Ion concentration polarization (ICP) is one of the preconcentration techniques which can acquire a high preconcentration factor. Still, the main hurdles of ICP are its instability and low efficiency under physiological conditions with high ionic strength and abundant biomolecules. Here, we suggested a sequentially driven ICP process, which enhanced the electrokinetic force required for preconcentration, enabling enrichment of highly ionic raw samples without increasing the electric field. We acquired a 13-fold preconcentration factor (PF) in human serum using a paper-based origami structure consisting of multiple layers for three-dimensional sequential ICP (3D seq-ICP). Moreover, we demonstrated a paper-based enzyme-linked immunosorbent assay (ELISA) by 3D seq-ICP using tau protein, showing a 6-fold increase in ELISA signals.

Sample preparation steps (e.g., preconcentration and separation) are key to enhancing sensitivity and reliability in biomedical and analytical chemistry. However, conventional methods (e.g., ultracentrifugation) cause significant loss of sample as well as their contamination. In this study, we developed a paper-based three-dimensional (3D) origami ion concentration polarization preconcentrator (POP) for highly efficient and facile sample preparation. The unique design of POP enables simultaneous preconcentration and spatial separation of target analytes rapidly and economically. The POP comprises accordion-like multifolded layers with convergent wicking areas that can separate analytes based on their sizes in different layers, which can then be easily isolated by unfolding the POP. We first demonstrated 100-fold preconcentration of albumin and its isolation on the specific layers. Then, we demonstrated the simultaneous preconcentration and spatial separation of microspheres of three different sizes (with diameters of 0.02, 0.2, and 2 μm) on the different layers.

Au-coated vertical silicon nanowire electrode array (VSNEA) was fabricated using a combination of bottom-up and top-down approaches by chemical vapor deposition and complementary metal-oxide-semiconductor process for biomolecule sensing. To verify the feasibility for the detection of biomolecules, Au-coated VSNEA was functionalized using peptides having a fluorescent probe. Cyclic voltammograms of the peptide-functionalized Au-coated VSNEA show a steady-state electrochemical current behavior. Because of the critically small dimension and vertically aligned nature of VSNEA, the current density of Au-coated VSNEA was dramatically higher than that of Au film electrodes. Au-coated VSNEA further showed a large current difference with and without peptides that was nine times more than that of Au film electrodes. These results indicate that Au-coated VSENA is highly effective device to detect peptides compared to conventional thin-film electrodes. Au-coated VSNEA can also be used as a divergent biosensor platform in many applications.

Ion concentration polarization (ICP) is one of the preconcentration techniques which can acquire a high preconcentration factor. Still, the main hurdles of ICP are its instability and low efficiency under physiological conditions with high ionic strength and abundant biomolecules. Here, we suggested a sequentially driven ICP process, which enhanced the electrokinetic force required for preconcentration, enabling enrichment of highly ionic raw samples without increasing the electric field. We acquired a 13-fold preconcentration factor (PF) in human serum using a paper-based origami structure consisting of multiple layers for three-dimensional sequential ICP (3D seq-ICP). Moreover, we demonstrated a paper-based enzyme-linked immunosorbent assay (ELISA) by 3D seq-ICP using tau protein, showing a 6-fold increase in ELISA signals.

Sample preparation steps (e.g., preconcentration and separation) are key to enhancing sensitivity and reliability in biomedical and analytical chemistry. However, conventional methods (e.g., ultracentrifugation) cause significant loss of sample as well as their contamination. In this study, we developed a paper-based three-dimensional (3D) origami ion concentration polarization preconcentrator (POP) for highly efficient and facile sample preparation. The unique design of POP enables simultaneous preconcentration and spatial separation of target analytes rapidly and economically. The POP comprises accordion-like multifolded layers with convergent wicking areas that can separate analytes based on their sizes in different layers, which can then be easily isolated by unfolding the POP. We first demonstrated 100-fold preconcentration of albumin and its isolation on the specific layers. Then, we demonstrated the simultaneous preconcentration and spatial separation of microspheres of three different sizes (with diameters of 0.02, 0.2, and 2 μm) on the different layers.

Au-coated vertical silicon nanowire electrode array (VSNEA) was fabricated using a combination of bottom-up and top-down approaches by chemical vapor deposition and complementary metal-oxide-semiconductor process for biomolecule sensing. To verify the feasibility for the detection of biomolecules, Au-coated VSNEA was functionalized using peptides having a fluorescent probe. Cyclic voltammograms of the peptide-functionalized Au-coated VSNEA show a steady-state electrochemical current behavior. Because of the critically small dimension and vertically aligned nature of VSNEA, the current density of Au-coated VSNEA was dramatically higher than that of Au film electrodes. Au-coated VSNEA further showed a large current difference with and without peptides that was nine times more than that of Au film electrodes. These results indicate that Au-coated VSENA is highly effective device to detect peptides compared to conventional thin-film electrodes. Au-coated VSNEA can also be used as a divergent biosensor platform in many applications.

