Showing papers by "University of Electronic Science and Technology of China published in 2019"

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Abstract: This paper presents a comprehensive literature review on applications of deep reinforcement learning (DRL) in communications and networking. Modern networks, e.g., Internet of Things (IoT) and unmanned aerial vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and large-scale networks, the state and action spaces are usually large, and the reinforcement learning may not be able to find the optimal policy in reasonable time. Therefore, DRL, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings. In this survey, we first give a tutorial of DRL from fundamental concepts to advanced models. Then, we review DRL approaches proposed to address emerging issues in communications and networking. The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks, such as 5G and beyond. Furthermore, we present applications of DRL for traffic routing, resource sharing, and data collection. Finally, we highlight important challenges, open issues, and future research directions of applying DRL.

6G Wireless Communications: Vision and Potential Techniques

Abstract: With the fast development of smart terminals and emerging new applications (e.g., real-time and interactive services), wireless data traffic has drastically increased, and current cellular networks (even the forthcoming 5G) cannot completely match the quickly rising technical requirements. To meet the coming challenges, the sixth generation (6G) mobile network is expected to cast the high technical standard of new spectrum and energy-efficient transmission techniques. In this article, we sketch the potential requirements and present an overview of the latest research on the promising techniques evolving to 6G, which have recently attracted considerable attention. Moreover, we outline a number of key technical challenges as well as the potential solutions associated with 6G, including physical-layer transmission techniques, network designs, security approaches, and testbed developments.

Computation Offloading and Resource Allocation For Cloud Assisted Mobile Edge Computing in Vehicular Networks

Abstract: Computation offloading services provide required computing resources for vehicles with computation-intensive tasks. Past computation offloading research mainly focused on mobile edge computing (MEC) or cloud computing, separately. This paper presents a collaborative approach based on MEC and cloud computing that offloads services to automobiles in vehicular networks. A cloud-MEC collaborative computation offloading problem is formulated through jointly optimizing computation offloading decision and computation resource allocation. Since the problem is non-convex and NP-hard, we propose a collaborative computation offloading and resource allocation optimization (CCORAO) scheme, and design a distributed computation offloading and resource allocation algorithm for CCORAO scheme that achieves the optimal solution. The simulation results show that the proposed algorithm can effectively improve the system utility and computation time, especially for the scenario where the MEC servers fail to meet demands due to insufficient computation resources.

Electromagnetic Interference Shielding Polymers and Nanocomposites - A Review

Abstract: Intrinsically conducting polymers (ICP) and conductive fillers incorporated conductive polymer-based composites (CPC) greatly facilitate the research in electromagnetic interference (EMI) s...

Freestanding 1T MoS2/graphene heterostructures as a highly efficient electrocatalyst for lithium polysulfides in Li–S batteries

Abstract: A novel approach to effectively suppress the “polysulfide shuttle” in Li–S batteries is presented by designing a freestanding, three-dimensional graphene/1T MoS2 (3DG/TM) heterostructure with highly efficient electrocatalysis properties for lithium polysulfides (LiPSs). The 3DG/TM heterostructure is constructed by few-layered graphene nanosheets sandwiched by hydrophilic, metallic, few-layered 1T MoS2 nanosheets with abundant active sites. The porous 3D structure and the hydrophilic feature of 1T-MoS2 are beneficial for electrolyte penetration and Li-ion transfer, and the high conductivities of both graphene and the 1T MoS2 nanosheets facilitate electron transfer. These merits lead to a high electrocatalytic efficiency for LiPSs due to excellent ion/electron transfer and the presence of sufficient electrocatalytic active sites. Therefore, the cells with 3DG/TM exhibit outstanding electrochemical performance, with a high reversible discharge capacity of 1181 mA h g−1 and a capacity retention of 96.3% after 200 cycles. The electrocatalysis mechanism of LiPSs is further experimentally and theoretically revealed, which provides new insights and opportunities to develop advanced Li–S batteries with highly efficient electrocatalysts for LiPS conversion.

Three-in-One Oxygen Vacancies: Whole Visible-Spectrum Absorption, Efficient Charge Separation, and Surface Site Activation for Robust CO 2 Photoreduction.

Abstract: Hongjian Yu, Jieyuan Li, Yihe Zhang, Songqiu Yang, Keli Han, Fan Dong, Tianyi Ma, Hongwei Huang

Neural Speech Synthesis with Transformer Network.

