Showing papers by "Beihang University published in 2015"
TL;DR: This review highlights the recent research efforts toward the synthesis of noble metal-free electrocatalysts, especially at the nanoscale, and their catalytic properties for the hydrogen evolution reaction (HER), and summarizes some important examples showing that non-Pt HER electrocatsalysts could serve as efficient cocatalysts for promoting direct solar-to-hydrogen conversion in both photochemical and photoelectrochemical water splitting systems, when combined with suitable semiconductor photocatalyst.
Abstract: Sustainable hydrogen production is an essential prerequisite of a future hydrogen economy. Water electrolysis driven by renewable resource-derived electricity and direct solar-to-hydrogen conversion based on photochemical and photoelectrochemical water splitting are promising pathways for sustainable hydrogen production. All these techniques require, among many things, highly active noble metal-free hydrogen evolution catalysts to make the water splitting process more energy-efficient and economical. In this review, we highlight the recent research efforts toward the synthesis of noble metal-free electrocatalysts, especially at the nanoscale, and their catalytic properties for the hydrogen evolution reaction (HER). We review several important kinds of heterogeneous non-precious metal electrocatalysts, including metal sulfides, metal selenides, metal carbides, metal nitrides, metal phosphides, and heteroatom-doped nanocarbons. In the discussion, emphasis is given to the synthetic methods of these HER electrocatalysts, the strategies of performance improvement, and the structure/composition-catalytic activity relationship. We also summarize some important examples showing that non-Pt HER electrocatalysts could serve as efficient cocatalysts for promoting direct solar-to-hydrogen conversion in both photochemical and photoelectrochemical water splitting systems, when combined with suitable semiconductor photocatalysts.
4,351 citations
TL;DR: A comparison with different topologies of dynamic neural networks as well as other prevailing parametric and nonparametric algorithms suggests that LSTM NN can achieve the best prediction performance in terms of both accuracy and stability.
Abstract: Neural networks have been extensively applied to short-term traffic prediction in the past years. This study proposes a novel architecture of neural networks, Long Short-Term Neural Network (LSTM NN), to capture nonlinear traffic dynamic in an effective manner. The LSTM NN can overcome the issue of back-propagated error decay through memory blocks, and thus exhibits the superior capability for time series prediction with long temporal dependency. In addition, the LSTM NN can automatically determine the optimal time lags. To validate the effectiveness of LSTM NN, travel speed data from traffic microwave detectors in Beijing are used for model training and testing. A comparison with different topologies of dynamic neural networks as well as other prevailing parametric and nonparametric algorithms suggests that LSTM NN can achieve the best prediction performance in terms of both accuracy and stability.
1,521 citations
TL;DR: It is found that the models designed specifically for salient object detection generally work better than models in closely related areas, which provides a precise definition and suggests an appropriate treatment of this problem that distinguishes it from other problems.
Abstract: We extensively compare, qualitatively and quantitatively, 41 state-of-the-art models (29 salient object detection, 10 fixation prediction, 1 objectness, and 1 baseline) over seven challenging data sets for the purpose of benchmarking salient object detection and segmentation methods. From the results obtained so far, our evaluation shows a consistent rapid progress over the last few years in terms of both accuracy and running time. The top contenders in this benchmark significantly outperform the models identified as the best in the previous benchmark conducted three years ago. We find that the models designed specifically for salient object detection generally work better than models in closely related areas, which in turn provides a precise definition and suggests an appropriate treatment of this problem that distinguishes it from other problems. In particular, we analyze the influences of center bias and scene complexity in model performance, which, along with the hard cases for the state-of-the-art models, provide useful hints toward constructing more challenging large-scale data sets and better saliency models. Finally, we propose probable solutions for tackling several open problems, such as evaluation scores and data set bias, which also suggest future research directions in the rapidly growing field of salient object detection.
1,372 citations
TL;DR: Design, and Applications Shutao Wang,“, Kesong Liu, Xi Yao, and Lei Jiang*,†,‡,§ †Laboratory of Bio-inspired Smart Interface Science, Technical Institute of Physics and Chemistry, and ‡Beijing National Laboratory for Molecular Science.
