First-principles Study of High-Pressure Phase Stability and Superconductivity of Bi4I4

Bismuth iodide Bi4I4 exhibits intricate crystal structures and topological insulating states that are highly susceptible to influence by environments, making its physical properties highly tunable by external conditions. In this work, we study the evolution of structural and electronic properties of Bi4I4 at high pressure using an advanced structure search method in conjunction with first-principles calculations. Our results indicate that the most stable ambient-pressure monoclinic α−Bi4I4 phase in C2/m symmetry transforms to a trigonal P31c structure (ɛ−Bi4I4) at 8.4 GPa, then to a tetragonal P4/mmm structure (ζ−Bi4I4) above 16.6 GPa. In contrast to the semiconducting nature of ambient-pressure Bi4I4, the two high-pressure phases are metallic, in agreement with reported electrical measurements. The ɛ−Bi4I4 phase exhibits distinct ionic states of Iδ− and (Bi4I3)δ + (δ=0.4123 e), driven by a pressure-induced volume reduction. We show that both ɛ- and ζ−Bi4I4 are superconductors, and the emergence of pressure-induced superconductivity might be intimately linked to the underlying structural phase transitions

Effects of Palmitate on the Expression of Inflammatory Cytokines in Microglial Cells

Obesity has become a pandemic health issue for the development of more serious illnesses such as insulin resistance, cardiovascular diseases, and hypertension. Among those pathological conditions, low grade chronic inflammation triggered by saturated fatty acids (SFA) such as palmitate (PA) has been suggested to be the culprit of crimes and various signaling modulators ranging from cellular surface receptors such as toll like receptors (TLRs) and G- protein – coupled receptor 40 (GPR40) to intracellular kinases like mitogen-activated protein kinase (MAPK) and protein kinase C (PKC) have been postulated. In this study, mouse BV2 cells were treated with different concentrations of PA and time and the mRNA expression levels of inflammatory cytokines, such as tumor necrosis factor (TNF)-α, interleukin (IL)-6, and IL-1β were examined. In order to determine how PA induced cytokine expression in BV2 cells, these cells were pre-treated with various pharmacological inhibitors followed by treatment with PA or BSA control, and the mRNA expressions of TNF-α, IL-6, and IL-1β were examined. Inhibition of PI3K and GPR40 by Wortmannin and DC260126 attenuated PA induced expression of these inflammatory cytokines, suggesting that PI3K and GPR40 might be involved in PA-induced production of inflammatory mediators in BV2 cells

Plant and soil microbe responses to light, warming and nitrogen addition in a temperate forest

1. Temperate forests across Europe and eastern North America have become denser since the 1950s due to less intensive forest management and global environmental changes such as nitrogen deposition and climate warming. Denser tree canopies result in lower light availability at the forest floor. This shade may buffer the effects of nitrogen deposition and climate warming on understorey plant communities. 2. We conducted an innovative in situ field experiment to study the responses of co-occurring soil microbial and understorey plant communities to nitrogen addition, enhanced light availability and experimental warming in a full-factorial design. 3. We determined the effects of multiple environmental drivers and their interactions on the soil microbial and understorey plant communities, and assessed to what extent the soil microbial and understorey plant communities covary. 4. High light led to lower biomass of the soil microbes (analysed by phospholipid fatty acids), but the soil microbial structure, i.e. the ratio of fungal biomass to bacterial biomass, was not affected by light availability. The composition of the soil bacterial community (analysed by high-throughput sequencing) was affected by both light availability and warming (and their interaction), but not by nitrogen addition. Yet, the number of unique operational taxonomic units was higher in plots with nitrogen addition, and there were significant interactive effects of light and nitrogen addition. Light availability also determined the composition of the plant community; no effects of nitrogen addition and warming were observed. The soil bacterial and plant communities were co-structured, and light availability explained a large part of the variance of this co-structure. 5. We provide robust evidence for the key role of light in affecting both the soil microbial and plant communities in forest understoreys. Our results advocate for more multifactor global change experiments that investigate the mechanism underlying the (in) direct effects of light on the plant-soil continuum in forests

Research and Design of VR Based Unmanned Aerial Vehicle Model and Database

In response to the application of drones in real life, drones are more susceptible to interference and influence from various external factors such as weather, site, airspace, etc. during flight operations or related tasks. Not only can they not guarantee the completion of expected goals or tasks, but they are also prone to problems such as falling, collision, or accidental injury caused by the unstable state of drones. The drone flight simulation, virtual training, and drone database system developed based on VR technology has improved the safety, diversity, and instability of drones in practical applications, and reduced the interference of external adverse factors on drone flight. A comprehensive drone model database system has been established. This provides effective guarantees for the application and implementation of drones in various fields

