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Terahertz (THz) quantum cascade lasers (QCLs) are technologically important laser sources for the THz range but are complex to model. An efficient extended rate equation model is developed here by incorporating the resonant tunneling mechanism from the density matrix formalism, which permits to simulate THz QCLs with thick carrier injection barriers within the semi-classical formalism. A self-consistent solution is obtained by iteratively solving the Schrödinger–Poisson equation with this transport model. Carrier–light coupling is also included to simulate the current behavior arising from stimulated emission. As a quasi-ab initio model, intermediate parameters, such as pure dephasing time and optical linewidth, are dynamically calculated in the convergence process, and the only fitting parameters are the interface roughness correlation length and height. Good agreement has been achieved by comparing the simulation results of various designs with experiments, and other models such as density matrix Monte Carlo and non-equilibrium Green's function method that, unlike here, require important computational resources. The accuracy, compatibility, and computational efficiency of our model enable many application scenarios, such as design optimization and quantitative insights into THz QCLs. Finally, the source code of the model is also provided in the supplementary material of this article for readers to repeat the results presented here, investigate, and optimize new designs. [ABSTRACT FROM AUTHOR]

In this paper, for the remote sensing images acquired under the GIS model, the wavelet thresholding denoising method is used to reduce the noise to improve the quality of computer images, combined with the image filtering method to complete the grayscale preprocessing of the image, and fused different operators to extract the edge features. To improve image alignment and decrease the amount of splicing calculations, a proposed raster DEM data fusion and splicing algorithm utilizes image feature information. An experimental environment has been created for preprocessing computer vision images by comparing peak signal-to-noise values at different levels to find the best parameters for wavelet thresholding denoising. The feasibility of the preprocessing method in this paper is tested by placing unpreprocessed and preprocessed images in DeepLabv3 and DAM-DeepLab models for training purposes. Test the effectiveness of this paper’s algorithm for fuzzy image target feature description by simulated image edge extraction process. Real-time and effective evaluation of image stitching verifies the reliability of the algorithm. The data show that the splicing results of the four different methods under different scene images are 6.6932 for the global transform algorithm, 6.6831 for the APAP algorithm, 6.6449 for the AANAP algorithm, and 6.6948 for the algorithm of this paper. This paper’s algorithm has seen an improvement in average information entropy by 0.16%, 1.17%, and 4.99% when compared to other algorithms. The theory of graphics application technology within GIS is enriched through the study of computer graphics processing technology within the GIS model.

Anxiety disorder is a universal disease related to neuro-inflammation. Solanesol has shown positive effects because of its anti-inflammatory, anti-tumor, and anti-ulcer properties. This study focused on determining whether solanesol could ameliorate anxiety-like behaviors in a mouse model of neuro-inflammation and identify its working targets. Complete Freund's adjuvant (CFA)-induced mice that were intra-peritoneally administered with solanesol (50 mg/kg) for 1 week showed a statistically significant reduction in anxiety-like behaviors, as measured by open field and elevated plus-maze tests. Western blot analysis revealed that CFA-induced upregulation of the levels of pro-inflammatory cytokines interleukin (IL)-1β and tumor necrosis factor α (TNF-α), which played crucial roles in regulating anxiety, returned to normal in the anterior cingulate cortex (ACC) after solanesol treatment. The level of T cell-restricted intracellular antigen-1 (TIA1), a key component of stress granules, also decreased in the ACC. Moreover, immunofluorescence results indicated that solanesol suppressed CFA-induced microglial and astrocytic activation in the ACC. CFA was injected in the hind paws of TIA1Nestin conditional knockout (cKO) mice to confirm whether TIA1 is a potential modulatory molecule that influences pro-inflammatory cytokines and anxiety-like behaviors. Anxiety-like behaviors could not be observed in cKO mice after CFA injection with IL-1β and TNF-α levels not remarkedly increasing. Our findings suggest that solanesol inhibits neuro-inflammation by decreasing the TIA1 level to reduce IL-1β and TNF-α expression, meanwhile inhibiting microglial and astrocytic activation in the ACC and ultimately ameliorating anxiety-like behaviors in mice. [ABSTRACT FROM AUTHOR]

