•Recent advances using alginates in flame-retardant fields are reviewed.•Flame retardancy and pyrolysis behaviors of alginates are discussed in depth.•Outlooks of flame-retardant applications of ...alginates are provided.
Alginates, a kind of naturally occurring polysaccharides, have been exploited for functional materials owing to their versatility, sustainability, nontoxicity, and relatively low cost. Inherent flame retardancy is one of the most attractive features of alginates, as it enables the high-value-added utilization of alginates for eco-friendly flame-retardant materials. Now, the influence of metal ions on the flame retardancy and pyrolysis behaviors of alginates has been systematically studied; besides, the applications of alginates for flame-retardant materials have been greatly developed, such as for preparing flame-retardant fibers, fabrics, aerogel composites, and foams, as well as serving as a component or modifier of functional coatings, hybrids, and additives. This review will give an overview of the recent progress and the prospects of using alginates in flame-retardant fields, which can guide the design of bio-based flame retardants and benefit the further development of more diverse applications of alginates.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
AbstractObjectiveTo study the clinical characteristics of patients in Zhejiang province, China, infected with the 2019 severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) responsible for ...coronavirus disease 2019 (covid-2019).DesignRetrospective case series.SettingSeven hospitals in Zhejiang province, China.Participants62 patients admitted to hospital with laboratory confirmed SARS-Cov-2 infection. Data were collected from 10 January 2020 to 26 January 2020.Main outcome measuresClinical data, collected using a standardised case report form, such as temperature, history of exposure, incubation period. If information was not clear, the working group in Hangzhou contacted the doctor responsible for treating the patient for clarification.ResultsOf the 62 patients studied (median age 41 years), only one was admitted to an intensive care unit, and no patients died during the study. According to research, none of the infected patients in Zhejiang province were ever exposed to the Huanan seafood market, the original source of the virus; all studied cases were infected by human to human transmission. The most common symptoms at onset of illness were fever in 48 (77%) patients, cough in 50 (81%), expectoration in 35 (56%), headache in 21 (34%), myalgia or fatigue in 32 (52%), diarrhoea in 3 (8%), and haemoptysis in 2 (3%). Only two patients (3%) developed shortness of breath on admission. The median time from exposure to onset of illness was 4 days (interquartile range 3-5 days), and from onset of symptoms to first hospital admission was 2 (1-4) days.ConclusionAs of early February 2020, compared with patients initially infected with SARS-Cov-2 in Wuhan, the symptoms of patients in Zhejiang province are relatively mild.
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BFBNIB, CMK, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
Tea is the world＇s oldest and most popular caffeine-containing beverage with immense economic, medicinal, and cultural importance. Here, we present the first high-quality nucleotide sequence of the ...repeat-rich （80.9%）, 3.02-Gb genome of the cultivated tea tree Camellia sinensis. We show that an extraordinarily large genome size of tea tree is resulted from the slow, steady, and long-term amplification of a few LTR retrotransposon families. In addition to a recent whole-genome duplication event, lineage-specific expansions of genes associated with flavonoid metabolic biosynthesis were discovered, which enhance catechin production, terpene enzyme activation, and stress tolerance, important features for tea flavor and adaptation. We demonstrate an independent and rapid evolution of the tea caffeine synthesis pathway relative to cacao and coffee. A comparative study among 25 Camellia species revealed that higher expression levels of most flavonoid- and caffeinebut not theanine-related genes contribute to the increased production of catechins and caffeine and thus enhance tea-processing suitability and tea quality. These novel findings pave the way for further metabolomic and functional genomic refinement of characteristic biosynthesis pathways and will help develop a more diversified set of tea flavors that would eventually satisfy and attract more tea drinkers worldwide.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Summary
Many plant viruses with monopartite or bipartite genomes have been developed as efficient expression vectors of foreign recombinant proteins. Nonetheless, due to lack of multiple insertion ...sites in these plant viruses, it is still a big challenge to simultaneously express multiple foreign proteins in single cells. The genome of Beet necrotic yellow vein virus (BNYVV) offers an attractive system for expression of multiple foreign proteins owning to a multipartite genome composed of five positive‐stranded RNAs. Here, we have established a BNYVV full‐length infectious cDNA clone under the control of the Cauliflower mosaic virus 35S promoter. We further developed a set of BNYVV‐based vectors that permit efficient expression of four recombinant proteins, including some large proteins with lengths up to 880 amino acids in the model plant Nicotiana benthamiana and native host sugar beet plants. These vectors can be used to investigate the subcellular co‐localization of multiple proteins in leaf, root and stem tissues of systemically infected plants. Moreover, the BNYVV‐based vectors were used to deliver NbPDS guide RNAs for genome editing in transgenic plants expressing Cas9, which induced a photobleached phenotype in systemically infected leaves. Collectively, the BNYVV‐based vectors will facilitate genomic research and expression of multiple proteins, in sugar beet and related crop plants.
