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Abstract The vacancy effect in quantum spin liquid (QSL) has been extensively studied. A finite density of random vacancies in the Kitaev model can lead to a pileup of low-energy density of states (DOS), which is generally experimentally determined by a scaling behavior of thermodynamic or magnetization quantities. Here, we report detailed muon spin relaxation (μSR) results of H3LiIr2O6, a Kitaev QSL candidate with vacancies. The absence of magnetic order is confirmed down to 80 mK, and the spin fluctuations are found to be persistent at low temperatures. Intriguingly, the time-field scaling law of longitudinal-field (LF)-μSR polarization is observed down to 0.1 K. This indicates a dynamical scaling, whose critical exponent of 0.46 is excellently consistent with the scaling behavior of specific heat and magnetization data. All the observations point to the finite DOS with the form $$N(E)\sim {E}^{-\nu }$$ N ( E ) ∼ E − ν , which is expected for the Kitaev QSL in the presence of vacancies. Our μSR study provides a dynamical fingerprint of the power-law low-energy DOS and introduces a crucial new insight into the vacancy effect in QSL.

Recent studies found that the intestinal flora is involved in the occurrence and development of CRC. In particular, Ruminococcus shows a decreased abundance in patients with CRC and can affect gastrointestinal health through cross-feeding and promoting butyrate production. Although the research on Ruminococcus is gradually increasing, no unified understanding of its influence on the occurrence and development of CRC has been established. This work summarizes the relationship of Ruminococcus with CRC and its possible anticancer mechanism to provide a theoretical basis for the further exploration of biological prevention and treatment of CRC in the future.

Abstract As urban areas expand globally, human activities are leading to a sustained increase in non-methane volatile organic compound (NMVOC) emissions, escalating both environmental and health-related concerns. Given their diverse origins, estimating anthropogenic NMVOC emissions levels from global urban areas remains challenging. Here, we integrate TROPOspheric Monitoring Instrument (TROPOMI) formaldehyde (HCHO) column data, Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light (NTL) radiance data, and the Emission Database for Global Atmospheric Research (EDGAR) to develop a method for estimating global anthropogenic NMVOC emissions. Furthermore, we construct a linear model to analyze the relationship between urbanization and anthropogenic NMVOC emissions. Our research reveals that meticulously filtered TROPOMI HCHO columns have a high Pearson correlation coefficient (r = 0.91) with anthropogenic NMVOC emissions, indicating its reliability as an indicator reflecting the levels of anthropogenic NMVOC emissions. We establish linear models at various scales, including global, continental, and national, linking HCHO columns (as indicators of anthropogenic NMVOC emissions) and NTL radiance (as an indicator of urbanization). The global-scale linear model exhibits an r of 0.81, with a slope of 0.42 × 1015 molec. cm−2 nanoWatts−1 cm2 sr and an intercept of 9.26 × 1015 molec. cm−2. This linear model reflects a positive correlation between urbanization and anthropogenic NMVOC emissions, also serving as a tool for estimating the levels of anthropogenic NMVOC emissions in urban areas. This study offers valuable insights for real-time monitoring of extensive anthropogenic NMVOC emissions.

The recognized importance of ambient fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) on human health has prompted the world to enact increasingly strict regulations on anthropogenic nitrogen oxides (NOx) emissions. However, the health concerns from soil NOx, potentially driven by fertilizer input but conventionally categorized as natural sources, remain less studied. Here, we emphasize the underappreciated roles of soil NOx emissions on health burden attributable to short-term PM2.5, O3, and NO2 exposure. Globally, we quantify acute health effects using machine-learning-based daily exposure estimates and identify influences of soil NOx emissions based on chemical transport model simulations. We find that 72.3% of the globe is affected by soil NOx emissions, whose contributions to short-term PM2.5, O3, and NO2 pollution lead to 13.9 (95% Confidence Interval [CI]: 9.1–18.8), 26.0 (18.2–34.2), and 13.9 (10.3–17.5) thousand premature mortality, respectively, in 2019. With distinct variations in regions, seasons, and pollutants, soil NOx-originated air pollution poses a global health concern, particularly for developing regions and intensively agricultural areas. In response to the intensive fertilizer use, South Asia, Southern Sub-Saharan Africa, and Central Europe witness the largest soil NOx-related health burden of up to 1.6 (95% CI: 1.1–2.1) mortality per 100k population. The overall health risk peaks in May, with O3 pollution typically dominating the soil NOx-attributable health burden during warm seasons and NO2 or PM2.5 during cold months. Our study highlights the necessity of dynamically adapted agricultural strategies for health-oriented multi-pollutant control, among which the improved use of synthetic fertilizers deserves priority under the ever-changing climate.