Background: Blood–brain barrier (BBB) dysfunction has been viewed as a potential underlying mechanism of neurodegenerative disorders, possibly involved in the pathogenesis and progression of Alzheimer's disease (AD). However, a relation between BBB dysfunction and dementia with Lewy bodies (DLB) has yet to be systematically investigated. Given the overlapping clinical features and neuropathology of AD and DLB, we sought to evaluate BBB permeability in the context of DLB and determine its association with plasma amyloid-β (Aβ) using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods: For this prospective study, we examined healthy controls (n = 24, HC group) and patients diagnosed with AD (n = 29) or DLB (n = 20) between December 2020 and April 2022. Based on DCE-MRI studies, mean rates of contrast agent transfer from intra- to extravascular spaces (Ktrans) were calculated within regions of interest. Spearman's correlation and multivariate linear regression were applied to analyze associations between Ktrans and specific clinical characteristics. Results: In members of the DLB (vs HC) group, Ktrans values of cerebral cortex (p = 0.024), parietal lobe (p = 0.007), and occipital lobe (p = 0.014) were significantly higher; and Ktrans values of cerebral cortex (p = 0.041) and occipital lobe (p = 0.018) in the DLB group were significantly increased, relative to those of the AD group. All participants also showed increased Ktrans values of parietal (β = 0.391; p = 0.001) and occipital (β = 0.357; p = 0.002) lobes that were significantly associated with higher scores of the Clinical Dementia Rating, once adjusted for age and sex. Similarly, increased Ktrans values of cerebral cortex (β = 0.285; p = 0.015), frontal lobe (β = 0.237; p = 0.043), and parietal lobe (β = 0.265; p = 0.024) were significantly linked to higher plasma Aβ1-42/Aβ1-40 ratios, after above adjustments. Conclusion: BBB leakage is a common feature of DLB and possibly is even more severe than in the setting of AD for certain regions of the brain. BBB leakage appears to correlate with plasma Aβ1-42/Aβ1-40 ratio and dementia severity. [ABSTRACT FROM AUTHOR]

Aiming at the limitations of the existing railway foreign object detection methods based on two-dimensional (2D) images, such as short detection distance, strong influence of environment and lack of distance information, we propose Rail-PillarNet, a three-dimensional (3D) LIDAR (Light Detection and Ranging) railway foreign object detection method based on the improvement of PointPillars. Firstly, the parallel attention pillar encoder (PAPE) is designed to fully extract the features of the pillars and alleviate the problem of local fine-grained information loss in PointPillars pillars encoder. Secondly, a fine backbone network is designed to improve the feature extraction capability of the network by combining the coding characteristics of LIDAR point cloud feature and residual structure. Finally, the initial weight parameters of the model were optimised by the transfer learning training method to further improve accuracy. The experimental results on the OSDaR23 dataset show that the average accuracy of Rail-PillarNet reaches 58.51%, which is higher than most mainstream models, and the number of parameters is 5.49 M. Compared with PointPillars, the accuracy of each target is improved by 10.94%, 3.53%, 16.96% and 19.90%, respectively, and the number of parameters only increases by 0.64 M, which achieves a balance between the number of parameters and accuracy. [ABSTRACT FROM AUTHOR]

Hyperuricemia (HUA) is characterized by elevated blood uric acid levels, which can increase the risk of erectile dysfunction (ED). Clinical studies have demonstrated satisfactory efficacy of a traditional Chinese medicine formula QYHT decoction in improving ED. Furthermore, the main monomeric components of this formula, linoleyl acetate and mandenol, demonstrate promise in the treatment of ED. This study established an ED rat model induced by HUA and the animals were administered with linoleyl acetate and mandenol. HE and TUNEL were performed to detect tissue changes, ELISA to measure the levels of serum testosterone (T), MDA, NO, CRP, and TNF‐α and qPCR and WB to assess the expression levels of NLRP3, ASC, Caspase‐1, JAK2, and STAT3 in whole blood. The findings showed that linoleyl acetate and mandenol improved kidney tissue morphology, reduced cell apoptosis in penile tissue, significantly increased T and NO levels, while substantially decreasing levels of MDA, CRP, and TNF‐α. Meanwhile, the expression of NLRP3, ASC, and Caspase‐1 mRNAs and proteins was markedly reduced, and the phosphorylation of JAK2 and STAT3 was inhibited. These findings were further validated through faecal microbiota transplantation results. Taken together, linoleyl acetate and mandenol could inhibit NLRP3 inflammasome activation, reduce inflammatory and oxidative stress responses, suppress the activity of JAK–STAT signalling pathway, ultimately providing a potential treatment for HUA‐induced ED. [ABSTRACT FROM AUTHOR]

