Conductive polymer composite with segregated structure has been well demonstrated to achieve high electromagnetic interference shielding effectiveness (EMI SE) due to the selectively distributed ...electrical nanofillers to establish desirable conductive networks. Nevertheless, the formation of segregated structure in low-melt-viscosity semi-crystalline polymer is still challenged and the segregated composite always suffers poor mechanical performance. Herein, elevated pressure and temperature were utilized to make a typical semi-crystalline polymer, polypropylene (PP), hold solid phase to restrict the diffusion of carbon nanotube (CNT) into its interior. Segregated CNT networks were facilely constructed in the resultant CNT/PP composite and imparted it with a superior EMI SE of 48.3 dB at 2.2 mm thickness and 5.0 wt% CNT loading, the highest EMI shielding level among the reported CNT/polymer composites at equivalent material thickness and CNT loading. Moreover, the elevated pressure and temperature effect dramatically increase the compressive, tensile, and flexural strength (modulus) of the CNT/PP composite by 133% (65%), 74% (130%) and 53% (50%), respectively, in comparison to those for conventional segregated CNT/PP composite, really overcoming the major mechanical shortcoming in the development of segregated composites for EMI shielding. Our work provides a facile strategy to fabricate the efficient EMI shielding and robust material with the construction of typical segregated structure in low-melt-viscosity semi-crystalline polymers.
IoT plays an important role in daily life; commands and data transfer rapidly between the servers and objects to provide services. However, cyber threats have become a critical factor, especially for ...IoT servers. There should be a vigorous way to protect the network infrastructures from various attacks. IDS (Intrusion Detection System) is the invisible guardian for IoT servers. Many machine learning methods have been applied in IDS. However, there is a need to improve the IDS system for both accuracy and performance. Deep learning is a promising technique that has been used in many areas, including pattern recognition, natural language processing, etc. The deep learning reveals more potential than traditional machine learning methods. In this paper, sequential model is the key point, and new methods are proposed by the features of the model. The model can collect features from the network layer via tcpdump packets and application layer via system routines. Text-CNN and GRU methods are chosen because the can treat sequential data as a language model. The advantage compared with the traditional methods is that they can extract more features from the data and the experiments show that the deep learning methods have higher F1-score. We conclude that the sequential model-based intrusion detection system using deep learning method can contribute to the security of the IoT servers.
A well-designed multilayered waterborne polyurethane (WPU) shielding composites with absorption-dominated shielding feature are realized by constructing a controllable electro-magnetic gradient. ...Using the layer-by-layer casting method with reasonable arrangement of Fe3O4@rGO and MWCNT nanofiller, an ordered multilayer shielding network can be constructed to provide the WPU composites with positive electrical conductivity gradient and negative magnetic gradient. Hence, the penetrating microwave would undergo a particular “absorption-reflection-reabsorption” process and interface polarization loss induced absorption process between impedance matching layer and high conductive layer, leading to rather low microwave reflection with effect electromagnetic interference shielding effeteness (EMI SE). With the increase of electro-magnetic gradient, the EMI SE of the Fe3O4@rGO/MWCNT/WPU composites reaches 35.9 dB, while the power coefficient of reflectivity can be significantly decreased to 0.27. This work offers a feasible strategy for designing absorption-dominated shielding material with tunable electromagnetic performance that suitable for next-generated smart electronic devices.
Video clip retrieval and captioning tasks play an essential role in multimodal research and are the fundamental research problem for multimodal understanding and generation. The CLIP (Contrastive ...Language-Image Pre-training) model has demonstrated the power of visual concepts learning from web collected image-text datasets. In this paper, we propose a CLIP4Clip model to transfer the knowledge of the image-text pretrained CLIP model to video-text tasks in an end-to-end manner. Furthermore, we conduct several empirical studies including 1) Whether image feature is enough for video-text retrieval and captioning? 2) How a post-pretraining on a large-scale video-text dataset based on the CLIP affect the performance? 3) What is the practical mechanism to model temporal dependency between video frames? And 4) The Hyper-parameters sensitivity of the model. Extensive experimental results present that the CLIP4Clip model transferred from the CLIP can achieve SOTA results on various video-text datasets, including MSR-VTT, MSVD, LSMDC, and DiDeMo for multimodal understanding and generation tasks.
