or IV. They can also differentiate individual cancer stages except those between Stage III and Stage IV. Due to the simplicity, high sensitivity, and high efficiency, the DISVT with the AI-assisted dual imaging analysis can be widely used for both basic research and clinical applications to quantitatively characterize molecularly targeted EV subtypes in biofluids.

Evaluating the performance of automatic wave speed estimators of natural shear waves to assess myocardial stiffness

Abstract Introduction Cardiac Shear Wave Elastography is an emerging non-invasive technique using ultrafast echocardiographic imaging. Natural shear waves (SW) occur after mitral valve closure (MVC) and aortic valve closure (AVC), and their propagation velocities are directly related to myocardial stiffness. However, the current assessment of SW velocities lacks a gold standard method, and relies on manual measurements with their inherent variability. Purpose To explore new automated methods for shear wave velocity estimates, aiming to achieve greater accuracy, less variability, and high feasibility. Methods To visualize shear waves, anatomical M-modes were drawn (4±1 cm) along ventricular septum in parasternal long axis views (PLAX), acquired with high-frame-rate echocardiography (1055±159 Hz). M-modes were colour coded for tissue acceleration. SWs appear as tilted green bands after MVC & AVC, the slope of which represents the SW propagation velocity. One hundred-twenty different slopes from healthy volunteers (n=67) and patients (n=53) were measured manually and with 9 different automatic methods. All automatic methods were initiated by indicating the approximate position of the shear wave in the M-mode with a mouse click of the user. Measurements were repeated three times to assess measurement reproducibility. (figure 1, A). In order to determine the influence of display settings, three different acceleration colour scales were tested (0.5, 1, 2 m/s²). (Figure 1, B). Failure of correctly identifying shear waves was counted to assess feasibility. Due to the lack of a gold standard, manual measurements of an experienced reader served as reference. Results Three of nine automatic methods: Radon Transformation (RT), Regression Segmentation Maximum value (RM), Regression Segmentation Weighted Maximum (RMW), revealed the least variability with repeated measurements (0.0±0.0 m/s for all) and colour scales (0.0±0.0, 0.1±0.2 and 0.1±0.2 m/s, resp.). Those three methods showed moderate to good agreement (r=0.66, ICC 0.77 [CI 0.64- 0.85]; r= 0.67, ICC 0.75 [CI 0.50 -0.86]; and r= 0.77, ICC 0.85 [CI 0.73-0.90], resp.) with manual measurements (r=0.96, ICC 0.98 [CI 0.97- 0.99] (figure 2). Conclusion RT, RM, and RMW showed a good reproducibility while RMW showed the best agreement with manual measurements. Our data suggest, that the RMW method could offer an objective alternative for SW velocity estimations. Further research is needed to explore their applicability in clinical scenarios.

Automatic Scheduling Method for Customs Inspection Vehicle Relocation Based on Automotive Electronic Identification and Biometric Recognition

This study presents an innovative automatic scheduling method for the relocation of customs inspection vehicles, leveraging Vehicle Electronic Identification (EVI) and biometric recognition technologies. With the expansion of global trade, customs authorities face increasing pressure to enhance logistics efficiency. Traditional vehicle scheduling often relies on manual processes and simplistic algorithms, resulting in prolonged waiting times and inefficient resource allocation. This research addresses these challenges by integrating EVI and biometric systems into a comprehensive framework aimed at improving vehicle scheduling. The proposed method utilizes genetic algorithms and intelligent optimization techniques to dynamically allocate resources and prioritize vehicle movements based on real-time data. EVI technology facilitates rapid identification of vehicles entering customs facilities, while biometric recognition ensures that only authorized personnel can operate specific vehicles. This dual-layered approach enhances security and streamlines the inspection process, significantly reducing delays. A thorough analysis of the existing literature on customs vehicle scheduling identifies key limitations in current methodologies. The automatic scheduling algorithm is detailed, encompassing vehicle prioritization criteria, dynamic path planning, and real-time driver assignment. The genetic algorithm framework allows for adaptive responses to varying operational conditions. Extensive simulations using real-world data from customs operations validate the effectiveness of the proposed method. Results indicate a significant reduction in vehicle waiting times—up to 30%—and an increase in resource utilization rates by approximately 25%. These findings demonstrate the potential of integrating EVI and biometric technologies to transform customs logistics management. Additionally, a comparison against state-of-the-art scheduling algorithms, such as NSGA-II and MOEA/D, reveals superior efficiency and adaptability. This research not only addresses pressing challenges faced by customs authorities but also contributes to optimizing logistics operations more broadly. In conclusion, the automatic scheduling method presented represents a significant advancement in customs logistics, providing a robust solution for managing complex vehicle scheduling scenarios. Future research directions will focus on refining the algorithm to handle peak traffic periods and exploring predictive analytics for enhanced scheduling optimization. Advancements in the intersection of technology and logistics aim to support more efficient and secure customs operations globally.