The Performance Analysis of Spectrum Sharing between UAV enabled Wireless Mesh Networks and Ground Networks

Unmanned aerial vehicle (UAV) has the advantages of large coverage and flexibility, which could be applied in disaster management to provide wireless services to the rescuers and victims. When UAVs forms an aerial mesh network, line-of-sight (LoS) air-to-air (A2A) communications have long transmission distance, which extends the coverage of multiple UAVs. However, the capacity of UAV is constrained due to the multiple hop transmissions in aerial mesh networks. In this paper, spectrum sharing between UAV enabled wireless mesh networks and ground networks is studied to improve the capacity of UAV networks. Considering two-dimensional (2D) and three-dimensional (3D) homogeneous Poisson point process (PPP) modeling for the distribution of UAVs within a vertical range {\Delta}h, stochastic geometry is applied to analyze the impact of the height of UAVs, the transmit power of UAVs, the density of UAVs and the vertical range, etc., on the coverage probability of ground network user and UAV network user. Besides, performance improvement of spectrum sharing with directional antenna is verified. With the object function of maximizing the transmission capacity, the optimal altitude of UAVs is obtained. This paper provides a theoretical guideline for the spectrum sharing of UAV enabled wireless mesh networks, which may contribute significant value to the study of spectrum sharing mechanisms for UAV enabled wireless mesh networks.Comment: 12 pages, 13 figures, IEEE Sensors Journa

Identifying Strongly Lensed Gravitational Waves with the Third-generation Detectors

The joint detection of GW signals by a network of instruments will increase the detecting ability of faint and far GW signals with higher signal-to-noise ratios (SNRs), which could improve the ability of detecting the lensed GWs as well, especially for the 3rd generation detectors, e.g. Einstein Telescope (ET) and Cosmic Explorer (CE). However, identifying Strongly Lensed Gravitational Waves (SLGWs) is still challenging. We focus on the identification ability of 3G detectors in this article. We predict and analyze the SNR distribution of SLGW signals and prove only 50.6\% of SLGW pairs detected by ET alone can be identified by Lens Bayes factor (LBF), which is a popular method at present to identify SLGWs. For SLGW pairs detected by CE\&ET network, owing to the superior spatial resolution, this number rises to 87.3\%. Moreover, we get an approximate analytical relation between SNR and LBF. We give clear SNR limits to identify SLGWs and estimate the expected yearly detection rates of galaxy-scale lensed GWs that can get identified with 3G detector network.Comment: 9 pages, 7 figure

How complex is the microarray dataset? A novel data complexity metric for biological high-dimensional microarray data

Data complexity analysis quantifies the hardness of constructing a predictive model on a given dataset. However, the effectiveness of existing data complexity measures can be challenged by the existence of irrelevant features and feature interactions in biological micro-array data. We propose a novel data complexity measure, depth, that leverages an evolutionary inspired feature selection algorithm to quantify the complexity of micro-array data. By examining feature subsets of varying sizes, the approach offers a novel perspective on data complexity analysis. Unlike traditional metrics, depth is robust to irrelevant features and effectively captures complexity stemming from feature interactions. On synthetic micro-array data, depth outperforms existing methods in robustness to irrelevant features and identifying complexity from feature interactions. Applied to case-control genotype and gene-expression micro-array datasets, the results reveal that a single feature of gene-expression data can account for over 90% of the performance of multi-feature model, confirming the adequacy of the commonly used differentially expressed gene (DEG) feature selection method for the gene expression data. Our study also demonstrates that constructing predictive models for genotype data is harder than gene expression data. The results in this paper provide evidence for the use of interpretable machine learning algorithms on microarray data

Spectrum Sharing between UAV-based Wireless Mesh Networks and Ground Networks

The unmanned aerial vehicle (UAV)-based wireless mesh networks can economically provide wireless services for the areas with disasters. However, the capacity of air-to-air communications is limited due to the multi-hop transmissions. In this paper, the spectrum sharing between UAV-based wireless mesh networks and ground networks is studied to improve the capacity of the UAV networks. Considering the distribution of UAVs as a three-dimensional (3D) homogeneous Poisson point process (PPP) within a vertical range, the stochastic geometry is applied to analyze the impact of the height of UAVs, the transmit power of UAVs, the density of UAVs and the vertical range, etc., on the coverage probability of ground network user and UAV network user, respectively. The optimal height of UAVs is numerically achieved in maximizing the capacity of UAV networks with the constraint of the coverage probability of ground network user. This paper provides a basic guideline for the deployment of UAV-based wireless mesh networks.Comment: 6 pages, 6 figure