Abstract: Design, and Applications Shutao Wang,†,‡ Kesong Liu, Xi Yao, and Lei Jiang*,†,‡,§ †Laboratory of Bio-inspired Smart Interface Science, Technical Institute of Physics and Chemistry, and ‡Beijing National Laboratory for Molecular Science, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, People’s Republic of China Key Laboratory of Bio-Inspired Smart Interfacial Science and Technology of Ministry of Education, School of Chemistry and Environment, BeiHang University, Beijing 100191, People’s Republic of China Department of Biomedical Sciences, City University of Hong Kong, Hong Kong P6903, People’s Republic of China
1,218 citations
TL;DR: In this paper, the authors highlight the recent progress on mechanical exfoliation for graphene production during the last decade, focusing on the widely used sonication method with the latest insight into sonication-induced defects, newly explored ball milling method, the fluid dynamics method that has emerged in the last three years, and the innovative supercritical fluid method.
Abstract: Mass production and commercial availability are prerequisites for the viability and wide application of graphene. The exfoliation of graphite to give graphene is one of the most promising ways to achieve large-scale production at an extremely low cost. This review focuses on discussing different exfoliation techniques based on a common mechanical mechanism; because a deep understanding of the exfoliation mechanism can provide fruitful information on how to efficiently achieve high-quality graphene by optimizing exfoliation techniques. We highlight the recent progress on mechanical exfoliation for graphene production during the last decade. The emphasis is set on the widely used sonication method with the latest insight into sonication-induced defects, the newly explored ball milling method, the fluid dynamics method that has emerged in the last three years, and the innovative supercritical fluid method. We also give an outlook on how to achieve high-quality graphene efficiently using mechanical exfoliation techniques. We hope this review will point towards a rational direction for the scalable production of graphene.
1,178 citations
TL;DR: In this article, a review of thermoelectric properties, applications and parameter relationships is presented, including modifications of electronic band structures and band convergence to enhance Seebeck coefficients; nanostructuring and all-scale hierarchical architecturing to reduce the lattice thermal conductivity.
866 citations
TL;DR: Insight is gained into how van der Waals interaction and chemical binding contribute to the adsorption of Li2Sn species for anchoring materials with strong, medium, and weak interactions, and it is discovered that too strong binding strength can cause decomposition of Li1Sn species.
Abstract: Although the rechargeable lithium–sulfur battery system has attracted significant attention due to its high theoretical specific energy, its implementation has been impeded by multiple challenges, especially the dissolution of intermediate lithium polysulfide (Li2Sn) species into the electrolyte. Introducing anchoring materials, which can induce strong binding interaction with Li2Sn species, has been demonstrated as an effective way to overcome this problem and achieve long-term cycling stability and high-rate performance. The interaction between Li2Sn species and anchoring materials should be studied at the atomic level in order to understand the mechanism behind the anchoring effect and to identify ideal anchoring materials to further improve the performance of Li–S batteries. Using first-principles approach with van der Waals interaction included, we systematically investigate the adsorption of Li2Sn species on various two-dimensional layered materials (oxides, sulfides, and chlorides) and study the de...
739 citations
TL;DR: This paper investigates the fixed-time consensus tracking problem for second-order multi-agent systems in networks with directed topology with a proposed framework that eliminates the singularity and the settling time is assignable for any initial conditions.
716 citations
TL;DR: In this paper, the branching fraction ratio R(D)(()*()) of (B) over bar → D-(*())tau(-)(nu)over bar (tau) relative to (B), where l = e or mu, was measured using the full Belle data sample.
Abstract: We report a measurement of the branching fraction ratios R(D)(()*()) of (B) over bar -> D-(*())tau(-)(nu) over bar (tau) relative to (B) over bar -> D-(*())l(-)(nu) over barl (where l = e or mu) using the full Belle data sample of 772 x 10(6)B (B) over bar pairs collected at the Upsilon(4S) resonance with the Belle detector at the KEKB asymmetric-energy e(+)e(-) collider. The measured values are R(D) = 0.375 +/- 0.064(stat) +/- 0.026(syst) and R(D*) = 0.293 +/- 0.038 (stat) +/- 0.015 (syst). The analysis uses hadronic reconstruction of the tag-side B meson and purely leptonic t decays. The results are consistent with earlier measurements and do not show a significant deviation from the standard model prediction.
