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We present a robust manganese-catalyzed cationic reversible addition-fragmentation chain transfer (RAFT) polymerization induced by visible light. Well-defined poly(vinyl ether)s with controlled molecular weight and molecular weight distributions (MWDs) can be conveniently prepared at room temperature without monomer purification. The commercially available manganese carbonyl bromide is used as the photocatalyst for cationic RAFT polymerization. Moreover, this method has been further applied in both batch and continuous flow systems, providing a visible light induced flow cationic polymerization under mild conditions.

In this paper, we employed a facile approach to prepare flexible and porous metal-organic frameworks (MOFs) containing cellulose nanofiber (CNF) aerogels (MNCAs) through freeze-drying MOF-containing cellulose nanofiber suspensions. After coating with methyltrimethoxysilane (MTMS) by chemical vapor deposition, recycled and hydrophobic MTMS-coated MNCAs (MMNCAs) were obtained. Due to the low density (0.009 g/cm3), high porosity (97%) and good mechanical properties of the aerogel, the adsorption capacity of MMNCAs reached up to 210 g/g, which was nearly 3–5 times that of pure CNF aerogels. These prepared aerogels showed excellent oil/water selectivity and high capacity to adsorb oil and organic solvents. This kind of cellulose-based aerogel may be applicable in the field of environmental protection.

MXene based materials are theoretical predicted as one of the most promising cathode materials for lithium-oxygen batteries due to their unique electronic structure, large surface area and lattice matching with Li2O2, but still be impeded by the lack of high electrocatalytic performance, inhomogeneous surface condition and poor understanding for reaction kinetics and mechanism during ORR/OER process. In the present work, we fabricated the Ti2C MXene decorated with -O and -F multifunctional groups. DFT calculations revealed that the Ti2CO2 surface provided a high efficient single electron reaction pathway for the adsorption-nucleation-decomposition process of Li2O2 acting as the main catalytic sites compared to the Ti2CF2 surface, meanwhile, the bare Ti2C surface degraded the catalytic capability of Ti2C MXene due to the strong chemical binding with Li2O2. Further, the inhomogeneous surface condition was supposed to promote the polarized nucleation and growth of discharge products to form the porous structure from spatial-direction accumulated nanoflakes, which can provide an efficient pathway for mass transfer. As a result, the T2C MXene cathode exhibited excellent performance in terms of superior capacity and long cycle stability. The present work provides intrinsic insights toward the catalytic mechanism and reaction kinetics of Ti2C MXene decorated with multifunctional groups, which contributes to the rational design of high performance MXene based materials in Li-O2 batteries.

This work extends B-spline parameterization method to topology optimization of elastic contact problems. Unlike the traditional density-based method, design variables directly refer to the control parameters of the B-spline. A continuous pseudo-density field representing the material distribution over the concerned design domain is constructed by means of B-spline parameterization and then discretized onto the finite element (FE) mesh. The threshold projection is further introduced to regularize the B-spline pseudo-density field for the reduction of gray areas related to the local support property of B-spline. 2D and 3D frictionless and frictional problems are solved to demonstrate the effectiveness of the proposed method. Results are also compared with those obtained by the traditional density-based method. It is shown that the B-spline parameterization is independent of the FE model and suitable to deal with contact problems of inherent contact nonlinearity. The optimized configuration with refined details and smoothed boundaries can be obtained.

Owing to the rapid development of deep learning and the high efficiency of hashing, hashing methods based on deep learning models have been extensively adopted in the area of cross-modal retrieval. In general, in existing deep model-based methods, modality-specific features play an important role during the hash learning. However, most existing methods only use the modality-specific features from the final fully connected layer, ignoring the semantic relevance among modality-specific features with different scales in multiple layers. To address this issue, in this study, we put forward an end-to-end deep hashing method called deep multiscale fusion hashing (DMFH) for cross-modal retrieval. For the proposed DMFH, we first design different network branches for two modalities and then adopt multiscale fusion models for each branch network to fuse the multiscale semantics, which can be used to explore the semantic relevance. Furthermore, the multi-fusion models also embed the multiscale semantics into the final hash codes, making the final hash codes more representative. In addition, the proposed DMFH can learn common hash codes directly without a relaxation, thereby avoiding a loss in accuracy during hash learning. Experimental results on three benchmark datasets prove the relative superiority of the proposed method.

Blumea was the resource for medicine such as l-borneol showing a great economic value. Interestingly, Blumea laciniata was widely used as a folk medicine in south of China and Asia. But the chemical compounds and specific pharmacological activity were rarely reported. Therefore, in this present work, the chemical components from B. laciniata were determined and the antioxidant ability on scavenging radicals and the protection on Hela cells against H2O2 were evaluated. The results showed the antioxidant ability was associated with the presence of polyphenols via response surface method. Additionally, chlorogenic acid, isoorientin, rutin, luteolin-4’-O-glucoside and cinnamic acid were firstly identified in B. laciniata. Moreover, the extract from B. laciniata (EBL) showed a strong antioxidant on clearing DPPH and ABTS free radicals with a lower EC50. Besides, EBL showed no toxicity on Hela cells and even could protect cells from H2O2 induced damage by sharply reducing the excessive reactive oxygen species, improving the mitochondrial membrane potential and then decreasing the generation of cell apoptosis. These outcomes could provide a promising understanding on the potential antioxidant.

The growing demand for portable and flexible electronics, such as roll-up displays, electronic papers, and wearable sensing devices, has stimulated the development of flexible energy storage devices. Herein, we demonstrate the fabrication of a high-voltage solid-state flexible free-standing supercapacitor with an ionogel separator. The negative electrode, titanium niobate, was hydrothermally grown onto a carbon cloth to ensure intimate contact. And the ionogel separator is based on EMIMBF4/P(VDF-HFP)/LiTFSI to ensure safety and high voltage. Our study shows that this device could exhibit reasonable area specific capacitance, relatively wide working potential (0–4 V), and outstanding high-temperature performances. And with that, it could deliver a super-high energy density of 397.3 μWh cm−2, and almost no decay was observed after 1000 cycles. This strategy, combining high area specific capacitance electrodes (0.26 mAh cm−2) and ionogel separators, will pave a new road for the development of solid-state flexible lithium-ion supercapacitors with a high energy density.

