Parkinson’s disease is a common, incurable neurodegenerative disorder that is clinically heterogeneous: it is likely that different cellular mechanisms drive the pathology in different individuals. So far it has not been possible to define the cellular mechanism underlying the neurodegenerative disease in life. We generated a machine learning-based model that can simultaneously predict the presence of disease and its primary mechanistic subtype in human neurons. We used stem cell technology to derive control or patient-derived neurons, and generated different disease subtypes through chemical induction or the presence of mutation. Multidimensional fluorescent labelling of organelles was performed in healthy control neurons and in four different disease subtypes, and both the quantitative single-cell fluorescence features and the images were used to independently train a series of classifiers to build deep neural networks. Quantitative cellular profile-based classifiers achieve an accuracy of 82%, whereas image-based deep neural networks predict control and four distinct disease subtypes with an accuracy of 95%. The machine learning-trained classifiers achieve their accuracy across all subtypes, using the organellar features of the mitochondria with the additional contribution of the lysosomes, confirming the biological importance of these pathways in Parkinson’s. Altogether, we show that machine learning approaches applied to patient-derived cells are highly accurate at predicting disease subtypes, providing proof of concept that this approach may enable mechanistic stratification and precision medicine approaches in the future.

Developing energy-efficient and multifunctional wearable electronic textiles (E-textiles) is a significant challenge. This study investigates MXene-coated cellulose hybrid fibers, focusing on their electrical properties, heating performance, and thermal stability. The fabrication process involves continuous dipping of cellulose fibers into an aqueous MXene solution, resulting in the creation of MXene-coated cellulose hybrid fibers. We confirm the uniform coating of MXene sheets on the cellulose fiber surfaces, with increasing content throughout the dip coating cycle, as evidenced by X-ray diffraction and scanning electron microscopy analysis. The high thermal conductivity of MXene acts as a heat source, impacting the thermal stability of cellulose fibers at lower temperatures. Additionally, the electrical properties of MXene/cellulose hybrid fiber composites are influenced at elevated temperatures. Remarkably, the longitudinal electrical conductivity of the MXene-coated cellulose fiber composites exhibits a notable increase of 0.06 S/cm after the final coating cycle, demonstrating the effective and conductive nature of the layer-by-layer MXene network formed on the cellulose fibers.

Carbon aerogel is a promising material in various applications, such as water treatment, insulators, catalysts, and sensors, due to its porosity, low density, conductivity, and good chemical stability. In this study, an inexpensive carbon aerogel was prepared through lyophilization and post-pyrolysis using waste paper. However, carbon aerogel, in the form of short belts, is randomly entangled without a crosslinking agent and has weak mechanical properties, thus limiting its applications, which would otherwise be various. In this paper, a novel strategy is proposed to fabricate a PDMS-coated carbon aerogel (Aerogel@PDMS). Benefiting from microwave heating, precise PDMS coating onto the carbon frame was able to be carried out in a short amount of time. PDMS coating firmly tied the carbon microstructure, maintaining a unique aerogel property without blocking its porous structure. FE-SEM, RAMAN, XPS, and FT-IR were all used to confirm the surface change in PDMS coating. Compressible stability and water contact angle measurement showed that Aerogel@PDMS is a perspective organic solvent absorbent due to its good resilience and its hydrophobicity, and, as a result, its organic solvent absorption capacity and repeated absorption were evaluated, ultimately suggesting a promising material in oil clean-up and pollution remediation in water. Based on our experimental results, we identified elastic carbon aerogels provided by a novel coating technology. In the future, then, the developed carbon/PDMS composite can be examined as a promising option for various applications, such as environmental sensors, virus sensors, and wearable sensors.