Abstract: Although end-to-end neural text-to-speech (TTS) methods (such as Tacotron2) are proposed and achieve state-of-theart performance, they still suffer from two problems: 1) low efficiency during training and inference; 2) hard to model long dependency using current recurrent neural networks (RNNs). Inspired by the success of Transformer network in neural machine translation (NMT), in this paper, we introduce and adapt the multi-head attention mechanism to replace the RNN structures and also the original attention mechanism in Tacotron2. With the help of multi-head self-attention, the hidden states in the encoder and decoder are constructed in parallel, which improves training efficiency. Meanwhile, any two inputs at different times are connected directly by a self-attention mechanism, which solves the long range dependency problem effectively. Using phoneme sequences as input, our Transformer TTS network generates mel spectrograms, followed by a WaveNet vocoder to output the final audio results. Experiments are conducted to test the efficiency and performance of our new network. For the efficiency, our Transformer TTS network can speed up the training about 4.25 times faster compared with Tacotron2. For the performance, rigorous human tests show that our proposed model achieves state-of-the-art performance (outperforms Tacotron2 with a gap of 0.048) and is very close to human quality (4.39 vs 4.44 in MOS).

Advances in designs and mechanisms of semiconducting metal oxide nanostructures for high-precision gas sensors operated at room temperature

Abstract: High-precision gas sensors operated at room temperature are attractive for various real-time gas monitoring applications, with advantages including low energy consumption, cost effectiveness and device miniaturization/flexibility. Studies on sensing materials, which play a key role in good gas sensing performance, are currently focused extensively on semiconducting metal oxide nanostructures (SMONs) used in the conventional resistance type gas sensors. This topical review highlights the designs and mechanisms of different SMONs with various patterns (e.g. nanoparticles, nanowires, nanosheets, nanorods, nanotubes, nanofilms, etc.) for gas sensors to detect various hazardous gases at room temperature. The key topics include (1) single phase SMONs including both n-type and p-type ones; (2) noble metal nanoparticle and metal ion modified SMONs; (3) composite oxides of SMONs; (4) composites of SMONs with carbon nanomaterials. Enhancement of the sensing performance of SMONs at room temperature can also be realized using a photo-activation effect such as ultraviolet light. SMON based mechanically flexible and wearable room temperature gas sensors are also discussed. Various mechanisms have been discussed for the enhanced sensing performance, which include redox reactions, heterojunction generation, formation of metal sulfides and the spillover effect. Finally, major challenges and prospects for the SMON based room temperature gas sensors are highlighted.

The amazing potential of fungi: 50 ways we can exploit fungi industrially

Abstract: Fungi are an understudied, biotechnologically valuable group of organisms. Due to the immense range of habitats that fungi inhabit, and the consequent need to compete against a diverse array of other fungi, bacteria, and animals, fungi have developed numerous survival mechanisms. The unique attributes of fungi thus herald great promise for their application in biotechnology and industry. Moreover, fungi can be grown with relative ease, making production at scale viable. The search for fungal biodiversity, and the construction of a living fungi collection, both have incredible economic potential in locating organisms with novel industrial uses that will lead to novel products. This manuscript reviews fifty ways in which fungi can potentially be utilized as biotechnology. We provide notes and examples for each potential exploitation and give examples from our own work and the work of other notable researchers. We also provide a flow chart that can be used to convince funding bodies of the importance of fungi for biotechnological research and as potential products. Fungi have provided the world with penicillin, lovastatin, and other globally significant medicines, and they remain an untapped resource with enormous industrial potential.

Review on Metal Sulphide-based Z-scheme Photocatalysts

Abstract: Semiconductor‐based Z‐scheme heterojunction photocatalysts have received considerable attention for solar energy conversion and environmental purification due to their spatially separated reduction and oxidation sites, effective separation and transportation of photo‐excited charge carriers and strong redox ability. With their wide visible‐light responsive range and high photocatalytic activity, metal sulphide is an important material in developing photocatalysts. This review summarizes and highlights recent research progress in sulphide‐based direct Z‐scheme photocatalysts, followed by analysis on the limitations over all‐solid‐state Z‐scheme photocatalyst. Furthermore, the applications and characterization methods of sulphide‐based direct Z‐scheme photocatalyst are summarized. Finally, the challenges and perspectives of sulphide‐based Z‐scheme photocatalyst are discussed.

Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA.