652 citations
TL;DR: The origin and main issues facing the smart city concept are introduced, and the fundamentals of a smart city by analyzing its definition and application domains are presented.
Abstract: Rapid urbanization creates new challenges and issues, and the smart city concept offers opportunities to rise to these challenges, solve urban problems and provide citizens with a better living environment. This paper presents an exhaustive literature survey of smart cities. First, it introduces the origin and main issues facing the smart city concept, and then presents the fundamentals of a smart city by analyzing its definition and application domains. Second, a data-centric view of smart city architectures and key enabling technologies is provided. Finally, a survey of recent smart city research is presented. This paper provides a reference to researchers who intend to contribute to smart city research and implementation.
536 citations
TL;DR: This research presents a new generation of Bio-Inspired Smart Interfacial Science and Technology Materials and Devices for Multidisciplinary Science, developed by the team of Prof. J. Heeger and Prof. Y. Sun at Beihang University, Beijing.
Abstract: Prof. L. Huo, T. Liu, Prof. X. Sun, Y. Cai, Prof. Y. Sun Key Laboratory of Bio-Inspired Smart Interfacial Science and Technology of Ministry of Education Beijing Key Laboratory of Bio-Inspired Energy Materials and Devices School of Chemistry and Environment Beihang University Beijing 100191 , P. R. China E-mail: sunym@buaa.edu.cn Prof. L. Huo, T. Liu, Prof. X. Sun, Y. Cai, Prof. A. J. Heeger, Prof. Y. Sun Heeger Beijing Research and Development Center International Research Institute for Multidisciplinary Science Beihang University Beijing 100191 , P. R. China
TL;DR: The results demonstrate that fine-tuning of PBI-based materials is a promising way to improve the PCEs of non-fullerene BHJ organic solar cells.
Abstract: A novel perylene bisimide (PBI) dimer-based acceptor material, SdiPBI-S, was developed. Conventional bulk-heterojunction (BHJ) solar cells based on SdiPBI-S and the wide-band-gap polymer PDBT-T1 show a high power conversion efficiency (PCE) of 7.16% with a high open-circuit voltage of 0.90 V, a high short-circuit current density of 11.98 mA/cm2, and an impressive fill factor of 66.1%. Favorable phase separation and balanced carrier mobilites in the BHJ films account for the high photovoltaic performance. The results demonstrate that fine-tuning of PBI-based materials is a promising way to improve the PCEs of non-fullerene BHJ organic solar cells.
TL;DR: The detailed variation with rigidity of the helium flux spectral index is presented for the first time and the spectral index progressively hardens at rigidities larger than 100 GV.
Abstract: Knowledge of the precise rigidity dependence of the helium flux is important in understanding the
origin, acceleration, and propagation of cosmic rays. A precise measurement of the helium flux in primary
cosmic rays with rigidity (momentum/charge) from 1.9 GV to 3 TV based on 50 million events is presented
and compared to the proton flux. The detailed variation with rigidity of the helium flux spectral index is
presented for the first time. The spectral index progressively hardens at rigidities larger than 100 GV. The
rigidity dependence of the helium flux spectral index is similar to that of the proton spectral index though
the magnitudes are different. Remarkably, the spectral index of the proton to helium flux ratio increases
with rigidity up to 45 GV and then becomes constant; the flux ratio above 45 GV is well described by a
single power law.
01 Jul 2015
TL;DR: This paper introduces multi-column convolutional neural networks (MCCNNs) to understand questions from three different aspects and learn their distributed representations and develops a method to compute the salience scores of question words in different column networks.
Abstract: Answering natural language questions over a knowledge base is an important and challenging task. Most of existing systems typically rely on hand-crafted features and rules to conduct question understanding and/or answer ranking. In this paper, we introduce multi-column convolutional neural networks (MCCNNs) to understand questions from three different aspects (namely, answer path, answer context, and answer type) and learn their distributed representations. Meanwhile, we jointly learn low-dimensional embeddings of entities and relations in the knowledge base. Question-answer pairs are used to train the model to rank candidate answers. We also leverage question paraphrases to train the column networks in a multi-task learning manner. We use FREEBASE as the knowledge base and conduct extensive experiments on the WEBQUESTIONS dataset. Experimental results show that our method achieves better or comparable performance compared with baseline systems. In addition, we develop a method to compute the salience scores of question words in different column networks. The results help us intuitively understand what MCCNNs learn.
