Semantic memory-based dynamic neural network using memristive ternary CIM and CAM for 2D and 3D vision.

The brain is dynamic, associative, and efficient. It reconfigures by associating the inputs with past experiences, with fused memory and processing. In contrast, AI models are static, unable to associate inputs with past experiences, and run on digital computers with physically separated memory and processing. We propose a hardware-software co-design, a semantic memory-based dynamic neural network using a memristor. The network associates incoming data with the past experience stored as semantic vectors. The network and the semantic memory are physically implemented on noise-robust ternary memristor-based computing-in-memory (CIM) and content-addressable memory (CAM) circuits, respectively. We validate our co-designs, using a 40-nm memristor macro, on ResNet and PointNet++ for classifying images and three-dimensional points from the MNIST and ModelNet datasets, which achieves not only accuracy on par with software but also a 48.1 and 15.9% reduction in computational budget. Moreover, it delivers a 77.6 and 93.3% reduction in energy consumption.

Causal relationship between mitochondrial-associated proteins and cerebral aneurysms: a Mendelian randomization study.

Background: Cerebral aneurysm is a high-risk cerebrovascular disease with a poor prognosis, potentially linked to multiple factors. This study aims to explore the association between mitochondrial-associated proteins and the risk of cerebral aneurysms using Mendelian randomization (MR) methods. Methods: We used GWAS summary statistics from the IEU Open GWAS project for mitochondrial-associated proteins and from the Finnish database for cerebral aneurysms (uIA, aSAH). The association between mitochondrial-associated exposures and cerebral aneurysms was evaluated using MR-Egger, weighted mode, IVW, simple mode and weighted median methods. Reverse MR assessed reverse causal relationship, while sensitivity analyses examined heterogeneity and pleiotropy in the instrumental variables. Significant causal relationship with cerebral aneurysms were confirmed using FDR correction. Results: Through MR analysis, we identified six mitochondrial proteins associated with an increased risk of aSAH: AIF1 (OR: 1.394, 95% CI: 1.109-1.752, p = 0.0044), CCDC90B (OR: 1.318, 95% CI: 1.132-1.535, p = 0.0004), TIM14 (OR: 1.272, 95% CI: 1.041-1.553, p = 0.0186), NAGS (OR: 1.219, 95% CI: 1.008-1.475, p = 0.041), tRNA PusA (OR: 1.311, 95% CI: 1.096-1.569, p = 0.003), and MRM3 (OR: 1.097, 95% CI: 1.016-1.185, p = 0.0175). Among these, CCDC90B, tRNA PusA, and AIF1 demonstrated a significant causal relationship with an increased risk of aSAH (FDR q < 0.1). Three mitochondrial proteins were associated with an increased risk of uIA: CCDC90B (OR: 1.309, 95% CI: 1.05-1.632, p = 0.0165), tRNA PusA (OR: 1.306, 95% CI: 1.007-1.694, p = 0.0438), and MRM3 (OR: 1.13, 95% CI: 1.012-1.263, p = 0.0303). In the reverse MR study, only one mitochondrial protein, TIM14 (OR: 1.087, 95% CI: 1.004-1.177, p = 0.04), showed a causal relationship with aSAH. Sensitivity analysis did not reveal heterogeneity or pleiotropy. The results suggest that CCDC90B, tRNA PusA, and MRM3 may be common risk factors for cerebral aneurysms (ruptured and unruptured), while AIF1 and NAGS are specifically associated with an increased risk of aSAH, unrelated to uIA. TIM14 may interact with aSAH. Conclusion: Our findings confirm a causal relationship between mitochondrial-associated proteins and cerebral aneurysms, offering new insights for future research into the pathogenesis and treatment of this condition.

O-Targeted Carbon Hybrid Orbital Conversion to Produce sp2-Rich Closed Pores for Sodium-Storage Hard Carbon.