Abstract: N6-Methyladenosine (m6A) refers to methylation modification of the adenosine nucleotide acid at the nitrogen-6 position. Many conventional computational methods for identifying N6-methyladenosine sites are limited by the small amount of data available. Taking advantage of the thousands of m6A sites detected by high-throughput sequencing, it is now possible to discover the characteristics of m6A sequences using deep learning techniques. To the best of our knowledge, our work is the first attempt to use word embedding and deep neural networks for m6A prediction from mRNA sequences. Using four deep neural networks, we developed a model inferred from a larger sequence shifting window that can predict m6A accurately and robustly. Four prediction schemes were built with various RNA sequence representations and optimized convolutional neural networks. The soft voting results from the four deep networks were shown to outperform all of the state-of-the-art methods. We evaluated these predictors mentioned above on a rigorous independent test data set and proved that our proposed method outperforms the state-of-the-art predictors. The training, independent, and cross-species testing data sets are much larger than in previous studies, which could help to avoid the problem of overfitting. Furthermore, an online prediction web server implementing the four proposed predictors has been built and is available at http://server.malab.cn/Gene2vec/.

LncRNADisease 2.0: an updated database of long non-coding RNA-associated diseases.

Abstract: Mounting evidence suggested that dysfunction of long non-coding RNAs (lncRNAs) is involved in a wide variety of diseases. A knowledgebase with systematic collection and curation of lncRNA-disease associations is critically important for further examining their underlying molecular mechanisms. In 2013, we presented the first release of LncRNADisease, representing a database for collection of experimental supported lncRNA-disease associations. Here, we describe an update of the database. The new developments in LncRNADisease 2.0 include (i) an over 40-fold lncRNA-disease association enhancement compared with the previous version; (ii) providing the transcriptional regulatory relationships among lncRNA, mRNA and miRNA; (iii) providing a confidence score for each lncRNA-disease association; (iv) integrating experimentally supported circular RNA disease associations. LncRNADisease 2.0 documents more than 200 000 lncRNA-disease associations. We expect that this database will continue to serve as a valuable source for potential clinical application related to lncRNAs. LncRNADisease 2.0 is freely available at http://www.rnanut.net/lncrnadisease/.

Over 56.55% Faradaic efficiency of ambient ammonia synthesis enabled by positively shifting the reaction potential.

Abstract: Ambient electrochemical N2 reduction is emerging as a highly promising alternative to the Haber–Bosch process but is typically hampered by a high reaction barrier and competing hydrogen evolution, leading to an extremely low Faradaic efficiency. Here, we demonstrate that under ambient conditions, a single-atom catalyst, iron on nitrogen-doped carbon, could positively shift the ammonia synthesis process to an onset potential of 0.193 V, enabling a dramatically enhanced Faradaic efficiency of 56.55%. The only doublet coupling representing 15NH4+ in an isotopic labeling experiment confirms reliable NH3 production data. Molecular dynamics simulations suggest efficient N2 access to the single-atom iron with only a small energy barrier, which benefits preferential N2 adsorption instead of H adsorption via a strong exothermic process, as further confirmed by first-principle calculations. The released energy helps promote the following process and the reaction bottleneck, which is widely considered to be the first hydrogenation step, is successfully overcome. While direct N2 reduction using electrochemistry offers an appealing method to obtain usable nitrogen, materials typically show poor activities and efficiencies. Here, authors demonstrate a single-atom catalyst, iron on N-doped carbon, to have dramatically enhanced N2 reduction efficiencies.

A survey on terahertz communications

Abstract: With the exponential growth of the data traffic in wireless communication systems, terahertz (THz) frequency band is envisioned as a promising candidate to support ultra-broadband for future beyond fifth generation (5G), bridging the gap between millimeter wave (mmWave) and optical frequency ranges. The purpose of this paper is to provide a comprehensive literature review on the development towards THz communications and presents some key technologies faced in THz wireless communication systems. Firstly, despite the substantial hardware problems that have to be developed in terms of the THz solid state superheterodyne receiver, high speed THz modulators and THz antennas, the practical THz channel model and the efficient THz beamforming are also described to compensate for the severe path attenuation. Moreover, two different kinds of lab-level THz communication systems are introduced minutely, named a solid state THz communication system and a spatial direct modulation THz communication system, respectively. The solid state THz system converts intermediate frequency (IF) modulated signal to THz frequency while the direct modulation THz system allows the high power THz sources to input for approving the relatively long distance communications. Finally, we discuss several potential application scenarios as well as some vital technical challenges that will be encountered in the future THz communications.