Abstract Purpose Coronavirus Disease 2019 (COVID‐19) and sepsis are closely related. This study aims to identify pivotal diagnostic candidate genes in COVID‐19 patients with sepsis. Patients and Methods We obtained a COVID‐19 data set and a sepsis data set from the Gene Expression Omnibus (GEO) database. Identification of differentially expressed genes (DEGs) and module genes using the Linear Models for Microarray Data (LIMMA) and weighted gene co‐expression network analysis (WGCNA), functional enrichment analysis, protein–protein interaction (PPI) network construction, and machine learning algorithms (least absolute shrinkage and selection operator (LASSO) regression and Random Forest (RF)) were used to identify candidate hub genes for the diagnosis of COVID‐19 patients with sepsis. Receiver operating characteristic (ROC) curves were developed to assess the diagnostic value. Finally, the data set GSE28750 was used to verify the core genes and analyze the immune infiltration. Results The COVID‐19 data set contained 3,438 DEGs， and 595 common genes were screened in sepsis. sepsis DEGs were mainly enriched in immune regulation. The intersection of DEGs for COVID‐19 and core genes for sepsis was 329, which were also mainly enriched in the immune system. After developing the PPI network, 17 node genes were filtered and thirteen candidate hub genes were selected for diagnostic value evaluation using machine learning. All thirteen candidate hub genes have diagnostic value, and 8 genes with an Area Under the Curve (AUC) greater than 0.9 were selected as diagnostic genes. Conclusion Five core genes (CD3D, IL2RB, KLRC, CD5, and HLA‐DQA1) associated with immune infiltration were identified to evaluate their diagnostic utility COVID‐19 patients with sepsis. This finding contributes to the identification of potential peripheral blood diagnostic candidate genes for COVID‐19 patients with sepsis.

Abstract Events of stratospheric intrusions to the surface (SITS) can lead to severe ozone (O3) pollution. Still, to what extent SITS events impact surface O3 on a national scale over years remains a long-lasting question, mainly due to difficulty of resolving three key SITS metrics: frequency, duration and intensity. Here, we identify 27,616 SITS events over China during 2015-2022 based on spatiotemporally dense surface measurements of O3 and carbon monoxide, two effective indicators of SITS. An overview of the three metrics is presented, illustrating large influences of SITS on surface O3 in China. We find that SITS events occur preferentially in high-elevation regions, while those in plain regions are more intense. SITS enhances surface O3 by 20 ppbv on average, contributing to 30-45% of O3 during SITS periods. Nationally, SITS-induced O3 peaks in spring and autumn, while over 70% of SITS events during the warm months exacerbate O3 pollution. Over 2015-2022, SITS-induced O3 shows a declining trend. Our observation-based results can have implications for O3 mitigation policies in short and long terms.

Glioma is the most common malignant tumor of the central nervous system. Drug-assisted chemotherapy is an important adjuvant treatment post-surgery, but currently, effective chemotherapy drugs for glioma are lacking. Expediting new and effective chemotherapy drugs is a persistent problem that needs to be solved. In this study, a tetramic acid derivative, trichobotrysin B, was extracted from the ascidian-derived fungus Trichobotrys effusa 4729 (denoted ADFTe4729). There is significant cytotoxicity of trichobotrysin B against glioma proliferation, which triggers apoptosis and cell cycle arrest. Furthermore, studies have found that trichobotrysin B inhibits glioma proliferation in a manner closely related to IL-6-mediated STAT3 phosphorylation and JAK2 activation. In conclusion, this study demonstrates that the small-molecule compound trichobotrysin B inhibits glioma proliferation and induces apoptosis through the IL-6-mediated STAT3/JAK2 signaling pathway, suggesting that trichobotrysin B has potential antiglioma efficiency and provides a new way to explore new small-molecule drugs with anticancer effects.

