Explore the vast range of titles available.

Currently, the trend of elderly people living alone is rising due to rapid aging and shifts in family structures. Accordingly, the efficient implementation and management of monitoring systems tailored for elderly people living alone have become paramount. Monitoring systems are generally implemented based on multiple sensors, and the collected data are processed on a server to provide monitoring services to users. Due to the use of multiple sensors and a reliance on servers, there are limitations to economical maintenance and a risk of highly personal information being leaked. In this paper, we propose an intelligent monitoring system with privacy preservation based on edge AI. The proposed system achieves cost competitiveness and ensures high security by blocking communication between the camera module and the server with an edge AI module. Additionally, applying edge computing technology allows for the efficient processing of data traffic. The edge AI module was designed with Verilog HDL and was implemented on a field-programmable gate array (FPGA). Through experiments conducted on 6144 frames, we achieved 95.34% accuracy. Synthesis results in a 180 nm CMOS technology indicated a gate count of 1516 K and a power consumption of 344.44 mW.

Recently, supplying healthcare services with wearable devices has been investigated. To realize this for true wireless stereo (TWS), which has limited resources (e.g. space, power consumption, and area), implementing multiple functions with one sensor simultaneously is required. The Photoplethysmography (PPG) sensor is a representative healthcare sensor that measures repeated data according to the heart rate. However, since the PPG data are biological, they are influenced by motion artifact and subject characteristics. Hence, noise reduction is needed for PPG data. In this paper, we propose the distance estimation algorithm for PPG signals of TWS. For distance estimation, we designed a waveform adjustment (WA) filter that minimizes noise while maintaining the relationship between before and after data, a lightweight deep learning model called MobileNet, and a PPG monitoring testbed. The number of criteria for distance estimation was set to three. In order to verify the proposed algorithm, we compared several metrics with other filters and AI models. The highest accuracy, precision, recall, and f1 score of the proposed algorithm were 92.5%, 92.6%, 92.8%, and 0.927, respectively, when the signal length was 15. Experimental results of other algorithms showed higher metrics than the proposed algorithm in some cases, but the proposed model showed the fastest inference time.

Stochastic computing requires random number generators to generate stochastic sequences that represent probability values. In the case of an 8-bit operation, a 256-bit length of a stochastic sequence is required, which results in latency issues. In this paper, a stochastic computing architecture is proposed to address the latency issue by employing parallel linear feedback shift registers (LFSRs). The proposed architecture reduces the latency in the stochastic sequence generation process without losing accuracy. In addition, the proposed architecture achieves area efficiency by reducing 69% of flip-flops and 70.4% of LUTs compared to architecture employing shared LFSRs, and 74% of flip-flops and 58% of LUTs compared to the architecture applying multiple LFSRs with the same computational time.

Stereo matching, utilized in diverse fields, poses a challenge to systems in resource-constrained environments due to the significant growth of computational load with image resolution. The challenge is crucial for the systems because fields utilizing stereo matching require short operational time for real-time applications and low power architecture. Stochastic computing (SC) is able to be a valuable approach to address the challenge by reducing the computational load by representing binary numbers with stochastic sequences, which are encoded as a probability value, and by leveraging the concept of mathematical probability. Also, it is possible for a system to be error-tolerant by utilizing the characteristics of stochastic computing. Therefore, in this paper, we propose an approach for lightweight and error-tolerant stereo matching with a hardware-implemented stochastic computing processor. To verify the feasibility and error tolerance of the proposed system, we implemented the proposed system and conducted experiments comparing depth maps with or without stochastic computing by calculating similarities. According to the experimental results, the proposed system indicated no significant differences in output depth maps and achieved an improvement in the depth maps from error-injected input images by an average of 58.95%. Therefore, we demonstrated that stereo matching with stochastic computing is feasible and error-tolerant.