Summary Despite their intrinsic safety and cost-effectiveness, aqueous zinc (Zn)-organic batteries have been struggling with the rapid performance degradation arising from the poor reversibility of Zn anodes and the dissolution of cathodes. Here, we present a new aqueous eutectic electrolyte by coupling a hydrated Zn salt (Zn(ClO4)2·6H2O) exclusively with a neutral ligand (succinonitrile) to mitigate these issues. The unique aqua Zn2+ solvates with a succinonitrile-assisted solvation shell enable an unusual Zn/Zn2+ reversibility of 98.4% Coulombic efficiency along with smooth Zn deposition. Moreover, all water molecules contribute to the formation of the eutectic network, resulting in a delayed oxidation and suppressed solvating ability. When a quinone-based polymer material (38 wt % sulfur content) is utilized as a cathode, the Zn-organic battery with this aqueous eutectic electrolyte exhibits an unprecedented cyclability with a low capacity decay rate (0.004% per cycle over 3,500 cycles) and superior low-temperature performance.

A porous carbon nanocomposite with embedded TiO2 nanowires (NWs) was synthesized using a two-step synthetic method in which carbon matrix was obtained by carbonizing a vacuum dried gel. This unique structure in which TiO2 nanowires uniformly distributed in and tightly bonded to the carbon matrix shortened the electron transport path and reduced the transmission resistance. Nanoporous structure ensured continuous transfer of Li+/Na+ and supplied a large specific surface area of 280.82 m2 g−1 to provide more active sites. Different from other existing works on TiO2@C anode materials with TiO2 loading higher than 60 wt%, the obtained very small amount of TiO2 (~12 wt%) improved the electrochemical and long-cycle performance of carbon substrate with TiO2 NWs embedded significantly, due to uniformly distributed TiO2 NWs throughout the carbon matrix. These TiO2@C composite anodes could deliver a specific capacity of 286 mA h g−1 at 0.3 C, 197 mA h g−1 at 0.15 C for lithium and sodium ion batteries, respectively. It maintained remarkably stable reversible capacities of 128 and 125 mA h g−1 for lithium and sodium ion batteries at 3 C during 2500 cycles, respectively. Smaller fluctuations and smoother curves demonstrated that sodium ion storage was more stable than lithium ion storage for the TiO2@C composite anode. In addition, the capacitive contributions of TiO2@C in both systems are quantified by kinetics analysis.

In this paper, a quantized control problem is investigated for a class of networked switched systems under the Round-Robin protocol. A more general switching rule is employed, under which the probability distribution of switching is dependent on both the sojourn time and the system mode. The Round-Robin protocol is adopted to accommodate the limitation of the network bandwidth from the viewpoint of fairness. The quantized measurement signal is sent to the controller as a feedback signal at each sampling instant. The aim of the problem addressed is to design a quantized controller such that, under the Round-Robin protocol, the switched system with the known joint distribution is mean-square stable. Considering the sojourn time and the periodicity of Round-Robin protocol, we present an interval-dependent periodic condition to guarantee the stability of addressed systems and then derive the explicit expressions of the controller for each subsystem by solving a set of matrix inequalities. Finally, a simulation example is given to demonstrate the effectiveness of the controller design method.

Effective processing and use of coal slime is of great significance to protect the environment and save resources. Different coal slimes (untreated with 43 wt% ash content, crushed and flotation treated with 10 wt% ash content, and pre-carbonized) were activated with KOH to prepare porous activated carbon. The results show the activated carbon prepared from coal slime with 10 wt% ash had high specific surface area (3037 m2/g) and pore volume (1.66 cm3/g), which was ascribed to the suitable contents of minerals as template and oxygen-containing functional groups. Electrochemical measurements exhibited the best specific capacitance of 220 F/g at 0.1 A/g and the cycle stability of over 100% capacitance retention after 1000 cycles at 5 A/g in 6 M KOH solution. Due to the high specific surface area, superior electrochemical performance, and facile and low cost, developing highly porous activated carbon for supercapacitors is one alternative way for effective use of coal slime waste.

Despite the excellent electrochemical performance of MnO-based electrodes, a large capacity increase cannot be avoided during long-life cycling, which makes it difficult to seek out appropriate cathode materials to match for commercial applications. In this work, a grape-like MnO-Ni@C framework from interfacial superassembly with remarkable electrochemical properties was fabricated as anode materials for lithium-ion batteries. Electrochemical analysis indicates that the introduction of Ni not only contributes to the excellent rate capability and high specific capacity but also prevents further oxidation of MnO to the higher valence states for ultrastable long-life cycling performance. Furthermore, thermodynamic calculation proves that the ultrastable long cycling life of the Ni-Mn-O system originated from a buffer composition region to stabilize the MnO structure. Because of the unique grape-like structure and performance of the Ni-Mn-O system, the MnO-Ni@C electrode displayed an invertible specific capacity of 706 mA h g-1 after 200 cycles at a current density of 0.1 A g-1 and excellent cycling stability maintained a capacity of 476.8 mA h g-1 after 2100 cycles at 1.0 A g-1 without obvious capacity change. This new nanocomposite material could offer a novel fabrication strategy and insight for MnO-based materials and other metal oxides as anodes for improved electrochemical performance.

Clock power, skew and maximum latency are three key metrics for clock distribution in low-power and high-performance designs. An H-tree offers minimum clock skew and good robustness against variations, but at the cost of large wirelength and clock power. On the other hand, a “fishbone” clock network with spine-ribs structures has smaller wirelength, latency and clock power, but larger skew, as compared to an H-tree. No previous work enables systematic exploration of the regime between H-tree and spine to achieve an optimal tradeoff among clock power, skew, and latency. In this paper, we study the concept of a generalized H-tree (GH-tree)—a topologically balanced tree with an arbitrary sequence of branching factors—and propose a dynamic programming-based method to determine optimal clock power, skew, and latency, in the space of GH-tree solutions. Our method co-optimizes clock tree topology and buffering along branches according to fitted electrical models. We further propose a balanced ${K}$ -means clustering and a linear programming (LP)-guided buffer placement approach to embed the GH-tree with respect to a given sink placement. We validate our solutions in commercial clock tree synthesis (CTS) tool flows, in a commercial foundry’s 28LP technology. The results show up to 30% clock power reduction while achieving similar skew and maximum latency as CTS solutions from recent versions of leading commercial place-and-route tools. Our proposed approach also achieves up to 56% clock power reduction while achieving similar skew and maximum latency as compared to CTS solutions from a state-of-the-art academic tool.

