105 research outputs found

    Secrecy outage analysis for Alamouti space-time block coded non-orthogonal multiple access

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    This letter proposed a novel transmission technique for physical layer security by applying the Alamouti Space-Time Block Coded Non-orthogonal Multiple Access (STBC-NOMA) scheme. The secure outage performance under both perfect successive interference cancellation (pSIC) and imperfect successive interference cancellation (ipSIC) are investigated. In particular, novel exact and asymptotic expressions of secrecy outage probability are derived. Numerical and theoretical results are presented to corroborate the derived expressions and to demonstrate the superiority of STBC-NOMA and its ability to enhance the secrecy outage performance compared to conventional NOMA

    Guaranteed Lower Eigenvalue Bound of Steklov Operator with Conforming Finite Element Methods

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    For the eigenvalue problem of the Steklov differential operator, by following Liu's approach, an algorithm utilizing the conforming finite element method (FEM) is proposed to provide guaranteed lower bounds for the eigenvalues. The proposed method requires the a priori error estimation for FEM solution to nonhomogeneous Neumann problems, which is solved by constructing the hypercircle for the corresponding FEM spaces and boundary conditions. Numerical examples are also shown to confirm the efficiency of our proposed method.Comment: 21 pages, 4 figures, 4 table

    The Relationship between Fungal Growth Rate and Temperature and Humidity

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    In order to determine the relation among the three factors of wood fiber decomposition rate, mycelial elongation and moisture resistance, our team resorted to the Monod equation and the modified Logistic equation. Combing with the kinetic principle and the law of mass action, the equations between the decomposition rate of wood fiber, the elongation rate of mycelium and the moisture resistance were established. In the course of solving the model, we found that when the temperature ranges from 24℃ to 28℃ and the relative humidity from 60% to75%, the growth rate of fungi is the fastest

    PointGPT: Auto-regressively Generative Pre-training from Point Clouds

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    Large language models (LLMs) based on the generative pre-training transformer (GPT) have demonstrated remarkable effectiveness across a diverse range of downstream tasks. Inspired by the advancements of the GPT, we present PointGPT, a novel approach that extends the concept of GPT to point clouds, addressing the challenges associated with disorder properties, low information density, and task gaps. Specifically, a point cloud auto-regressive generation task is proposed to pre-train transformer models. Our method partitions the input point cloud into multiple point patches and arranges them in an ordered sequence based on their spatial proximity. Then, an extractor-generator based transformer decoder, with a dual masking strategy, learns latent representations conditioned on the preceding point patches, aiming to predict the next one in an auto-regressive manner. Our scalable approach allows for learning high-capacity models that generalize well, achieving state-of-the-art performance on various downstream tasks. In particular, our approach achieves classification accuracies of 94.9% on the ModelNet40 dataset and 93.4% on the ScanObjectNN dataset, outperforming all other transformer models. Furthermore, our method also attains new state-of-the-art accuracies on all four few-shot learning benchmarks.Comment: 9 pages, 2 figure

    Neighbourhood satisfaction in rural resettlement residential communities: the case of Suqian, China

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    Against the background of large-scale urbanisation and rural land expropriation, rural resettlement residential housing has been built to accommodate local rural residents in the peripheral areas of China. To explore the context-specific policy implications for improving neighbourhood satisfaction (NS) of residents in rural resettlement residential communities (RRRCs), this paper examines the determinants of NS, and their spatial effects, in rural resettlement residential neighbourhoods using Suqian, in Jiangsu Province, as a case study. This study contributes to the current literature in two ways: it constitutes the first attempt to examine NS among RRRCs; second, our spatial model helps to gain further understanding of horizontal and vertical spatial dependence effects. Our results indicate that income, gender, age, family structure, number of years living in a community, transport and architectural age all have significant effects on NS in RRRCs

    Construction and validation of a risk prediction model for aromatase inhibitor-associated bone loss

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    PurposeTo establish a high-risk prediction model for aromatase inhibitor-associated bone loss (AIBL) in patients with hormone receptor-positive breast cancer.MethodsThe study included breast cancer patients who received aromatase inhibitor (AI) treatment. Univariate analysis was performed to identify risk factors associated with AIBL. The dataset was randomly divided into a training set (70%) and a test set (30%). The identified risk factors were used to construct a prediction model using the eXtreme gradient boosting (XGBoost) machine learning method. Logistic regression and least absolute shrinkage and selection operator (LASSO) regression methods were used for comparison. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the model in the test dataset.ResultsA total of 113 subjects were included in the study. Duration of breast cancer, duration of aromatase inhibitor therapy, hip fracture index, major osteoporotic fracture index, prolactin (PRL), and osteocalcin (OC) were found to be independent risk factors for AIBL (p < 0.05). The XGBoost model had a higher AUC compared to the logistic model and LASSO model (0.761 vs. 0.716, 0.691).ConclusionThe XGBoost model outperformed the logistic and LASSO models in predicting the occurrence of AIBL in patients with hormone receptor-positive breast cancer receiving aromatase inhibitors

    Oregano essential oil modulates colonic homeostasis and intestinal barrier function in fattening bulls

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    Oregano essential oil (OEO) primarily contains phenolic compounds and can serve as a dietary supplement for fattening bulls. However, the precise molecular mechanism underlying this phenomenon remains largely elusive. Therefore, this study investigated the impact of adding OEO to diet on the integrity of the intestinal barrier, composition of the colonic microbiome, and production of microbial metabolites in fattening bulls. Our goal was to provide insights into the utilization of plant essential oil products in promoting gastrointestinal health and welfare in animals. We employed amplicon sequencing and metabolome sequencing techniques to investigate how dietary supplementation with OEO impacted the intestinal barrier function in bulls. The inclusion of OEO in the diet resulted in several notable effects on the colon of fattening bulls. These effects included an increase in the muscle thickness of the colon, goblet cell number, short-chain fatty acid concentrations, digestive enzyme activity, relative mRNA expression of intestinal barrier-related genes, and relative expression of the anti-inflammatory factor IL-10. Additionally, α-amylase activity and the relative mRNA expression of proinflammatory cytokines decreased. Moreover, dietary OEO supplementation increased the abundance of intestinal Bacteroides, Coprobacillus, Lachnospiraceae_UCG_001, and Faecalitalea. Metabolomic analysis indicated that OEO primarily increased the levels of 5-aminovaleric acid, 3-methoxysalicylic acid, and creatinine. In contrast, the levels of maltose, lactulose, lactose, and D-trehalose decreased. Correlation analysis showed that altered colonic microbes and metabolites affected intestinal barrier function. Taken together, these results demonstrate that OEO facilitates internal intestinal environmental homeostasis by promoting the growth of beneficial bacteria while inhibiting harmful ones
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