Surface charge printing for programmed droplet transport

Abstract: The directed, long-range and self-propelled transport of droplets on solid surfaces is crucial for many applications from water harvesting to bio-analysis1-9. Typically, preferential transport is achieved by topographic or chemical modulation of surface wetting gradients that break the asymmetric contact line and overcome the resistance force to move droplets along a particular direction10-16. Nonetheless, despite extensive progress, directional droplet transport is limited to low transport velocity or short transport distance. Here we report the high-velocity and ultralong transport of droplets elicited by surface charge density gradients printed on diverse substrates. We leverage the facile water droplet printing on superamphiphobic surfaces to create rewritable surface charge density gradients that stimulate droplet propulsion under ambient conditions17 and without the need for additional energy input. Our strategy provides a platform for programming the transport of droplets on flat, flexible and vertical surfaces that may be valuable for applications requiring a controlled movement of droplets17-19.

Intelligent Reflecting Surface: A Programmable Wireless Environment for Physical Layer Security

Abstract: In this paper, we introduce an intelligent reflecting surface (IRS) to provide a programmable wireless environment for physical layer security. By adjusting the reflecting coefficients, the IRS can change the attenuation and scattering of the incident electromagnetic wave so that it can propagate in the desired way toward the intended receiver. Specifically, we consider a downlink multiple-input single-output (MISO) broadcast system, where the base station (BS) transmits independent data streams to multiple legitimate receivers and keeps them secret from multiple eavesdroppers. By jointly optimizing the beamformers at the BS and reflecting coefficients at the IRS, we formulate a minimum-secrecy-rate maximization problem under various practical constraints on the reflecting coefficients. The constraints capture the scenarios of both continuous and discrete reflecting coefficients of the reflecting elements. Due to the non-convexity of the formulated problem, we propose an efficient algorithm based on the alternating optimization and the path-following algorithm to solve it in an iterative manner. Besides, we show that the proposed algorithm can converge to a local (global) optimum. Furthermore, we develop two suboptimal algorithms with some forms of closed-form solutions to reduce computational complexity. Finally, the simulation results validate the advantages of the introduced IRS and the effectiveness of the proposed algorithms.

Greatly Improving Electrochemical N2 Reduction over TiO2 Nanoparticles by Iron Doping.

Abstract: Titanium-based catalysts are needed to achieve electrocatalytic N2 reduction to NH3 with a large NH3 yield and a high Faradaic efficiency (FE). One of the cheapest and most abundant metals on earth, iron, is an effective dopant for greatly improving the nitrogen reduction reaction (NRR) performance of TiO2 nanoparticles in ambient N2 -to-NH3 conversion. In 0.5 m LiClO4 , Fe-doped TiO2 catalyst attains a high FE of 25.6 % and a large NH3 yield of 25.47 μg h-1 mgcat-1 at -0.40 V versus a reversible hydrogen electrode. This performance compares favorably to those of all previously reported titanium- and iron-based NRR electrocatalysts in aqueous media. The catalytic mechanism is further probed with theoretical calculations.

Quantum optical microcombs

Abstract: A key challenge for quantum science and technology is to realize large-scale, precisely controllable, practical systems for non-classical secured communications, metrology and, ultimately, meaningful quantum simulation and computation. Optical frequency combs represent a powerful approach towards this goal, as they provide a very high number of temporal and frequency modes that can result in large-scale quantum systems. The generation and control of quantum optical frequency combs will enable a unique, practical and scalable framework for quantum signal and information processing. Here, we review recent progress on the realization of energy–time entangled optical frequency combs and discuss how photonic integration and the use of fibre-optic telecommunications components can enable quantum state control with new functionalities, yielding unprecedented capability. This Review describes quantum frequency combs that operate via photon entanglement, beginning with mode-locked quantum frequency combs followed by energy–time entanglement methods. The use of photonic integration and fibre-optic telecommunications components in enabling the quantum state control are also discussed.

Broadband Metamaterial Absorbers

Abstract: This work was supported by National Basic Research Program of China (Project No. 2013CB933301) and National Natural Science Foundation of China (Project No. 51272038). L.V.B. was supported by China Postdoctoral Science Foundation. A.O.G. was supported by the Volkswagen Foundation (Germany) and via the Chang Jiang (Yangtze River) Chair Professorship (China). G.P.W. acknowledges support from the Center for Nanoscale Materials, a U.S. Department of Energy Office of Science User Facility, and support by the U.S. Department of Energy, Office of Science, under Contract No. DE-AC02-06CH11357. In addition, the authors acknowledge financial support obtained from the Virtual Institute for Theoretical Photonics and Energy. This review is part of the Advanced Optical Materials Hall of Fame article series, which recognizes the excellent contributions of leading researchers to the field of optical materials science.