TL;DR: A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS) data from taxi to extend deep learning theory into large-scale transportation network analysis.
Abstract: Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS) and Internet of Things (IoT), transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis. A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS) data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88% within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU)-based parallel computing environment. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation.
TL;DR: The existing applications of “Big Data” in PLM are summarized, and the potential applications of the concept and techniques can be employed in manufacturing to enhance the intelligence and efficiency of design, production, and service process.
Abstract: Recently, “Big Data” has attracted not only researchers’ but also manufacturers’ attention along with the development of information technology. In this paper, the concept, characteristics, and applications of “Big Data” are briefly introduced first. Then, the various data involved in the three main phases of product lifecycle management (PLM) (i.e., beginning of life, middle of life, and end of life) are concluded and analyzed. But what is the relationship between these PLM data and the term “Big Data”? Whether the “Big Data” concept and techniques can be employed in manufacturing to enhance the intelligence and efficiency of design, production, and service process, and what are the potential applications? Therefore, in order to answer these questions, the existing applications of “Big Data” in PLM are summarized, and the potential applications of “Big Data” techniques in PLM are investigated and pointed out.
TL;DR: The results indicated the essential role of gut bacteria in PS biodegradation and mineralization, confirmed the presence of PS-degrading gut bacteria, and demonstrated the biodegrading of PS by mealworms.
Abstract: The role of gut bacteria of mealworms (the larvae of Tenebrio molitor Linnaeus) in polystyrene (PS) degradation was investigated. Gentamicin was the most effective inhibitor of gut bacteria among six antibiotics tested. Gut bacterial activities were essentially suppressed by feeding gentamicin food (30 mg/g) for 10 days. Gentamicin-feeding mealworms lost the ability to depolymerize PS and mineralize PS into CO2, as determined by characterizing worm fecula and feeding with (13)C-labeled PS. A PS-degrading bacterial strain was isolated from the guts of the mealworms, Exiguobacterium sp. strain YT2, which could form biofilm on PS film over a 28 day incubation period and made obvious pits and cavities (0.2-0.3 mm in width) on PS film surfaces associated with decreases in hydrophobicity and the formation of C-O polar groups. A suspension culture of strain YT2 (10(8) cells/mL) was able to degrade 7.4 ± 0.4% of the PS pieces (2500 mg/L) over a 60 day incubation period. The molecular weight of the residual PS pieces was lower, and the release of water-soluble daughter products was detected. The results indicated the essential role of gut bacteria in PS biodegradation and mineralization, confirmed the presence of PS-degrading gut bacteria, and demonstrated the biodegradation of PS by mealworms.
TL;DR: In this paper, a novel hybrid anode is synthesized consisting of ultrafine, few-layered SnS2 anchored on few-layer reduced graphene oxide (rGO) by a facile solvothermal route.
Abstract: Na-ion Batteries have been considered as promising alternatives to Li-ion batteries due to the natural abundance of sodium resources. Searching for high-performance anode materials currently becomes a hot topic and also a great challenge for developing Na-ion batteries. In this work, a novel hybrid anode is synthesized consisting of ultrafine, few-layered SnS2 anchored on few-layered reduced graphene oxide (rGO) by a facile solvothermal route. The SnS2/rGO hybrid exhibits a high capacity, ultralong cycle life, and superior rate capability. The hybrid can deliver a high charge capacity of 649 mAh g−1 at 100 mA g−1. At 800 mA g−1 (1.8 C), it can yield an initial charge capacity of 469 mAh g−1, which can be maintained at 89% and 61%, respectively, after 400 and 1000 cycles. The hybrid can also sustain a current density up to 12.8 A g−1 (≈28 C) where the charge process can be completed in only 1.3 min while still delivering a charge capacity of 337 mAh g−1. The fast and stable Na-storage ability of SnS2/rGO makes it a promising anode for Na-ion batteries.
TL;DR: 2,6-Diphenylanthracene OLED arrays are successfully driven by DPA field-effect transistor arrays, demonstrating that DPA is a high mobility emissive organic semiconductor with potential in organic optoelectronics.
