Current Issue : April-June Volume : 2026 Issue Number : 2 Articles : 5 Articles
Designing soft robotic components with desired functionalities relies on selecting and accurately characterizing elastomeric materials. However, literature often lacks comprehensive and standardized reporting of elastomer properties, limiting reproducibility and comparability across studies. This work highlights a broader need for standardization in soft robotics research. To address this gap, a unified material testing framework is proposed that encompasses quasielastic mechanical properties, viscoelastic response, thermal stability, and processing characteristics, along with the model parameters required for accurate simulations. The framework is demonstrated through the systematic characterization of ten elastomers commonly used in soft robotics, including seven thermosetting elastomers, two thermoplastic elastomers, and one covalent adaptable network (CAN) elastomer. Inclusion of the CAN shows the framework’s applicability to advanced elastomer systems. All experimental data and model parameters are provided in an open-access repository to facilitate informed material selection, improve modeling accuracy, and promote transparency and collaboration in the soft robotics community. In future work, this framework may be extended to include application-specific properties relevant for sensor or actuator development....
In this paper, we investigate the discretization of fuzzy implications using rounding functions. Our discretization method is a unified framework of the upper and lower discretization methods of Munar-Covas et al. Furthermore, we examine the extent to which the essential properties of these fuzzy implications are preserved in our discretization process....
Background/Objectives: Magnetic resonance imaging (MRI) is a widely used non-invasive imaging modality that provides high-fidelity soft-tissue contrast without ionizing radiation. However, acquiring high-resolution MRI scans is time-consuming, necessitating accelerated acquisition and reconstruction methods. Recently, self-supervised learning approaches have been introduced for reconstructing undersampled MRI data without external fully sampled ground truth. Methods: In this work, we propose a logarithmic scaled scheme for conventional loss functions (e.g., 1, 2) to enhance self-supervised MRI reconstruction. Standard self-supervised methods typically compute loss in the k-space domain, which tends to overemphasize low spatial frequencies while under-representing high-frequency information. Our method introduces a logarithmic scaling to adaptively rescale residuals, emphasizing highfrequency contributions and improving perceptual quality. Results: Experiments on public datasets demonstrate consistent quantitative improvements when the proposed log-scaled loss is applied within a self-supervised MRI reconstruction framework. Conclusions: The proposed approach improves reconstruction fidelity and perceptual quality while remaining lightweight, architecture-agnostic, and readily integrable into existing self-supervised MRI reconstruction pipelines....
Background/Objectives: Establishing the identity of unknown individuals has always been one of the primary objectives of anthropologists and forensic pathologists in judicial contexts. Particularly when human remains are found in advanced stages of decomposition, carbonization, or fragmentation conditions that may compromise the efficacy of techniques such as DNA analysis or dental comparison innovative methodologies, including craniofacial superimposition, are employed, often supplemented by further examinations. This study presents the discovery of an individual in an advanced state of decomposition, transitioning from the colliquative to the semi-skeletal phase, demonstrating how degenerative processes can alter soft tissues to the extent of hindering genetic investigations. Methods: The multidisciplinary investigation conducted to resolve the case is described in two phases: the first, of an anthropological and medico-legal nature, aimed at reconstructing the biological profile (sex, age, stature, ancestry); the second, anthropological in focus, directed toward identification through craniofacial superimposition, applying two established methods from the literature the linear method and the computer-assisted comparison approach. Results: The results obtained from both investigative phases proved decisive, providing a significant and anticipated resolution for the authorities involved. Conclusions: This judicial case ultimately reaffirms the critical importance of multidisciplinary collaboration in forensic investigations....
Organic memristors have emerged as leading candidates for “soft” biorealistic systems due to their intrinsic processability, biocompatibility, low power consumption, and low cost. They are highly compatible with wearable and flexible technologies, ultimately enabling seamlessly integrable biorealistic architectures, such as wearable in-sensor computing systems. However, conventional organic memristors have notoriously been limited by low device yield and reliability, primarily due to the inherent semicrystallinity of mainstream conjugated (macro-) molecules. In this context, utilizing a nonconjugated radical polymer can dramatically mitigate these issues by leveraging its intrinsic memristivity, amorphous nature, and the molecular tunability of the nonconjugated backbones. This work demonstrates a high-yield and multifunctional organic memory device and a soft in-sensor computing architecture based on a radical polymer. Specifically, this study achieves an on/off ratio of >106, state retention of over 4 × 105 s, stable switching performance over 500 cycles, and remarkable flexibility, all with a very high yield (>95%) in an organic memristive array. Additionally, the polymer’s intrinsic chemical sensitivity is leveraged for application in soft in-sensor computing. This work presents a radical molecular engineering strategy to enhance the physical properties of organic memristive materials on demand, significantly broadening the scope of organic materials for next-generation data processing technologies....
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