Adsorption and Diffusion of CH4, N2, and Their Mixture in MIL-101(Cr): A Molecular Simulation Study

A comprehensive quantitative grasp of methane (CH4), nitrogen (N2), and their mixture’s adsorption and diffusion in MIL-101(Cr) is crucial for wide and important applications, e.g., natural gas upgrading and coal-mine methane capturing. Previous studies often overlook the impact of gas molecular configuration and MIL-101 topology structure on adsorption, lacking quantitative assessment of primary and secondary adsorption sites. Additionally, understanding gas mixture adsorption mechanisms remains a research gap. To bridge this gap and to provide new knowledge, we utilized Monte Carlo and molecular dynamics simulations for computing essential MIL-101 properties, encompassing adsorption isotherms, density profiles, self-diffusion coefficients, radial distribution function (RDF), and CH4/N2 selectivity. Several novel and distinctive findings are revealed by the atomic-level analysis, including (1) the significance of C═C double bond of the benzene ring within MIL-101 for CH4 and N2 adsorption, with Cr and O atoms also exerting notable effects. (2) Density distribution analysis reveals CH4’s preference for large and medium cages, while N2 is evenly distributed along pentagonal and triangular window edges and small tetrahedral cages. (3) Calculations of self-diffusion and diffusion activation energies suggest N2’s higher mobility within MIL-101 compared to CH4. (4) In the binary mixture, the existence of CH4 can decrease the diffusion coefficient of N2. In summary, this investigation provides valuable microscopic insights into the adsorption and diffusion phenomena occurring in MIL-101, thereby contributing to a comprehensive understanding of its potential for applications, e.g., natural gas upgrading and selective capture of coal-mine methane

Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey

Adversarial attacks and defenses in machine learning and deep neural network have been gaining significant attention due to the rapidly growing applications of deep learning in the Internet and relevant scenarios. This survey provides a comprehensive overview of the recent advancements in the field of adversarial attack and defense techniques, with a focus on deep neural network-based classification models. Specifically, we conduct a comprehensive classification of recent adversarial attack methods and state-of-the-art adversarial defense techniques based on attack principles, and present them in visually appealing tables and tree diagrams. This is based on a rigorous evaluation of the existing works, including an analysis of their strengths and limitations. We also categorize the methods into counter-attack detection and robustness enhancement, with a specific focus on regularization-based methods for enhancing robustness. New avenues of attack are also explored, including search-based, decision-based, drop-based, and physical-world attacks, and a hierarchical classification of the latest defense methods is provided, highlighting the challenges of balancing training costs with performance, maintaining clean accuracy, overcoming the effect of gradient masking, and ensuring method transferability. At last, the lessons learned and open challenges are summarized with future research opportunities recommended.Comment: 46 pages, 21 figure

When Internet of Things meets Metaverse: Convergence of Physical and Cyber Worlds

In recent years, the Internet of Things (IoT) is studied in the context of the Metaverse to provide users immersive cyber-virtual experiences in mixed reality environments. This survey introduces six typical IoT applications in the Metaverse, including collaborative healthcare, education, smart city, entertainment, real estate, and socialization. In the IoT-inspired Metaverse, we also comprehensively survey four pillar technologies that enable augmented reality (AR) and virtual reality (VR), namely, responsible artificial intelligence (AI), high-speed data communications, cost-effective mobile edge computing (MEC), and digital twins. According to the physical-world demands, we outline the current industrial efforts and seven key requirements for building the IoT-inspired Metaverse: immersion, variety, economy, civility, interactivity, authenticity, and independence. In addition, this survey describes the open issues in the IoT-inspired Metaverse, which need to be addressed to eventually achieve the convergence of physical and cyber worlds.info:eu-repo/semantics/publishedVersio

L2hgdh Deficiency Accumulates l-2-Hydroxyglutarate with Progressive Leukoencephalopathy and Neurodegeneration

l-2-Hydroxyglutarate aciduria (L-2-HGA) is an autosomal recessive neurometabolic disorder caused by a mutation in the l-2-hydroxyglutarate dehydrogenase (L2HGDH) gene. In this study, we generated L2hgdh knockout (KO) mice and observed a robust increase of l-2-hydroxyglutarate (L-2-HG) levels in multiple tissues. The highest levels of L-2-HG were observed in the brain and testis, with a corresponding increase in histone methylation in these tissues. L2hgdh KO mice exhibit white matter abnormalities, extensive gliosis, microglia-mediated neuroinflammation, and an expansion of oligodendrocyte progenitor cells (OPCs). Moreover, L2hgdh deficiency leads to impaired adult hippocampal neurogenesis and late-onset neurodegeneration in mouse brains. Our data provide in vivo evidence that L2hgdh mutation leads to L-2-HG accumulation, leukoencephalopathy, and neurodegeneration in mice, thereby offering new insights into the pathophysiology of L-2-HGA in humans