Abstract: The integration of high charge carrier mobility and high luminescence in an organic semiconductor is challenging. However, there is need of such materials for organic light-emitting transistors and organic electrically pumped lasers. Here we show a novel organic semiconductor, 2,6-diphenylanthracene (DPA), which exhibits not only high emission with single crystal absolute florescence quantum yield of 41.2% but also high charge carrier mobility with single crystal mobility of 34 cm(2) V(-1) s(-1). Organic light-emitting diodes (OLEDs) based on DPA give pure blue emission with brightness up to 6,627 cd m(-2) and turn-on voltage of 2.8 V. 2,6-Diphenylanthracene OLED arrays are successfully driven by DPA field-effect transistor arrays, demonstrating that DPA is a high mobility emissive organic semiconductor with potential in organic optoelectronics.
TL;DR: The discovery of the rapid biodegradation of PS in the larval gut reveals a new fate for plastic waste in the environment.
Abstract: Polystyrene (PS) is generally considered to be durable and resistant to biodegradation. Mealworms (the larvae of Tenebrio molitor Linnaeus) from different sources chew and eat Styrofoam, a common PS product. The Styrofoam was efficiently degraded in the larval gut within a retention time of less than 24 h. Fed with Styrofoam as the sole diet, the larvae lived as well as those fed with a normal diet (bran) over a period of 1 month. The analysis of fecula egested from Styrofoam-feeding larvae, using gel permeation chromatography (GPC), solid-state (13)C cross-polarization/magic angle spinning nuclear magnetic resonance (CP/MAS NMR) spectroscopy, and thermogravimetric Fourier transform infrared (TG-FTIR) spectroscopy, substantiated that cleavage/depolymerization of long-chain PS molecules and the formation of depolymerized metabolites occurred in the larval gut. Within a 16 day test period, 47.7% of the ingested Styrofoam carbon was converted into CO2 and the residue (ca. 49.2%) was egested as fecula with a limited fraction incorporated into biomass (ca. 0.5%). Tests with α (13)C- or β (13)C-labeled PS confirmed that the (13)C-labeled PS was mineralized to (13)CO2 and incorporated into lipids. The discovery of the rapid biodegradation of PS in the larval gut reveals a new fate for plastic waste in the environment.
TL;DR: In this paper, the grain morphology evolution behaviors of laser-deposition titanium alloy components were investigated via basic study on solidification nucleation and growth mechanisms of the local melt pool during the layer-by-layer deposition process.
TL;DR: This paper devise an efficient hierarchical codebook by jointly exploiting sub-array and deactivation (turning-off) antenna processing techniques, where closed-form expressions are provided to generate the codebook.
Abstract: In millimeter-wave communication, large antenna arrays are required to achieve high power gain by steering towards each other with narrow beams, which poses the problem to efficiently search the best beam direction in the angle domain at both Tx and Rx sides. As the exhaustive search is time consuming, hierarchical search has been widely accepted to reduce the complexity, and its performance is highly dependent on the codebook design. In this paper, we propose two basic criteria for the hierarchical codebook design, and devise an efficient hierarchical codebook by jointly exploiting sub-array and deactivation (turning-off) antenna processing techniques, where closed-form expressions are provided to generate the codebook. Performance evaluations are conducted under different system and channel models. Results show superiority of the proposed codebook over the existing alternatives.
TL;DR: The promising adsorption performance of S(x)-LDH composites for uranyl ions from a variety of aqueous solutions including seawater shows superior selectivity for UO2(2+), over previously reported sorbents.
Abstract: There is a need to develop highly selective and efficient materials for capturing uranium (normally as UO22+) from nuclear waste and from seawater. We demonstrate the promising adsorption performance of Sx-LDH composites (LDH is Mg/Al layered double hydroxide, [Sx]2– is polysulfide with x = 2, 4) for uranyl ions from a variety of aqueous solutions including seawater. We report high removal capacities (qm = 330 mg/g), large KdU values (104–106 mL/g at 1–300 ppm U concentration), and high % removals (>95% at 1–100 ppm, or ∼80% for ppb level seawater) for UO22+ species. The Sx-LDHs are exceptionally efficient for selectively and rapidly capturing UO22+ both at high (ppm) and trace (ppb) quantities from the U-containing water including seawater. The maximum adsorption coeffcient value KdU of 3.4 × 106 mL/g (using a V/m ratio of 1000 mL/g) observed is among the highest reported for U adsorbents. In the presence of very high concentrations of competitive ions such as Ca2+/Na+, Sx-LDH exhibits superior selectivi...
