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Thin-film transistors made from solution-processed single-walled carbon nanotubes are used to fabricate large-scale integrated arrays of complementary static random access memory cells. Over the past two decades, extensive research on single-walled carbon nanotubes (SWCNTs) has elucidated their many extraordinary properties 1 , 2 , 3 , making them one of the most promising candidates for solution-processable, high-performance integrated circuits 4 , 5 . In particular, advances in the enrichment of high-purity semiconducting SWCNTs 6 , 7 , 8 have enabled recent circuit demonstrations including synchronous digital logic 9 , flexible electronics 10 , 11 , 12 , 13 , 14 and high-frequency applications 15 . However, due to the stringent requirements of the transistors used in complementary metal–oxide–semiconductor (CMOS) logic as well as the absence of sufficiently stable and spatially homogeneous SWCNT thin-film transistors 16 , 17 , 18 , the development of large-scale SWCNT CMOS integrated circuits has been limited in both complexity and functionality 19 , 20 , 21 . Here, we demonstrate the stable and uniform electronic performance of complementary p-type and n-type SWCNT thin-film transistors by controlling adsorbed atmospheric dopants and incorporating robust encapsulation layers. Based on these complementary SWCNT thin-film transistors, we simulate, design and fabricate arrays of low-power static random access memory circuits, achieving large-scale integration for the first time based on solution-processed semiconductors.

At present, the seismic structure of recoverable functional bridges based on seismic resilience is one of the hotspots in bridge seismic engineering research. Therefore, a new type of hybrid piers is designed in this paper, which mainly relies on replaceable components to achieve repairable structural performance after earthquakes. At the same time, four-level seismic fortification objectives based on seismic resilience is proposed, and the follow-up stiffness phenomenon is found on this basis. The finite element software OPENSEES was used to perform IDA analysis on a hybrid pier and an ordinary reinforced concrete (RC) pier. The fragility curves and seismic resilience curves of two piers were compared, and the seismic resilience performance and the follow-up stiffness phenomenon of the hybrid pier were studied. The results show that under the action of different seismic waves, the top displacement angle of the pier of the hybrid pier is slightly larger than that of the ordinary RC pier, but the overall difference is not large. The fragility curve of the hybrid pier is slightly larger than that of the ordinary RC pier. However, with the damage to the hybrid pier, the follow-up stiffness phenomenon impacts the seismic performance, which reduces the seismic force acting on the structure and improves the seismic resilience of the structure. The post-earthquake recovery time of two piers under different damage states was determined. Combined with the fragility curves, the seismic resilience curves of two piers were presented. The resilient index of the hybrid pier was always maintained at 0.9–1, and the seismic resilience performance was excellent.

The “welfare-to-work” program is a comprehensive supportive policy in the 14th five-year plan period in China. In this paper, a systematical assessment of the long-run effectiveness of the welfare-to-work policy on poverty reduction is of great significance to stimulate the internal impetus of people who are lifted out of poverty to achieve income growth and prosperity and promote regional economic development. Based on the data at the county (city) level in China from 2000 to 2019 and the sustainable development theory, in this paper, a county-relative poverty evaluation system was constructed. Besides, the double difference method was employed to evaluate the effect of the welfare-to-work policy on poverty reduction and test its action mechanism. The findings are as follows: (1) the welfare-to-work policy has a significant poverty reduction effect and presents an inverted “U” shape. In addition, significant achievements have been made in “maintaining employment stability, ensuring income, strengthening skills, and supporting the economy” ; (2) the welfare-to-work policy boosts poverty reduction in counties through infrastructure construction, fiscal intervention, and financial tools; however, the financial tools play a positive role in poverty reduction in the northwest region and suppressed role in the southwest region, and has an insignificant effect in the central region; and (3) there are differences in the effect of poverty alleviation policies of the counties with different sustainable development levels, and the regions with higher development level have a stronger driving effect.

