Lin Zhou

Resistive Random-Access Memory (ReRAM)—a core component of nanoscale mixed-signal storage circuits—faces a unique data-dependent challenge, sneak path (SP) interference, which severely limits its use in high-density integrated systems. With the rapid advancement of high-density ReRAM crossbar architectures, independently designed constrained coding or error-correcting coding (ECC) often introduces excessive hardware redundancy. It fails to synergistically optimize SP suppression and channel noise correction, reducing the integration and energy efficiency of ReRAM-based circuits. To address this, this paper proposes a unified multi-module collaborative storage error-control framework, achieving “suppression-correction” dual optimization for SP interference in one system. Constrained codes built on the maximum mutual information principle significantly lower SP occurrence probability while preserving channel capacity. An optimized adaptive belief propagation (BP) detection algorithm enables precise estimation of ReRAM array-stored data, and polar codes efficiently correct residual errors. Simulation results show the unified framework greatly improves array data discrimination accuracy. This work not only offers a new paradigm for balancing reliability and efficiency in practical ReRAM storage circuit design but also connects information theory with nanoscale mixed-signal circuit implementation.

An analytical framework integrating performance characterization and coding theory is proposed to mitigate sneak path (SP) interference in resistive random-access memory (ReRAM) crossbar arrays. The core innovation is identified in the mathematical decomposition of ReRAM’s non-ergodic data-dependent channel into multiple stationary memoryless subchannels. Through information-theoretic analysis, an approximate finite-length characterization of the theoretical lower bound for decoding word error probability (WEP) is established. This is achieved by systematically analyzing the SP occurrence rate in constrained array geometries combined with comprehensive evaluation of both mutual information and dispersion metrics across the decomposed channel components. Building upon this decomposition paradigm, a systematic code construction methodology is developed using density evolution principles for sparse-graph code design. The designed codes not only exhibit capacity-approaching decoding thresholds but also yield word error rate simulation results that are close to the derived WEP bound under practical crossbar configurations.

Due to the duality between source coding and channel coding, high-performance channel codes are often adopted for addressing source coding problems. In this paper, we investigate the design and analysis of protograph low-density parity-check (P-LDPC) codes in lossy source coding systems. First, we propose a novel lossy source coding architecture based on P-LDPC codes, and empirically verify that although high-performance channel codes perform exceptionally well in their intended domain, they are not inherently optimal for lossy compression. To facilitate analysis, we introduce the lossy compression protograph extrinsic information transfer (LC-PEXIT) algorithm, which aids in evaluating the rate-distortion (RD) performance of P-LDPC codes. Additionally, the LC-PEXIT algorithm allows for the prediction of key parameters, such as the prior coefficient P and the reinforcement rate R, both of which critically influence system performance. We further propose a design algorithm for lossy P-LDPC codes and provide two sets of design examples. Experimental results show strong alignment between the predicted and actual values of P and Rf, with the designed P-LDPC codes exhibiting superior RD performance compared to classical P-LDPC and state-of-the-art ultra-sparse LDPC (US-LDPC) codes. In particular, the designed P-LDPC codes over benchmark codes increases the system coding efficiency of up to approximate 70% and achieves a performance gain of 1.69 ~ 3.26 dB, making it highly effective for lossy compression applications.

With the development of deep learning, the Super-Resolution (SR) reconstruction of microscopic images has improved significantly. However, the scarcity of microscopic images for training, the underutilization of hierarchical features in original Low-Resolution (LR) images, and the high-frequency noise unrelated with the image structure generated during the reconstruction process are still challenges in the Single Image Super-Resolution (SISR) field. Faced with these issues, we first collected sufficient microscopic images through Motic, a company engaged in the design and production of optical and digital microscopes, to establish a dataset. Secondly, we proposed a Residual Dense Attention Generative Adversarial Network (RDAGAN). The network comprises a generator, an image discriminator, and a feature discriminator. The generator includes a Residual Dense Block (RDB) and a Convolutional Block Attention Module (CBAM), focusing on extracting the hierarchical features of the original LR image. Simultaneously, the added feature discriminator enables the network to generate high-frequency features pertinent to the image’s structure. Finally, we conducted experimental analysis and compared our model with six classic models. Compared with the best model, our model improved PSNR and SSIM by about 1.5 dB and 0.2, respectively.

This letter proposes a high-order probabilistic shaped (PS) system for joint source-channel coding (JSCC) using polar codes, which are employed for both source compression and channel coding. The modulation is based on a selection rule derived from the proposed bit classification distribution matching (BCDM) algorithm. An external binary random sequence is used to select and redundantly insert the codeword sequence, which makes the modulation symbol probability approach the Gaussian distribution, instead of being uniformly distributed as in the conventional constellation mapping. At the receiver, a joint belief propagation (JBP) decoder, which consists of a source and a channel BP decoder, is applied. Simulation results demonstrate that the proposed PS joint source-channel polar-coded modulation (PS-JSCPCM) system yields a significant enhancement to the bit error rate (BER) performance compared with the traditional unshaped joint source-channel code-modulation (JSCCM) system.