TL;DR: It is found that the widely-used degree and PageRank fail in ranking users' influence and the best spreaders are consistently located in the k-core across dissimilar social platforms such as Twitter, Facebook, Livejournal and scientific publishing in the American Physical Society.
Abstract: A number of predictors have been suggested to detect the most influential spreaders of information in online social media across various domains such as Twitter or Facebook. In particular, degree, PageRank, k-core and other centralities have been adopted to rank the spreading capability of users in information dissemination media. So far, validation of the proposed predictors has been done by simulating the spreading dynamics rather than following real information flow in social networks. Consequently, only model-dependent contradictory results have been achieved so far for the best predictor. Here, we address this issue directly. We search for influential spreaders by following the real spreading dynamics in a wide range of networks. We find that the widely-used degree and PageRank fail in ranking users' influence. We find that the best spreaders are consistently located in the k-core across dissimilar social platforms such as Twitter, Facebook, Livejournal and scientific publishing in the American Physical Society. Furthermore, when the complete global network structure is unavailable, we find that the sum of the nearest neighbors' degree is a reliable local proxy for user's influence. Our analysis provides practical instructions for optimal design of strategies for “viral” information dissemination in relevant applications.
TL;DR: In this article, a fixed-time terminal sliding-mode control methodology for a class of second-order nonlinear systems in the presence of matched uncertainties and perturbations is presented.
Abstract: This study addresses a fixed-time terminal sliding-mode control methodology for a class of second-order non-linear systems in the presence of matched uncertainties and perturbations. A newly defined non-singular terminal sliding surface is constructed and a guaranteed closed-loop convergence time independent of initial states is derived based on the phase plane analysis and Lyapunov tools. The simulation results of a single inverted pendulum in the end are included to show the effectiveness of the proposed methodology.
TL;DR: In this paper, a bio-inspired beehive-like hierarchical nanoporous carbon (BHNC) with a high specific surface area of 1472 m2 g−1 and a good electronic conductivity of 4.5 S cm−1 is synthesized by carbonizing the industrial waste of bamboo-based byproduct.
Abstract: Bio-inspired beehive-like hierarchical nanoporous carbon (BHNC) with a high specific surface area of 1472 m2 g−1 and a good electronic conductivity of 4.5 S cm−1 is synthesized by carbonizing the industrial waste of bamboo-based by-product. The BHNC sample exhibits remarkable electrochemical performances as a supercapacitor electrode material, such as a high specific capacitance of 301 F g−1 at 0.1 A g−1, still maintaining a value of 192 F g−1 at 100 A g−1, negligible capacitance loss after 20 000 cycles at 1 A g−1, and a high power density of 26 000 W kg−1 at an energy density of 6.1 W h kg−1 based on active electrode materials in an aqueous electrolyte system. Moreover, an enhanced power density of 42 000 W kg−1 at a high energy density of 43.3 W h kg−1 is obtained in an ionic liquid electrolyte system, which places the BHNC-based supercapacitors in the Ragone chart among the best energy–power synergetic outputting properties ever reported for carbon-based supercapacitors.
TL;DR: In this article, the authors characterize the traffic percolation process as a transition between isolated local flows and global flows, where the giant cluster of local flows disintegrates when the second largest cluster reaches its maximum.
Abstract: A critical phenomenon is an intrinsic feature of traffic dynamics, during which transition between isolated local flows and global flows occurs. However, very little attention has been given to the question of how the local flows in the roads are organized collectively into a global city flow. Here we characterize this organization process of traffic as “traffic percolation,” where the giant cluster of local flows disintegrates when the second largest cluster reaches its maximum. We find in real-time data of city road traffic that global traffic is dynamically composed of clusters of local flows, which are connected by bottleneck links. This organization evolves during a day with different bottleneck links appearing in different hours, but similar in the same hours in different days. A small improvement of critical bottleneck roads is found to benefit significantly the global traffic, providing a method to improve city traffic with low cost. Our results may provide insights on the relation between traffic dynamics and percolation, which can be useful for efficient transportation, epidemic control, and emergency evacuation.