This paper presents a systematical numerical investigation into the lining performance of a tunnel with cavities around surrounding rocks, focusing on the influences of cavity size and multicavity distribution. The study demonstrates that the cavities around surrounding rocks have much influence on tunnel stability and may induce damages in tunnel structures, in which cavity width has a more severe effect on the stress state of tunnel structures than cavity depth. Moreover, the numerical investigation also illustrates that the nonadjacent distribution of multicavities has more serious influence on tunnel structures than that from adjacent distribution of multicavities as well as that from a single cavity.

KNN-Based outlier detection over IoT streaming data is a fundamental problem, which has many applications. However, due to its computational complexity, existing efforts cannot efficiently work in the IoT streaming data. In this paper, we propose a novel framework named GAAOD(Grid-based Approximate Average Outlier Detection) to support KNN-Based outlier detection over IoT streaming data. Firstly, GAAOD introduces a grid-based index to manage summary information of streaming data. It can self-adaptively adjust the resolution of cells, and achieve the goal of efficiently filtering objects that almost cannot become outliers. Secondly, GAAOD uses a min-heap-based algorithm to compute the distance upper-/lower-bound between objects and their k-th nearest neighbors respectively. Thirdly, GAAOD utilizes a k-skyband based algorithm to maintain outliers and candidate outliers. Theoretical analysis and experimental results verify the efficiency and accuracy of GAAOD. INDEX TERMS IoT streaming data, KNN-based outliers, indexes, error guarantee.

Sparse tensor contraction sequence has been widely employed in many fields, such as chemistry and physics. However, how to efficiently implement the sequence faces multiple challenges, such as redundant computations and memory operations, massive memory consumption, and inefficient utilization of hardware. To address the above challenges, we introduce Athena, a high-performance framework for SpTC sequences. Athena introduces new data structures, leverages emerging Optane-based heterogeneous memory (HM) architecture, and adopts stage parallelism. In particular, Athena introduces shared hash table-represented sparse accumulator to eliminate unnecessary input processing and data migration; Athena uses a novel data-semantic guided dynamic migration solution to make the best use of the Optane-based HM for high performance; Athena also co-runs execution phases with different characteristics to enable high hardware utilization. Evaluating with 12 datasets, we show that Athena brings 327-7362× speedup over the state-of-the-art SpTC algorithm. With the dynamic data placement guided by data semantics, Athena brings performance improvement on Optane-based HM over a state-of-the-art software-based data management solution, a hardware-based data management solution, and PMM-only by 1.58×, 1.82×, and 2.34× respectively. Athena also showcases its effectiveness in quantum chemistry and physics scenarios.

Bioinspired mineralization is a powerful method for designing and preparing nanomaterials. In this work, we developed a bioinspired mineralization approach under freezing conditions and fabricated ...

In this paper, we report a novel and facile structure of graphene aerogel dispersed with multi-walled carbon nanotubes (CNTs) loaded with zeolitic imidazolate framework (ZIF-8). ZIF-8 was well intertwined by CNTs and this interconnecting composite framework was dispersed in the aerogel matrix. This continuous interconnected network enhanced the conductivity of ZIF-8 while the porous structure of ZIF-8 facilitated fast mass transport of electrolytes to all the electrochemical active materials. The obtained aerogel electrode delivered high area capacitance of 961 mF cm−2 at 1 mA cm−2 according to galvanostatic charge–discharge measurement. This facile structure of graphene aerogel may promote the direct use of MOFs as electrodes in the field of energy storage.

An ultra-high strength (over 1500 MPa) and enhanced ductility (30%) of cold-rolled medium-Mn steel is developed by quenching and tempering heat treatment. The highest product of strength and elongation of 45.7 GPa·% is obtained when tempering at 300 °C. The microstructure consists of martensite matirx, substantial nano-carbide and film-like/blocky retained austenite. The ultra-high strength derives from the precipitate strengthening of nano-carbide as efficient obstacles to dislocation motion. While, transformation induced plasticity effect of retained austenite enhances the plasticity.

Inability or insufficient access to modern forms of energy is an important issue in development, which makes the notion of energy poverty a widely discussed topic. A consensus has been reached that energy poverty has serious health, education, and other socio-economic impacts for people in a country. However, measurements of energy poverty have generally been absent or inaccurate, especially for developing countries at the micro level. This paper begins with the multidimensional nature of energy poverty and uses household-level survey data in China to construct a quantitative measure of energy poverty, covering both affordability of and accessibility to a broad range of forms of energy. It then builds an econometric model to address empirically how much energy poverty affects health. A statistically significant and robust negative impact on health from energy poverty is confirmed. Our results have important policy relevance in terms of understanding the current status quo of energy poverty in China and its consequences. The concept can also be expanded to investigate similar issues in other developing countries.

Microstructural evolution and tensile deformation behavior of cold-rolled Fe-27Mn-12Al-0.9C steel were investigated as a function of annealing temperature in a range from 900°C to 1050°C. The austenitic duplex steels exhibited an excellent combination of ultimate tensile strength of about 1000 MPa and elongation of about 40%. The influence of annealing temperature on deformation behavior was analyzed using a Hollomon equation, and the cold-rolled steel presented a three-stage strain hardening behavior during plastic deformation at room temperature. The superior mechanical properties substantially came from stage II and stage III. The annealing temperature mainly affected the deformation behavior by controlling the steady plastic deformation capacity and strain hardening capacity in stage II. The dislocation substructure of austenite showed planar dislocation glide characteristics. The deformation characteristics formed at a relatively lower strain than those in hot-rolled steel due to the grain refinement and internal stress caused by the cold rolling process.