Biomass-based hard carbon has the advantages of a balanced cost and electrochemical performance, making it the most promising anode material for sodium-ion batteries. However, due to the structural limitations of biomass (such as macropores and impurities), it still faces the problems of low specific capacity and initial Coulombic efficiency (ICE). Herein, an integrated strategy of biomass liquefaction and oxidation treatment is proposed to fabricate hard carbon with low ash content and sp2-rich closed pores. Specifically, liquefaction treatment can break through the inherent constraints of biomass, while oxidation treatment with O-targeted effect can directionally convert C─C/C─O bonds into C═O/O═C─O bonds, which would promote the formation of closed pores and the rearrangement into sp2-carbon within the graphene layer. Moreover, it is well demonstrated that the hard carbon interface rich in sp2 hybridization can induce the generation of an inorganic-rich solid electrolyte interface, contributing to fast ion migration and excellent interfacial stability. As a result, the optimized hard carbon with maximum closed pore volume and sp2/sp3 ratio can exhibit a high capacity of 347.3 mAh g-1 at 20 mA g-1 with the ICE of 90.5%, and a capacity of 110.4 mAh g-1 at 5.0 A g-1 after 10 000 cycles.

Inhibition of STRA6 suppresses NSCLC growth via blocking STAT3/SREBP-1c axis-mediated lipogenesis.

Dysregulation in lipid metabolism is among the most prominent metabolic alterations in cancer. Stimulated by retinoic acid 6 (STRA6), a vitamin A transporter has shown to be involved in the pathogenesis of cancers. Nevertheless, the function of STRA6 in non-small cell lung cancer (NSCLC) progression remains undefined. We obtained cancer and adjacent tissues from NSCLC patients and conducted functional experiments on STRA6 on NSCLC cell lines and mice. High STRA6 expression is correlated with poor prognosis in patients with NSCLC. Results from in vitro and in vivo animal studies showed that STRA6 knockdown suppressed the proliferation, migration, and invasion of NSCLC cells in vitro and tumor growth in vivo through regulation of lipid synthesis. Mechanistically, STRA6 activated a Janus kinase 2/signal transducer and activator of transcription 3 (JAK2-STAT3) signaling cascade which inducing the expression of STAT3 target gene. By inducing the expression of the target gene of STAT3, sterol regulatory element binding protein 1 (SREBP-1), STRA6 promotes SREBP-1-mediated adipogenesis and provides energy for NSCLC cell growth. Our study uncovers a novel STRA6/STAT3/SREBP-1 regulatory axis that enhances NSCLC metastasis by reprogramming of lipid metabolism. These results demonstrate the potential use of STRA6 as a biomarker for diagnosing NSCLC, which may therefore potentially serve as a therapeutic target for NSCLC.

Comparative Analysis of AI-SONICTM Thyroid System and Six Thyroid Risk Stratification Guidelines in Papillary Thyroid Cancer: A Retrospective Cohort Study.

Aim: The study aimed to compare the diagnostic performance of AI-SONICTM Thyroid System (AI-SONICTM) with six thyroid nodule ultrasound risk stratification systems, as well as the interobserver agreement among different-year ultrasound examiners using the same diagnostic approach. Methods: This retrospective study included patients who underwent thyroid ultrasound examination and surgery between 2010 and 2022. Three ultrasound examiners with 2, 5, and 10 years of experience, respectively, used AI-SONICTM and six guidelines to risk-stratify the nodules. The diagnostic performance and interobserver agreement were assessed. Results: A total of 370 thyroid nodules were included, including 195 papillary thyroid carcinomas (PTC) and 175 benign nodules. For physicians of varying seniority from low to high, AI-SONICTM had a moderate sensitivities of 82.56%, 83.08%, 84.62%, respectively, while AACE/ACE/AME had the highest diagnostic sensitivities (96.41%, 95.38%, 96.41%, respectively); And relatively higher specificities were 85.14%, 85.71%, 85.71% for KSThR, while moderate specificities with values of 84.0%, 85.14%, and 85.71%, respectively were found for AI-SONICTM; The accuracy was highest for ATA (excluding non-classifiable nodules), with values of 87.26%, 87.93%, and 88.82%, respectively, while the accuracy for AI-SONICTM were 83.24%, 84.05%, and 85.14%, respectively. The Kendall's tau coefficient indicated strong or moderate interobserver agreement among all examiners using different diagnostic methods (Kendall's tau coefficient >0.6, P<0.001). AI-SONICTM showed the highest interobserver agreement (Kendall's tau coefficient=0.995, P<0.001). A binary probit regression analysis showed that nodules with cystic components had a significantly higher regression coefficient value of 0.983 (P=0.002), indicating that AI-SONICTM may have higher accuracy for nodules with cystic components. Conclusion: AI-SONICTM and the six thyroid nodule ultrasound risk stratification systems showed high diagnostic performance for papillary thyroid carcinoma. All examiners showed strong or moderate interobserver agreement when using different diagnostic methods. AI-SONICTM may have higher accuracy for nodules with cystic components.

