The rediscovery of graphene in 2004 triggered an explosive expansion of research on various van der Waals (vdW) materials. The atomic layers of these vdW materials do not have surface crystal defects ...and are bonded by weak vdW interactions, thus the vdW materials can be stacked onto each other to form vdW heterojunction structures without needing to consider the lattice mismatch issue. In addition, the broad library of vdW materials makes it possible to design diverse types of heterojunctions with a wide range of band alignments, bandgaps, and electron affinities. Vertical vdW heterostructures especially offer numerous possibilities for the realization of high-performance electronic and optoelectronic devices. Therefore, these vdW heterostructures have received significant attention, and extensive relevant experimental results have been reported in the past few years. In this review, we first introduce the transfer techniques to form vdW heterojunction structures. Next, we discuss recent progress in vdW heterostructure-based electronic and optoelectronic devices, including vertical field effect transistors, negative differential resistance devices, memories, photodetectors, photovoltaic devices, and light-emitting diodes. Finally, we conclude this review by discussing the current challenges facing vdW heterojunction structure-based devices and our perspective on future research directions.
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In this paper, we investigate cooperative spectrum sensing (CSS) in a cognitive radio network (CRN) where multiple secondary users (SUs) cooperate in order to detect a primary user, which possibly ...occupies multiple bands simultaneously. Deep cooperative sensing (DCS), which constitutes the first CSS framework based on a convolutional neural network (CNN), is proposed. In DCS, instead of the explicit mathematical modeling of CSS, the strategy for combining the individual sensing results of the SUs is learned autonomously with a CNN using training sensing samples regardless of whether the individual sensing results are quantized or not. Moreover, both spectral and spatial correlation of individual sensing outcomes are taken into account such that an environment-specific CSS is enabled in DCS. Through simulations, we show that the performance of CSS can be greatly improved by the proposed DCS.
In this letter, deep power control (DPC), which is the first transmit power control framework based on a convolutional neural network (CNN), is proposed. In DPC, the transmit power control strategy ...to maximize either spectral efficiency (SE) or energy efficiency (EE) is learned by means of a CNN. While conventional power control schemes require a considerable number of computations, in DPC, the transmit power of users can be determined using far fewer computations enabling real-time processing. We also propose a form of DPC that can be performed in a distributed manner with local channel state information, allowing the signaling overhead to be greatly reduced. Through simulations, we show that the DPC can achieve almost the same or even higher SE and EE than a conventional power control scheme, with a much lower computation time.
Heterostructures of compositionally and electronically variant two-dimensional (2D) atomic layers are viable building blocks for ultrathin optoelectronic devices. We show that the composition of ...interfacial transition region between semiconducting WSe2 atomic layer channels and metallic NbSe2 contact layers can be engineered through interfacial doping with Nb atoms. W x Nb1–x Se2 interfacial regions considerably lower the potential barrier height of the junction, significantly improving the performance of the corresponding WSe2-based field-effect transistor devices. The creation of such alloyed 2D junctions between dissimilar atomic layer domains could be the most important factor in controlling the electronic properties of 2D junctions and the design and fabrication of 2D atomic layer devices.
High peak-to-average power ratio (PAPR) has been one of the major drawbacks of orthogonal frequency division multiplexing (OFDM) systems. In this letter, we propose a novel PAPR reduction scheme, ...known as PAPR reducing network (PRNet), based on the autoencoder architecture of deep learning. In the PRNet, the constellation mapping and demapping of symbols on each subcarrier is determined adaptively through a deep learning technique, such that both the bit error rate (BER) and the PAPR of the OFDM system are jointly minimized. We used simulations to show that the proposed scheme outperforms conventional schemes in terms of BER and PAPR.
An ultrahigh performance MoS2 photodetector with high photoresponsivity (1.94 × 106 A W–1) and detectivity (1.29 × 1012 Jones) under 520 nm and 4.63 pW laser exposure is demonstrated. This ...photodetector is based on a methyl‐ammonium lead halide perovskite/MoS2 hybrid structure with (3‐aminopropyl)triethoxysilane doping. The performance degradation caused by moisture is also minimized down to 20% by adopting a new encapsulation bilayer of octadecyltrichlorosilane/polymethyl methacrylate.
We investigate the stability of the transmission efficiency (TE) in nonradiative wireless power transfer (WPT) using a relay resonator. An equivalent circuit model is used to show mathematically that ...two suppositions are satisfied in an overcoupled region: first, the TE of relay-based WPT systems is always larger than that of conventional two-coil-based WPT systems, and second, as the coupling coefficient increases, the TE of two-coil-based WPT systems decreases severely, while the TE of relay-based WPT systems increases slightly. Consequently, we surmise that an intermediate relay can be used to achieve higher TE and stability, compared with conventional two-coil-based WPT systems. We verify that our analytical results are in good agreement with the experimental ones.