This work optimizes tensor-times-dense matrix multiply (Ttm) for general sparse and semi-sparse tensors on CPU and NVIDIA GPU platforms. Ttm is a computational kernel in tensor methods-based data analytics and data mining applications, such as the popular Tucker decomposition. We first design an in-place sequential SpTtm to avoid explicit data reorganizing between a tensor and a matrix in its conventional approach. We further optimize SpTtm on NVIDIA GPU platforms. Five approaches including employing fine thread granularity, arranging coalesced memory access, rank blocking, and using fast GPU shared memory are developed for GPU-SpTtm. We also optimize semi-sparse tensor-times-dense matrix multiply (SspTtm) to take advantage of the inside dense sub-structures. The optimized SpTtm and SspTtm are applied to Tucker decomposition to improve its overall performance. Our sequential SpTtm is 3 – 120 × faster than the SpTtm from Tensor Toolbox library. GPU-SpTtm obtains 6 – 19 × speedup on NVIDIA K40c and 23 – 67 × speedup on NVIDIA P100 over CPU-SpTtm respectively. Our GPU-SpTtm is 3 . 9 × faster than the state-of-the-art GPU implementation. Our SspTtm implementations outperform SpTtms by up to 4 . 5 × , which handles the input semi-sparse tensor in a general way. Tucker decomposition achieves up to 3 . 2 × speedup after applying the optimized Ttms. The code will be publicly released in ParTI! library: https://github.com/hpcgarage/ParTI .

For many years, discrete gate sizing has been widely used for timing and power optimization in VLSI designs. The importance of gate sizing optimization has been emphasized by academia for many years, especially since the 2012/2013 ISPD gate sizing contests [1, 2]. These contests have provided practical impetus to academic sizers through the use of realistic constraints and benchmark formats. At the same time, due to simplified delay/power Liberty models and timing constraints, the contests fail to address real-world criteria for gate sizing that are highly challenging in practice. We observe that lack of consideration of practical issues such as electrical and multi-corner constraints – along with limited sets of benchmarks – can misguide the development of contest-focused academic sizers. Thus, we study implications of the “gap” between academic sizers and product design use cases. In this paper, we note important constraints of modern industrial designs that are generally not comprehended by academic sizers. We also point out that various optimization techniques used in academic sizers can fail to offer benefits in product design contexts due to differences in the underlying optimization formulation and constraints. To address this gap, we develop a new robust academic sizer, Sizer, from a fresh implementation of Trident [3]. Experimental results show that Sizer is able to achieve up to 10% leakage power and 4% total power reductions compared to leading commercial tools on designs implemented with foundry technologies, and 7% leakage power reduction on a modern industrial design in the multi-corner multi-mode (MCMM) context.

Geobacter metallireducens is known to be capable of removing nitroaromatic compounds via an oxidation mode. However, little attention has been paid to investigate the reductive removal of chlorinated nitroaromatic compounds by G. metallireducens. In this study, G. metallireducens was used to reduce chloramphenicol (CAP), a typical chlorinated nitroaromatic antibiotic. Cyclic voltammograms and chronoamperometry highlighted a higher peak current for CAP reduction by G. metallireducens compared to the control without bacteria. G. metallireducens efficiently reduced CAP (20 mg/L) with acetate as the sole electron donor, and the removal efficiency reached (97.6±4.9)% within 6 d. Aromatic amine (AMCl2), AMCl (dechlorinated AMCl2) and AM (dechlorinated AMCl) were identified as reduction products by liquid chromatography-mass spectrometry. However, the removal efficiency declined to (25.0±3.6)% when the CAP dosage increased to 80 mg/L. Transcriptomic analysis indicated the significant upregulation of genes related to electron transfer, such as pilus assembly protein gene (2.8 folds), NADH-quinone oxidoreductase subunit K2 gene (4.5 folds) and many c-type cytochrome genes such as cytochrome c biogenesis protein ResB (Gmet 2901, 4.6 folds), cytochrome c (Gmet 0335, 4.4 folds) and cytochrome c7 (Gmet 2902, 3.4 folds). Furthermore, a gene related to chlorinated contaminant removal (Gmet 1046, 5.4 folds) was also upregulated, possibly resulting in enhanced CAP reduction. This work deepened our knowledge of the bioremediation ability of G. metallireducens with respect to environmental contaminants and provided a potential strategy to treat antibiotics with electrochemically active bacteria.

In this paper, a finite-horizon H∞ consensus control problem is studied for multi-agent systems under the limited energy constraint. Due to the limited energy, only a part of agents can use high energy to transmit information infallibly, and the remaining agents are randomly allocated low energy with several levels, which may lead to packet loss in some sense. Different levels result in different packet dropout probability. The purpose of this paper is to design a probability-dependent controller such that, for all probabilistic energy allocation and packet dropout, the H∞ consensus performance can be guaranteed for multi-agent systems over a finite horizon. To this end, a stochastic and high-availability energy allocation method is first presented via stratified multi-objective optimization methods and stochastic analysis methods. Based on this novel allocation, a H∞ consensus controller depending on the varying energy allocation is established. Furthermore, in terms of the probability information of both energy allocation and packet dropout, important results are obtained to guarantee the desired performance of the designed probability-dependent controller, and the controller are explicitly parameterized by means of the solutions to a set of linear matrix inequalities. Finally, a simulation example is utilized to illustrate the usefulness of the proposed controller design method.

To study the bonding mechanical properties of the interface between sprayed FRP and concrete substrate, seven double shear specimens were made to do the double shear tests. The failure mode, load and deformation, shear strength and deformation, distribution of strain and stress were studied. Furthermore, the influence of three main factors including fiber volume ratio, thickness of sprayed FRP and bonding length were analyzed in the paper as well. The results show that the thickness of sprayed FRP and bonding length have great influence on the bonding mechanical properties, while fiber volume ratio has little. The bonding interface that participate in bearing shear load can only develop to a certain length and then move from loading end to the other end with the development of debonding. The study can provide reference to the further research on sprayed FRP material used in concrete structure reinforcement.

