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In the field of intelligent manufacturing, image anomaly detection plays a pivotal role in automated product quality inspection. Most existing anomaly detection methods are adept at capturing local features of images, achieving high detection accuracy for structural anomalies such as cracks and scratches. However, logical anomalies typically appear normal within local regions of an image and are difficult to represent well by the anomaly score map, requiring the model to possess the capability to extract global context features. To address this challenge while balancing the detection of both structural and logical anomalies, this paper proposes a lightweight anomaly detection framework built upon EfficientAD. This framework integrates the reconstruction difference constraint (RDC) and a logical anomaly detection module. Specifically, the original EfficientAD relies on the coarse-grained reconstruction difference between the student and the autoencoder to detect logical anomalies; but, false detection may be caused by the local fine-grained reconstruction difference between the two models. RDC can promote the consistency of the fine-grained reconstruction between the student and the autoencoder, thereby effectively alleviating this problem. Furthermore, in order to detect anomalies that are difficult to represent by feature maps more effectively, the proposed logical anomaly detection module extracts and aggregates the context features of the image, and combines the feature-based method to calculate the overall anomaly score. Extensive experiments demonstrate our method’s significant improvement in logical anomaly detection, achieving 94.2 AU-ROC on MVTec LOCO, while maintaining strong structural anomaly detection performance at 98.4 AU-ROC on MVTec AD. Compared to the baseline, like EfficientAD, our framework achieves a state-of-the-art balance between both anomaly types.

The new energy vehicle (NEV) industry is a key sector for achieving dual carbon goals and advancing regional green transformation. Its sustainable development depends on the deep coupling of the industrial chain and the innovation chain. Drawing on data from Shaanxi’s NEV industry covering the period 2014–2023, this study employed kernel density estimation (KDE), the entropy weight method, the coupling coordination degree model, and the optimal parameter geographical detector. Specifically, we examine Shaanxi’s national positioning and spatial pattern within the NEV industry, the spatiotemporal evolution of the coupling coordination degree between its industrial and innovation chains, and the key driving factors along with their interaction mechanisms. The results indicate that Shaanxi is situated within the secondary core growth zone of central and western China. Within the province, the industry exhibits a pronounced spatial pattern characterized by single core concentration in Xi’an, contiguous support across the Guanzhong region, and point-like distribution in northern and southern Shaanxi. The dual-chain coupling coordination degree in Shaanxi’s NEV industry has improved steadily, resulting in a four-tier structure comprising core breakthrough, secondary catch-up, weak foundation, and lagging predicament categories. The dominant driving factors are Industrial Agglomeration Degree, Research and Development (R&D) Funding Input, and Resource Utilization Rate. The interaction between Resource Utilization Rate and Integration Degree exerts the strongest effect.

Autophagy degrades phagocytosed damaged organelles, misfolded proteins, and various pathogens through lysosomes as an essential way to maintain cellular homeostasis. Autophagy is a tightly regulated cellular self-degradation process that plays a crucial role in maintaining normal cellular function and homeostasis in the body. The NLRP3 inflammasome in neuroinflammation is a vital recognition receptor in innate cellular immunity, sensing external invading pathogens and endogenous stimuli and further triggering inflammatory responses. The NLRP3 inflammasome forms an inflammatory complex by recognizing DAMPS or PAMPS, and its activation triggers caspase-1-mediated cleavage of pro-IL-1β and pro-IL-18 to promote the inflammatory response. In recent years, it has been reported that there is a complex interaction between autophagy and neuroinflammation. Strengthening autophagy can regulate the expression of NLRP3 inflammasome to reduce neuroinflammation in disease and protect neurons. However, the related mechanism is not entirely clear. The formation of protein aggregates is one of the common features of Alzheimer's diseases(AD) and Parkinson's diseases(PD). A large number of toxic protein aggregates can induce inflammation. In theory, activation of the autophagy pathway can remove the potential toxicity of protein aggregates and delay the progression of the disease. This article aims to review recent research on the interaction of autophagy, NLRP3 inflammasome, and protein aggregates in and PD, analyze the mechanism, and provide theoretical reference for further primary research in the future.

