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Flight tests and wind tunnel experiments face difficulties in investigating the impact of aircraft carrier air-wake on the landing process. Meanwhile, numerical methods generally exhibit low overall computational efficiency in solving such problems. To address the computational challenges posed by the disparate spatiotemporal scales of the ship air-wake and aircraft motion, a domain precursor inflow method is developed to efficiently generate unsteady inflow boundary conditions from precomputed full-domain air-wake simulations. This study investigates the aerodynamic variability of carrier-based aircraft during landing through the turbulent air-wake generated by an aircraft carrier, employing a hybrid RANS-LES methodology on dynamic unstructured overset grids. The numerical framework integrates a delayed detached-eddy simulation (DDES) model with a parallel dynamic overset grid approach, enabling high-fidelity simulations of coupled aircraft carrier interactions. Validation confirms the accuracy of the precursor inflow method in reproducing air-wake characteristics and aerodynamic loads compared to full-domain simulations. Parametric analyses of 15 distinct landing trajectories reveal significant aerodynamic variability, particularly within 250 m of the carrier, where interactions with island-generated vortices induce fluctuations in lift (up to 25%), drag (18%), and pitching moments (30%). Ground effects near the deck further amplify load variations, while lateral deviations in landing paths generate asymmetric forces and moments. The proposed methodology demonstrates computational efficiency for multi-scenario analysis, providing critical insights into aerodynamic uncertainties during carrier operations.

At cryogenic temperatures, gases exhibit significant deviations from ideal behaviour, and the commonly employed gas model may inadequately represent the thermodynamic properties of cryogenic gases, subsequently impacting numerical simulations using various thermodynamic and transport models at cryogenic temperatures. The findings of this study reveal that the relative errors in aerodynamic characteristics obtained through different isentropic relations are noteworthy, with the maximum relative error in the drag coefficient reaching 16%. The impact of the equation of state, viscosity model, and thermal conductivity model is relatively minor, with relative errors in the pressure drag coefficient and viscous drag coefficient remaining well below 1%. Nevertheless, the relative error in the skin friction coefficient cannot be ignored due to transonic shock wave/boundary layer interactions. Consequently, when conducting numerical simulations of cryogenic flow, it is imperative to select appropriate gas models to attain precise results.

Aiming at the question about armor training simulator whose image visual is not vivid and function is not perfect in our country [1, 6], the paper introduces RV engine in simulation training system of armored vehicle. It has researched establishment of entity and fire control system model based on RV, and then accomplished the simulation of range setting state, including the changing rule of gunner viewer-sight of the Image-stabilization fire control system, the principle on the measuring distance with laser, and the calculation of the location and height lead.

A potential therapeutic target to curb obesity and diabetes is thermogenic beige adipocytes. However, beige adipocytes quickly transition into white adipocytes upon removing stimuli. Here, we define the critical role of cyclin dependent kinase inhibitor 2A (Cdkn2a) as a molecular pedal for the beige-to-white transition. Beige adipocytes lacking Cdkn2a exhibit prolonged lifespan, and male mice confer long-term metabolic protection from diet-induced obesity, along with enhanced energy expenditure and improved glucose tolerance. Mechanistically, Cdkn2a promotes the expression and activity of beclin 1 (BECN1) by directly binding to its mRNA and its negative regulator BCL2 like 1 (BCL2L1), activating autophagy and accelerating the beige-to-white transition. Reactivating autophagy by pharmacological or genetic methods abolishes beige adipocyte maintenance induced by Cdkn2a ablation. Furthermore, hyperactive BECN1 alone accelerates the beige-to-white transition in mice and human. Notably, both Cdkn2a and Becn1 exhibit striking positive correlations with adiposity. Hence, blocking Cdkn2a -mediated BECN1 activity holds therapeutic potential to sustain beige adipocytes in treating obesity and related metabolic diseases. Beige adipocytes quickly transition into white adipocytes upon the removal of stimuli, limiting their therapeutic potential for chronic metabolic diseases. In this study, the authors show that inhibiting Cdkn2a-Becn1 mediated autophagy can maintain beige adipocytes and provide long term metabolic health benefits in mice.