Quality evaluation for Ficus hirta Vahl granules, using TLC and HPLC fingerprint combined with chemical pattern recognition.

Ficus hirta Vahl is a healthy food with both medicinal and culinary properties and with anti-inflammatory and anti-aging effects. There is currently no standardized or universally accepted research strategy for evaluating the quality of Ficus hirta Vahl granules (FHGs). Therefore, the development of a comprehensive quality evaluation method is crucial for the quality control of FHGs. In this study, we used n-hexane : trichloromethane : ethyl acetate : glacial acetic acid = 20 : 4 : 7 : 1 as the optimal developing agent for TLC to separate and identify 15 batches of FHGs from different origins. Using HPLC, a fingerprint with 7 common peaks was established, and peaks 6 and 7 were attributed to psoralen and bergapten, respectively. The content of the identified components was determined. Further quality evaluation of FHGs was performed using chemical pattern recognition, and the results showed that hierarchical cluster analysis (HCA) could cluster 15 batches of FHGs into 2 categories. Principal component analysis (PCA) showed that 2 principal components can show the similarities and differences between different batches of FHGs. Orthogonal partial least squares discrimination (OPLS-DA) showed that components 5, 6 (psoralen) and 7 (bergapten) are landmark components that cause differences in FHG quality from different regions. By integrating the analytical modes of TLC, HPLC fingerprint and chemical pattern recognition, a scientific basis is provided for the comprehensive control and evaluation of herbal medicine quality.

Advancing driver fatigue detection in diverse lighting conditions for assisted driving vehicles with enhanced facial recognition technologies.

Against the backdrop of increasingly mature intelligent driving assistance systems, effective monitoring of driver alertness during long-distance driving becomes especially crucial. This study introduces a novel method for driver fatigue detection aimed at enhancing the safety and reliability of intelligent driving assistance systems. The core of this method lies in the integration of advanced facial recognition technology using deep convolutional neural networks (CNN), particularly suited for varying lighting conditions in real-world scenarios, significantly improving the robustness of fatigue detection. Innovatively, the method incorporates emotion state analysis, providing a multi-dimensional perspective for assessing driver fatigue. It adeptly identifies subtle signs of fatigue in rapidly changing lighting and other complex environmental conditions, thereby strengthening traditional facial recognition techniques. Validation on two independent experimental datasets, specifically the Yawn and YawDDR datasets, reveals that our proposed method achieves a higher detection accuracy, with an impressive 95.3% on the YawDDR dataset, compared to 90.1% without the implementation of Algorithm 2. Additionally, our analysis highlights the method's adaptability to varying brightness levels, improving detection accuracy by up to 0.05% in optimal lighting conditions. Such results underscore the effectiveness of our advanced data preprocessing and dynamic brightness adaptation techniques in enhancing the accuracy and computational efficiency of fatigue detection systems. These achievements not only showcase the potential application of advanced facial recognition technology combined with emotional analysis in autonomous driving systems but also pave new avenues for enhancing road safety and driver welfare.

2D Reconfigurable Memory Device Enabled by Defect Engineering for Multifunctional Neuromorphic Computing.