Although soil moisture is a key factor of hydrologic and climate applications, global continuous high resolution soil moisture datasets are still limited. Here we use physics-informed machine learning to generate a global, long-term, spatially continuous high resolution dataset of surface soil moisture, using International Soil Moisture Network (ISMN), remote sensing and meteorological data, guided with the knowledge of physical processes impacting soil moisture dynamics. Global Surface Soil Moisture (GSSM1 km) provides surface soil moisture (0–5 cm) at 1 km spatial and daily temporal resolution over the period 2000–2020. The performance of the GSSM1 km dataset is evaluated with testing and validation datasets, and via inter-comparisons with existing soil moisture products. The root mean square error of GSSM1 km in testing set is 0.05 cm 3 /cm 3 , and correlation coefficient is 0.9. In terms of the feature importance, Antecedent Precipitation Evaporation Index (APEI) is the most important significant predictor among 18 predictors, followed by evaporation and longitude. GSSM1 km product can support the investigation of large-scale climate extremes and long-term trend analysis.

The integration of various two-dimensional (2D) materials on wafers enables a more-than-Moore approach for enriching the functionalities of devices 1 – 3 . On the other hand, the additive growth of 2D materials to form heterostructures allows construction of materials with unconventional properties. Both may be achieved by materials transfer, but often suffer from mechanical damage or chemical contamination during the transfer. The direct growth of high-quality 2D materials generally requires high temperatures, hampering the additive growth or monolithic incorporation of different 2D materials. Here we report a general approach of growing crystalline 2D layers and their heterostructures at a temperature below 400 °C. Metal iodide (MI, where M = In, Cd, Cu, Co, Fe, Pb, Sn and Bi) layers are epitaxially grown on mica, MoS 2 or WS 2 at a low temperature, and the subsequent low-barrier-energy substitution of iodine with chalcogens enables the conversion to at least 17 different 2D crystalline metal chalcogenides. As an example, the 2D In 2 S 3 grown on MoS 2 at 280 °C exhibits high photoresponsivity comparable with that of the materials grown by conventional high-temperature vapour deposition (~700–1,000 °C). Multiple 2D materials have also been sequentially grown on the same wafer, showing a promising solution for the monolithic integration of different high-quality 2D materials. High-quality crystalline two-dimensional layers of metal halides can be on mica, MoS 2 or WS 2 at temperatures below 400 °C.

Reliable onboard fault detection and diagnosis (FDD) is essential for autonomous small-satellite constellation operations. The satellite telemetry streams are typically high-dimensional, strongly time-correlated, and severely imbalanced. These characteristics make rare but critical faults hard to recognize. To address these issues, this paper proposes an imbalance-aware spatiotemporal diagnostic framework based on three-dimensional convolutional neural networks (3D-CNNs). Multivariate telemetry is first converted into structured spatiotemporal volumes via sliding-window segmentation and grid-based embedding. This enables the model to jointly learn temporal evolution and cross-parameter coupling patterns. A lightweight residual 3D-CNN is developed to enable end-to-end multi-class classification. In addition, a class-balanced focal objective function is introduced to mitigate class-imbalance issues and enhance sensitivity to minority fault modes. The Lumelite series satellite telemetry dataset, comprising 23 fault types, is constructed for training and evaluation. The proposed lightweight residual 3D-CNN is benchmarked against long short-term memory–random forest (LSTM-RF), support vector machine (SVM), 2D-CNN, CNN-LSTM, and residual neural network models. Experimental results show that the proposed algorithm has the highest overall accuracy and Macro-F1 score. It also obtains higher Recall for low-frequency faults. The computational complexity studies indicate that the proposed algorithm has promising potential for real-time satellite health monitoring.