Binary Multi-View Clustering

Abstract: Clustering is a long-standing important research problem, however, remains challenging when handling large-scale image data from diverse sources. In this paper, we present a novel Binary Multi-View Clustering (BMVC) framework, which can dexterously manipulate multi-view image data and easily scale to large data. To achieve this goal, we formulate BMVC by two key components: compact collaborative discrete representation learning and binary clustering structure learning, in a joint learning framework. Specifically, BMVC collaboratively encodes the multi-view image descriptors into a compact common binary code space by considering their complementary information; the collaborative binary representations are meanwhile clustered by a binary matrix factorization model, such that the cluster structures are optimized in the Hamming space by pure, extremely fast bit-operations. For efficiency, the code balance constraints are imposed on both binary data representations and cluster centroids. Finally, the resulting optimization problem is solved by an alternating optimization scheme with guaranteed fast convergence. Extensive experiments on four large-scale multi-view image datasets demonstrate that the proposed method enjoys the significant reduction in both computation and memory footprint, while observing superior (in most cases) or very competitive performance, in comparison with state-of-the-art clustering methods.

Two-dimensional copper nanosheets for electrochemical reduction of carbon monoxide to acetate

Abstract: Upgrading carbon dioxide to high-value multicarbon (C2+) products is one promising avenue for fuel and chemical production. Among all the monometallic catalysts, copper has attracted much attention because of its unique ability to convert CO2 or CO into C2+ products with an appreciable selectivity. Although numerous attempts have been made to synthesize Cu materials that expose the desired facets, it still remains a challenge to obtain high-quality nanostructured Cu catalysts for the electroreduction of CO2/CO. Here we report a facile synthesis of freestanding triangular-shaped two-dimensional Cu nanosheets that selectively expose the (111) surface. In a 2 M KOH electrolyte, the Cu nanosheets exhibit an acetate Faradaic efficiency of 48% with an acetate partial current density up to 131 mA cm−2 in electrochemical CO reduction. Further analysis suggest that the high acetate selectivity is attributed to the suppression of ethylene and ethanol formation, probably due to the reduction of exposed (100) and (110) surfaces. Upgrading CO to high-value multicarbon products is a promising avenue for fuel and chemical feedstock production. Here triangular Cu nanosheets that selectively expose the (111) surface exhibit a high acetate partial current density (131 mA cm–2) and Faradaic efficiency (48%) in CO electroreduction.

PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search

Abstract: Differentiable architecture search (DARTS) provided a fast solution in finding effective network architectures, but suffered from large memory and computing overheads in jointly training a super-network and searching for an optimal architecture. In this paper, we present a novel approach, namely, Partially-Connected DARTS, by sampling a small part of super-network to reduce the redundancy in exploring the network space, thereby performing a more efficient search without comprising the performance. In particular, we perform operation search in a subset of channels while bypassing the held out part in a shortcut. This strategy may suffer from an undesired inconsistency on selecting the edges of super-net caused by sampling different channels. We alleviate it using edge normalization, which adds a new set of edge-level parameters to reduce uncertainty in search. Thanks to the reduced memory cost, PC-DARTS can be trained with a larger batch size and, consequently, enjoys both faster speed and higher training stability. Experimental results demonstrate the effectiveness of the proposed method. Specifically, we achieve an error rate of 2.57% on CIFAR10 with merely 0.1 GPU-days for architecture search, and a state-of-the-art top-1 error rate of 24.2% on ImageNet (under the mobile setting) using 3.8 GPU-days for search. Our code has been made available at: this https URL.

Large Intelligent Surface/Antennas (LISA): Making Reflective Radios Smart

Abstract: Large intelligent surface/antennas (LISA), a two-dimensional artificial structure with a large number of reflective-surface/antenna elements, is a promising reflective radio technology to construct programmable wireless environments in a smart way. Specifically, each element of the LISA adjusts the reflection of the incident electromagnetic waves with unnatural properties, such as negative refraction, perfect absorption, and anomalous reflection, thus the wireless environments can be software-defined according to various design objectives. In this paper, we introduce the reflective radio basics, including backscattering principles, backscatter communication, reflective relay, the fundamentals and implementations of LISA technology. Then, we present an overview of the state-of-the-art research on emerging applications of LISA-aided wireless networks. Finally, the limitations, challenges, and open issues associated with LISA for future wireless applications are discussed.