A 13-Gene Metabolic Prognostic Signature Is Associated With Clinical and Immune Features in Stomach Adenocarcinoma

Patients with advanced stomach adenocarcinoma (STAD) commonly show high mortality and poor prognosis. Increasing evidence has suggested that basic metabolic changes may promote the growth and aggressiveness of STAD; therefore, identification of metabolic prognostic signatures in STAD would be meaningful. An integrative analysis was performed with 407 samples from The Cancer Genome Atlas (TCGA) and 433 samples from Gene Expression Omnibus (GEO) to develop a metabolic prognostic signature associated with clinical and immune features in STAD using Cox regression analysis and least absolute shrinkage and selection operator (LASSO). The different proportions of immune cells and differentially expressed immune-related genes (DEIRGs) between high- and low-risk score groups based on the metabolic prognostic signature were evaluated to describe the association of cancer metabolism and immune response in STAD. A total of 883 metabolism-related genes in both TCGA and GEO databases were analyzed to obtain 184 differentially expressed metabolism-related genes (DEMRGs) between tumor and normal tissues. A 13-gene metabolic signature (GSTA2, POLD3, GLA, GGT5, DCK, CKMT2, ASAH1, OPLAH, ME1, ACYP1, NNMT, POLR1A, and RDH12) was constructed for prognostic prediction of STAD. Sixteen survival-related DEMRGs were significantly related to the overall survival of STAD and the immune landscape in the tumor microenvironment. Univariate and multiple Cox regression analyses and the nomogram proved that a metabolism-based prognostic risk score (MPRS) could be an independent risk factor. More importantly, the results were mutually verified using TCGA and GEO data. This study provided a metabolism-related gene signature for prognostic prediction of STAD and explored the association between metabolism and the immune microenvironment for future research, thereby furthering the understanding of the crosstalk between different molecular mechanisms in human STAD. Some prognosis-related metabolic pathways have been revealed, and the survival of STAD patients could be predicted by a risk model based on these pathways, which could serve as prognostic markers in clinical practice

Modeling and Performance Analysis of Flying Mesh Network

Maintaining good connectivity is a major concern when constructing a robust flying mesh network, known as FlyMesh. In a FlyMesh, multiple unmanned aerial vehicles (UAVs) collaborate to provide continuous network service for mobile devices on the ground. To determine the connectivity probability of the aerial link between two UAVs, the Poisson point process (PPP) is used to describe the spatial distribution of UAVs equipped with omnidirectional antennas. However, the PPP fails to reflect the fact that there is a minimum distance restriction between two neighboring UAVs. In this paper, the β-Ginibre point process (β-GPP) is adopted to model the spatial distribution of UAVs, with β representing the repulsion between nearby UAVs. Additionally, a large-scale fading method is used to model the route channel between UAVs equipped with directional antennas, allowing the monitoring of the impact of signal interference on network connectivity. Based on the β-GPP model, an analytical expression for the connectivity probability is derived. Numerical tests are conducted to demonstrate the effects of repulsion factor β, UAV intensity ρ, and beamwidth θ on network connectivity. The results indicate that an increase in UAV intensity decreases network connectivity when the repulsion factor β remains constant. These findings provide valuable insights for enhancing the service quality of the FlyMesh

Dyadic Green's functions for antenna radiation in the presence of three-dimensional arbitrarily shaped chiral bodies

Asia-Pacific Microwave Conference Proceedings, APMC2570-5730028

New Adversarial Image Detection Based on Sentiment Analysis

Deep Neural Networks (DNNs) are vulnerable to adversarial examples, while adversarial attack models, e.g., DeepFool, are on the rise and outrunning adversarial example detection techniques. This paper presents a new adversarial example detector that outperforms state-of-the-art detectors in identifying the latest adversarial attacks on image datasets. Specifically, we propose to use sentiment analysis for adversarial example detection, qualified by the progressively manifesting impact of an adversarial perturbation on the hidden-layer feature maps of a DNN under attack. Accordingly, we design a modularized embedding layer with the minimum learnable parameters to embed the hidden-layer feature maps into word vectors and assemble sentences ready for sentiment analysis. Extensive experiments demonstrate that the new detector consistently surpasses the state-of-the-art detection algorithms in detecting the latest attacks launched against ResNet and Inception neutral networks on the CIFAR-10, CIFAR-100 and SVHN datasets. The detector only has about 2 million parameters, and takes shorter than 4.6 milliseconds to detect an adversarial example generated by the latest attack models using a Tesla K80 GPU card