A Graph Regularized Point Process Model For Event Propagation Sequence

Point process is the dominant paradigm for modeling event sequences occurring at irregular intervals. In this paper we aim at modeling latent dynamics of event propagation in graph, where the event sequence propagates in a directed weighted graph whose nodes represent event marks (e.g., event types). Most existing works have only considered encoding sequential event history into event representation and ignored the information from the latent graph structure. Besides they also suffer from poor model explainability, i.e., failing to uncover causal influence across a wide variety of nodes. To address these problems, we propose a Graph Regularized Point Process (GRPP) that can be decomposed into: 1) a graph propagation model that characterizes the event interactions across nodes with neighbors and inductively learns node representations; 2) a temporal attentive intensity model, whose excitation and time decay factors of past events on the current event are constructed via the contextualization of the node embedding. Moreover, by applying a graph regularization method, GRPP provides model interpretability by uncovering influence strengths between nodes. Numerical experiments on various datasets show that GRPP outperforms existing models on both the propagation time and node prediction by notable margins.Comment: IJCNN 202

Measurement and Prediction of Coupling Coordination Level of Economic Development, Social Stability and Ecological Environment in Qinghai—Thoughts on Sustainable Societal Safety

Societal safety is the result of the coordinated development of several subsystems; the coupling–coordination relationship among economic development (ED), social stability (SS), and ecological environment (EE) is the premise of realizing sustainable societal safety (SSS). Taking Qinghai Province as an example, this paper quantitatively evaluates the development index of each subsystem by constructing ED-SS-EE evaluation index system, then analyzes the spatio-temporal evolution of ED, SS, and EE coupling coordination levels based on the coupling coordination model, and finally predicts the coupling coordination level of ED, SS, and EE by using the GM(1.1) model. The findings are as follows: (1) The economy is developing rapidly, but the regional development is extremely uneven; ED is driving SS, and SS lags behind ED and slowly improves; EE is improving continuously, and the spatial pattern is relatively stable. (2) The ED, SS, and EE systems have a high degree of coupling, showing a fluctuating upward trend. Coordination level is low, showing a slow upward trend, and the regional differences are obvious. (3) In the future, the coordination level of ED, SS, and EE systems in different regions will be upgraded from the level of near imbalance and barely coordination to the level of intermediate coordination, good coordination, and even high-quality coordination. The spatio-temporal description of ED, SS, and EE coupling coordination level not only reveals the development trend and problems of SSS in Qinghai, it also proves the feasibility of evaluating societal safety level based on the coupling coordination level of sustainable social structure system.publishedVersionPeer reviewe

End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation

Multivariate time series forecasting with hierarchical structure is pervasive in real-world applications, demanding not only predicting each level of the hierarchy, but also reconciling all forecasts to ensure coherency, i.e., the forecasts should satisfy the hierarchical aggregation constraints. Moreover, the disparities of statistical characteristics between levels can be huge, worsened by non-Gaussian distributions and non-linear correlations. To this extent, we propose a novel end-to-end hierarchical time series forecasting model, based on conditioned normalizing flow-based autoregressive transformer reconciliation, to represent complex data distribution while simultaneously reconciling the forecasts to ensure coherency. Unlike other state-of-the-art methods, we achieve the forecasting and reconciliation simultaneously without requiring any explicit post-processing step. In addition, by harnessing the power of deep model, we do not rely on any assumption such as unbiased estimates or Gaussian distribution. Our evaluation experiments are conducted on four real-world hierarchical datasets from different industrial domains (three public ones and a dataset from the application servers of Alipay's data center) and the preliminary results demonstrate efficacy of our proposed method

Simultaneous determination of nine phenolic compounds in imitation wild Dendrobium officinale samples using ultrahigh-performance liquid chromatography–tandem mass spectrometry

Dendrobium officinale Kimura et Migo (D. officinale), one of the nine everlasting types of grass, has gained increasing attention owing to its important roles in alternative medicines and drug discovery. Due to its natural resources being in danger of being extinct, imitation wild planting is becoming increasingly common. To assess the product’s quality completely, an efficient ultrahigh performance liquid chromatography-triple quadrupole tandem mass spectrometry (UHPLC-QQQ-MS/MS) method was established to simultaneously quantify nine phenolic compounds in D. officinale samples. The extraction parameters, including solvent, solvent concentration, solid–liquid ratio, and extraction time, were systematically optimized with the single-factor test. The results demonstrated that extraction with a 1:200 solid-to-liquid ratio of 80% methanol for 1.5 h was the most efficient condition for the extraction of flavonoids. Satisfactory retention times and resolution of the nine analytes were acquired on the Thermo Scientific Hypersil GOLD column with multiple reaction monitoring in negative ion scanning mode. The method was validated to demonstrate its selectivity, linearity, precision, accuracy, and robustness. Thus, the verified UHPLC-QQQ-MS/MS method was successfully applied to the quantification of phenolic components present in D. officinale samples. The results indicated that the quantity and composition of phenolic components in D. officinale from various provenances were significantly different. This work provides a theoretical foundation for the cultivation and assessment of wild D. officinale quality