Effective catalysts are indispensable for the preparation of fuel components from vegetable oils via the one‐step hydrothermal method. In this study, the Pt/MCM‐41 catalysts were prepared by etching with sulfuric acid, citric acid, or hydrochloric acid. The performance of the obtained Pt/MCM‐41 catalysts for the hydrothermal treatment of vegetable oils was evaluated in a fixed‐bed reactor using Jatropha oil as a model raw material (temperature = 360°C, liquid hourly space velocity = 1 h−1, hydrogen–oil ratio = 1000, and hydrogen pressure = 4 MPa). The modified catalysts were characterized using X‐ray diffraction, transmission electron microscopy, Brunauer–Emmett–Teller analysis, X‐ray fluorescence spectrometry, temperature‐programmed desorption of NH3, CO chemisorption, and pyridine adsorption infrared spectroscopy, respectively. Furthermore, the selectivity of various fuel components, including C8–C16 alkanes, C8–C16 iso‐alkanes, C8–C16 arenes, and C17–C18 alkanes, were analyzed based on the catalyst characteristics. Acid etching was found to decrease the surface area of Al‐MCM‐41 but increase the amount of acid sites, and among these acids, citric acid was proved as the priority additives, with better catalytic performance. Moreover, this catalyst exhibited the best conversion and the highest C8–C16 and C17–C18 selectivities. In this study, the effect of etching with three different acids (sulfuric acid, citric acid, or hydrochloric acid) on the performance of a Pt‐loaded Al‐MCM‐41 catalyst for the one‐step hydrotreatment of vegetable oils was examined using Jatropha oil as a model raw material.

The spatial structure of geochemical patterns is influenced by various geological processes, one of which may be mineralization. Thus, analysis of spatial geochemical patterns facilitates understanding of regional metallogenic mechanisms and recognition of geochemical anomalies related to mineralization. Convolutional neural networks (CNNs) used in previous studies to extract spatial features require regular data (e.g., raster maps) as input. Due to the complex and diverse geological environment, geochemical samples are inevitably irregularly distributed and even partially missing in many spaces, leading to the inapplicability of CNN-based methods for geochemical anomaly identification. Also, interpolation from samples to regular grids often introduces uncertainties. To address these problems, this study innovatively transformed geochemical sampled point data into graphs and introduced graph learning to extract the geochemical patterns. Correspondingly, a novel framework of geochemical identification named GAUGE (recognition of Geochemical Anomalies Using Graph lEarning) is proposed. To assess the performance of the proposed method, this study recognized anomalies related to Au deposits in the Longyan area, the Wuyishan polymetallic metallogenic belt, China. For a set of regularly distributed samples, GAUGE achieved an accuracy similar to that of a traditional convolution autoencoder. More importantly, GAUGE achieved an area under the curve of 0.833, outperforming one-class support vector machine, isolation forest, autoencoder, and deep autoencoder network for a set of irregularly distributed samples by 10.6, 5.2, 4.8, and 2.5%, respectively. By introducing graph learning into geochemical anomaly recognition, this study provides a new perspective of extracting both spatial structure and compositional relationships of multivariate geochemical patterns, which can be applied directly to irregularly distributed samples in irregularly shaped regions without the need for interpolation. Such an improvement greatly enhances the applicability of machine learning methods in geochemical anomaly recognition, providing support for mineral resources evaluation and exploration.

Backgroud Previous observational studies have shown that vitiligo usually co‐manifests with a variety of dysglycemic diseases, such as Type 1 diabetes mellitus (T1DM) and Type 2 diabetes mellitus (T2DM). Mendelian randomization (MR) analysis was performed to further evaluate the causal association between fasting plasma glucose, glycosylated hemoglobin (HbA1c), T1DM, T2DM and vitiligo. Materials and methods We used aggregated genome‐wide association data from the Integrative Epidemiology Unit (IEU) online database of European adults vitiligo; HbA1c data were from IEU. Fasting blood glucose data were obtained from the European Bioinformatics Institute (EBI). T1DM and T2DM data were from FinnGen. We used bidirectional two‐sample and multivariate MR analyses to test whether dysglycemic measures (fasting blood glucose, HbA1c), diabetes‐related measures (T1DM, T2DM) are causatively associated with vitiligo. Inverse variance weighting (IVW) method was used as the main test method, MR‐Egger, Weighted mode and Weighted median were used as supplementary methods. Results We found no statistically significant evidence to support a causal association between dysglycemic traits and vitiligo, but in the correlation analysis of diabetic traits, our data supported a positive causal association between T1DM and vitiligo (p = 0.018). In the follow‐up multivariate MR analysis, our results still supported this conclusion (p = 0.016), and suggested that HbA1c was not a mediator of T1DM affecting the pathogenesis of vitiligo. No reverse causality was found in any of the reverse MR Analyses of dysglycemic traits and diabetic traits. Conclusions Our findings support that T1DM is a risk factor for the development of vitiligo, and this conclusion may explain why the co‐presentation of T1DM and vitiligo is often seen in observational studies. Clinical use of measures related to T1DM may be a new idea for the prevention or treatment of vitiligo.