In this era of artificial intelligence and Internet of Things, emerging new computing paradigms such as in-sensor and in-memory computing call for both structurally simple and multifunctional memory devices. Although emerging two-dimensional (2D) memory devices provide promising solutions, the most reported devices either suffer from single functionalities or structural complexity. Here, this work reports a reconfigurable memory device (RMD) based on MoS2/CuInP2S6 heterostructure, which integrates the defect engineering-enabled interlayer defects and the ferroelectric polarization in CuInP2S6, to realize a simplified structure device for all-in-one sensing, memory and computing. The plasma treatment-induced defect engineering of the CuInP2S6 nanosheet effectively increases the interlayer defect density, which significantly enhances the charge-trapping ability in synergy with ferroelectric properties. The reported device not only can serve as a non-volatile electronic memory device, but also can be reconfigured into optoelectronic memory mode or synaptic mode after controlling the ferroelectric polarization states in CuInP2S6. When operated in optoelectronic memory mode, the all-in-one RMD could diagnose ophthalmic disease by segmenting vasculature within biological retinas. On the other hand, operating as an optoelectronic synapse, this work showcases in-sensor reservoir computing for gesture recognition with high energy efficiency.

Utility of Second-look Ultrasonography in Distinguishing BI-RADS 4 Calcifications Detected on Mammography: An observational study.

This study aimed to assess the utility of second-look ultrasonography (US) in differentiating breast imaging reporting and data system (BI-RADS) 4 calcifications initially detected on mammography (MG). BI-RADS 4 calcifications have a wide range of positive predictive values. We hypothesized that second-look US would help distinguish BI-RADS 4 calcifications without clinical manifestations and other abnormalities on MG. This study included 1622 pure BI-RADS 4 calcifications in 1510 women (112 patients with bilateral calcifications). The cases were randomly divided into training (85%) and testing (15%) datasets. Two nomograms were developed to differentiate BI-RADS 4 calcifications in the training dataset: the MG-US nomogram, based on multifactorial logistic regression and incorporated clinical information, MG, and second-look US characteristics, and the MG nomogram, based on clinical information and mammographic characteristics. Calibration of the MG-US nomogram was performed using calibration curves. The discriminative ability and clinical utility of both nomograms were compared using the area under the receiver operating characteristic curve (AUC) and the decision analysis curve (DCA) in the test dataset. The clinical information and imaging characteristics were comparable between the training and test datasets. The bias-corrected calibration curves of the MG-US nomogram closely approximate the ideal line for both datasets. In the test dataset, the MG-US nomogram exhibited a higher AUC than the MG nomogram (0.899 vs 0.852, P = .01). DCA demonstrated the superiority of the MG-US nomogram over the MG nomogram. Second-look US features, including ultrasonic calcifications, lesions, and moderate or marked color flow, were valuable for distinguishing BI-RADS 4 calcifications without clinical manifestations and other abnormalities on MG.

Melt Compounding of Poly(lactic acid)-Based Composites: Blending Strategies, Process Conditions, and Mechanical Properties.

Polylactic acid (PLA), derived from renewable resources, has the advantages of rigidity, thermoplasticity, biocompatibility, and biodegradability, and is widely used in many fields such as packaging, agriculture, and biomedicine. The excellent processability properties allow for melt processing treatments such as extrusion, injection molding, blow molding, and thermoforming in the preparation of PLA-based materials. However, the low toughness and poor thermal stability of PLA limit its practical applications. Compared with pure PLA, conditions such as processing technology, filler, and crystallinity affect the mechanical properties of PLA-based materials, including tensile strength, Young's modulus, and elongation at break. This review systematically summarizes various technical parameters for melt processing of PLA-based materials and further discusses the mechanical properties of PLA homopolymers, filler-reinforced PLA-based composites, PLA-based multiphase composites, and reactive composite strategies for PLA-based composites.

EZH2-STAT3 signaling pathway regulates GSDMD-mediated pyroptosis in glioblastoma.