Background: Chronic atrophic gastritis (CAG) is a chronic inflammatory disease and premalignant lesion of gastric cancer. As an antimicrobial peptide, hepcidin can maintain iron metabolic balance and is susceptible to inflammation. Objectives: The objective of this study was to clarify whether hepcidin is involved in abnormal iron metabolism and ferroptosis during CAG pathogenesis. Methods: Non-atrophic gastritis (NAG) and chronic atrophic gastritis (CAG) patient pathology slides were collected, and related protein expression was detected by immunohistochemical staining. The CAG rat model was established using MNNG combined with an irregular diet. Results: CAG patients and rats exhibited iron deposition in gastric tissue. CAG-induced ferroptosis in the stomach was characterized by decreased GPX4 and FTH levels and increased 4-HNE levels. Hepcidin, which is mainly located in parietal cells, was elevated in CAG gastric tissue. The high gastric level of hepcidin inhibited iron absorption in the duodenum by decreasing the protein expression of DMT1 and FPN1. In addition, the IL-6/STAT3 signaling pathway induced hepcidin production in gastric tissue. Conclusion: Our results showed that the high level of gastric hepcidin induced ferroptosis in the stomach but also inhibited iron absorption in the intestines. Inhibiting hepcidin might be a new strategy for the prevention of CAG in the future.

Nowadays, gastric cancer has become a significant issue in the global cancer burden, and its impact cannot be ignored. The rapid development of artificial intelligence technology is attempting to address this situation, aiming to change the clinical management landscape of gastric cancer fundamentally. In this transformative change, machine learning and deep learning, as two core technologies, play a pivotal role, bringing unprecedented innovations and breakthroughs in the diagnosis, treatment, and prognosis evaluation of gastric cancer. This article comprehensively reviews the latest research status and application of artificial intelligence algorithms in gastric cancer, covering multiple dimensions such as image recognition, pathological analysis, personalized treatment, and prognosis risk assessment. These applications not only significantly improve the sensitivity of gastric cancer risk monitoring, the accuracy of diagnosis, and the precision of survival prognosis but also provide robust data support and a scientific basis for clinical decision-making. The integration of artificial intelligence, from optimizing the diagnosis process and enhancing diagnostic efficiency to promoting the practice of precision medicine, demonstrates its promising prospects for reshaping the treatment model of gastric cancer. Although most of the current AI-based models have not been widely used in clinical practice, with the continuous deepening and expansion of precision medicine, we have reason to believe that a new era of AI-driven gastric cancer care is approaching. Graphical Abstract

Sparse Bayesian learning (SBL) has given renewed interest to the problem of direction-of-arrival (DOA) estimation. It is generally assumed that the measurement matrix in SBL is precisely known. Unfortunately, this assumption may be invalid in practice due to the imperfect manifold caused by unknown or misspecified mutual coupling. This paper describes a modified SBL method for joint estimation of DOAs and mutual coupling coefficients with uniform linear arrays (ULAs). Unlike the existing method that only uses stationary priors, our new approach utilizes a hierarchical form of the Student t prior to enforce the sparsity of the unknown signal more heavily. We also provide a distinct Bayesian inference for the expectation-maximization (EM) algorithm, which can update the mutual coupling coefficients more efficiently. Another difference is that our method uses an additional singular value decomposition (SVD) to reduce the computational complexity of the signal reconstruction process and the sensitivity to the measurement noise.

In this paper, we experimentally demonstrated a novel all-optical reservoir computer with an all optical input mask. The combination of the binary random masks and the time-stretched ultrashort pulses has increased the system’s classification performance. Compared with the traditional digital masks, this method shows superior classification performance in spoken-digit classification tasks and eliminates the need for high-speed modulation for digital masks.

