Transitional media vs. normative theories : Schramm, Altschull, and China

Wilbur Schramm’s “Soviet” communist model and J. Herbert Altschull’s “Marxist” approach have been widely used as general theoretical frameworks to examine press systems in the Marxist world in general and China in particular. Though a growing literature suggested significant changes in Chinese journalism in the past 2 decades, very few studies have sent a direct challenge to the 2 models’ theoretical wisdom through the Chinese case. This article finds neither of the 2 models is sufficient in conceptualizing the Chinese case because of Chinese news media’s transitional nature and the 2 models’ inner theoretical flaws as normative press theories. Furthermore, realizing the growing conflict between normative media theories and accelerated post-Cold War global media transformation, the author suggests using a transitional media approach to revisit the traditional normative media approach and calls for a more systematic study of the transitional phenomenon of global media systems

Delay Constrained Buffer-Aided Relay Selection in the Internet of Things with Decision-Assisted Reinforcement Learning

This paper investigates the reinforcement learning for the relay selection in the delay-constrained buffer-aided networks. The buffer-aided relay selection significantly improves the outage performance but often at the price of higher latency. On the other hand, modern communication systems such as the Internet of Things often have strict requirement on the latency. It is thus necessary to find relay selection policies to achieve good throughput performance in the buffer-aided relay network while stratifying the delay constraint. With the buffers employed at the relays and delay constraints imposed on the data transmission, obtaining the best relay selection becomes a complicated high-dimensional problem, making it hard for the reinforcement learning to converge. In this paper, we propose the novel decision-assisted deep reinforcement learning to improve the convergence. This is achieved by exploring the a-priori information from the buffer-aided relay system. The proposed approaches can achieve high throughput subject to delay constraints. Extensive simulation results are provided to verify the proposed algorithms

Beyond party propaganda : a case study of China's rising commercialised press

Beyond party propaganda : a case study of China's rising commercialised pres

Recent advances in homogeneous chromium catalyst design for ethylene tri-, tetra-, oligo- and polymerization

This review focuses on recent progress made using well-defined molecular chromium complexes that, upon suitable activation, can catalyze the tri-, tetra, oligo- and/or polymerization of ethylene. In particular, emphasis will be placed on the tuning of the performance characteristics of these homogeneous catalysts through structural modifications made to the multidentate ligand manifold (e.g., donor atoms, charge, backbone and strain) and the effects these changes have on the resulting ethylene derivatives. While the ability of these catalysts to mediate the formation of high molecular weight linear polyethylene continues to see many developments, their capacity to form polyethylene waxes and oligomers has witnessed some major advances. Moreover, the impressive selectivity of some chromium systems to generate commercially important 1-hexene and more recently 1-octene has seen the implementation of this technology at the industrial level. The types of precatalysts to be discussed will be divided broadly on the basis of their ability to generate either polymers/oligomers or short chain α-olefins; the effects of co-catalyst and reaction conditions (e.g., temperature, pressure, solvent) on catalytic activity and selectivity, will be also developed. In addition, current proposals as to the mechanistic details displayed by these versatile chromium catalysts will be highlighted

A bibliometric review of green building research 2000–2016

This study presents a summary of green building research through a bibliometric approach. A total of 2980 articles published in 2000–2016 were reviewed and analyzed. The results indicated that green building research had been concentrated on the subject categories of engineering, environmental sciences & ecology, and construction & building technology, and the keywords ‘building envelope’ and ‘living wall’ obtained citation bursts in the recent years. Additionally, based on the cluster analysis and content analysis, the hot research topics were identified: green and cool roof, vertical greening systems, water efficiency, occupants’ comfort and satisfaction, financial benefits of green building, life cycle assessment and rating systems, green retrofit, green building project delivery, and information and communication technologies in green building. Knowledge gaps were detected in the areas of corporate social responsibility, the validation of real performance of green building, the ICT application in green building, as well as the safety and health risks in the construction process of green projects. Future research directions are recommended to fill these gaps and extend the body of green building research

Applicable artificial intelligence for brain disease: A survey

Brain diseases threaten hundreds of thousands of people over the world. Medical imaging techniques such as MRI and CT are employed for various brain disease studies. As artificial intelligence succeeded in image analysis, scientists employed artificial intelligence, especially deep learning technologies, to assist brain disease studies. The AI applications for brain disease studies can be divided into two categories. The first category is preprocessing, including denoising, registration, skull-stripping, intensity normalization, and data augmentation. The second category is the clinical application that contains lesion segmentation, disease detection, grade classification, and outcome prediction. In this survey, we reviewed over one hundred representative papers on how to apply AI to brain disease studies. We first introduced AI-based preprocessing for brain disease studies. Second, we reviewed the influential works of AI-based brain disease studies. At last, we also discussed three development trends in the future. We hope this survey will inspire both expert-level researchers and entry-level beginners