Deep Reinforcement Learning for User Association and Resource Allocation in Heterogeneous Cellular Networks

Abstract: Heterogeneous cellular networks can offload the mobile traffic and reduce the deployment costs, which have been considered to be a promising technique in the next-generation wireless network. Due to the non-convex and combinatorial characteristics, it is challenging to obtain an optimal strategy for the joint user association and resource allocation issue. In this paper, a reinforcement learning (RL) approach is proposed to achieve the maximum long-term overall network utility while guaranteeing the quality of service requirements of user equipments (UEs) in the downlink of heterogeneous cellular networks. A distributed optimization method based on multi-agent RL is developed. Moreover, to solve the computationally expensive problem with the large action space, multi-agent deep RL method is proposed. Specifically, the state, action and reward function are defined for UEs, and dueling double deep Q-network (D3QN) strategy is introduced to obtain the nearly optimal policy. Through message passing, the distributed UEs can obtain the global state space with a small communication overhead. With the double-Q strategy and dueling architecture, D3QN can rapidly converge to a subgame perfect Nash equilibrium. Simulation results demonstrate that D3QN achieves the better performance than other RL approaches in solving large-scale learning problems.

Blockchain-Based Internet of Vehicles: Distributed Network Architecture and Performance Analysis

Abstract: The rapid growth of Internet of Vehicles (IoV) has brought huge challenges for large data storage, intelligent management, and information security for the entire system. The traditional centralized management approach for IoV faces the difficulty in dealing with real-time response. The blockchain, as an effective technology for decentralized distributed storage and security management, has already showed great advantages in its application of Bitcoin. In this paper, we investigate how the blockchain technology could be extended to the application of vehicle networking, especially with the consideration of the distributed and secure storage of big data. We define several types of nodes such as vehicle and roadside for vehicle networks and form several sub-blockchain networks. In this paper, we present a model of the outward transmission of vehicle blockchain data, and then give detail theoretical analysis and numerical results. This paper has shown the potential to guide the application of blockchain for future vehicle networking.

Modulating Electronic Structures of Inorganic Nanomaterials for Efficient Electrocatalytic Water Splitting.

Abstract: Electrocatalytic water splitting is one of the most promising sustainable energy conversion technologies, but is limited by the sluggish electrochemical reactions. Inorganic nanomaterials have been widely used as efficient catalysts for promoting the electrochemical kinetics. Several approaches to optimize the activities of these nanocatalysts have been developed. The electronic structures of the catalysts play a pivotal role in governing the activity and thus have been identified as an essential descriptor. However, the underlying working mechanisms related to the refined electronic structures remain elusive. To establish the structure-electronic-behavior-activity relationship, a comprehensive overview of the developed strategies to regulate the electronic structures is presented, emphasizing the surface modification, strain, phase transition, and heterostructure. Current challenges to the fundamental understanding of electron behaviors in the nanocatalysts are fully discussed.

Chemical and Biomolecule Sensing with Organic Field-Effect Transistors

Abstract: The strong and controllable chemical sensitivity of organic semiconductors (OSCs) and the amplification capability of transistors in circuits make use of OSC-based field-effect transistors compelling for chemical sensors. Analytes detected and assayed range from few-atom gas-phase molecules that may have adverse health and security implications to biomacromolecules (proteins, nucleic acids) that may be markers for physiological processes and medical conditions. This review highlights recent progress in organic field-effect transistor (OFET) chemical sensors, emphasizing advances from the past 5 years and including aspects of OSC morphology and the role of adjacent dielectrics. Design elements of the OSCs and various formats for the devices are illustrated and evaluated. Challenges associated with the present state of the art and future opportunities are also discussed.

Blockchain and Deep Reinforcement Learning Empowered Intelligent 5G Beyond

Abstract: Blockchain and AI are promising techniques for next-generation wireless networks. Blockchain can establish a secure and decentralized resource sharing environment. AI can be explored to solve problems with uncertain, time-variant, and complex features. Both of these techniques have recently seen a surge in interest. The integration of these two techniques can further enhance the performance of wireless networks. In this article, we first propose a secure and intelligent architecture for next-generation wireless networks by integrating AI and blockchain into wireless networks to enable flexible and secure resource sharing. Then we propose a blockchain empowered content caching problem to maximize system utility, and develop a new caching scheme by utilizing deep reinforcement learning. Numerical results demonstrate the effectiveness of the proposed scheme.