Improvements on the optical properties of Ge-Sb-Se chalcogenide glasses with iodine incorporation

International audienceDecreasing glass network defects and improving optical transmittance are essential work for material researchers. We studied the function of halogen iodine (I) acting as a glass network modifier in Ge–Sb–Se–based chalcogenide glass system. A systematic series of Ge20Sb5Se75-xIx (x = 0, 5, 10, 15, 20 at%) infrared (IR) chalcohalide glasses were investigated to decrease the weak absorption tail (WAT) and improve the mid-IR transparency. The mechanisms of the halogen I affecting the physical, thermal, and optical properties of Se-based chalcogenide glasses were reported. The structural evolutions of these glasses were also revealed by Raman spectroscopy and camera imaging. The progressive substitution of I for Se increased the optical bandgap. The WAT and scatting loss significantly decreased corresponding to the progressive decrease in structural defects caused by dangling bands and structure defects in the original Ge20Sb5Se75 glass. The achieved maximum IR transparency of Ge–Sb–Se–I glasses can reach up to 80% with an effective transmission window between 0.94 μm to 17 μm, whereas the absorption coefficient decreased to 0.029 cm-1 at 10.16 μm. Thus, these materials are promising candidates for developing low-loss IR fibers

Transformative skeletal motion analysis: optimization of exercise training and injury prevention through graph neural networks

IntroductionExercise is pivotal for maintaining physical health in contemporary society. However, improper postures and movements during exercise can result in sports injuries, underscoring the significance of skeletal motion analysis. This research aims to leverage advanced technologies such as Transformer, Graph Neural Networks (GNNs), and Generative Adversarial Networks (GANs) to optimize sports training and mitigate the risk of injuries.MethodsThe study begins by employing a Transformer network to model skeletal motion sequences, facilitating the capture of global correlation information. Subsequently, a Graph Neural Network is utilized to delve into local motion features, enabling a deeper understanding of joint relationships. To enhance the model's robustness and adaptability, a Generative Adversarial Network is introduced, utilizing adversarial training to generate more realistic and diverse motion sequences.ResultsIn the experimental phase, skeletal motion datasets from various cohorts, including professional athletes and fitness enthusiasts, are utilized for validation. Comparative analysis against traditional methods demonstrates significant enhancements in specificity, accuracy, recall, and F1-score. Notably, specificity increases by ~5%, accuracy reaches around 90%, recall improves to around 91%, and the F1-score exceeds 89%.DiscussionThe proposed skeletal motion analysis method, leveraging Transformer and Graph Neural Networks, proves successful in optimizing exercise training and preventing injuries. By effectively amalgamating global and local information and integrating Generative Adversarial Networks, the method excels in capturing motion features and enhancing precision and adaptability. Future research endeavors will focus on further advancing this methodology to provide more robust technological support for healthy exercise practices

Fabrication and characterization of Ge–Sb–Se–I glasses and fibers

International audienceChalcogenide glasses of the Ge20Sb5Se75−x I x (x = 0, 5, 10, 15, 20 at.%) system were prepared. This study was performed to examine some Ge–Sb–Se–I glass physical and optical properties, the structural evolution of the glass network, and the optical properties of the infrared glass fibers based on our previous studies. The variation process of the glass physical properties, such as transition temperature, glass density, and refractive index, was investigated from the glass of Ge20Sb5Se75 to the Ge20Sb5Se75−x I x glass series. The structural evolutions of these glasses were examined by Raman spectroscopy. The Ge20Sb5Se55I20 composition was selected for the preparation of the IR fiber. The Ge20Sb5Se55I20 glass was purified through distillation, and the intensity of the impurity absorption peaks caused by Ge–O, H2O, and Se–H was reduced or eliminated in the purified glasses. Then, Ge20Sb5Se55I20 chalcogenide glass fiber for mid-infrared transmission was fabricated using high-purity materials. The transmission loss of the Ge20Sb5Se55I20 fiber was greatly reduced compared with that of the Ge20Sb5Se75 glass fiber. The lowest losses obtained were 3.5 dB/m at 3.3 μm for Ge20Sb5Se75I20 fiber, which was remarkably improved compared with 48 dB/m of the unpurified Ge20Sb5Se75 fiber

