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Investigating mineral/melt Sn partitioning at high temperatures and pressures is a difficult task because Sn is a redox-sensitive multivalent element and easily alloys with noble metal sample capsules. To obtain accurate Sn partition coefficients between titanite, ilmenite, and granitic melts, we developed single capsule Pt or Au and double capsule Pt95Rh5(or Au)-Re designs to avoid significant Sn loss at a controlled oxygen fugacity (fO2). With these new capsule designs, we performed piston-cylinder experiments of Sn partitioning between titanite, ilmenite, and granitic melts. The experimental P-T-fO2conditions were 0.5–1.0 GPa, 850–1000 °C, and ~QFM+8 to ~QFM-4 (quartz-fayalite-magnetite, QFM, buffer), with fO2controlled by the solid buffers of Ru-RuO2, Re-ReO2, Co-CoO, graphite, and Fe-FeO. The obtained mineral/melt Sn partition coefficients (DSnmin/melt$\begin{array}{} \displaystyle D_{\mathrm{Sn}}^{\mathrm{min} / \mathrm{melt}} \end{array}$) are 0.48–184.75 for titanite and 0.03–69.45 for ilmenite at the experimental conditions. The DSnmin/melt$\begin{array}{} \displaystyle D_{\mathrm{Sn}}^{\mathrm{min} / \mathrm{melt}} \end{array}$values are largely dependent on fO2, although the effects of temperature and melt composition are also observed. DSnTtn/melt$\begin{array}{} \displaystyle D_{\mathrm{Sn}}^{\mathrm{Ttn} / \mathrm{melt}} \end{array}$strongly decreases with decreasing fO2, from ~46–185 at the most oxidizing conditions (Ru-RuO2buffer), to ~2–16 at moderately oxidizing to moderately reducing conditions (Re-ReO2to Co-CoO and graphite buffers), to < 1 at the most reducing conditions (Fe-FeO buffer). DSnIlm/melt$\begin{array}{} \displaystyle D_{\mathrm{Sn}}^{\mathrm{Ilm} / \mathrm{melt}} \end{array}$exhibits a variation trend similar to DSnTtn/melt$\begin{array}{} \displaystyle D_{\mathrm{Sn}}^{\mathrm{Ttn} / \mathrm{melt}} \end{array}$but is always lower than DSnTtn/melt$\begin{array}{} \displaystyle D_{\mathrm{Sn}}^{\mathrm{Ttn} / \mathrm{melt}} \end{array}$at a given fO2. These DSnmin/melt$\begin{array}{} \displaystyle D_{\mathrm{Sn}}^{\mathrm{min} / \mathrm{melt}} \end{array}$values can be applied to quantitatively assess the mineralization potential of granitic magmas. Using DSnTtn/melt$\begin{array}{} \displaystyle D_{\mathrm{Sn}}^{\mathrm{Ttn} / \mathrm{melt}} \end{array}$, we estimate that Sn contents are ~150–400 ppm in the pre-mineralization magmas of the tin-mineralized Qitianling plutons (South China).

Chronic inflammation and tissue fibrosis are common responses that worsen organ function, yet the molecular mechanisms governing their cross-talk are poorly understood. In diseased organs, stress-induced gene expression changes fuel maladaptive cell state transitions1and pathological interaction between cellular compartments. Although chronic fibroblast activation worsens dysfunction in the lungs, liver, kidneys and heart, and exacerbates many cancers2, the stress-sensing mechanisms initiating transcriptional activation of fibroblasts are poorly understood. Here we show that conditional deletion of the transcriptional co-activator Brd4in infiltrating Cx3cr1+macrophages ameliorates heart failure in mice and significantly reduces fibroblast activation. Analysis of single-cell chromatin accessibility and BRD4 occupancy in vivo in Cx3cr1+cells identified a large enhancer proximal to interleukin-1β (IL-1β, encoded by Il1b), and a series of CRISPR-based deletions revealed the precise stress-dependent regulatory element that controls Il1bexpression. Secreted IL-1β activated a fibroblast RELA-dependent (also known as p65) enhancer near the transcription factor MEOX1, resulting in a profibrotic response in human cardiac fibroblasts. In vivo, antibody-mediated IL-1β neutralization improved cardiac function and tissue fibrosis in heart failure. Systemic IL-1β inhibition or targeted Il1bdeletion in Cx3cr1+cells prevented stress-induced Meox1expression and fibroblast activation. The elucidation of BRD4-dependent cross-talk between a specific immune cell subset and fibroblasts through IL-1β reveals how inflammation drives profibrotic cell states and supports strategies that modulate this process in heart disease and other chronic inflammatory disorders featuring tissue remodelling.