Glioblastoma multiforme (GBM) is the most therapeutically challenging primary brain tumor owing to the unique physiological structure of the blood-brain barrier. Lately, research on targeted therapy for gliomas has shifted focus toward the tumor microenvironment and local immune responses. Pyroptosis is a newly identified cellular demise characterized by the release of numerous inflammatory factors. While pyroptosis shows promise in impeding the occurrence and progression of GBM, the regulatory mechanisms governing this process in gliomas still require further investigation. The function of the Enhancer of zeste homolog 2 (EZH2) in pyroptosis remains unexplored. In this study, we discovered that 3-Deazaneplanocin A (DZNep), an inhibitor of EZH2, can induce pyroptosis in GBM in vitro experiments. Moreover, our investigation unveiled that the signal transducer and activator of transcription (STAT3) could serve as a downstream regulator influenced by EZH2, impacting pyroptosis in GBM. Following treatment with DZNep and the STAT3 inhibitor (SH-4-54), there was an elevation in the levels of pyroptosis-related factors, namely NOD-like receptor thermal protein domain-associated protein 3 (NLRP3) and Gasdermin D (GSDMD). Moreover, simultaneous inhibition of both EZH2 and STAT3 led to the expression of inflammatory factors such as IL-1ß and IL-18. In summary, we have identified that EZH2 regulates pyroptosis in GBM through STAT3, and pyroptosis could potentially be targeted for immunotherapy in GBM.

Beyond Mechanical Protection: The Electron-Shielding Effect of SWCNT for the Stabilized SiO Interface.

The single-walled carbon nanotube (SWCNT) commonly serves as a conductive additive for SiO-based anode materials due to the excellent conductivity and mechanical properties. However, the potential action mechanisms for the SWCNT beyond conductivity and mechanical features have rarely been studied. Herein, an interfacial electron-shielding effect and preferential adsorption to the electrolyte components for the SWCNT are revealed through a series of advanced characterizations and density functional theory (DFT) simulations. It can be determined that SWCNT networks could restrict the transmission of the electron from SiO interface to electrolyte with the reduced decomposition, because of the typical axial conductivity of the SWCNT. Moreover, the SWCNT shows stronger adsorption energy for LiPF6 and ethylene carbonate (EC) molecules, rather than nonselectivity of traditional carbon additives, facilitating the generation of inorganic-rich and denser solid electrolyte interface (SEI) film. As a result, benefiting from the electron-shielding effect, preferential adsorption, and mechanical protection, the SWCNT endows the SiO@C anode with a higher average Coulombic efficiency (CE) value of 99.4% over 100 cycles and a long cycling stability.

Screening Methods for Cervical Cancer.

Cervical cancer seriously affects the health of women worldwide. Persistent infection of high-risk HPV (Human Papilloma Virus) can lead to cervical cancer. There is a great need for timely and efficient screening methods for cervical cancer. The current screening methods for cervical cancer are mainly based on cervical cytology and HPV testing. Cervical cytology is made of Pap smear and liquid-based cytology, while HPV testing is based on immunological and nucleic acid level detection methods. This review introduces cervical cancer screening methods based on cytology and human papillomavirus testing in detail. The advantages and limitations of the screening methods are also summarized and compared.

Quality markers of Polygala fallax Hemsl decoction based on qualitative and quantitative analysis combined with network pharmacology and chemometric analysis.

INTRODUCTION: Polygala fallax Hemsl (PFH) is a widely used herbal medicine in Guangxi, China. At present, research on PFH mainly focuses on extraction technology and cultivation, lacking quality control standards for systematic evaluation. OBJECTIVES: The study aimed to assess the quality of PFH from different sources and to predict markers that would help assess quality. METHODS: Fingerprinting of 15 batches of PFH samples was performed by ultra-high performance liquid chromatography (UPLC) and similarity was assessed using hierarchical cluster analysis (HCA), principal component analysis (PCA), and orthogonal partial least squares discrimination (OPLS-DA). Differential components were screened by mathematical analysis, and a "component-target-pathway" network map was constructed in combination with network pharmacology, quality markers (Q-markers) of PFH were predicted, and quantitative analysis was performed. RESULTS: Fifteen batches were fingerprinted for PFH, with 11 common peaks, and peak 5 was identified as 4-hydroxybenzoic acid, which was generally consistent with the results of HCA, PCA, and OPLS-DA. Network pharmacology screened 18 potential compounds, 45 core targets, and 20 key pathways, integrating fingerprinting, pattern recognition, and network pharmacology methods. One of the potential Q-markers that can identify the principle of testability, efficacy, and specificity is 4-hydroxybenzoic acid, whose content ranges from 0.0188 to 1.4517 mg/g. CONCLUSION: The potential Q-markers of PFH were predicted by integrating fingerprinting, pattern recognition, and network pharmacological analysis, which provided a scientific basis for the overall control and evaluation of the quality of PFH and a theoretical reference for the study of the quality standard of multi-base traditional Chinese medicine.