Study on the significance and mechanism of ASGR1 in hepatocellular carcinoma

Objective·To explore the significance and mechanism of asialoglycoprotein receptor 1 (ASGR1) in hepatocellular carcinoma.Methods·The expression of ASGR1 in patients with liver cancer in The Cancer Genome Atlas (TCGA) database was analyzed by R language and the related survival curves were drawn. The Human Protein Atlas (HPA) database was used to obtain the immunohistochemistry (IHC) data of normal human liver tissue and liver cancer tissue to analyze the protein expression of ASGR1. By using the hydrodynamic tail vein injection (HTVI) delivery method, Asgr1 was knocked out in the liver of fully immune mice to explore its tumorigenic function in vivo. Gene knockout efficiency was verified by Western blotting (WB). The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and correlation analysis were performed by using R language. The GSEA hallmark correlation pathway analysis was performed by using Gene Set Enrichment Analysis (GSEA) software. The expression level of key genes of glycolysis in mouse liver cancer tissue was verified by quantitative real-time PCR (qPCR).Results·ASGR1 was significantly low-expressed in liver cancer tissue, and the low expression of ASGR1 in liver cancer patients was associated with poorer overall survival (OS), disease-free interval (DFI), progression-free interval (PFI), and disease-specific survival (DSS). The higher the degree of tumor grade, the lower the expression level of ASGR1 in patients with liver cancer. Immunohistochemistry showed that the protein expression of ASGR1 in normal human liver tissue was significantly higher than that in liver cancer tissue. In an immunocompetent mouse model of hepatocellular carcinoma, knockout of endogenous Asgr1 in mice increased the size and number of tumor nodules in liver tissue. In the TCGA database, patients with liver cancer in the ASGR1 low-expression group were enriched in multiple cancer and metabolic pathways. The expression of ASGR1 was negatively correlated with some key genes of glycolysis. The level of glycolysis in liver cancer tissues of mice in the Asgr1 knockout group was higher than that in the control group. It was suggested that the low expression of ASGR1 be likely to promote the growth and development of liver cancer and strengthen metabolic reprogramming to promote the anabolic development of tumors.Conclusion·The expression of ASGR1 is significantly reduced in patients with liver cancer, which is positively correlated with the prognosis of patients. Knocking out Asgr1 in mice can promote the occurrence and development of hepatocellular carcinoma. ASGR1 can be used as a potential biomarker for poor prognosis of liver cancer and a new target for potential treatment

Undernutrition-induced substance metabolism and energy production disorders affected the structure and function of the pituitary gland in a pregnant sheep model

IntroductionUndernutrition spontaneously occurs in ewes during late gestation and the pituitary is an important hinge in the neurohumoral regulatory system. However, little is known about the effect of undernutrition on pituitary metabolism.MethodsHere, 10 multiparous ewes were restricted to a 30% feeding level during late gestation to establish an undernutrition model while another 10 ewes were fed normally as controls. All the ewes were sacrificed, and pituitary samples were collected to perform transcriptome, metabolome, and quantitative real-time PCR analysis and investigate the metabolic changes.ResultsPCA and PLS-DA of total genes showed that undernutrition changed the total transcriptome profile of the pituitary gland, and 581 differentially expressed genes (DEGs) were identified between the two groups. Clusters of orthologous groups for eukaryotic complete genomes demonstrated that substance transport and metabolism, including lipids, carbohydrates, and amino acids, energy production and conversion, ribosomal structure and biogenesis, and the cytoskeleton were enriched by DEGs. Kyoto encyclopedia of genes and genomes pathway enrichment analysis displayed that the phagosome, intestinal immune network, and oxidative phosphorylation were enriched by DEGs. Further analysis found that undernutrition enhanced the lipid degradation and amino acid transport, repressing lipid synthesis and transport and amino acid degradation of the pituitary gland. Moreover, the general metabolic profiles and metabolic pathways were affected by undernutrition, repressing the 60S, 40S, 28S, and 39S subunits of the ribosomal structure for translation and myosin and actin synthesis for cytoskeleton. Undernutrition was found also to be implicated in the suppression of oxidative phosphorylation for energy production and conversion into a downregulation of genes related to T cell function and the immune response and an upregulation of genes involved in inflammatory reactions enriching phagosomes.DiscussionThis study comprehensively analyses the effect of undernutrition on the pituitary gland in a pregnant sheep model, which provides a foundation for further research into the mechanisms of undernutrition-caused hormone secretion and metabolic disorders