Background: Infection with Helicobacter pylori ( H. pylori ) can cause chronic gastritis and other digestive tract diseases, and represents a public health concern. Current anti- H. pylori treatment can result in antibiotic resistance and other adverse reactions. Huangqi Jianzhong decoction (HQJZD) is a prescription form of traditional Chinese medicine for chronic gastritis that increases probiotics and inhibits H. pylori . In this study, its anti-bacterial activity against H. pylori receives a preliminary evaluation, and a pharmacology analysis is performed to predict its underlying mechanisms. Methods: Human GES-1 cells are divided into a blank control group, a model group, a HQJZD low-dose (2.08 mg·mL −1 ), a high-dose group (4.16 mg·mL −1 ), and a positive control group (amoxicillin, 5 μg·mL −1 ). After culture, the CCK-8 method is used to detect cell viability; flow cytometry is used to detect cell apoptosis rate; and RT-qPCR is used to detect the expression of mRNA virulence factors, including HpPrtC, OPiA, IceA1, and BabA2. Network pharmacology analysis and molecular docking were performed to explore the mechanisms of HQJZD in treating H. pylori gastritis, based on its anti- H. pylori infection effect. Results: We noted lower cell survival rates in the model group, but higher apoptosis rates and mRNA expressions of HpPrtC, OPiA, IceA1, and BabA2 than in the control group ( p < 0.05). Compared to the model group, the cell survival rate of each dosage group of Huangqi Jianzhong decoction and the positive control group increased significantly, while the apoptosis rate and the mRNA expressions of HpPrtC, OPiA, IceA1, and BabA2 were decreased significantly. The effect in each HQJZD group was dose-dependent ( p < 0.05). Network pharmacological analysis involving 159 signaling pathways was used to screen 6 key active components of HQJZD and 102 potential target proteins for the treatment of H. pylori -related gastritis. The molecular docking results revealed that the 6 active compounds had a strong binding ability with the target proteins of ALB, IL-6, AKT1, IL-1B, and JUN. Conclusion: HQJZD effectively increases the proliferation rate of human GES-1 cells after infection, while reducing the level of apoptosis. The mechanism may be related to multiple components, multiple targets and pathways, which provides a scientific basis for further elucidating the mechanism of action, the pharmacodynamic material basis, and the clinical application of HQJZD against H. pylori infection.

The generalization capability of the decision-making modules in unmanned ground vehicles (UGVs) is critical for their safe deployment in unseen environments. Prevailing evaluation methods, which rely on aggregated performance over static benchmark sets, lack the granularity to diagnose the root causes of model failure, as they often conflate the distinct influences of scenario similarity and intrinsic difficulty. To overcome this limitation, we introduce a fine-grained, dynamic evaluation framework that deconstructs generalization along the dual axes of multi-level difficulty and similarity. First, scenario similarity is quantified through a four-layer hierarchical decomposition, with results aggregated into a composite similarity score. Test scenarios are independently classified into ten discrete difficulty levels via a consensus mechanism integrating large language models and task-specific proxy models. By constructing a three-dimensional (3D) performance landscape across similarity, difficulty, and task performance, we enable detailed behavioral diagnosis. The framework assesses robustness by analyzing performance within the high-similarity band (90–100%), while the full 3D landscape characterizes generalization under distribution shift. Seven interpretable metrics are derived to quantify distinct facets of both generalization and robustness. This initial validation focuses on the path-planning layer under full state observability, establishing a foundational proof-of-concept for the framework. It not only ranks algorithms but also reveals non-trivial behavioral patterns, such as the decoupling between in-distribution robustness and out-of-distribution generalization. It provides a reliable and interpretable foundation for evaluating the readiness of UGVs for safe deployment in unseen environments.

This paper investigates the robustness properties of Pearson's rank-variate correlation coefficient (PRVCC) in scenarios where one channel is corrupted by impulsive noise and the other is impulsive noise-free. As shown in our previous work, these scenarios that frequently encountered in radar and/or sonar, can be well emulated by a particular bivariate contaminated Gaussian model (CGM). Under this CGM, we establish the asymptotic closed forms of the expectation and variance of PRVCC by means of the well known Delta method. To gain a deeper understanding, we also compare PRVCC with two other classical correlation coefficients, i.e., Spearman's rho (SR) and Kendall's tau (KT), in terms of the root mean squared error (RMSE). Monte Carlo simulations not only verify our theoretical findings, but also reveal the advantage of PRVCC by an example of estimating the time delay in the particular impulsive noise environment.