Epitope binning for multiple antibodies simultaneously using mammalian cell display and DNA sequencing.

Epitope binning, an approach for grouping antibodies based on epitope similarities, is a critical step in antibody drug discovery. However, conventional methods are complex, involving individual antibody production. Here, we established Epitope Binning-seq, an epitope binning platform for simultaneously analyzing multiple antibodies. In this system, epitope similarity between the query antibodies (qAbs) displayed on antigen-expressing cells and a fluorescently labeled reference antibody (rAb) targeting a desired epitope is analyzed by flow cytometry. The qAbs with epitope similar to the rAb can be identified by next-generation sequencing analysis of fluorescence-negative cells. Sensitivity and reliability of this system are confirmed using rAbs, pertuzumab and trastuzumab, which target human epidermal growth factor receptor 2. Epitope Binning-seq enables simultaneous epitope evaluation of 14 qAbs at various abundances in libraries, grouping them into respective epitope bins. This versatile platform is applicable to diverse antibodies and antigens, potentially expediting the identification of clinically useful antibodies.

NAD-Dependent Protein Deacetylase Sirtuin-1 Mediated Mitophagy Regulates Early Brain Injury After Subarachnoid Hemorrhage.

Background: This study focuses on the role of SIRT1 in neuroinflammation caused by early brain injury (EBI) after subarachnoid hemorrhage (SAH), and explores its mechanism in mitophagy after SAH. Methods: C57BL/6J mice and primary microglia SAH in vivo and in vitro models were constructed to explore the expression level of SIRT1 in neuroinflammation after SAH. Subsequently, the brain edema content, blood-brain barrier (BBB) damage and neurological function scores of the mice were observed after using the SIRT1 inhibitor EX-527. q-PCR and Western blot were used to detect relevant genes and proteins, and enzyme-linked immunosorbent assay (ELISA) was used to detect the levels of IL-6, IL-1ß, and TNF-α inflammatory factors. Immunofluorescence staining was used to observe the positive level of SIRT1 and the degree of mitochondria-lysosome fusion, and transmission electron microscopy was used to observe mitochondrial damage and autophagosome levels. Results: In in vivo and in vitro experiments, we found that SIRT1 expression increased after SAH, and neurological deficits, brain edema, and blood-brain barrier damage after SAH were aggravated. Inhibiting SIRT1 further aggravates the aforementioned damage. In addition, EX-527 can also inhibit the level of mitophagy and aggravate neuroinflammation after SAH. Conclusion: Our results indicated that SIRT1 promotes mitophagy and alleviates neuroinflammation after SAH.

Comprehensive analysis of CYBB as a prognostic marker and therapeutic target in glioma: A bioinformatics approach.

Background: In the central nervous system, glioma is the most common malignant tumor, and patients have a poor prognosis. Identification of novel marker genes and establishment of prognostic models are important for early diagnosis and prognosis determination. Methods: Download glioma data from the CGGA and TCG databases. Application of bioinformatics to analyze the impact of CYBB on the clinicopathological characteristics, immunological features and prognosis of gliomas. Using single-cell sequencing data from 7 glioblastoma patients in the CGGA database, the role of CYBB in the tumor microenvironment was analyzed. In addition, a prognostic model was constructed based on CYBB high and low differentially expressed genes and mitochondrial genes. Results: The expression of CYBB is closely related to various clinical features, immune cell infiltration level, immune checkpoint and survival time of patients. A 10-gene prediction model was constructed based on the differentially expressed genes of low and high CYBB and mitochondria-related genes. Glioma patients with higher risk scores had significantly lower survival probabilities. Receiver operating characteristic curves and nomograms were plotted over time to show the predictive accuracy and predictive value of the 10-gene prognostic model. Conclusions: Our study shows that CYBB is strongly correlated with clinical characteristics features and prognosis of glioma patients, and can be used as a potential therapeutic target. Prognostic models based on CYBB and mitochondrial genes have good performance in predicting prognosis of glioma patients.

Oscillatory Neural Network-Based Ising Machine Using 2D Memristors.