Image analysis-based artificial intelligence (AI) models leveraging convolutional neural networks (CNN) take a significant role in evaluating the ripeness of strawberry, contributing to the maximization of productivity. However, the convolution, which constitutes the majority of the CNN models, imposes significant computational burdens. Additionally, the dense operations in the fully connected (FC) layer necessitate a vast number of parameters and entail extensive external memory access. Therefore, reducing the computational burden of convolution operations and alleviating memory overhead is essential in embedded environment. In this paper, we propose a strawberry ripeness classification system utilizing a convolution-based feature extractor (CoFEx) for accelerating convolution operations and an edge AI processor, Intellino, for replacing FC layer operations. We accelerated feature map extraction utilizing the CoFEx constructed with systolic array (SA) and alleviated the computational burden and memory overhead associated with the FC layer operations by replacing them with the k-nearest neighbors (k-NN) algorithm. The CoFEx and the Intellino both were designed with Verilog HDL and implemented on a field-programmable gate array (FPGA). The proposed system achieved a high precision of 93.4%, recall of 93.3%, and F1 score of 0.933. Therefore, we demonstrated a feasibility of the strawberry ripeness classification system operating in an embedded environment.

Microscopy techniques are widely employed to confirm the effectiveness of electrodes and electrolytes designed to suppress dendrites in lithium metal anodes. Although optical microscopy (OM) and atomic force microscopy (AFM) are widely used, they are typically performed in isolation, preventing correlative analysis of the same regions. Moreover, OM suffers from limited spatial resolution, whereas AFM affords only localized insights. Thus, an integrated methodology combining operando OM and in situ electrochemical AFM (EC‐AFM) was invented for surface characterization of the lithium metal anode in lithium/lithium symmetric cells under 0.5 and 3 mA cm−2. OM visualizes microscale features such as dendrites, pit formation, and surface darkening during stripping, while EC‐AFM resolves nanoscale structures at the same darkened sites, unattainable by OM. This multiscale approach enables a qualitative and quantitative comparison of surface evolution, overcoming the individual limitations of each microscopy method and providing an analysis methodology that is expandable for evaluating other electrodes and electrolytes. A sequential multiscale imaging methodology integrates operando optical microscopy with in situ electrochemical atomic force microscopy to characterize lithium anode surface evolution during galvanostatic cycling under dual current densities. This unified approach enhances measurement consistency through correlative micro‐to‐nanoscale visualization, demonstrating the versatile applicability of electrode‐electrolyte evaluation methodologies for the development of lithium metal and other battery systems.

Background Most adjuvant therapies for hepatocellular carcinoma (HCC) have failed except for autologous cytokine-induced killer (CIK) therapy, which prolonged recurrence-free survival (RFS) in a previously reported randomized controlled trial (RCT) and real-world data (RWD). Methods This study aimed to assess the long-term outcomes of adjuvant CIK therapy using both an extended follow-up of the original RCT and a retrospective cohort study. An extended follow-up analysis of the RCT included 226 patients (114 in the CIK group and 112 in the control group). The follow-up duration was extended from 2 to 9 years after the enrollment of the last patient. In parallel, a retrospective RWD study was performed involving 577 patients from two tertiary centers in Korea, including 251 who received adjuvant CIK therapy and 326 controls. Propensity score matching (PSM) was applied to adjust for baseline imbalances. The primary endpoint was RFS in both studies. Results In the RWD study (median follow-up = 57.2 months), the CIK group demonstrated significantly prolonged RFS than controls both before PSM (median = 101.2 versus 64.7 months; HR = 0.69, 95% CI 0.53–0.90, P  = 0.006) and after PSM (median = 101.2 vs. 65.7 months; HR = 0.64, 95% CI 0.45–0.91, P  = 0.01). In the extended follow-up of the RCT (median follow-up = 116.1 months), the CIK group exhibited significantly prolonged RFS (median = 44.0 vs. 30.0 months; hazard ratio [HR] = 0.72, 95% confidence interval [CI] 0.54–0.97, P  = 0.033) compared to the control group. Conclusions Adjuvant CIK cell therapy significantly improved RFS in both a RWD study and a 9-year extended RCT follow-up, supporting its reproducible benefit in reducing recurrence after curative treatment of HCC. These consistent findings provide strong evidence for the clinical utility of CIK therapy as a durable adjuvant immunotherapeutic strategy for HCC.