Keap1 plays a crucial role in regulating the Nrf2-mediated cytoprotective response and is increasingly targeted for oxidative stress-related diseases. Using small molecules to disrupt the Keap1–Nrf2 protein–protein interaction (PPI) has emerged as a new strategy for developing Nrf2 activators. Through extensive structure–activity relationship studies, we identified compound 56, which features a unique 5-tetrahydroisoquinoline scaffold and acts as a potent inhibitor of the Keap1–Nrf2 PPI. Compound 56exhibited significant inhibitory activity (IC50= 16.0 nM) and tight Keap1 binding affinity (Kd= 3.07 nM), along with acceptable oral bioavailability (F= 20%). Notably, 56enhanced antioxidant defenses in HK-2 renal tubular epithelial cells and significantly reduced plasma creatinine and blood urea nitrogen levels in acute kidney injury (AKI) mice. These findings collectively position compound 56as a promising candidate for the treatment of AKI.

The use of porous media as a fixed bed for promoting methane hydrate formation has been endowed with great potential in hydrate-based energy storage. In this work, a graphene aerogel (GA) with a one-piece structure and ultralight weight was prepared and adopted for the first time as a fixed bed for methane hydrate formation. In particular, the effects of the filling ratio and hydrophobic–hydrophilic properties of GA on the promotion efficiency were comprehensively investigated. In the range of 84–95%, a lower filling ratio resulted in higher promotion efficiency, which at 84% increased the methane storage capacity and apparent storage capacity from 114.4 ± 7.6 and 52.4 ± 6.7 V/V to 146.3 ± 6.5 and 91.1 ± 13.0 V/V, respectively. Cryo-scanning electron microscopy (Cryo-SEM) demonstrated that the hydrates formed in the inner pores of GA but also revealed the existence of empty pores, indicating that part of the reaction solution migrated out of the GA. Fortunately, by regulating the hydrophilic–hydrophobic properties of GA, the migration of the reaction solution during hydrate growth could be controlled, and the methane storage capacity could be optimized. More importantly, GA produced excellent advantages in terms of gravimetric storage capacity over traditional media and, therefore, produced great potential in serving as an ultralight fixed bed for the industrial application of hydrate-based energy storage.

The membrane electrode assembly (MEA) is one of the critical components in proton exchange membrane fuel cells (PEMFCs). However, the conventional MEA cathode with a covered-type catalyst/ionomer interfacial structure severely limits oxygen transport efficiency and Pt activity, hardly achieving the theoretical performance upper bound of PEMFCs. Here, we design a noncovered catalyst/ionomer interfacial structure with low proton transport resistance and high oxygen transport efficiency in the cathode catalyst layer (CL). This noncovered interfacial structure employs the ionomer cross-linked carbon particles as long-range and fast proton transport channels and prevents the ionomer from directly covering the Pt/C catalyst surface in the CL, freeing the oxygen diffusion process from passing through the dense ionomer covering layer to the Pt surface. Moreover, the structure improves oxygen transport within the pores of the CL and achieves more than 20% lower pressure-independent oxygen transport resistance compared to the covered-type structure. Fuel-cell diagnostics demonstrate that the noncovered catalyst/ionomer interfacial structure provides exceptional fuel-cell performance across the kinetic and mass transport-limited regions, with 77% and 67% higher peak power density than the covered-type interfacial structure under 0 kPagaugeof oxygen and air conditions, respectively. This alternative interfacial structure provides a new direction for optimizing the electrode structure and improving mass-transport paths of MEA.