Neural networks are increasingly used to solve optimization problems in various fields, including operations research, design automation, and gene sequencing. However, these networks face challenges due to the nondeterministic polynomial time (NP)-hard issue, which results in exponentially increasing computational complexity as the problem size grows. Conventional digital hardware struggles with the von Neumann bottleneck, the slowdown of Moore's law, and the complexity arising from heterogeneous system design. Two-dimensional (2D) memristors offer a potential solution to these hardware challenges, with their in-memory computing, decent scalability, and rich dynamic behaviors. In this study, we explore the use of nonvolatile 2D memristors to emulate synapses in a discrete-time Hopfield neural network, enabling the network to solve continuous optimization problems, like finding the minimum value of a quadratic polynomial, and tackle combinatorial optimization problems like Max-Cut. Additionally, we coupled volatile memristor-based oscillators with nonvolatile memristor synapses to create an oscillatory neural network-based Ising machine, a continuous-time analog dynamic system capable of solving combinatorial optimization problems including Max-Cut and map coloring through phase synchronization. Our findings demonstrate that 2D memristors have the potential to significantly enhance the efficiency, compactness, and homogeneity of integrated Ising machines, which is useful for future advances in neural networks for optimization problems.

Dual-Function Electrolyte Additive Design for Long Life Alkaline Zinc Flow Batteries.

Alkaline zinc-based flow batteries (AZFBs) have emerged as a promising electrochemical energy storage technology owing to Zn abundance, high safety, and low cost. However, zinc dendrite growth and the formation of dead zinc greatly impede the development of AZFBs. Herein, a dual-function electrolyte additive strategy is proposed to regulate zinc nucleation and mitigate the hydroxide corrosion of zinc depositions for stable AZFBs. This strategy, as exemplified by urea, introduces an electrolyte additive to coordinate with Zn2+/Zn with proper strength, slowing zinc deposition kinetics to induce uniform nucleation and protecting the deposited zinc surface from attack by hydroxide ions through preferable adsorption. The zincate complexes with urea are identified to be Zn(OH)2(urea)(H2O)2 and Zn2(OH)4(H2O)4(urea), which exhibit slow zinc deposition kinetics, allowing instantaneous nucleation. Calculation results reveal an additional energy barrier of 1.29 eV for the subsequent adsorption of an OH- group when a urea molecule absorbs on the zinc cluster, significantly mitigating the formation of dead zinc. Consequently, prolonged cell cycling of the prototype alkaline zinc-iron flow battery demonstrates stable operation for over 130 h and an average coulombic efficiency of 98.5%. It is anticipated that this electrolyte additive strategy will pave the way for developing highly stable AZFBs.

Non-sweep DC component estimation method for a virtual-carrier assisted Kramers-Kronig receiver.

The Kramers-Kronig (KK) receiver has attracted much attention in short-range optical interconnection because of its ability to recover the phase of the signal from the intensity information through KK algorithm. In high-speed KK systems, such as virtual-carrier (VC) assisted ones, an alternating current (AC) coupled photo-detector (PD) is preferred due to relaxing the requirements of analog-to-digital converter (ADC) and electronic amplifier by filtering direct current (DC) component. However, the loss of the DC component will cause the KK algorithm to break down, so it is necessary to accurately recover DC value in the digital domain with multiple-sweep. In this paper, we propose what we believe is a novel non-sweep DC component estimation scheme based on optimized digital carrier-to-signal power ratio (OD-CSPR) method, which can accurately estimate the DC component with only 3-4 iterations in the scenario of VC-assisted KK receiver optical transmission. The scheme utilizes the one-dimensional search optimization algorithm based on golden section search and parabolic interpolation without sweeping. The simulation and experimental results of the proposed non-sweep OD-CSPR method show that the DC component can be estimated accurately in a large CSPR range, and the system performance is close to that of the conventional DC-sweep method. Compared with the typical defined digital CSPR (DD-CSPR) based optimization method, the proposed one can realize optical signal-to-noise ratio (OSNR) gains of 0.9 dB in the back-to-back (B2B) and 0.7 dB under 80 km fiber transmission scenarios respectively with a total bit rate of 160Gb/s.