Enhancer of zeste homolog 2 (EZH2) catalyzes H3K27me3, an epigenetic modification linked to gene silencing, and its overexpression contributes to the progression of hematological malignancies. This study compares the efficacy of a conventional EZH2 inhibitor with a PROTAC-based EZH2 degrader in human lymphoma cell lines. Furthermore, we investigate the anti-tumor effects of combining EZH2 degrader with anti-PD-1, an immune checkpoint inhibitor, focusing on immune cell interactions and underlying mechanisms. The cytotoxic effects of the EZH2 degrader and EZH2 inhibitor were evaluated in Burkitt's, B-cell, cutaneous T-cell, and Hodgkin's lymphoma cell lines. Additionally, the combination therapy of the EZH2 degrader and anti-PD-1 was assessed both and in a hu-PBMC-CDX mouse model. We evaluated the effects of an EZH2 degrader on seven lymphoma cell lines and observed significant reductions in cell viability compared to EZH2 inhibitor, particularly in Burkitt's lymphoma cell lines. EZH2 degrader treatment reduced EZH2 and c-Myc expression, induced G2/M cell cycle arrest, and increased apoptosis markers, including cleaved caspase-3 and cleaved PARP. Furthermore, Burkitt's lymphoma is a PD-L1 negative tumor; however, treatment with the EZH2 degrader resulted in a slight increase in PD-L1 expression. Combining EZH2 degrader with anti-PD-1 significantly enhanced anti-tumor effects compared to monotherapy. studies using a humanized lymphoma mouse model demonstrated a synergistic anti-tumor effect of EZH2 degrader and anti-PD-1, which was attributed to apoptosis-related pathways. These findings aim to provide insights into the therapeutic potential of targeting EZH2 in combination with immune checkpoint inhibitors for improved treatment of lymphomas.

This study presents a design approach for a uniform porous surface to control laminar-to-turbulent transition in hypersonic boundary layers. The focus is on suppressing the Mack second mode, which is a dominant instability in hypersonic boundary layers. The Mack second mode is acoustic-wave-like in the ultrasonic frequency range and can be effectively attenuated by porous surfaces. Previous studies have explored porous surfaces, either by targeting a specific frequency or by adopting geometrically complex configurations for various frequencies. In contrast, the present study proposes a porous surface design that effectively stabilizes the Mack second mode over a wide frequency range, while maintaining structural simplicity. In addition, this porous surface design incorporates constraints associated with practical fabrication to enhance manufacturability. The absorption characteristics of porous surfaces are evaluated with an acoustic impedance model, and the stabilization performance is assessed with linear stability theory. The proposed porous surface design is compared with a conventional design method that focuses on the Mack second mode with a single frequency. Consequently, the proposed design methodology demonstrates robust and consistent suppression of the Mack second mode in a broad frequency range. This approach improves both stabilization performance and manufacturability with a uniform porous surface, contributing to its practical application in high-speed vehicles.

The application of explainable artificial intelligence in nanomaterial research has emerged in the past few years, which has facilitated the discovery of novel physical findings. However, a fundamental question arises concerning the physical insights presented by deep neural networks; the model interpretation results have not been carefully evaluated. Herein, explainable artificial intelligence and quantum mechanical calculations is bridged to investigate the correlation between light scattering and emission in a WSe2 monolayer. Convolutional neural networks using light scattering and emission data are first trained, while expecting the networks to determine the relationships between them. The trained models are interpreted and the specific phonon contribution during the exciton relaxation process is derived. Finally, the findings are independently evaluated through quantum mechanical calculations, such as the Born–Oppenheimer molecular dynamics simulation and density functional perturbation theory. The study provides reliable fundamental physical insight by evaluating the results of neural networks and suggests a novel methodology that can be applied in materials science. A combination of explainable artificial intelligence‐applied correlative spectroscopy and quantum mechanical calculations enables the discovery and validation of novel physics. Deep neural networks using Raman scattering and photoluminescence images are trained, and their interpretation results provide novel physics. Molecular dynamics and density functional perturbation theory calculations are calculated to validate the physical findings.