In this study, a method for direct screening and identification of α-glucosidase inhibitors (AGIs) from extracts of natural products was established based on polyamidoamine (PAMAM) coated magnetic microspheres. A facile route to synthesize the magnetic PAMAM was employed and α-glucosidase was successfully covalently attached to its surface through cross linking of glutaraldehyde. Using the enzyme-loaded magnetic microspheres, potential inhibitors were fished out from crude extracts directly, followed by structure confirmation. The inhibitory activities of the screened components were further investigated by the enzyme-loaded magnetic microspheres. The Fe3O4 @PAMAM@α-Glu microspheres displayed favorable dispersibility, fast magnetic separation, large enzyme binding amount (42.9 μg•mg−1) and high enzyme activity. Moreover, the α-glucosidase on the surface of PAMAM coating maintained high storage stability and remarkable reusability. Taking advantage of specific interaction of the α-glucosidase with AGIs, the materials could selectively capture a known AGI (+)-catechin under the interference of an inactive compound salicylic acid, with a binding capacity as high as 15.4%. Additionally, using the Fe3O4 @PAMAM@α-Glu microspheres in the inhibition assay, the enzymatic reaction could be stopped by magnetic separation instead of the traditional addition of Na2CO3 solution, which not only eliminated the disturbance of termination reagent to the results, but also reused the immobilized α-glucosidase. The screening and inhibitory activity verification of potential ligands in Radix Paeoniae Rubra (“Chi-shao” in Chinese) extracts were achieved by using Fe3O4 @PAMAM@α-Glu microspheres, demonstrating practical applicability of our method. Therefore, the magnetic PAMAM-based screening approach could be a feasible and alternative strategy for discovering enzyme inhibitors from natural product extracts.

The electron-injection energy losses of dye-sensitized solar cells (DSSCs) are among the fundamental problems hindering their successful breakthrough application. Two triazatruxene (TAT)-based sensitizers, with one containing a flexible Z-type double bond and another a rigid single bond, coded as ZL001 and ZL003, respectively, have been synthesized and applied in DSSCs to probe the energy losses in the process of electron injection. Using time-resolved laser spectroscopic techniques in the kinetic study, ZL003 with the rigid single bond promotes much faster electron injection into the conductive band of TiO2 especially in the locally excited state (hot injection), which leads to higher electron density in TiO2 and a higher Voc. The devices based on ZL003 exhibited a champion power conversion efficiency (PCE) of 13.6% with Voc = 956 mV, Jsc = 20.73 mA cm–2, and FF = 68.5%, which are among the highest recorded results to date on single dye-sensitized DSSCs. An independent certified PCE of 12.4% has been obt...

This paper considers a parameter-dependent controller design problem for a class of discrete-time uncertain systems subject to censored measurement. First, a set of mutually independent stochastic variables obeying uniform distribution is used to describe the system uncertainty. Then, an array of new bounded variables is introduced to characterize the boundedness of the censored measurement. In addition, a novel definition, named as finite-time boundedness in probability (FTBP), is presented to depict the dynamic behavior of addressed systems in the sense of probability. In this case, the norm of controlled system states cannot exceed a given boundary under a probability constraint. By means of the hyper-rectangle depending on the value range of stochastic variables, a sufficient condition is presented to ensure that the system is FTBP. Finally, the corresponding controller design problem is formulated as an algorithm based on the recursive linear matrix inequality. Two simulation examples are given to illustrate the effectiveness of the proposed methodology.

We study the stratified bubbly wake of the research vessel Athena R/V during an overshoot maneuver using a polydisperse two-fluid model. The motions of ship, rudders and propellers are handled with a dynamic overset approach. The demands of the resolution of the flow and the small time step imposed by the rotating propellers make this problem very demanding and challenging. We discuss the dynamic response of the ship, focusing on trajectory, motions, forces and moments. Vortical structures and their effects on bubble and density transport are studied. Efforts are then focused on studying the bubbly flow and density distribution, and how vortices and flow field affect bubble entrainment, density and bubble transport, and size distribution. Results indicate that the void fraction and maximum depth in the wake increase during the maneuver, as do the disturbances to the stable stratification. Bubble entrainment during the maneuver increases in the transom stern but decreases around the hull, resulting in more bubbles in the wake. Due to transport of bubbles entrained around the hull into the inboard side of the turn, the resulting wake is also wider than in straight self-propulsion operation.

An ultrahigh tensile strength (over 1000 MPa) and enhanced elongation (65%) of hot-rolled 6Mn steel is designed through controlling the combination of fraction and stability of austenite by intercritical annealing (IA). During IA, austenite and ferrite duplex phases with plate-like and equiaxed morphologies are formed between initial martensite lath and junction. With the increase of IA temperature, grain size enlarges, plate-like grains transform into equiaxed and austenite fraction increases, which all reduce the stability of austenite. The austenite stability determines the three types of work hardening behavior. The increasing fraction and decreasing stability of austenite corporately affect the mechanical properties, so the product of strength and elongation increases firstly and then decreases as IA temperature increases. Austenite with high fraction (65 vol%) is obtained in sample after IA at 700 °C and efficiently transforms into martensite (40%) from 0.035 to fracture. The gradient of austenite stability inside IA700 leads to the discontinuous TRIP effect and work hardening rate (WHR) curve with both monotical decrease and fluctuation. Moreover, ferrite slip and cooperation of transformation induced plasticity (TRIP) and twin induced plasticity (TWIP) in austenite act together, producing 70 GPa·% grade 6Mn (medium-Mn) steel through facile hot rolling (HT) and IA process.

Theranostic platforms that combine therapeutic and imaging modalities have received increasing interest. The development of theranostic nanovectors that can release therapeutic agents at pathological tissues in an on-demand manner and provide instantaneous feedback through non-invasive imaging techniques is of urgent need. Herein, a new magnetic resonance imaging (MRI) contrast agent, gadolinium oxide (Gd2O3), and an anticancer drug, doxorubicin (DOX), were co-loaded into mesoporous silica nanoparticles (MSNs) with the formation of hybrid Gd2O3@MSN-DOX nanoparticles. The hybrid nanoparticles were further coated with pH-responsive polyelectrolytes that underwent a charge reversal process at acidic pH. Upon entering into cells by folic acid (FA) receptor-mediated endocytosis, the mildly acidic pH within endolysosomes triggered the disassociation of the absorbed polyelectrolytes on the surfaces of the Gd2O3@MSN-DOX nanoparticles and thus actuated the DOX release, thereby exerting an anti-cancer effect. More importantly, the confinement of paramagnetic Gd2O3 within MSNs led to a remarkable increase of MRI relaxivity (r1 = 9.14 mM-1 s-1vs. 3.68 mM-1 s-1 of the clinically applied MRI contrast agent), likely due to the increased tumbling time and coordination number of water molecules. This work provides a feasible strategy to fabricate theranostic nanovectors with controlled release behavior triggered by mildly acidic pH and high-performance MR imaging capability.