Joint Buffer-Aided Hybrid-Duplex Relay Selection and Power Allocation for Secure Cognitive Networks with Double Deep Q-Network

This paper applies the reinforcement learning in the joint relay selection and power allocation in the secure cognitive radio (CR) relay network, where the data buffers and full-duplex jamming are applied at the relay nodes. Two cases are considered: maximizing the throughput with the delay and secrecy constraints, and maximizing the secrecy rate with the delay constraint, respectively. In both cases, the optimization relies on the buffer states, the interference to/from the primary user, and the constraints on the delay and/or secrecy. This makes it mathematically intractable to apply the traditional optimization methods. In this paper, the double deep Q-network (DDQN) is used to solve the above two optimization problems. We also apply the a-priori information in the CR network to improve the DDQN learning convergence. Simulation results show that the proposed scheme outperforms the traditional algorithm significantly

Deep Reinforcement Learning Based Relay Selection in Intelligent Reflecting Surface Assisted Cooperative Networks

This paper proposes a deep reinforcement learning (DRL) based relay selection scheme for cooperative networks with the intelligent reflecting surface (IRS). We consider a practical phase-dependent amplitude model in which the IRS reflection amplitudes vary with the discrete phase-shifts. Furthermore, we apply the relay selection to reduce the signal loss over distance in IRS-assisted networks. To solve the complicated problem of joint relay selection and IRS reflection coefficient optimization, we introduce DRL to learn from the environment to obtain the solution and reduce the computational complexity. Simulation results show that the throughput is significantly improved with the proposed DRL-based algorithm compared to random relay selection and random reflection coefficients methods

Novel deep reinforcement learning-based delay-constrained buffer-aided relay selection in cognitive cooperative networks

In this Letter, a deep reinforcement learning-based approach is proposed for the delay-constrained buffer-aided relay selection in a cooperative cognitive network. The proposed learning algorithm can efficiently solve the complicated relay selection problem, and achieves the optimal throughput when the buffer size and number of relays are large. In particular, the authors use the deep-Q-learning to design an agent to estimate a specific action for each state of the system, which is then utilised to provide an optimum trade-off between throughput and a given delay constraint. Simulation results are provided to demonstrate the advantages of the proposed scheme over conventional selection methods. More specifically, compared to the max-ratio selection criteria, where the relay with the highest signal-to-interference ratio is selected, the proposed scheme achieves a significant throughput gain with higher throughput-delay balance

Chromium ethylene polymerization catalysts bearing sterically enhanced α,α′-bis(imino)-2,3:5,6-bis(pentamethylene)pyridines: Tuning activity and molecular weight

The ortho-benzhydryl-substituted α,α′-bis(arylimino)-2,3:5,6-bis(pentamethylene)pyridine-chromium(III) chlorides, [2,3:5,6-{C 4 H 8 C(N(2-R 1 -4-R 2 -6-(CHPh 2 )C 6 H 2 )} 2 C 5 HN]CrCl 3 [R 1 = R 2 = Me Cr1, R 1 = Me, R 2 = CHPh 2 Cr2, R 1 = Et, R 2 = CHPh 2 Cr3, R 1 = i-Pr, R 2 = CHPh 2 Cr4, R 1 = Cl, R 2 = CHPh 2 Cr5, R 1 = F, R 2 = CHPh 2 Cr6], differing in the electronic and/or steric properties of their aryl-R 1 and -R 2 groups, have been prepared by a one-pot template approach involving α,α′-dioxo-2,3:5,6-bis(pentamethylene)pyridine, the corresponding aniline and CrCl 3 (THF) 3 in acetic acid. The molecular structure of six-coordinate Cr1 reveals the carbocyclic-fused N,N,N-ligand to adopt a mer configuration with the puckered sections of the two fused rings arranged mutually cis. On activation with MAO or MMAO, Cr1 - Cr6 displayed high activities (up to 1.83 × 10 6 g (PE) mol −1 (Cr) h −1 ) for the polymerization of ethylene with the MAO-promoted polymerizations in most cases more productive than with MMAO. In general, the chromium complexes appended with ortho-halide substituents (Cr6 (F)) and (Cr5 (Cl)), proved the most active with the overall order being: Cr6 > Cr5 > Cr1 > Cr2 > Cr3 > Cr4. All catalysts formed linear polyethylene displaying a wide range of molecular weights (from 2.17 to 300.4 kg mol −1 ) that were highly dependent on the nature of the ortho-R 1 substituent with fluoride Cr6 forming the lowest molecular weight and the most sterically demanding Cr4 (i-Pr) the highest