Accurate estimation of the ambient temperature of mountain freeways enables freeway management agencies to provide weather-related information to drivers. This article proposed an image-based data-driven method, namely the visual temperature estimation network (VTENet), to estimate freeway ambient temperature based on images captured by roadside cameras. The VTENet had a convolutional neural network (CNN) architecture to extract temperature-related image features, and two extra networks to capture space-time information on data collection and time-series image features. The VTENet was trained and tested based on a self-established dataset collected at a mountain freeway. The results showed that the VTENet can estimate freeway ambient temperature with high accuracy. The model gives a more accurate temperature estimation with data collected from 10 to 11 A.M. and 2 to 3 P.M. than other periods. It also performed better using four-day or five-day sequence images than other data inputs.

Septic acute kidney injury (AKI) is a fatal disease in the intensive care unit, with ferroptosis playing a crucial role in its pathogenesis. Long non‐coding RNA (LncRNA) metastasis‐associated lung adenocarcinoma transcript 1 (MALAT1) has been implicated in septic‐induced AKI inflammation and apoptosis. However, its regulatory role in ferroptosis and underlying mechanisms remain unclear. In vivo and in vitro models of septic AKI were established using cecal ligation and puncture (CLP) and lipopolysaccharide (LPS) challenge, respectively. Serum levels of creatinine (Cr), blood urea nitrogen (BUN), kidney injury molecule‐1 (Kim‐1), neutrophil gelatinase‐associated lipocalin (NGAL), and inflammatory cytokine in kidney tissues were determined by ELISA kits. Histopathological alterations and apoptosis were evaluated by HE staining and TUNEL. Ferroptosis was accessed by measuring MDA, GSH, Fe2+, total and lipid ROS levels, and mitochondrial ultrastructure changes. Target molecular levels were determined using RT‐qPCR, Western blotting, and immunofluorescence. Interactions among MALAT1, acyl‐CoA synthetase family member 2 (ACSF2) and FUS RNA binding protein (FUS) were validated by RIP and RNA‐pull down. MALAT1 level was significantly elevated in both in vivo and in vitro septic AKI models, of which knockdown impeded ferroptosis to alleviate septic AKI. Mechanistically, high MALAT1 expression increased ACSF2 mRNA stability via interaction with FUS. Rescue experiments showed that ACSF2 overexpression partially reversed the ferroptosis inhibition mediated by MALAT1 silencing. MALAT1 induces ferroptosis and exacerbates septic AKI by stabilizing ACSF2 mRNA with the assistance of FUS. These findings provide theoretical evidence for MALAT1 as a potential therapeutic target for septic AKI.

Aerial object detection is crucial in various computer vision tasks, including video monitoring, early warning systems, and visual tracking. While current methods can accurately detect normal-sized objects, they face challenges distinguishing small objects from cluttered backgrounds. Developing methods that can be deployed on edge devices to achieve fast, accurate, and energy-efficient performance is also an urgent challenge. This paper proposes a network for aerial object detection by incorporating an attention mechanism to enhance feature extraction and elevate the accuracy of aerial moving object detection. Additionally, we optimize the channel dimensions of the feature extraction framework, resulting in a reduction in model parameters, acceleration of inference speed, and alleviation of the computational burden. Ulteriorly, we optimize the Spatial Pyramid Pooling (SPP) module to enhance detection accuracy and processing speed. Inspired by the ResNet and RepVGG structures, we design a feature fusion module to combine early-extracted features, improving speed and accuracy. Based on the design mentioned above principles, we develop a neural network method with an impressively small model size of only 4.5M. The proposed approach achieves state-of-the-art performance on five benchmark datasets. Besides its superior performance, our method demonstrates excellent throughput on edge computing devices. Experimental results show that even when running on low-performance computing devices, the CPU and GPU temperatures remain below 50∘C and achieve a detection speed of 14.8 frames per second (fps) and power consumption of only 2.9W. These findings suggest that a high-accuracy, low-power, low-latency, and low-memory footprint aerial object detection solution is achievable.