U-shape networks are widely used in salient object detection. Recently, CTDNet with a Comprehensive Triangular Decoder improved detection efficiency, which made some improvement with respect to the complexity and slow training of U-shape networks. However, CTDNet is still not lightweight enough, and the use of Global Average Pooling for top-level semantic features can lead to the loss of global structural information. This paper proposes Trilateral Enhanced Network (TENet), a faster salient detection model based on CTDNet, for industrial application. TENet uses MobileNetV3 as a backbone network so that TENet only needs 3.72M parameters, which lightweight the network consequently. TENet contains a feature fusion module called Channel Attraction Enhanced Feature Fusion Model, which integrates high-level semantics to improve accuracy. Additionally, Convolutional Block Feature Enhancement Module is proposed, which can further enhance accuracy. In comparison with CTDNet, TENet is a lightweight network with faster detection speed and more detection accuracy. TENet robustly detects defects in salient texture images, indicating insensitivity to texture interference. Experiments show TENet maintains strong performance on salient textures detection, demonstrating suitability for industrial optical inspection.

The solar insecticidal lamp (SIL) is an electronic device designed for physical pest control, widely utilized in orchards and farmland. Currently, the characteristic of the phototactic rhythm of pest is commonly ignored in the design of SILs, hindering pest control. This phenomenon is particularly evident in the prolonged turning on/offlamp, which leads to inefficient energy utilization due to the lack of adjustment for peak pest activity. To address this issue, four models based on the phototactic rhythm of pests are developed to adjust the insecticidal timing of SIL for precise pest control. These mathematical models are established considering the phototactic rhythm of four pests that exert the most significant impact on crops, namely Mythimna seperata, Helicoverpa armigera, Proxenus lepigone, and Cnaphalocrocis medinalis. The results indicate that mathematical modeling of the phototactic rhythm of the pest is valuable in capturing their nocturnal activity patterns. The proposed mathematical model can help to optimize the on/offtime of SIL for pest control. The integration of electronic devices, such as SIL in pest management represents a noteworthy advancement in agricultural electronics, contributing to the progress of smart and sustainable agriculture.

The development of the β-solidifying γ-TiAl alloys with good oxidation resistance and thermal deformation ability has always been crucial for the practical application. However, increasing the content of the antioxidant elements, such as Nb, Al, etc., tends to be detrimental to the deformability of the alloys. In this work, a novel Nb, Mn containing β-solidifying TiAl alloy with the nominal composition of Ti-43Al-1.5Mn-3Nb-0.2C-0.2Si-0.1B (at. %) (named as NbMn-TiAl) is invented, which goes through the single β phase as well as the α phase during solidification, resulting in the final as-cast microstructure of nearly full lamellar structure (NFL). Subsequently, the isothermal compression tests were performed within a temperature range of 1100 °C–1300 °C and a strain rate range of 0.001 s−1-10 s−1, the hot processing maps and constitutive equation were established. Moreover, the deformation mechanisms were clarified, including the bending/kinking of the α2/γ lamellar colonies, the dynamic recrystallization (DRX) of γ phase and α phase at different temperatures. Rolled bars with a diameter of 12 mm were then directly fabricated from the ingot by conventional hot rolling process. The as-rolled bars exhibited excellent oxidation resistance and mechanical properties that outperform many available TiAl alloys manufactured using various methods.

This paper proposes a novel censored autoregressive conditional Fréchet (CAcF) model with a flexible evolution scheme for the time-varying parameters, which allows deciphering tail risk dynamics constrained by price limits from the viewpoints of different risk preferences. The proposed model can well accommodate many important empirical characteristics of financial data, such as heavy-tailedness, volatility clustering, extreme event clustering, and price limits. We then investigate tail risk dynamics via the CAcF model in the price-limited stock markets, taking entropic value at risk (EVaR) as a risk measurement. Our findings suggest that tail risk will be seriously underestimated in price-limited stock markets when the censored property of limit prices is ignored. Additionally, the evidence from the Chinese Taiwan stock market shows that widening price limits would lead to a decrease in the incidence of extreme events (hitting limit-down) but a significant increase in tail risk. Moreover, we find that investors with different risk preferences may make opposing decisions about an extreme event. In summary, the empirical results reveal the effectiveness of our model in interpreting and predicting time-varying tail behaviors in price-limited stock markets, providing a new tool for financial risk management.