Hybrid activated carbon/graphene materials are prospective candidates for use as high performance supercapacitor electrode materials, since they have the superior characteristics of high surface area, abundant micro/mesoporous structure due to the presence of activated carbon and good electrical conductivity as a result of the presence of graphene. In this work, the electrochemical performance of facile and low-cost graphene-coated activated mesocarbon microbeads (g-AM) is carefully studied. The results show that g-AM can only be formed at a very high temperature over a long activation time, resulting in the formation of a large pore size and low specific surface area, further resulting in poor electrochemical performance (110 F g−1 at 0.1 A g−1 in 6 M KOH solution). Ball milling for a short time is an effective way to improve the electrochemical performance (191 F g−1 at 0.1 A g−1 in 6 M KOH solution). Moreover, due to the strong resistance to aggregation and good electrical conductivity of graphene flowers, the g-AM had nearly 100% rate capability when increasing the current density from 5 to 50 A g−1. The as-assembled two-electrode symmetric supercapacitor exhibits a high energy and power density (5.28 W h kg−1 at 10 000 W kg−1) in organic LiPF6 electrolyte, due to its better electrical conductivity. It is expected that this type of hybrid structure holds great potential for scalable industrial manufacture as supercapacitor electrodes.

Conductive materials/minerals can promote direct interspecies electron transfer (DIET) between syntrophic bacteria and methanogens in defined co-culture systems and artificial anaerobic digesters; however, little is known about the stimulation strategy of carbon material on methane production in natural environments. Herein, the effect of carbon cloth, as a representative of conductive carbon materials, on methane production with incubated wetland soil was investigated. Carbon cloth significantly promoted methanogenesis. With the application of electrochemical technology, calculation of the apparent electron transfer rate constant showed that carbon cloth significantly increased electron transfer rate (ETR) compared with the control experiment in presence of cotton cloth, from 0.0017 ± 0.0003 to 0.0056 ± 0.0015 s−1. Results obtained from both stable carbon isotope measurements and application of specific inhibitor (CH3F) for acetoclastic methanogenesis indicated that carbon cloth obviously promoted acetoclastic methanogenesis instead of CO2 reduction. High-throughput sequencing showed that methane production may stem from the involvement of Methanosarcina for both treatments. Our findings suggested that conductive carbon material can promote acetoclastic methanogenesis instead of CO2 reduction in a natural environment.

In this paper, we report a facile and cost-effective approach to synthesis carbon fiber paper (CFP) based electrode coated with reduced graphene oxide (RGO) by vacuum filtration and carbonization. The SEM image showed that graphene was deposited on each individual carbon fiber and the hybrid electrode paper retained its three dimensional structure which provided efficient percolation path ways for electrons. The specific capacitance of the hybrid paper reached 1076 mF cm−2 at 5 mV s−1 according to cyclic voltammetry measurement. Further, the assembled symmetric supercapacitor delivered a high capacitance of 344 mF cm−2 at 0.5 mA cm−2. In a word, this feasible method should be appropriate for the fabrication of carbon fiber paper based electrodes.

In the present study, the microstructure, tensile property and deformation behavior were investigated in Fe-8Mn-8Al-0.8C steel after quenching and tempering treatment (Q&T). The steel exhibited a ferrite-austenite-martensite mixed microstructure, and a good combination of ultrahigh ultimate tensile strength (UTS) of ~1500 MPa and total elongation (TE) of ~30%. The tensile property was improved after tempering at 200 °C, which is mainly due to an improvement of austenite stability. Tempering process promoted the uniform distribution of carbon element in austenite, resulting in an elimination of the serrated behavior in the strain hardening rate curve. The element partitioning from the supersaturated martensite and δ-ferrite to austenite during tempering, as well as its contributing effects to austenite stability, was also discussed. Two methods were used to quantitatively monitor austenite stability as a function of tempering time with regard to transformation-induced plasticity (TRIP) effect. After an optimal tempering treatment at 200 °C for 60 min, the continuous strain-induced martensite transformation resulting from an optimized austenite stability led to an ultrahigh tensile strength and a product of strength and elongation (PSE) of 41 GPa·%. A fracture transition from brittle cleavage to ductile fracture was observed, which is due to variation of the local stress distribution between coarse δ-ferrite and adjacent austenite.

Sparse tensor contractions appear commonly in many applications. Efficiently computing a two sparse tensor product is challenging: It not only inherits the challenges from common sparse matrix-matrix multiplication (SpGEMM), i.e., indirect memory access and unknown output size before computation, but also raises new challenges because of high dimensionality of tensors, expensive multi-dimensional index search, and massive intermediate and output data. To address the above challenges, we introduce three optimization techniques by using multi-dimensional, efficient hashtable representation for the accumulator and larger input tensor, and all-stage parallelization. Evaluating with 15 datasets, we show that Sparta brings 28 -- 576× speedup over the traditional sparse tensor contraction with sparse accumulator. With our proposed algorithm- and memory heterogeneity-aware data management, Sparta brings extra performance improvement on the heterogeneous memory with DRAM and Intel Optane DC Persistent Memory Module (PMM) over a state-of-the-art software-based data management solution, a hardware-based data management solution, and PMM-only by 30.7% (up to 98.5%), 10.7% (up to 28.3%) and 17% (up to 65.1%) respectively.