The event camera, as an innovative perception sensor, generates a high-temporal-resolution event stream, filtering out redundant visual information and offering a wide dynamic range for object detection. Nevertheless, it is nontrivial to effectively utilize the advantages of the event stream to enhance the accuracy of object detection. Initially, the current event representation methods require hyperparameters tuning to achieve optimal performance for event-based object detection, which is very time-consuming. Moreover, most existing methods rely on large-scale models to enhance detection accuracy. In addition, they fail to leverage the temporal clues provided by the event stream efficiently. In this work, we propose a detection pipeline, which better achieves equilibrium between model speed and accuracy. First, we design an event representation without hyperparameter tuning named dynamic temporal representation (DTR), which efficiently utilizes the spatial-temporal asynchronous event stream. Then, a module named spatial-temporal focus (STF), capable of extracting the rich temporal information from the DTR tensor, is proposed. Besides, a memory module is implemented, leveraging rich temporal clues named light recurrent convolution (LRC). Finally, experiments demonstrate that our method outperforms the state-of-the-art (SOTA) approaches on two classic event camera datasets. Specifically, in the Gen1 Automotive Dataset and the 1Mpx Detection Dataset, the mean average precision (mAP) increases by 0.5% and 0.4%, respectively, while inference times decrease by 34.3% and 32.5%. Additionally, the parameters of the model are reduced by 50.1%. Therefore, our method demonstrates competitive advantages in terms of accuracy, efficiency, and parameters.

We propose a general methodology and reconfigurable architecture for computing $\boldsymbol {A^{B}}$ -like functions in the complex field. CORDIC and the parabolic synthesis approximation method help the proposed method to be flexible to adjust the precision and complexity of computation. The proposed architecture is reconfigurable to adapt to different functions. The MATLAB test results show that our calculation method’s average relative error is the order of $\boldsymbol {6\times 10^{-5}}$ . The synthesis report under TSMC 28nm CMOS technology shows that the proposed design has an area consumption of $\boldsymbol {7149.86~\mu m^{2}}$ at the frequency of 1 GHz and the power consumption is 2.0935 mW.

ABSTRACTThis work sought to determine how lipopolysaccharide (LPS)-induced pro-inflammatory factor production in BV2 microglia was influenced by myeloid cell 2 (TREM2) expressions. LPS (0.1, 1, and 10 µg/mL) induced inflammation in BV2 cells, MTT and QPCR were used to detect the occurrence of inflammation; TREM2 activation and inhibition vectors were used to activate and inhibit TREM2; Cell Proliferation was detected using CCK-8 and cell cloning experiments. LY294002 was used to inhibit the activity of PI3K/AKT signal pathway; Western blot and ELISA were used to detect cell polarization and signal pathway changes. CCK-8 and cell clone experiments found that the activation of TERM2 can promote the proliferation of BV2 cells; and the activation of TERM2 can promote the expression of IL6, IL1β, TNFα and the expression of M2 cell phenotype molecules Arg-1 and CD206. The effect of adding LY294002 signaling pathway by TERM2 activation was inhibited, indicating that TERM2 can affect the occurrence of inflammation by regulating the activity of PI3K/AKT signaling pathway. Finally, Western blotting and ELISA showed that activation of TERM2 can promote the expression of Arg-1 and CD206 in BV2 cells, and promote the transformation of BV2 cells to M2 polarization. TERM2 can affect the inflammatory response in microglia through the PI3K/AKT signaling pathway, suggesting that TERM2 may be a target for the treatment of inflammatory response in glial cells. This study provides a treatment plan for alleviating the impact of inflammation on central nervous system.