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BFBNIB, DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UILJ, UKNU, UL, UM, UPUK
A balance of sustainability and high fire resistance and smoke suppression is important for the preparation of epoxy resins (EPs). Herein, bio-based iron alginate was used to ameliorate the fire ...safety of EP matrix. The addition of iron alginate reduced the initial decomposition temperature and temperature at maximum weight-loss rate of EP matrix, whereas obviously improved the char residues at the higher temperature zones. The peak heat release rate, smoke production rate and total smoke production were dramatically decreased by 61.3%, 60.4% and 42.2%, respectively, compared with those of EP matrix. And the presence of iron alginate obtained obviously smoke-suppressant effect on EP/iron alginate composites. Furthermore, the incorporation of iron alginate had no seriously destructive effect on the mechanical properties of EP matrix, while EP/iron alginate-3 exhibited a 13.5% improvement in the impact strength, compared with that of EP matrix. Such desirable features including higher fire resistance and proper smoke suppression make iron alginate a significant strategy for producing fire-safety EP compositions.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Achieving weighted throughput maximization (WTM) through power control has been a long standing open problem in interference-limited wireless networks. The complicated coupling between the mutual ...interferences of links gives rise to a non-convex optimization problem. Previous work has considered the WTM problem in the high signal to interference-and-noise ratio (SINR) regime, where the problem can be approximated and transformed into a convex optimization problem through proper change of variables. In the general SINR regime, however, the approximation and transformation approach does not work. This paper proposes an algorithm, MAPEL, which globally converges to a global optimal solution of the WTM problem in the general SINR regime. The MAPEL algorithm is designed based on three key observations of the WTM problem: (1) the objective function is monotonically increasing in SINR, (2) the objective function can be transformed into a product of exponentiated linear fraction functions, and (3) the feasible set of the equivalent transformed problem is always ldquonormalrdquo, although not necessarily convex. The MAPEL algorithm finds the desired optimal power control solution by constructing a series of polyblocks that approximate the feasible SINR region in an increasing precision. Furthermore, by tuning the approximation factor in MAPEL, we could engineer a desirable tradeoff between optimality and convergence time. MAPEL provides an important benchmark for performance evaluation of other heuristic algorithms targeting the same problem. With the help of MAPEL, we evaluate the performance of several existing algorithms through extensive simulations.
In this paper, we consider a mobile-edge computing (MEC) system, where an access point (AP) assists a mobile device (MD) to execute an application consisting of multiple tasks following a general ...task call graph. The objective is to jointly determine the offloading decision of each task and the resource allocation (e.g., CPU computing power) under time-varying wireless fading channels and stochastic edge computing capability, so that the energy-time cost (ETC) of the MD is minimized. Solving the problem is particularly hard due to the combinatorial offloading decisions and the strong coupling among task executions under the general dependency model. Conventional numerical optimization methods are inefficient to solve such a problem, especially when the problem size is large. To address the issue, we propose a deep reinforcement learning (DRL) framework based on the actor-critic learning structure. In particular, the actor network utilizes a DNN to learn the optimal mapping from the input states (i.e., wireless channel gains and edge CPU frequency) to the binary offloading decision of each task. Meanwhile, by analyzing the structure of the optimal solution, we derive a low-complexity algorithm for the critic network to quickly evaluate the ETC performance of the offloading decisions output by the actor network. With the low-complexity critic network, we can quickly select the best offloading action and subsequently store the state-action pair in an experience replay memory as the training dataset to continuously improve the action generation DNN. To further reduce the complexity, we show that the optimal offloading decision exhibits an one-climb structure, which can be utilized to significantly reduce the search space of action generation. Numerical results show that for various types of task graphs, the proposed algorithm achieves up to 99.1% of the optimal performance while significantly reducing the computational complexity compared to the existing optimization methods.