Ceramsites are extensively employed as substrates for adsorbents in studies focused on phosphorus adsorption, leaving ceramsites crafted from diatomite less explored. Diatomite-based ceramsite, with its distinct porous architecture and surface silanol functionalities, is adept at supporting a variety of metal oxides, presenting a distinct advantage over other ceramsite variants. In light of this, the present study embarked on producing diatomite-based ceramsite using diatomite as the foundational material, subsequently enhancing it through the incorporation of Fe, thus yielding an Fe-modified diatomite-based ceramsite. Through adsorption testing, the modified ceramsite demonstrated a significantly improved adsorption capacity of 4.06 mg P/g, marking a substantial enhancement from the initial capacity of 0.9 mg P/g. The process of phosphorus adsorption exhibited a strong alignment with the Langmuir isotherm model and the pseudo-second-order kinetic model. In-depth analyses employing XRD, FTIR, zeta potential, and XPS techniques have revealed that the principal mechanisms driving the adsorption process are centered on interactions involving electrostatic forces, the formation of chemical precipitates, and the exchange of ligands. This investigation not only opens new avenues for the application of diatomite-based ceramsite but also lays down a theoretical foundation for its modification, thereby enriching the spectrum of its utility. [ABSTRACT FROM AUTHOR]

The Shendong Mining Area, being the largest coal base in the world, has significant challenges in the intensive development and utilization of coal resources, as well as the impact of a dry climate, which can have serious negative effects on the growth of flora in the region. Investigating the spatial and temporal patterns of how meteorological drought affects vegetation in the Shendong Mining Area at various time scales can offer a scientific foundation for promoting sustainable development and ecological restoration in the region. This study utilizes the Standardized Precipitation Evapotranspiration Index (SPEI) and Normalized Difference Vegetation Index (NDVI) data from 1986 to 2020 in the Shendong Mining Area. It employs Slope trend analysis, a Mann–Kendall test, a Geographic Detector, and other methods to examine the spatiotemporal distribution characteristics of meteorological drought at various time scales. Additionally, the study investigates the influence of these drought patterns on vegetation growth in the Shendong Mining Area. Across the mining area, there was a general decrease in the monthly average SPEI on an annual basis. However, on a seasonal, semi-annual, and annual basis, there was a gradual increase in the annual average SPEI, with a higher rate of increase in the southern region compared to the northern region. When considering the spatial variation trend in different seasons, both positive and negative trends were observed in winter and summer. The negative trend was mainly observed in the western part of the mining area, while the positive trend was observed in the eastern part. In spring, the mining area generally experienced drought, while in autumn, it generally experienced more precipitation. The mining area exhibits a prevailing distribution of vegetation, with a greater extent in the southeast and a lesser extent in the northwest. The vegetation coverage near the mine is insufficient, resulting in a low NDVI value, which makes the area prone to drought. Over the past few years, the mining area has experienced a significant increase in vegetation coverage, indicating successful ecological restoration efforts. Various forms of land use exhibit distinct responses to drought, with forests displaying the most positive correlation and barren land displaying the strongest negative correlation. Various types of landforms exhibit varying responses to drought. Loess ridge and hill landforms demonstrate the most pronounced positive association with monthly-scale SPEI values, whereas alluvial and floodplain landforms display the poorest positive correlation with yearly scale SPEI values. The general findings of this research can be summarized as follows: (1) The mining area exhibits a general pattern of increased humidity, with the pace of humidity increase having intensified in recent times. Seasonal variations exhibit consistent cyclic patterns. (2) There are distinct regional disparities in NDVI values, with a notable peak in the southeast and a decline in the northwest. The majority of the mining area exhibits a positive trend in vegetation recovery. (3) Regional meteorological drought is a significant element that influences changes in vegetation coverage in the Shendong Mining Area. Nevertheless, it displays complexity and is more obviously impacted by other factors at a small scale. (4) It should be noted that forests and barren land exert a more significant influence on SPEI values, despite their relatively lesser spatial coverage. The predominant land use type in most locations is grasslands; however, they have a relatively minor influence on SPEI. (5) A shorter time period, higher elevation, and steeper slope gradient all contribute to a larger correlation with drought. [ABSTRACT FROM AUTHOR]