TL;DR: In this paper, the authors presented a system which can collect in-use EV data and vehicle driving data and analyzed both EV performance and driver behaviors to measure and estimate EVs' energy consumption.
Abstract: Use of electric vehicles (EVs) has been viewed by many as a way to significantly reduce oil dependence, operate vehicles more efficiently, and reduce carbon emissions. Due to the potential benefits of EVs, the federal and local governments have allocated considerable funding and taken a number of legislative and regulatory steps to promote EV deployment and adoption. With this momentum, it is not difficult to see that in the near future EVs could gain a significant market penetration, particularly in densely populated urban areas with systemic air quality problems. We will soon face one of the biggest challenges: how to improve efficiency for EV transportation system? This research takes the first step in tackling this challenge by addressing a fundamental issue, i.e. how to measure and estimate EVs’ energy consumption. In detail, this paper first presents a system which can collect in-use EV data and vehicle driving data. This system then has been installed in an EV conversion vehicle built in this research as a test vehicle. Approximately 5 months of EV data have been collected and these data have been used to analyze both EV performance and driver behaviors. The analysis shows that the EV is more efficient when driving on in-city routes than driving on freeway routes. Further investigation of this particular EV driver’s route choice behavior indicates that the EV user tries to balance the trade-off between travel time and energy consumption. Although more data are needed in order to generalize this finding, this observation could be important and might bring changes to the traffic assignment for future transportation system with a significant share of EVs. Additionally, this research analyzes the relationships among the EV’s power, the vehicle’s velocity, acceleration, and the roadway grade. Based on the analysis results, this paper further proposes an analytical EV power estimation model. The evaluation results using the test vehicle show that the proposed model can successfully estimate EV’s instantaneous power and trip energy consumption. Future research will focus on applying the proposed EV power estimation model to improve EVs’ energy efficiency.
TL;DR: A high solubility limit of >9 mol% for MnTe alloying in SnTe is demonstrated, and the room-temperature Seebeck coefficients of Mn-doped SnTe are significantly higher than those predicted by theoretical Pisarenko plots for pure SnTe, indicating a modified band structure.
Abstract: We demonstrate a high solubility limit of >9 mol% for MnTe alloying in SnTe. The electrical conductivity of SnTe decreases gradually while the Seebeck coefficient increases remarkably with increasing MnTe content, leading to enhanced power factors. The room-temperature Seebeck coefficients of Mn-doped SnTe are significantly higher than those predicted by theoretical Pisarenko plots for pure SnTe, indicating a modified band structure. The high-temperature Hall data of Sn1–xMnxTe show strong temperature dependence, suggestive of a two-valence-band conduction behavior. Moreover, the peak temperature of the Hall plot of Sn1–xMnxTe shifts toward lower temperature as MnTe content is increased, which is clear evidence of decreased energy separation (band convergence) between the two valence bands. The first-principles electronic structure calculations based on density functional theory also support this point. The higher doping fraction (>9%) of Mn in comparison with ∼3% for Cd and Hg in SnTe gives rise to a muc...
TL;DR: In this article, 40 state-of-the-art models (28 salient object detection, 10 fixation prediction, 1 objectness, and 1 baseline) were evaluated over 6 challenging datasets for the purpose of benchmarking salient object detector and segmentation methods.
Abstract: We extensively compare, qualitatively and quantitatively, 40 state-of-the-art models (28 salient object detection, 10 fixation prediction, 1 objectness, and 1 baseline) over 6 challenging datasets for the purpose of benchmarking salient object detection and segmentation methods. From the results obtained so far, our evaluation shows a consistent rapid progress over the last few years in terms of both accuracy and running time. The top contenders in this benchmark significantly outperform the models identified as the best in the previous benchmark conducted just two years ago. We find that the models designed specifically for salient object detection generally work better than models in closely related areas, which in turn provides a precise definition and suggests an appropriate treatment of this problem that distinguishes it from other problems. In particular, we analyze the influences of center bias and scene complexity in model performance, which, along with the hard cases for state-of-the-art models, provide useful hints towards constructing more challenging large scale datasets and better saliency models. Finally, we propose probable solutions for tackling several open problems such as evaluation scores and dataset bias, which also suggest future research directions in the rapidly-growing field of salient object detection.