Protein tyrosine phosphatase 1B (PTP1B) is a member of the non-transmembrane phosphotyrosine phosphatase family. Recently, PTP1B has been proposed to be a novel target of anti-cancer and ...anti-diabetic drugs. However, the role of PTP1B in the central nervous system is not clearly understood. Therefore, in this study, we sought to define PTP1B's role in brain inflammation.
PTP1B messenger RNA (mRNA) and protein expression levels were examined in mouse brain and microglial cells after LPS treatment using RT-PCR and western blotting. Pharmacological inhibitors of PTP1B, NF-κB, and Src kinase were used to analyze these signal transduction pathways in microglia. A Griess reaction protocol was used to determine nitric oxide (NO) concentrations in primary microglia cultures and microglial cell lines. Proinflammatory cytokine production was measured by RT-PCR. Western blotting was used to assess Src phosphorylation levels. Immunostaining for Iba-1 was used to determine microglial activation in the mouse brain.
PTP1B expression levels were significantly increased in the brain 24 h after LPS injection, suggesting a functional role for PTP1B in brain inflammation. Microglial cells overexpressing PTP1B exhibited an enhanced production of NO and gene expression levels of TNF-α, iNOS, and IL-6 following LPS exposure, suggesting that PTP1B potentiates the microglial proinflammatory response. To confirm the role of PTP1B in neuroinflammation, we employed a highly potent and selective inhibitor of PTP1B (PTP1Bi). In LPS- or TNF-α-stimulated microglial cells, in vitro blockade of PTP1B activity using PTP1Bi markedly attenuated NO production. PTP1Bi also suppressed the expression levels of iNOS, COX-2, TNF-α, and IL-1β. PTP1B activated Src by dephosphorylating the Src protein at a negative regulatory site. PTP1B-mediated Src activation led to an enhanced proinflammatory response in the microglial cells. An intracerebroventricular injection of PTP1Bi significantly attenuated microglial activation in the hippocampus and cortex of LPS-injected mice compared to vehicle-injected mice. The gene expression levels of proinflammatory cytokines were also significantly suppressed in the brain by a PTP1Bi injection. Together, these data suggest that PTP1Bi has an anti-inflammatory effect in a mouse model of neuroinflammation.
This study demonstrates that PTP1B is an important positive regulator of neuroinflammation and is a promising therapeutic target for neuroinflammatory and neurodegenerative diseases.
Alcoholic liver disease (ALD) refers to the damages to the liver and its functions due to alcohol overconsumption. It consists of fatty liver/steatosis, alcoholic hepatitis, steatohepatitis, chronic ...hepatitis with liver fibrosis or cirrhosis, and hepatocellular carcinoma. However, the mechanisms behind the pathogenesis of alcoholic liver disease are extremely complicated due to the involvement of immune cells, adipose tissues, and genetic diversity. Clinically, the diagnosis of ALD is not yet well developed. Therefore, the number of patients in advanced stages has increased due to the failure of proper early detection and treatment. At present, abstinence and nutritional therapy remain the conventional therapeutic interventions for ALD. Moreover, the therapies which target the TNF receptor superfamily, hormones, antioxidant signals, and MicroRNAs are used as treatments for ALD. In particular, mesenchymal stem cells (MSCs) are gaining attention as a potential therapeutic target of ALD. Therefore, in this review, we have summarized the current understandings of the pathogenesis and diagnosis of ALD. Moreover, we also discuss the various existing treatment strategies while focusing on promising therapeutic approaches for ALD.
Deep Learning-Aided SCMA Kim, Minhoe; Kim, Nam-I; Lee, Woongsup ...
IEEE communications letters,
2018-April, 2018-4-00, Letnik:
22, Številka:
4
Journal Article
Recenzirano
Sparse code multiple access (SCMA) is a promising code-based non-orthogonal multiple-access technique that can provide improved spectral efficiency and massive connectivity meeting the requirements ...of 5G wireless communication systems. We propose a deep learning-aided SCMA (D-SCMA) in which the codebook that minimizes the bit error rate (BER) is adaptively constructed, and a decoding strategy is learned using a deep neural network-based encoder and decoder. One benefit of D-SCMA is that the construction of an efficient codebook can be achieved in an automated manner, which is generally difficult due to the non-orthogonality and multi-dimensional traits of SCMA. We use simulations to show that our proposed scheme provides a lower BER with a smaller computation time than conventional schemes.
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