Unintentional islanding events cause potential threats to the safety of dc microgrids. Selected frequency islanding detection is considered a promising technology thanks to its good power quality and high detection accuracy. However, the conventional frequency-domain-based islanding detection parameter boundary cannot consider the impact of detection time, which causes a quite slow detection speed and thus leads to detection failure. To overcome this obstacle, a linear model of the islanding dc system is developed first to analyze the steady-state response of the voltage at the point of common coupling (PCC). On top of that, the components of the islanding system characteristic equation are analyzed based on modal analysis, which lays a good foundation for simplifying the time-domain response model of the PCC voltage. Then, the oscillation trajectory of the PCC voltage triggered by the islanding event is characterized in the time domain, which facilitates the analysis and calculation of islanding detection time. Furthermore, the boundary of the islanding detection parameters considering the detection time effect is accurately depicted to guide the resonator design. In this manner, the effect of resonant parameters on the detection time can be evaluated visually while the fast detection speed is also ensured. Finally, the proposed method is validated in simulations and hardware-in-loop experiments.

A comprehensive investigation is conducted to explore the heavy-ions-induced leakage current (HIILC) modes and degradation mechanism in silicon carbide (SiC) MOSFET. The ions used in irradiation experiments had different linear energy transfer (LET) values, which aimed to simulate complex ion conditions in aerospace applications. The distinct HIILC degradation modes of SiC MOSFETs are observed first under heavy ion irradiation with different LET values. Under irradiation with low LET ions, the overall leakage current degradation is dominated by the degradation of the drain current. Conversely, gate current has a higher degradation rate under irradiation with high LET ions. The degradation mechanisms of leakage current are discussed by combining the defect analysis of irradiated samples and simulation results. We propose and verify for the first time that the generation of stacking faults (SFs) in SiC MOSFETs under heavy ion irradiation is the cause of drain current degradation regardless of LET values. Additionally, under high LET ion irradiation, damage caused by a high electric field in the gate oxide is presumed to be attributed to the rapid degradation in gate current. The proposed theory provides a foundation and guidance for reinforcing against HIILC degradation of SiC MOSFET by addressing the process and material characteristics of the SiC.

A quantitative relationship between grain size and Young’s modulus of metal composites has not been established by considering the coupling effect of the reinforcements on microstructural deformation. The objective of this study is to investigate the mechanism of deformation coordination between the carbon nanotube and the composite microstructure using molecular dynamics simulations. Through the analysis of stress contributions from grain boundaries and grains, a grain size-dependent continuum model for the pure metal modulus is established. On this basis, a predictive model for the modulus of metallic composites is developed by considering the coupling deformation between carbon nanotubes and the grains. The proposed model is capable of accurately capturing the relationship between grain size and the modulus of metal composites. This study provides a guideline for microstructure-dependent multiscale modeling.

Microwave frequency-hopping signal generation is one of the backbone components in modern wireless communication system. In this paper, we propose a novel photoelectric integrated approach to implement broadband microwave frequency-hopping signal generation. The coherent dual optical combs are constituted by two Mach Zehnder modulators (MZMs) and one acousto-optic frequency modulator (AOM). By using dense wavelength division multiplexing (DWDM) and single-sideband modulation with carrier suppression, the proposed method extends the input signal's frequency hopping bandwidth by six times. Due to the photoelectric hybrid structure, the proposed method can achieve precise control for using electronic technology while also providing the advantages of high bandwidth and reconfigurability through photonics. In the experiment, the frequencies of generated signals can hop ranging from 0 to 6 GHz. Meanwhile, the frequency hopping signals can maintain a well signal quality with the EVM lower than 6.4%.

Understanding the maintenance and shift in reproductive strategies is a fundamental question in evolutionary research. Although many efforts have been made to compare different reproductive strategies, the association between reproductive strategies and lineage divergence is largely unknown. To explore the impact of different reproductive strategies on lineage divergence, we investigated the evolution of clonality in Saxifragasect. Irregulares+Heterisia. By integrating several lines of evidence, we found that the loss of clonality in Irregulares+Heterisiawas associated with a progressive increase in diversification rate and intraspecific morphological diversity but with a reduction in species distribution range. Our findings provide insights into the ecological and evolutionary effects of different reproductive strategies, suggesting the necessity of integrating clonality into ecological and evolutional research.