Backgroud Previous observational studies have shown that vitiligo usually co‐manifests with a variety of dysglycemic diseases, such as Type 1 diabetes mellitus (T1DM) and Type 2 diabetes mellitus (T2DM). Mendelian randomization (MR) analysis was performed to further evaluate the causal association between fasting plasma glucose, glycosylated hemoglobin (HbA1c), T1DM, T2DM and vitiligo. Materials and methods We used aggregated genome‐wide association data from the Integrative Epidemiology Unit (IEU) online database of European adults vitiligo; HbA1c data were from IEU. Fasting blood glucose data were obtained from the European Bioinformatics Institute (EBI). T1DM and T2DM data were from FinnGen. We used bidirectional two‐sample and multivariate MR analyses to test whether dysglycemic measures (fasting blood glucose, HbA1c), diabetes‐related measures (T1DM, T2DM) are causatively associated with vitiligo. Inverse variance weighting (IVW) method was used as the main test method, MR‐Egger, Weighted mode and Weighted median were used as supplementary methods. Results We found no statistically significant evidence to support a causal association between dysglycemic traits and vitiligo, but in the correlation analysis of diabetic traits, our data supported a positive causal association between T1DM and vitiligo (p = 0.018). In the follow‐up multivariate MR analysis, our results still supported this conclusion (p = 0.016), and suggested that HbA1c was not a mediator of T1DM affecting the pathogenesis of vitiligo. No reverse causality was found in any of the reverse MR Analyses of dysglycemic traits and diabetic traits. Conclusions Our findings support that T1DM is a risk factor for the development of vitiligo, and this conclusion may explain why the co‐presentation of T1DM and vitiligo is often seen in observational studies. Clinical use of measures related to T1DM may be a new idea for the prevention or treatment of vitiligo.

This study presents broadband ultraviolet photodetectors (BUV PDs) based on Ga2O3/GaN core-shell micro-nanorod arrays with excellent performance. Micro-Nanoarchitectonics of Ga2O3/GaN core-shell rod arrays were fabricated with high-temperature oxidization of GaN micro-nanorod arrays. The PD based on the microrod arrays exhibited an ultrahigh responsivity of 2300 A/W for 280 nm at 7 V, the peak responsivity was approximately 400 times larger than those of the PD based on the planar Ga2O3/GaN film. The responsivity was over 1500 A/W for the 270–360 nm band at 7 V. The external quantum efficiency was up to 1.02 × 106% for 280 nm. Moreover, the responsivity was further increased to 2.65 × 104 A/W for 365 nm and over 1.5 × 104 A/W for 270–360 nm using the nanorod arrays. The physical mechanism may have been attributed to the large surface area of the micro-nanorods coupled with the Ga2O3/GaN heterostructure, which excited more photogenerated holes to be blocked at the Ga2O3 surface and Ga2O3/GaN interface, resulting in a larger internal gain. The overall high performance coupled with large-scale production makes it a promising candidate for practical BUV PD.

The argyrodite compounds (A (12− n ) m/m+ B n + X 6 2− (A m + = Li + , Cu + , and Ag+; B n + = Ga 3+ , Si 4+ , Ge 4+ , Sn 4+ , P 5+ , and As 5+ ; and X 2− = S 2− , Se 2− , or Te 2− )) have attracted great attention as excellent thermoelectric (TE) materials due to their extremely low lattice thermal conductivity Among them, Ag 8 SnSe 6 -based TE materials have high potential for TE applications. However, the pristine Ag 8 SnSe 6 materials have low carrier concentration (< 10 17 cm −3 ), resulting in low power factors. In this study, a hydrothermal method was used to synthesize Ag 8 SnSe 6 with high purity, and the introduction of SnBr 2 into the pristine Ag 8 SnSe 6 powders has been used to simultaneously increase the power factor and decrease the thermal conductivity ( κ ). On the one hand, a portion of the Br − ions acted as electrons to increase the carrier concentration, increasing the power factor to a value of ∼698 µW·m −1 ·K −2 at 736 K. On the other hand, some of the dislocations and nanoprecipitates (SnBr 2 ) were generated, resulting in a decrease of κ 1 (−0.13 W·m −1 ·K −1 ) at 578 K. As a result, the zT value reaches ∼1.42 at 735 K for the sample Ag 8 Sn 1.03 Se 5.94 Br 0.06 , nearly 30% enhancement in contrast with that of the pristine sample (−1.09). The strategy of synergistic manipulation of carrier concentration and microstructure by introducing halogen compounds could be applied to the argyrodite compounds to improve the TE properties.