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This paper presents a single-phase and three-phase compatible isolated on-board charger for electric vehicles. The charger is compatible with the single-phase and three-phase AC voltage input and is composed of two-stage circuits. Based on the three-phase six-switch power factor correction (PFC) circuit, by switching on and off, the front stage is transformed into a cascade topology of a totem-pole PFC circuit and boost circuit. The rear stage is a full-bridge LLC resonant converter. In the single-phase or three-phase working mode, the front-stage control strategy is a double closed-loop control. The current inner loop controller is improved for the three-phase six-switch PFC circuit. The PI controller and repetitive controller are connected in parallel to form the current inner loop compound controller, which can effectively reduce the total harmonic distortion of the input current. By using simulation software MATLAB/Simulink, the circuit model was built. The simulation results verify the correctness of the theoretical analysis.

Background Colonic polyps can be detected and resected during a colonoscopy before cancer development. However, about 1/4th of the polyps could be missed due to their small size, location or human errors. An artificial intelligence (AI) system can improve polyp detection and reduce colorectal cancer incidence. We are developing an indigenous AI system to detect diminutive polyps in real-life scenarios that can be compatible with any high-definition colonoscopy and endoscopic video– capture software. Methods We trained a masked region-based convolutional neural network model to detect and localize colonic polyps. Three independent datasets of colonoscopy videos comprising 1,039 image frames were used and divided into a training dataset of 688 frames and a testing dataset of 351 frames. Of 1,039 image frames, 231 were from real-life colonoscopy videos from our centre. The rest were from publicly available image frames already modified to be directly utilizable for developing the AI system. The image frames of the testing dataset were also augmented by rotating and zooming the images to replicate real-life distortions of images seen during colonoscopy. The AI system was trained to localize the polyp by creating a ‘bounding box’. It was then applied to the testing dataset to test its accuracy in detecting polyps automatically. Results The AI system achieved a mean average precision (equivalent to specificity) of 88.63% for automatic polyp detection. All polyps in the testing were identified by AI, i.e., no false-negative result in the testing dataset (sensitivity of 100%). The mean polyp size in the study was 5 (± 4) mm. The mean processing time per image frame was 96.4 minutes. Conclusions This AI system, when applied to real-life colonoscopy images, having wide variations in bowel preparation and small polyp size, can detect colonic polyps with a high degree of accuracy.

License \"incompatibility\" in free and open source software licensing means that, when two differently licensed pieces of software are combined, one cannot comply with both licenses at the same time. It is commonly accepted that the GNU General Public License version 2 is incompatible with the Apache License, version 2 because certain provisions of the Apache License would be considered \"further restrictions \" not permitted by the GPLv2. However, this article will explain why there is no legally cognizable claim for combining the two, either under a copyright infringement theory or a breach of contract theory.

Ensuring the safety, security, robustness, and fault tolerance of advanced robotic systems is essential for various fields, including healthcare, industry, and space. To address these issues, it is necessary to use software testing techniques and standards, similar to those applied in other safety-critical applications. The Robot Operating System (ROS) is a popular choice for developing robotic systems, so it is important to have specialized testing libraries and methods for it. In this study, a novel mutation testing library for ROS was developed and integrated it into the automated/tailored mutation-based software fault injection tool (IM-FIT). IM-FIT is an open-source automated software testing tool that is used to evaluate the software robustness of safety-critical systems using mutation-based tests. The proposed ROSMutation library mutates ROS-specific code snippets (publisher, subscriber, params, services, etc.) in the Python code within ROS-compatible software packages using IM-FIT. We evaluated the effectiveness of the ROSMutation library in two scenarios (basic and advanced), applying it to ROS-compatible code through IM-FIT and measuring its ability to assess the software robustness of ROS-compatible and Python-based software packages. The results showed that the ROSMutation library is effective in evaluating software robustness criteria for ROS-compatible and Python based software.

Focusing on the complex mechanical responses exhibited by semi-crystalline polymers under the coupled influences of strain hardening, strain-rate strengthening, and temperature softening, this paper proposes a phenomenological constitutive model employing a three-branch parallel structure. Using a hybrid global optimization algorithm, the optimal parameters for polypropylene were identified, attaining a coefficient of determination of 0.9834 and controlling the average absolute relative error within 6.4%. Moreover, the effectiveness of the proposed constitutive model was accurately validated through two material models from the LS-Dyna software 4.8.29 database, and the simulation results exhibited high consistency with the theoretical model. This study provides a high-confidence material model suitable for high-strain-rate simulation scenarios.