In this paper, we investigate over-the-air model aggregation in a federated edge learning (FEEL) system. We introduce a Markovian probability model to characterize the intrinsic temporal structure of ...the model aggregation series. With this temporal probability model, we formulate the model aggregation problem as to infer the desired aggregated update given all the past observations from a Bayesian perspective. We develop a message passing based algorithm, termed temporal-structure-assisted gradient aggregation (TSA-GA), to fulfil this estimation task with low complexity and near-optimal performance. We further establish the state evolution (SE) analysis to characterize the behaviour of the proposed TSA-GA algorithm, and derive an explicit bound of the expected loss reduction of the FEEL system under certain standard regularity conditions. In addition, we develop an expectation maximization (EM) strategy to learn the unknown parameters in the Markovian model. We show that the proposed TSA-GA significantly outperforms the state-of-the-art analog compression scheme, and is able to achieve comparable learning performance as the error-free benchmark in terms of final test accuracy.
State estimation is critical to the monitoring and control of smart grids. Recently, the false data injection attack (FDIA) is emerging as a severe threat to state estimation. Conventional FDIA ...detection approaches are limited by their strong statistical knowledge assumptions, complexity, and hardware cost. Moreover, most of the current FDIA detection approaches focus on detecting the presence of FDIA, while the important information of the exact injection locations is not attainable. Inspired by the recent advances in deep learning, we propose a deep-learning-based locational detection architecture (DLLD) to detect the exact locations of FDIA in real time. The DLLD architecture concatenates a convolutional neural network (CNN) with a standard bad data detector (BDD). The BDD is used to remove the low-quality data. The followed CNN, as a multilabel classifier, is employed to capture the inconsistency and co-occurrence dependency in the power flow measurements due to the potential attacks. The proposed DLLD is "model-free" in the sense that it does not leverage any prior statistical assumptions. It is also "cost-friendly" in the sense that it does not alter the current BDD system and the runtime of the detection process is only hundreds of microseconds on a household computer. Through extensive experiments in the IEEE bus systems, we show that DLLD can perform locational detection precisely under various noise and attack conditions. In addition, we also demonstrate that the employed multilabel classification approach effectively enhances the presence-detection accuracy.
The emerging mobile edge computing (MEC) technology has been recently applied to improve the Quality of Experience (QoE) of network services, such as live video streaming. In this paper, we study an ...energy-aware adaptive live streaming scheme in wireless edge networks. In particular, we aim to design a joint uplink transmission and edge transcoding algorithm maximizing the video followers' QoE, while minimizing the energy consumption of the video streamer. We formulate the problem as a Markov decision process (MDP), and propose a deep reinforcement learning (DRL) based framework, named SACCT, to determine the streamer's encoding bitrate, the uploading power as well as the edge transcoding bitrates and frequency. We decompose the MDP problem into inter-frame and intra-frame problems to address the key design challenges that arise from continuous-discrete hybrid action space, time-varying state and action spaces, and unknown network variation. By doing so, SACCT integrates model-based optimization and model-free DRL to determine the intra-frame continuous resource allocation decisions and the inter-frame discrete bitrate adaptation decisions, respectively. To integrate both the numerical features (e.g., channel gain) and the categorical features (e.g., bitrate), we propose a communication Transformer (CT) as a backbone of SACCT by representing network states as communication tokens and running Transformers to model multi-scale dependencies. Extensive simulations manifest that compared with state-of-the-art approaches, SACCT can provide 128.23% (on average) extra reward. As such, by leveraging joint uplink adaption and edge transcoding, the proposed scheme enables an intelligent wireless network edge with QoE-assured and energy-aware live streaming services.
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