International audience; Electroactive microorganisms and electrochemical technologies have been separately used for environmental remediation such as antibiotics removal, yet the efficiency of coupling these two methods for chlorinated antibiotics removal is poorly known. Here we tested the synergy of Geobacter sulfurreducens PCA, an electroactive bacteria, and an electrical field, on chloramphenicol removal. Removal is increased twofold by increasing the temperature from 30°C to 37°C. The cyclic voltammograms and chronoamperometry tests demonstrated that G. sulfurreducens PCA catalyzed chloramphenicol chemical reduction with electrode as excusive electron donor. A critical voltage, − 0.6 to − 0.5 V vs. Ag/AgCl, was discovered for chloramphenicol degradation with an increase of removal rate about 2.62-folds, from 31.06% to 81.41%. Combined removal with both G. sulfurreducens PCA and an electrical field increased the apparent rate constant and reached 82.77% removal at − 0.5 V. Specially, the combined removal at − 0.5 V even presented more robust removal efficiency compared to − 0.6 V (78.64%) without G. sulfurreducens PCA. Mass spectrometry of degradation products indicates the reduction of nitro into amine groups, and dechlorination into less toxic compounds. Overall, combined biocatalysis and an electrical field is a promising method to remove antibiotics from polluted environments.

Given a source node, a destination node and a request list, the result of the requirement-oriented route planning is a path which has minimum distance/travel time/money and could satisfy all the requests at the same time. Most of the previous studies supposed that each Point-Of-Interest (POI) provides only one type of service, which is known as the trip planning query (TPQ). The problem is more complicated if each POI provides multiple services, which is called multi-request route planning (MRRP) problem. The existing MRRP method is designed based on A* algorithm in the POI network. However, they need a huge amount of calculations to get the heuristic values based on the detour distances, which is inefficient especially for large scale road networks. Moreover, it doesn't pay attention to the request on the list which is hard to satisfy. If some required service can be provided only by one node, this node should be regarded as a key point, who will definitely be visited. Based on the above observation, we propose a new framework to solve the MRRP problem, which takes the overall distribution of the POIs who provide the required services into account. Furthermore, we design two algorithms GOD and SPGOD using the framework with the help of a grid index. They can identify the POIs that will definitely be visited earlier, and guide the expansion towards such POIs which can further improve the effectiveness and efficiency. Extensive experiments have been conducted to evaluate and compare the performance of the proposed algorithms and the existing algorithm.

To effectively manage and analyze graph data in many real-life graph applications, SCAN algorithm can efficiently help users to detect useful clusters, such as social networks, communication networks, gene networks, and so on. However, dirty data exist in graph data, for example, some edges are missing in graph data. In this case, the clustering results of SCAN over the dirty graph data cannot meet the users' requirements, such as, the missing nodes are not included in the desired clusters. To address this kind of problem, in this paper, we explore an effective explanation model to make the missing nodes be included in the desired clusters. The problem of explaining missing nodes in light of data modification is to explain why the missing nodes are not included in the desired clusters and how to make the missing nodes appear in the corresponding desired clusters by modifying the original graph dataset. To achieve this purpose, first, the clustering rational of SCAN algorithm is analyzed, and an unified explanation framework is proposed based on the analysis above. Moreover, based on the common explanation principle, the original clustering results should be maintained as much as possible in the new clustering results of SCAN, we design a penalty function to achieve this purpose. Then, we propose two explanation algorithms for making the missing nodes appear in the desired clusters by modifying the original graph dataset with minimum penalty value. Finally, comprehensive experiments are conducted, which demonstrate that the explored explanation model can efficiently explain the missing nodes on structural graph clustering.

In order to solve the problem of insufficient generalization performance and kernel parameter selection of the kernel extreme learning machine, a new model based on optimized multiple kernel extreme learning machine by beetle antennae search algorithm (BAS-MK-ELM) is proposed. In the model, the weighted combination of polynomial kernel function and Gaussian kernel function is selected as the kernel function of multiple kernel extreme learning machine (MK-ELM), and beetle antennae search algorithm is introduced to optimize the kernel function parameters to get the optimal kernel function parameters. This classification model is applied to sEMG gesture recognition, and four time-domain features of the pre-processed signalsl are extracted, and signal features are classified and recognized by BAS-MK-ELM. The experimental results show that the average recognition rate of the proposed classification model is higher than that of single-kernel extreme learning machine, multi-kernel support vector machine, MK-ELM and particle swarm optimization multi-kernel extreme learning machine, which verifies the effectiveness and superiority of the proposed classification model.

The Emu Chick prototype implements migratory memory-side processing in a novel hardware system. Rather than transferring large amounts of data across the system interconnect, the Emu Chick moves lightweight thread contexts to near-memory cores before the beginning of each remote memory read. Previous work has characterized the performance of the Chick prototype in terms of memory bandwidth and programming differences from more typical, non-migratory platforms, but there has not yet been an analysis of algorithms on this system. This work evaluates irregular algorithms that could benefit from the lightweight, memory-side processing of the Chick and demonstrates techniques and optimization strategies for achieving performance in sparse matrix-vector multiply operation (SpMV), breadth-first search (BFS), and graph alignment across up to eight distributed nodes encompassing 64 nodelets in the Chick system. We also define and justify relative metrics to compare prototype FPGA-based hardware with established ASIC architectures. The Chick currently supports up to 68x scaling for graph alignment, 80 MTEPS for BFS on balanced graphs, and 50% of measured STREAM bandwidth for SpMV.

Phototherapy and immunogenic cell death (ICD) are powerful strategies to fight cancer. However, their therapeutic outcomes are diminished by immunosuppressive and hypoxia microenvironment. Herein, a photo-based, immunomodulating and hypoxia-alleviated nanosystem, PDA-ICG@CAT-DTA-1, is proposed to achieve the synergism between phototherapy and immunotherapy. Catalase (CAT) and anti-GITR antibody (DTA-1) are loaded to photothermal agent and photosensitizer composed PDA-ICG nanoparticles. The PDA-ICG@CAT-DTA-1 exhibits intrinsic local hyperthermia and enhanced ROS generation in tumor, and abrogates tumor immune suppression. It results in reduction of intratumoral FOXP3+ regulatory T cells (4.3-fold) and increase of CD4+ effector T cells (1.5-fold) compare with the control, and promotes damage associated molecular patterns generation to reinvigorate ICD effect. The potent antitumor of PDA-ICG@CAT-DTA-1 is proved in 4T1 bilateral tumor-bearing mice, with inhibition ratio of 95.1% for primary cancers and 68.7% for abscopal cancers. Our findings highlight great promise of the constructed versatility nanosystem to fix bottlenecks for cancer therapy.