An efficient RS encoder for a parity replacer circuit of ATSC M/H mobile DTV receiver is presented. First, we derive the non-systematic RS encoder architecture which requires the well-known Forney algorithm to evaluate the parity symbol values located at arbitrary positions. We notice that only at most three information symbols corresponding to the trellis initialization symbol determine the RS parity symbols to be replaced in the parity replacer circuit. So we derive another form of the Forney algorithm to represent the parity symbol value as a linear combination of the input information symbols. With the new form, efficient architecture for implementing the RS encoder of a parity replacer is presented.

Ultrasound imaging is widely employed for breast cancer detection; however, its diagnostic reliability is often constrained by operator dependence and subjective interpretation. Deep learning-based computer-aided diagnosis (CADx) systems offer potential to improve diagnostic consistency, yet their effectiveness is frequently limited by the scarcity of annotated medical images. This work introduces a training framework to enhance the performance and training stability of a YOLO-based object detection model for breast tumor localization, particularly in data-constrained scenarios. The proposed method integrates a detail-to-context curriculum learning scheme using hierarchically zoomed-in B-mode images, with progression difficulty determined by the tumor-to-background area ratio. A preprocessing step resizes all images to 640 × 640 pixels while preserving aspect ratio to improve intra-dataset consistency. Our evaluation indicates that aspect ratio-preserving resizing is associated with a 2.3% increase in recall and a reduction in the standard deviation of stability metrics by more than 20%. Moreover, the curriculum learning approach reached 97.2% of the final model performance using only 35% of the training data required by conventional methods, while achieving a more balanced precision–recall profile. These findings suggest that the proposed framework holds potential as an effective strategy for developing more robust and efficient tumor detection models, particularly for deployment in resource-limited clinical environments.

Predicting the wear-out lifetime of memory cells is one of the most important task in the reliability of NAND flash memory. Since the lifetime is inversely proportional to the stress voltage across the cell-oxide layer during repeated program-erase cycles, in order to accurately estimate the lifetime, it is critical to calculate the exact acceleration factor for each test. This paper introduces a precise lifetime-estimation method based on the analysis of the energy-band behavior caused by the electric field during program-erase cycles. The experimental results show that the predicted result with 99% accuracy when the proposed method was applied to the wafer of the mass-produced product.

This study was designed to determine the characteristics and tendencies of drones, which have emerged as a key weapon system in the Russian-Ukrainian War since 2022, and their use in modern warfare. A spatiotemporal analysis was conducted on 5,491 coordinates of drone-based battles within the city with a multidimensional model. The analysis revealed extensive drone operations by both Russia and Ukraine, with Ukraine shifting to offensive actions in 2023, and distinct temporal patterns by day of the week at battle sites, as indicated by the frequency of drone-based battles. Moreover, Russia maintained the momentum of offensive drone operations, intercepting 85.1% of Ukrainian drones and achieving a 54.0% success rate in drone-based attacks, whereas Ukraine intercepted 43.3% of Russian drones, with a success rate of only 14.3%. Based on this study, the spatiotemporal analysis of drone-based combat across Ukraine enabled an examination of the operating areas, roles, and efficiency of this weapon system as well as an understanding of the impact and multifaceted characteristics associated with its deployment on the battlefield.

Breast ultrasound (BUS) is an effective clinical modality for diagnosing breast abnormalities in women. Deep-learning techniques based on convolutional neural networks (CNN) have been widely used to analyze BUS images. However, the low quality of B-mode images owing to speckle noise and a lack of training datasets makes BUS analysis challenging in clinical applications. In this study, we proposed an end-to-end CNN framework for BUS analysis using multiple parametric images generated from radiofrequency (RF) signals. The entropy and phase images, which represent the microstructural and anatomical information, respectively, and the traditional B-mode images were used as parametric images in the time domain. In addition, the attenuation image, estimated from the frequency domain using RF signals, was used for the spectral features. Because one set of RF signals from one patient produced multiple images as CNN inputs, the proposed framework overcame the limitation of datasets in a broad sense of data augmentation while providing complementary information to compensate for the low quality of the B-mode images. The experimental results showed that the proposed architecture improved the classification accuracy and recall by 5.5% and 11.6%, respectively, compared with the traditional approach using only B-mode images. The proposed framework can be extended to various other parametric images in both the time and frequency domains using deep neural networks to improve its performance.