Abstract
We present a deep learning approach towards the large-scale prediction and analysis of bird acoustics from 100 different bird species. We use spectrograms constructed on bird audio ...recordings from the Cornell Bird Challenge (CBC)2020 dataset, which includes recordings of multiple and potentially overlapping bird vocalizations with background noise. Our experiments show that a hybrid modeling approach that involves a Convolutional Neural Network (CNN) for learning the representation for a slice of the spectrogram, and a Recurrent Neural Network (RNN) for the temporal component to combine across time-points leads to the most accurate model on this dataset. We show results on a spectrum of models ranging from stand-alone CNNs to hybrid models of various types obtained by combining CNNs with other CNNs or RNNs of the following types: Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU), and Legendre Memory Units (LMU). The best performing model achieves an average accuracy of 67% over the 100 different bird species, with the highest accuracy of 90% for the bird species, Red crossbill. We further analyze the learned representations visually and find them to be intuitive, where we find that related bird species are clustered close together. We present a novel way to empirically interpret the representations learned by the LMU-based hybrid model which shows how memory channel patterns change over time with the changes seen in the spectrograms.
Iron metabolism and atherosclerosis Guo, Qian; Qian, Christopher; Qian, Zhong-Ming
Trends in endocrinology and metabolism,
July 2023, 2023-07-00, 20230701, Letnik:
34, Številka:
7
Journal Article
Recenzirano
The fact that hereditary hemochromatosis (HH) patients do not show any increased incidence of atherosclerosis is often cited as the most convincing evidence against the role of iron in ...atherosclerosis.The key question about whether atherosclerosis occurs is whether there is excess iron accumulation in the artery wall, the actual location of atherosclerosis.An increase in atherosclerosis should not be observed in HH because the iron homeostasis of the arterial wall in HH is not affected.Iron content in the aortic tissue was not significantly affected in those animal studies that reported the conflicting results against iron’s role in atherogenesis.The reviewed findings, discussion, and analysis strongly suggest a causal link between iron and atherosclerosis.
Despite several decades of study, whether iron is involved in the development of atherosclerosis remains a controversial and unresolved issue. Here, we focus on the up-to-date advances in studies on role of iron in atherosclerosis and discuss possible reasons why patients with hereditary hemochromatosis (HH) do not show any increased incidence of atherosclerosis. In addition, we analyze conflicting results concerning the role of iron in atherogenesis from several epidemiological and animal studies. We argue that atherosclerosis is not observed in HH because iron homeostasis in the arterial wall, the actual location of atherosclerosis, is not significantly affected, and support a causal link between iron in the arterial wall and atherosclerosis.
Abstract
Observations show that supermassive black holes (SMBHs) with a mass of ∼10
9
M
⊙
exist when the universe is just 6% of its current age. We propose a scenario where a self-interacting dark ...matter halo experiences gravothermal instability and its central region collapses into a seed black hole. The presence of baryons in protogalaxies could significantly accelerate the gravothermal evolution of the halo and shorten collapse timescales. The central halo could dissipate its angular momentum remnant via viscosity induced by the self-interactions. The host halo must be on high tails of density fluctuations, implying that high-
z
SMBHs are expected to be rare in this scenario. We further derive conditions for triggering general relativistic instability of the collapsed region. Our results indicate that self-interacting dark matter can provide a unified explanation for diverse dark matter distributions in galaxies today and the origin of SMBHs at redshifts
z
∼ 6–7.
Green cloud is an emerging new technology in the computing world in which memory is a critical component. Phase-change memory (PCM) is one of the most promising alternative techniques to the dynamic ...random access memory (DRAM) that faces the scalability wall. Recent research has been focusing on the multi-level cell (MLC) of PCM. By precisely arranging multiple levels of resistance inside a PCM cell, more than one bit of data can be stored in one single PCM cell. However, the MLC PCM suffers from the degradation of performance compared to the single-level cell(SLC) PCM, due to the longer memory access time. In this paper, we present a genetic-based optimization algorithm for chip multiprocessor (CMP) equipped with PCM memory in green clouds. The proposed genetic-based algorithm not only schedules and assigns tasks to cores in the CMP system, but also provides a PCM MLC configuration that balances the PCM memory performance as well as the efficiency. The experimental results show that our genetic-based algorithm can significantly reduce the maximum memory usage by 76.8 percent comparing with the uniform SLC configuration, and improve the efficiency of memory usage by 127 percent comparing with the uniform 4 bits/cell MLC configuration. Moreover, the performance of the system is also improved by 24.5 percent comparing with the uniform 4 bits/cell MLC configuration in terms of total execution time.
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