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Robotic testing systems are commonly utilized for the study of orthopaedic biomechanics. Quantification of system error is essential for reliable use of robotic systems. Therefore, the purpose of this study was to quantify a 6-DOF robotic system's repeatability during knee biomechanical testing and characterize the error induced in passive path repeatability by removing and reinstalling the knee. We hypothesized removing and reinstalling the knee would substantially alter passive path repeatability. Testing was performed on four fresh-frozen cadaver knees. To determine repeatability and reproducibility, the passive path was collected three times per knee following the initial setup (intra-setup), and a single time following two subsequent re-setups (inter-setup). Repeatability was calculated as root mean square error. The intra-setup passive path had a position repeatability of 0.23 mm. In contrast, inter-setup passive paths had a position repeatability of 0.89 mm. When a previously collected passive path was replayed following re-setup of the knee, resultant total force repeatability across the passive path increased to 28.2N (6.4N medial-lateral, 25.4N proximal-distal, and 10.5 N anterior-posterior). This study demonstrated that removal and re-setup of a knee can have substantial, clinically significant changes on our system's repeatability and ultimately, accuracy of the reported results.
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http://dx.doi.org/10.1016/j.medengphy.2014.06.022 | DOI Listing |
Sensors (Basel)
August 2025
Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai 600036, India.
Contemporary visual assistive devices often lack immersive user experience due to passive control systems. This study introduces a neuronally controlled visual assistive device (NCVAD) that aims to assist visually impaired users in performing reach tasks with active, intuitive control. The developed NCVAD integrates computer vision, electroencephalogram (EEG) signal processing, and robotic manipulation to facilitate object detection, selection, and assistive guidance.
View Article and Find Full Text PDFEnviron Manage
August 2025
Plant Ecophysiology Laboratory, Biology Institute, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil.
Monitoring restoration areas is crucial for understanding how ecological succession changes over time and whether the trajectories of planted communities are following the desired path of recovery. A functional trait-based approach coupling the functional trajectory analysis (FTA) with species abundance modelling may have a significant potential application in restoration assessment. In the present study, we surveyed a 10-year-old restoration tree community in the Brazilian Atlantic Forest, planted in rows (2 × 2 m spacing) and clusters (13 individuals planted 0.
View Article and Find Full Text PDFSensors (Basel)
July 2025
Research and Development Engineering, SmOp CleanTech, Manchester M40 8WN, UK.
Terahertz (THz) communications and simultaneous wireless information and power transfer (SWIPT) hold the potential to energize battery-less Internet-of-Things (IoT) devices while enabling multi-gigabit data transmission. However, severe path loss, blockages, and rectifier nonlinearity significantly hinder both throughput and harvested energy. Additionally, high-power THz beams pose safety concerns by potentially exceeding specific absorption rate (SAR) limits.
View Article and Find Full Text PDFMolecules
August 2025
Department of Pharmaceutics and Biopharmaceutics, Marburg University, Robert-Koch-Str. 4, 35037 Marburg, Germany.
Nanocrystals, defined as crystalline particles with dimensions in the nanometer range (<1000 nm), exhibit unique properties that enhance the efficacy of poorly soluble active compounds. This review explores the fundamental aspects of nanocrystals, including their characteristics and various preparation methods, while addressing critical factors that influence their stability and incorporation into final products. A key focus of the review is the advantages offered by nanocrystals in dermal applications.
View Article and Find Full Text PDFAnimals (Basel)
July 2025
College of Information Engineering, Sichuan Agriculture University, Ya'an 625014, China.
This research presents DuSAFNet, a lightweight deep neural network for fine-grained bird audio classification. DuSAFNet combines dual-path feature fusion, spectral-temporal attention, and a multi-band ArcMarginProduct classifier to enhance inter-class separability and capture both local and global spectro-temporal cues. Unlike single-feature approaches, DuSAFNet captures both local spectral textures and long-range temporal dependencies in Mel-spectrogram inputs and explicitly enhances inter-class separability across low, mid, and high frequency bands.
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