Baicalin is an active compound extracted from Scutellaria baicalensis with antioxidant and anti‐inflammatory properties. Bone mesenchymal stem cells (BMSCs)‐derived exosomes have shown promise for the treatment of hepatic ischemia–reperfusion (I/R) injury. This study aims to investigate the role of Baicalin‐pretreated BMSCs‐derived exosomes in hepatic I/R injury and its mechanisms. BMSCs were pretreated with or without Baicalin, and their exosomes (Ba‐Exo and Exo) were collected and characterized. These exosomes were administered to mice via tail vein injection. Treatment with Exo and Ba‐Exo significantly suppressed the elevation of ALT and AST induced by hepatic injury. Additionally, both Exo and Ba‐Exo treatments resulted in a reduction in the liver weight‐to‐body weight ratio. RT‐PCR results revealed a significant downregulation of pro‐inflammatory cytokines with Exo and Ba‐Exo treatment. Both Exo and Ba‐Exo treatment improved the Th17/Treg cell imbalance induced by I/R and reduced hepatic injury. Additionally, exosomes were cocultured with normal liver cells, and the expression of fibroblast growth factor 21 (FGF21) in liver cells was elevated through Ba‐Exo treatment. After treatment, the JAK2/STAT3 pathway was inhibited, and FOXO1 expression was upregulated. Finally, recombinant FGF21 was injected into mouse tail veins to assess its effects. Recombinant FGF21 injection further inhibited the JAK2/STAT3 pathway, increased FOXO1 expression, and improved the Th17/Treg cell imbalance. In conclusion, this study confirms the protective effects of Exo and Ba‐Exo against hepatic I/R injury. Ba‐Exo mitigates hepatic I/R injury, achieved through inducing FGF21 expression in liver cells, inhibiting the JAK2/STAT3 pathway, and activating FOXO1 expression. Therefore, baicalin pretreatment emerges as a promising strategy to enhance the therapeutic capability of BMSCs‐derived exosomes for hepatic I/R. [ABSTRACT FROM AUTHOR]

Accurate and reliable medium- and long-term load forecasting is crucial for the rational planning and operation of power systems. However, existing methods often struggle to accurately extract and capture long-term dependencies in load data, leading to poor predictive accuracy. Therefore, this paper proposes a medium- and long-term residential load forecasting method based on FEDformer, aiming to capture long-term temporal dependencies of load data in the frequency domain while considering factors such as electricity prices and temperature, ultimately improving the accuracy of medium- and long-term load forecasting. The proposed model employs Discrete Cosine Transform (DCT) for frequency domain transformation of time-series data to address the Gibbs phenomenon caused by the use of Discrete Fourier Transform (DFT) in FEDformer. Additionally, causal convolution and attention mechanisms are applied in the frequency domain to enhance the model's capability to capture long-term dependencies. The model is evaluated using real-world load data from power systems, and experimental results demonstrate that the proposed model effectively learns the temporal and nonlinear characteristics of load data. Compared to other baseline models, DCTformer improves prediction accuracy by 37.5% in terms of MSE, 26.9% in terms of MAE, and 26.24% in terms of RMSE. [ABSTRACT FROM AUTHOR]