Dicranum Hedw. is a highly diverse and widely distributed genus within Dicranaceae. The species diversity and distribution of this genus in China, however, remain not well known. A new revision of Dicranum in China using morphological and molecular phylogenetic methods confirms that China has 39 species, including four newly reported species, D. bardunovii Tubanova & Ignatova, D. dispersum Engelmark, D. schljakovii Ignatova & Tubanova, and D. spadiceum J.E.Zetterst. Dicranum psathyrum Klazenga is transferred to Dicranoloma (Renauld) Renauld as a new synonym of Dicranoloma fragile Broth. Two species, Dicranum brevifolium (Lindb.) Lindb. and D. viride (Sull. & Lesq.) Lindb. are excluded from the bryoflora of China. A key to the Chinese Dicranum species is also provided. These results indicate an underestimation of the distribution range of numerous Dicranum species, underscoring the need for further in-depth investigations into the worldwide Dicranum diversity.

Abstract Metal halide perovskites have shown great promise as a potential candidate for next-generation solid state lighting and display technologies. However, a generic organic ligand-free and antisolvent-free solution method to fabricate highly efficient full-color perovskite light-emitting diodes has not been realized. Herein, by utilizing porous alumina membranes with ultra-small pore size as templates, we have successfully fabricated crystalline all-inorganic perovskite quantum wire arrays with ultrahigh density and excellent uniformity, using a generic organic ligand-free and anti-solvent-free solution method. The quantum confinement effect, in conjunction with the high light out-coupling efficiency, results in high photoluminescence quantum yield for blue, sky-blue, green and pure-red perovskite quantum wires arrays. Consequently, blue, sky-blue, green and pure-red LED devices with spectrally stable electroluminescence have been successfully fabricated, demonstrating external quantum efficiencies of 12.41%, 16.49%, 26.09% and 9.97%, respectively, after introducing a dual-functional small molecule, which serves as surface passivation and hole transporting layer, and a halide vacancy healing agent.

Abstract Surface ozone increased unexpectedly over northern China during the COVID-19 lockdown (CLD) period (23 January–29 February 2020), which was characterized by vigorous emission reduction. The reasons for this ozone enhancement have been speculated from perspectives of chemical responses to the emissions and meteorology. As known, the processes of natural stratospheric ozone injecting to the troposphere are most active in winter and spring. Yet, little attention was paid to stratospheric influences on this ozone enhancement. Here we report a stratospheric intrusion (SI) that reached the surface over northern China on 15–17 February during the CLD. The coevolution of enhanced ozone and sharply declined carbon monoxide and relative humidity (RH) was indicative of the SI occurrence. We show that the SI was facilitated by a cutoff low system that led to abnormally high surface ozone in most part of northern China. We estimate that over the SI period, the injected stratospheric ozone constituted up to 40–45% of the surface ozone over northern China. If the stratospheric ozone inputs were scaled over the entire CLD period, these inputs would account for 4–8% of the surface ozone. In view of the unexpected ozone increase during the CLD, this SI event could explain up to 18% of the ozone increase in some cities, and average 5–10% over larger areas that were affected. Hence, the nonnegligible stratospheric influences urge extra consideration of natural ozone sources in disentangling the role of emission reduction and meteorological conditions during the CLD in China and elsewhere in the world.

Abstract The frequency, intensity, and duration of extreme heatwaves are projected to increase in the global context of climate change. However, evidence of how anthropogenic emissions respond to heatwaves and further impact air quality remains elusive. Here, we use satellite remote sensing measurements alongside chemical transport model simulations to reveal abrupt variations in primary and secondary air pollutants introduced by extreme heatwaves. We highlight evidence from China and India, where satellite sulfur dioxide (SO2) and nitrogen dioxide (NO2) columns over thermal power plants enhance consistently responding to heatwaves. We attribute such spiked emissions to soaring electricity use and demonstrate that bottom‐up inventories underestimate the emissions from the power sector by 34.9% for the selected case. Elevated emissions facilitate fine particulate matter (PM2.5) and ozone (O3) formation over thermal power plants in an inhomogeneous manner, due to the combined effect of atmospheric oxidizing capacity, thermal decomposition of peroxyacetyl nitrate, planetary boundary layer rise, and air stagnation. Our results underscore the emerging challenge of pollution control attributable to the increasing climate penalty and the necessity of targeted control strategies and alternative energy sources during heatwaves.