In this paper, we propose a Linux-based operating system, namely, DicomOS, tailored for medical imaging and enhanced interoperability, addressing user-friendly functionality and the main critical needs in radiology workflows. Traditional operating systems in clinical settings face limitations, such as fragmented software ecosystems and platform-specific restrictions, which disrupt collaborative workflows and hinder diagnostic efficiency. Built on Ubuntu 22.04 LTS, DicomOS integrates essential DICOM functionalities directly into the OS, providing a unified, cohesive platform for image visualization, annotation, and sharing. Methods include custom configurations and the development of graphical user interfaces (GUIs) and command-line tools, making them accessible to medical professionals and developers. Key applications such as ITK-SNAP and 3D Slicer are seamlessly integrated alongside specialized GUIs that enhance usability without requiring extensive technical expertise. As preliminary work, DicomOS demonstrates the potential to simplify medical imaging workflows, reduce cognitive load, and promote efficient data sharing across diverse clinical settings. However, further evaluations, including structured clinical tests and broader deployment with a distributable ISO image, must validate its effectiveness and scalability in real-world scenarios. The results indicate that DicomOS provides a versatile and adaptable solution, supporting radiologists in routine tasks while facilitating customization for advanced users. As an open-source platform, DicomOS has the potential to evolve alongside medical imaging needs, positioning it as a valuable resource for enhancing workflow integration and clinical collaboration.

Reproducible approaches are needed to bring AI/ML for medical image analysis closer to the bedside. Investigators wishing to shadow test cross-sectional medical imaging segmentation algorithms on new studies in real-time will benefit from simple tools that integrate PACS with on-premises image processing, allowing visualization of DICOM-compatible segmentation results and volumetric data at the radiology workstation. In this work, we develop and release a simple containerized and easily deployable pipeline for shadow testing of segmentation algorithms within the clinical workflow. Our end-to-end automated pipeline has two major components- 1. A router/listener and anonymizer and an OHIF web viewer backstopped by a DCM4CHEE DICOM query/retrieve archive deployed in the virtual infrastructure of our secure hospital intranet, and 2. An on-premises single GPU workstation host for DICOM/NIfTI conversion steps, and image processing. DICOM images are visualized in OHIF along with their segmentation masks and associated volumetry measurements (in mL) using DICOM SEG and structured report (SR) elements. Since nnU-net has emerged as a widely-used out-of-the-box method for training segmentation models with state-of-the-art performance, feasibility of our pipleine is demonstrated by recording clock times for a traumatic pelvic hematoma nnU-net model. Mean total clock time from PACS send by user to completion of transfer to the DCM4CHEE query/retrieve archive was 5 min 32 s (± SD of 1 min 26 s). This compares favorably to the report turnaround times for whole-body CT exams, which often exceed 30 min, and illustrates feasibility in the clinical setting where quantitative results would be expected prior to report sign-off. Inference times accounted for most of the total clock time, ranging from 2 min 41 s to 8 min 27 s. All other virtual and on-premises host steps combined ranged from a minimum of 34 s to a maximum of 48 s. The software worked seamlessly with an existing PACS and could be used for deployment of DL models within the radiology workflow for prospective testing on newly scanned patients. Once configured, the pipeline is executed through one command using a single shell script. The code is made publicly available through an open-source license at \"https://github.com/vastc/,\" and includes a readme file providing pipeline config instructions for host names, series filter, other parameters, and citation instructions for this work.

Recently, expanded metal mesh has been used on the facades of many buildings in Taiwan. Therefore, in this study, we evaluated the impact of expanded metal mesh on natural lighting and energy consumption in office buildings. First, the compatibility of EnergyPlus and DIVA simulation software with expanded metal mesh was verified using field measurements. The results show a high correlation between simulation and measurement, except for some periods of direct sunlight. Then, we evaluated the effects of window-to-wall ratio (WWR), glass, and expanded metal mesh on energy consumption and lighting. The results show that WWR has a significant influence on both lighting and energy consumption. The greater the WWR, the greater the energy saving potential of the expanded metal mesh and glass. If the SHGC of the glass is lower, the potential of the expanded metal mesh to save air conditioning energy consumption is smaller, and, as a result, the expanded metal mesh may increase the total energy consumption. Of the 36 simulation cases performed, three cases met the LEED lighting standard. The case with minimum energy consumption is achieved when SHGC = 50%, using laminated clear glass and expanded metal mesh with a 21% perforated ratio.

The Argument Web is maturing as both a platform built upon a synthesis of many contemporary theories of argumentation in philosophy and also as an ecosystem in which various applications and application components are contributed by different research groups around the world. It already hosts the largest publicly accessible corpora of argumentation and has the largest number of interoperable and cross compatible tools for the analysis, navigation and evaluation of arguments across a broad range of domains, languages and activity types. Such interoperability is key in allowing innovative combinations of tool and data reuse that can further catalyse the development of the field of computational argumentation. The aim of this paper is to summarise the key foundations, the recent advances and the goals of the Argument Web, with a particular focus on demonstrating the relevance to, and roots in, philosophical argumentation theory.