Scenario engineering plays a vital role in various Industry 5.0 applications. In the field of autonomous driving systems, driving scenario data are important for the training and testing of critical modules. However, the corner scenario cases are usually rare and necessary to be extended. Existing methods cannot handle the interpretation and reasoning of the generation process well, which reduces the reliability and usability of the generated scenarios. With the rapid development of Foundation Models, especially the large language model (LLM), we can conduct scenario generation via more powerful tools. In this article, we propose LLMScenario, a novel LLM-driven scenario generation framework, which is composed of scenario prompt engineering, LLM scenario generation, and evaluation feedback tuning. The minimum scenario description specific to LLM is given by scenario analysis and ablation studies. We also appropriately design the score functions in terms of reality and rarity to evaluate the generated scenarios. The model performance is further enhanced through chain-of-thoughts and experiences. Different LLMs are also compared with our framework. Experimental results on naturalistic datasets demonstrate the effectiveness of LLMScenario, which can provide solid support for scenario engineering in Industry 5.0.

The influence of the QLT (quenching + lamellarizing + tempering) heat treatment process on the structure and properties of ASTM 5% Ni steel is analyzed. Using QLT heat treatment is to improve the cryogenic toughness of steel, which is generally just related to the reversed austenite. The present study deals with the analysis of the role of QLT heat treatment on structure transformation by researching the relationship among QLT, reversed austenite, and cryogenic toughness. After QLT heat treatment, the structure of ASTM 5% Ni steel transforms from coarse F + B to uniform and fine-tempered sorbite. Grain refinement and high dislocation density bring excellent comprehensive mechanical properties. Alloying elements influence the process and results of the phase transformation of austenite and martensite by getting into them to form new segregation structural units. The oscillographic impact results show that unstable crack growth does not occur during the fracture process. The fracture is upper platform type, which indicates that 5% Ni steel has good cryogenic crack arrest performance. Austenite is not found in the structure under X-ray and transmission electron microscope. The results of the electronic probe indicate the condition of creating conversed austenite is not met. So, the good low-temperature toughness is not from austenite but grain refinement and the increase of the ratio of the large angular grain boundary. This study not only provides proof that the QLT process is not the only factor in the forming of reversed austenite, but also gives inspiration that cryogenic toughness should not rely too much on the reversed austenite.

Liver X receptors (LXRs) which link lipid metabolism and inflammation, were overexpressed in experimental rheumatoid arthritis (RA) rats as observed in our previous studies, while suppression of LXRα by silybin ameliorates arthritis and abnormal lipid metabolism. However, the role of LXRs in RA remains undefined. In this study, we investigated the inhibition role of LXRs in the polarization and activation of M1 macrophage by using a special LXRs inverse agonist SR9243, which led to ameliorating the progression of adjuvant-induced arthritis (AIA) in rats. Mechanistically, SR9243 disrupted the LPS/IFN-γ-induced Warburg effect in M1 macrophages, while glycolysis inhibitor 2-DG attenuated the inhibition effect of SR9243 on M1 polarization and the cytokines expression of M1 macrophages including iNOS, TNF-α, and IL-6 in vitro. Furthermore, SR9243 downregulated key glycolytic enzymes, including LDH-A, HK2, G6PD, GLUT1, and HIF-1α in M1 macrophages, which is mediated by increased phosphorylation of AMPK (Thr172) and reduced downstream phosphorylation of mTOR (Ser2448). Importantly, gene silencing of LXRs compromises the inhibition effect of SR9243 on M1 macrophage polarization and activation. Collectively, for the first time, our findings suggest that the LXR inverse agonist SR9243 mitigates adjuvant-induced rheumatoid arthritis and protects against bone erosion by inhibiting M1 macrophage polarization and activation through modulation of glycolytic metabolism via the AMPK/mTOR/HIF-1α pathway.

Larger capacity has become a trend in the development of high-temperature superconducting magnetic energy storage system (HTS-SMES). A 10 MJ/5 MW HTS-SMES is under construction, and the cost of the conductor can be effectively reduced by introducing a hybrid winding structure with low-cost MgB2 cable in the low-field region. In this article, the AC losses of a 10 MJ/5 MW SMES magnet under different working conditions are investigated and it provides a reference for the design of the cooling system and control strategy.