Accurate stroke assessment and consequent favorable clinical outcomes rely on the early identification and quantification of aneurysmal subarachnoid hemorrhage (aSAH) in non-contrast computed tomography (NCCT) images. However, hemorrhagic lesions can be complex and difficult to distinguish manually. To solve these problems, here we propose a novel Hybrid 2D/3D UNet deep-learning framework for automatic aSAH identification and quantification in NCCT images. We evaluated 1824 consecutive patients admitted with aSAH to four hospitals in China between June 2018 and May 2022. Accuracy and precision, Dice scores and intersection over union (IoU), and interclass correlation coefficients (ICC) were calculated to assess model performance, segmentation performance, and correlations between automatic and manual segmentation, respectively. A total of 1355 patients with aSAH were enrolled: 931, 101, 179, and 144 in four datasets, of whom 326 were scanned with Siemens, 640 with Philips, and 389 with GE Medical Systems scanners. Our proposed deep-learning method accurately identified (accuracies 0.993–0.999) and segmented (Dice scores 0.550–0.897) hemorrhage in both the internal and external datasets, even combinations of hemorrhage subtypes. We further developed a convenient AI-assisted platform based on our algorithm to assist clinical workflows, whose performance was comparable to manual measurements by experienced neurosurgeons (ICCs 0.815–0.957) but with greater efficiency and reduced cost. While this tool has not yet been prospectively tested in clinical practice, our innovative hybrid network algorithm and platform can accurately identify and quantify aSAH, paving the way for fast and cheap NCCT interpretation and a reliable AI-based approach to expedite clinical decision-making for aSAH patients.

The search of quantum spin liquid (QSL), an exotic magnetic state with strongly fluctuating and highly entangled spins down to zero temperature, is a main theme in current condensed matter physics. However, there is no smoking gun evidence for deconfined spinons in any QSL candidate so far. The disorders and competing exchange interactions may prevent the formation of an ideal QSL state on frustrated spin lattices. Here we report comprehensive and systematic measurements of the magnetic susceptibility, ultralow-temperature specific heat, muon spin relaxation (μSR), nuclear magnetic resonance (NMR), and thermal conductivity for NaYbSe2 single crystals, in which Yb3+ ions with effective spin-1/2 form a perfect triangular lattice. All these complementary techniques find no evidence of long-range magnetic order down to their respective base temperatures. Instead, specific heat, μSR, and NMR measurements suggest the coexistence of quasi-static and dynamic spins in NaYbSe2. The scattering from these quasi-static spins may cause the absence of magnetic thermal conductivity. Thus, we propose a scenario of fluctuating ferrimagnetic droplets immersed in a sea of QSL. This may be quite common on the way pursuing an ideal QSL, and provides a brand new platform to study how a QSL state survives impurities and coexists with other magnetically ordered states.

Abstract X‐ray flaw detection is widely used in non‐destructive area. The intuitive defect information can be obtained through the X‐ray film, which is usually digitized into high greyscale image by a 12‐bit or 16‐bit, called super 8‐bit, industrial scanner. When an ordinary 8‐bit monitor displays a super 8‐bit greyscale image, it appears loss of detail information, blurring of the image appears and other problems. Therefore, in this paper, a pseudo‐colour enhancement algorithm for displaying high‐bit RAW images on low‐bit monitor was proposed according to the chromatographic mapping relationship based on the visual characteristics of human eye. First, a high grey‐scale pseudo‐colour enhancement algorithm, called HOTM‐HGL, based on hot metal coding was proposed based on the standard film, whose enhancement effect is better than current mainstream algorithms. Second, aiming at the non‐standard film, a new stretching function called RAW‐Optical‐Stretching was reconstructed to improve HOTM‐HGL algorithm, called HOTM‐HGLS algorithm, whose display effect on ordinary monitors was improved in further. Finally, HOTM‐HGLS algorithm was applied in the detection of X‐ray film defect, which was convenient for the capture of defect information in the weld. Compared with the existing algorithms, various indicators have been greatly improved, enriching the amount of information and strengthening the image recognition effects.

Technologies for making devices communicate seamlessly over the air are expected to be adopted more and more in future digital ecosystems, including cyber-physical systems. In the latter case, machine-to-machine (M2M) interactions are often characterized by tight timing constraints. When communication takes place on shared and noisy media, timeliness is customarily specified as the probability that a message is delivered within specific deadlines, which depend on the dynamics of the controlled system. A fair amount of effort was spent in the past two decades to improve wireless communication technologies, and there is now the expectation that in the future, they could be (almost) as dependable as wires.