With the development of deep learning, the Super-Resolution (SR) reconstruction of microscopic images has improved significantly. However, the scarcity of microscopic images for training, the underutilization of hierarchical features in original Low Resolution (LR) images, and the high-frequency noise unrelated with the image structure generated during reconstruction process are still challenges in the Single Image Super-Resolution (SISR) field. Faced with these issues, we first collected sufficient microscopic images through Motic, a company engaged in the design and production of optical and digital microscopes, to establish a dataset. Secondly, we proposed a Residual Dense Attention Generative Adversarial Network (RDAGAN). The network comprises a generator, an image discriminator, and a feature discriminator. The generator includes a Residual Dense Block (RDB) and a Convolutional Block Attention Module (CBAM), focusing on extracting the hierarchical features of the original LR image. Simultaneously, the added feature discriminator enables the network to generate high-frequency features pertinent to the image’s structure. Finally, we conducted experimental analysis and compared our model with six classic models. Compared with the best model, our model improved PSNR and SSIM by about 1.5dB and 0.2, respectively.

Background: Social frailty (SF) is associated with multiple adverse health outcomes, yet there has been an inadequate focus on social frailty. The convoy model portrays the social networks through the perspective of the life course, thus providing a framework to explain the occurrence of social frailty. This study aimd to figure out the prevalence of social frailty and loneliness among community-dwelling older adults and to explore their correlations based on convoy model. Methods: This was a cross-sectional study, and 295 older adults from 10 communities of Zhengzhou in Henan Province participated in the study. Social frailty and loneliness were assessed separately with the Social Frailty Scale and University of California at Los Angeles-Loneliness Scale. The scores of social frailty of the older adults in different characteristic communities were compared by independent sample t-test and single factor analysis of variance. The influencing factors of social frailty were analysed by multiple stepwise linear regression and the structural equation model. The correlation between social frailty and loneliness was analysed by Pearson correlation analysis. Results: The total scores of social frailty and loneliness of the older adults in the community were (2.09 ± 1.53) and (43.19 ± 8.91), respectively. There was a moderate positive correlation between social frailty and loneliness (r = 0.621, P < 0.01). The results of multiple stepwise linear regression analysis showed that age, living styles, balance of payments, and loneliness were the main influencing factors of the social frailty of older adults in the community (F = 27.180, P < 0.001). The structural equation model of social frailty fitted well (χ² = 47.292, df = 26, χ²/df = 1.819, P = 0.007; RMSEA = 0.053, 95%CI (0.028, 0.076), P = 0.359; GFI = 0.971; AGFI = 0.939; NFI = 0.904; IFI = 0.955; TLI = 0.918; CFI = 0.953; SRMR = 0.0466). Conclusions: The convoy model had certain applicability in explanation of the relationship between loneliness and social frailty among older adults in community. The incidence of social frailty among the older adults in the community was high, and loneliness was at a medium level. It is necessary to strengthen the intervention of social frailty and loneliness of the older adults in the community, improve the quality of life of the older adults, and promote the development of healthy aging.

In recent years, polyvinyl alcohol hydrogel has been repeatedly confronted with urgent needs for more enhanced bio-activity in the field of bone tissue engineering. We used sulfydryl-alkyne click chemical reaction to prepare a bifunctional polyvinyl alcohol hydrogel containing RGD peptide and hyaluronic acid. Polyvinyl alcohol grafted with hyaluronic acid can reduce degradation rate and improve mechanical properties. RGD peptide can improve cell adhesion and migration on material surface. The bifunctional polyvinyl alcohol hydrogel is prepared by click chemistry reaction and characterized by scanning electron microscopy, ¹H NMR, Ellman’s test and Kjeldahl nitrogen determination. The physical properties of bifunctional polyvinyl alcohol hydrogel are evaluated in terms of tensile properties and water contact angle test. According to varying hydrogel composition, five groups are designed to conduct the experiment as follows: Control group, PVA group, PVA-HA group, PVAPA-RGD group, and PVA-HA-SH/PVAPA-RGD group, each of which is to be co-cultured with MC3T3-E1 cells to observe cell morphology by laser scanning confocal microscope. Then, CCK-8 method and cell counting method were used to detect the proliferation activity and cell adhesion rate, respectively. ALP activity and qRT-PCR were used to detect osteogenic differentiation and expression of osteogenic marker genes ALP, OCN, Runx2, Col I and OPN. The experimental results show that the tensile strength of the bifunctional polyvinyl alcohol hydrogel is 1.42±0.61 MPa, with tensile modulus as 0.58±0.03 MPa, and elongation at break as 297.53±42.36%. The water contact angle is 18.40±2.2°. After three days of culture, the proliferation activity ranks in order as PVA-HA-SH/PVAPA-RGD group > PVA-RGD group > PVA-HA group > PVA group > Control group. After 4 hours of culture, cell adhesion is sequenced as PVA-HA-SH/PVAPA-RGD group > bicomponent group > PVA group > Control group. After 48 hours of culture, immunofluorescence staining demonstrates more sufficient cellular spreading and adherence, clearer actin fibers, more vinculin numbers in PVA-HA-SH/PVAPA-RGD group. In addition, significantly higher ALP activity (after 3 days’ culturing) and osteogenic gene expression level (after 7 days’ culturing) have been witnessed in PVA-HA-SH/PVAPA-RGD group than those of other groups. The overall experimental results have confirmed its excellent hydrophilicity and fine mechanical properties of the bifunctional polyvinyl alcohol hydrogel system, which would exert a remarkable effect on proliferation, adhesion and osteogenic differentiation of MC3T3-E1 cells.