Cold atmospheric plasma (CAP), a novel promising anti-cancer modality, has shown its selective anti-cancer capacity on dozens of cancer cell lines in vitro and on subcutaneous xenograft tumors in mice. Over the past five years, the CAP-stimulated solutions (PSS) have also shown their selective anti-cancer effect over different cancers in vitro and in vivo . The solutions used to make PSS include several bio-adaptable solutions, mainly cell culture medium and simple buffered solutions. Both the CAP-stimulated medium (PSM) and the CAP-stimulated buffered solution (PSB) are able to significantly kill cancer cells in vitro . In this study, we systematically compared the anti-cancer effect of PSM and PSB over pancreatic adenocarcinoma cells and glioblastoma cells. We demonstrated that pancreatic cancer cells and glioblastoma cells were specifically vulnerable to PSM and PSB, respectively. The specific response such as the rise of intracellular reactive oxygen species of two cancer cell lines to the H 2 O 2 -containing environments might result in the specific vulnerabilities to PSM and PSB. In addition, we demonstrated a basic guideline that the toxicity of PSS on cancer cells could be significantly modulated through controlling the dilutability of solution.

Background China is ranked third globally in terms of burden and has a moderately high to high prevalence of tuberculosis (TB). This study meticulously investigated the notification rates of TB and assessed the epidemic in China from 2000 to 2021. The aim of the study was to provide robust supporting data that is crucial for enhancing TB prevention and control strategies. Methods Extensive data regarding TB notification rates in China between 2000 and 2021 was collected. The joinpoint regression model was subsequently utilized to assess the temporal trends in the notification rates of TB, which were analyzed through the annual percentage change (APC) and the average annual percentage change (AAPC). Results During the study period (2000–2021), the standardized notification rates of TB in China ranged from 38.89/100,000 to 101.15/100,000, with a significant annual average decrease of 4.43% ( P  < 0.05). Before the COVID-19 pandemic, a marked acceleration in this decline was observed from 2006 to 2015, with an APC of 4.62% ( P  < 0.05). Stratified by age and sex, the age group with the most significant annual decline in overall standardized notification rates of TB among males in China was < 15 years old, followed by 55–64 years old, and the group with the least decrease was 25–44 years old. Similarly, the age group with the most significant annual decline in standardized notification rates of TB among females was < 15 years old. Conclusions The epidemic of TB in China exhibited a downward trajectory between 2000 and 2021. However, it is imperative to prioritize the attention given to males and older adults, and to promote specific and effective prevention and control strategies for these populations.

Zwitterionic hydrogels have emerged as promising candidates for diverse applications, especially in epidermal electronics, due to their prominent hemocompatibility, superhydration, and nonfouling properties. However, their practical applications are often severely hindered by inadequate mechanical properties and limited functionalities. Here, we develop a mechanically robust zwitterionic hydrogel with an optimal combination of functions (RHOCF) by constructing a consolidated dynamic supramolecular framework and a spatially multiscale hierarchical structure. By finely introducing a reinforced entangled supramolecular network, along with hierarchical architectures across multiple length scales into the zwitterionic hydrogel system, we can engineer highly stiff and tough zwitterionic hydrogels that seamlessly integrate typically incompatible mechanical properties, including excellent stretchability, notable tensile strength, high fracture toughness, considerable stiffness, and great resilience. The RHOCF further integrates optical transparency, ionic conductivity, self-adhesion, and freezing tolerance, enabling conformal contact with dynamic, irregular surfaces for stable motion sensing and artifact-free electrophysiological signal acquisition. Zwitterionic hydrogels have potential in a range of applications due to favourable properties, but often mechanical properties are not suitable. Here, the authors report a hydrogel with an entangled supramolecular network for hierarchical networks, for zwitterionic materials with favourable material properties.

Electron cyclotron maser (ECM) emission is an important coherent emission mechanism for the direct amplification of electromagnetic waves by nonthermal electrons in a magnetized plasma. This paper will report on our recent study on ECM emission by fast electron beams with moderately relativistic energy. The results show that, similar to the spontaneous emission by the magnetic cyclotron motion of energetic electrons in a magnetic field, the coherent emission also exhibits the characteristic of a gradual transition from harmonic emission to continuous emission as the energy of the energetic electrons increases from subrelativistic to relativistic. The effects of the characteristic beam electrons energy (Ec) and the plasma-to-cyclotron frequency ratio (ωpe/ωce) on the growth rate, the peak-value frequency, and the spectral width are discussed further. These results are helpful for us to understand phenomena associated with astrophysical radio bursts.