The successful launch of Korea Multipurpose Satellite-3/Advanced Earth Imaging Sensor System (KOMPSAT-3/AEISS) on 18 May 2012 allowed the Republic of Korea to meet the growing demand for high-resolution satellite imagery. However, like all satellite sensors, KOMPSAT-3/AEISS experienced temporal changes post-launch and thus requires ongoing evaluation and calibration. Although more than a decade has passed since launch, the KOMPSAT-3/AEISS mission and its multi-year data archive remain widely used. This study focused on the cross-calibration of KOMPSAT-3/AEISS with Sentinel-2A/Multispectral Instrument (MSI) by comparing the radiometric responses of the two satellite sensors under similar observation conditions, leveraging the linear relationship between Digital Numbers (DN) and top-of-atmosphere (TOA) radiance. Cross-calibration was performed using near-simultaneous satellite images of the same region, and the Spectral Band Adjustment Factor (SBAF) was calculated and applied to account for differences in spectral response functions (SRF). Additionally, Bidirectional Reflectance Distribution Function (BRDF) correction was applied using MODIS-based kernel models to minimize angular reflectance effects caused by differences in viewing and illumination geometry. This study aims to evaluate the radiometric consistency of KOMPSAT-3/AEISS relative to Sentinel-2A/MSI over Baotou scenes acquired in 2022–2023, derive band-specific calibration coefficients and compare them with prior results, and conduct a side-by-side comparison of cross-calibration and vicarious calibration. Furthermore, the cross-calibration yielded band-specific gains of 0.0196 (Blue), 0.0237 (Green), 0.0214 (Red), and 0.0136 (NIR). These findings offer valuable implications for Earth observation, environmental monitoring, and the planning and execution of future satellite missions.

Measuring torsional movement is crucial for tracking human kinematics in applications such as virtual reality, biomechanics, rehabilitation, and soft robotic joints. However, continuous, non-invasive torsion measurement remains challenging due to multi-axial deformations, where existing methods rely heavily on empirical data fitting or computationally expensive deep learning models requiring vast training datasets. To address these limitations, this paper proposes a training-free, highly precise torsion estimation framework that converts soft resistive strain sensor data into forearm rotation angles using closed-form kinematic-geometric modeling. Two geometric representations—the Cylinder Model and Truncated Cone Model—are established to analytically map non-linear tensile strain to torsion angles. Additionally, an online calibration algorithm is integrated to mitigate cumulative measurement drift caused by viscoelastic hysteresis. Under static conditions, the proposed models achieved a high coefficient of determination (R² = 0.9888) against reference IMU data. In dynamic trials, the hysteresis compensation algorithm maintained robust tracking performance (R² = 0.8033), achieving an angular root-mean-square error (RMSE) of 4.12° and a cycle peak prominence relative error of 0.42% for the Truncated Cone Model. In this single-subject proof-of-concept feasibility study (n = 1), the proposed framework demonstrated the feasibility of continuous torsional motion tracking through a training-free geometric approach, providing a preliminary foundation for future evaluation in wearable systems and soft robotics.

This paper proposes a novel Neural Network Coding Layer (NNCL) that applies network coding theory to provide structured redundancy and enable reconstruction of lost features, thereby mitigating information loss problems in deep learning models. Unlike existing skip connections or attention mechanisms, NNCL embeds reconstructible redundancy into intermediate features through a learnable coding process, providing an explicit algebraic restoration mechanism for the original features. This design adapts the core principle of network coding—encoding information recoverably across a channel—for application within the computational graph of a neural network. Extensive experiments on the CIFAR-10 and CIFAR-100 datasets validate NNCL’s effectiveness. Even under a more conventional feature erasure rate of 20%, NNCL consistently improved classification accuracy by up to 8.3 percentage points. The model’s robustness becomes even more pronounced under extreme conditions. In a deliberate stress test involving 60% feature erasure—a scenario where baseline model performance collapses—NNCL dramatically boosted accuracy by up to 40.3 percentage points (e.g., from 19.8% to 60.1% on CIFAR-100). The proposed layer is designed to be modular and has been successfully integrated into various modern architectures, including ResNet and EfficientNet, and Vision Transformer, proving its broad applicability.