Tensor computations present significant performance challenges that impact a wide spectrum of applications ranging from machine learning, healthcare analytics, social network analysis, data mining to quantum chemistry and signal processing. Efforts to improve the performance of tensor computations include exploring data layout, execution scheduling, and parallelism in common tensor kernels. This work presents a benchmark suite for arbitrary-order sparse tensor kernels using state-of-the-art tensor formats: coordinate (COO) and hierarchical coordinate (HiCOO) on CPUs and GPUs. It presents a set of reference tensor kernel implementations that are compatible with real-world tensors and power law tensors extended from synthetic graph generation techniques. We also propose Roofline performance models for these kernels to provide insights of computer platforms from sparse tensor view. This benchmark suite along with the synthetic tensor generator is publicly available.

Today's data-driven analytics and machine learning workload have been largely driven by the General-Purpose Graphics Processing Units (GPGPUs). To accelerate dense matrix multiplications on the GPUs, Tensor Core Units (TCUs) have been introduced in recent years. In this paper, we study linear-algebra-based and vertex-centric algorithms for various graph kernels on the GPUs with an objective of applying this new hardware feature to graph applications. We identify the potential stages in these graph kernels that can be executed on the Tensor Core Units. In particular, we leverage the reformulation of the reduction and scan operations in terms of matrix multiplication [1] on the TCUs. We demonstrate that executing these operations on the TCUs, available inside different graph kernels, can assist in establishing an end-to-end pipeline on the GPGPUs without depending on hand-tuned external libraries and still can deliver comparable performance for various graph analytics.

This study attempts to measure the drag effect of carbon emissions on China&rsquo, s economic growth by incorporating carbon emissions as an endogenous variable into an economic growth model and by relaxing the assumption that the size of the economy will remain unchanged. The drag effect of carbon emissions on the process of urbanisation is derived based on the intrinsic relationship between economic growth and urban development. Then, unit root and cointegration tests are performed using panel data from 30 provincial regions in Mainland China from 2003 to 2016 to prove and estimate the resistance caused by carbon emission in the process of urbanisation. Results show that the drag effect of carbon emission between 2003 and 2016 has a certain negative impact on the process of urbanisation in China. Due to the constraints of carbon emissions, the growth rate of China&rsquo, s economic growth and urbanization level is 0.74% and 4.96% lower than that without constraints, respectively. Therefore, in the process of rapid urbanisation, formulating a reasonable carbon emission reduction strategy by the provincial government is conducive to the healthy and sustainable development of urbanisation.

Saxitoxin, which is one of the most typical paralytic shellfish poisoning toxins, ranks the highest intoxication rate of marine biological poisoning cases globally. Efficient clean-up and extraction of saxitoxin from complex biological matrices are imperative for the analysis and concentration monitoring of the toxin when correlative poisoning cases happen. Herein, l-cysteine-modified magnetic microspheres based on metal-organic coordination were synthesized by a facile approach and applied for magnetic solid-phase extraction of saxitoxin from rat plasma samples before liquid chromatography-tandem mass spectrometry detection. Parameters, including adsorbent amount, extraction time, desorption solution, and desorption time that could affect the extraction efficiency, were respectively investigated. The developed method demonstrated good linearity in the range of 5-300 ng/mL (R2 = 0.9985) with a limit of quantification of 5 ng/mL and a limit of detection of 0.5 ng/mL, acceptable accuracy. and precision of within-run and between-run.

Tensor computations present significant performance challenges that impact a wide spectrum of applications ranging from machine learning, healthcare analytics, social network analysis, data mining to quantum chemistry and signal processing. Efforts to improve the performance of tensor computations include exploring data layout, execution scheduling, and parallelism in common tensor kernels. This work presents a benchmark suite for arbitrary-order sparse tensor kernels using state-of-the-art tensor formats: coordinate (COO) and hierarchical coordinate (HiCOO) on CPUs and GPUs. It presents a set of reference tensor kernel implementations that are compatible with real-world tensors and power law tensors extended from synthetic graph generation techniques. We also propose Roofline performance models for these kernels to provide insights of computer platforms from sparse tensor view., 13 pages, 7 figures

Using the zone fire model CFAST as the simulation engine, time series data for building sensors, such as heat detectors, smoke detectors, and other targets at any arbitrary locations in multi-room compartments with different geometric configurations, can be obtained. An automated process for creating inputs files and summarizing model results, CData, is being developed as a companion to CFAST. An example case is presented to demonstrate the use of CData where synthetic data is generated for a wide range of fire scenarios. Three machine learning algorithms: support vector machine (SVM), decision tree (DT), and random forest (RF), are used to develop classification models that can predict the location of a fire based on temperature data within a compartment. Results show that DT and RF have excellent performance on the prediction of fire location and achieve model accuracy in between 93 % and 96 %. For SVM, model performance is sensitive to the size of training data. Additional study shows that results obtained from DT and RT can be used to examine the importance of each input feature. This paper contributes a learning-by-synthesis approach to facilitate the utilization of a machine learning paradigm to enhance situational awareness for fire fighting in buildings.

In the environment of human-computer interaction of information systems, people are paying more attention to user identity authentication based on operation behaviors. Behavior science research shows that each user has a his/her own behavioral pattern that reflects the unique habits, and maintains stability over a period. As known, most of the previous research have explored the user’s behavior using static authentication models. However, the user’s behavior is evolutionary, even the same user will develop different behavioral tendencies under various times and conditions (job position change or promotion, business content change, increase in age, etc.), causing the difficulty of user authentication under the evolution of user’s behavior. This paper proposes a method named time-lapse detection attempting to establish the authentication model based on the evolution of user’s behavior. We obtained the log data of several years period of the information system of a publishing house. Firstly, we extracted the data of employees’ early operation behaviors and the Bayesian network is used to identify a detection model. Next, the behavior data are divided into multiple test sets according to the time series, and multiple authentication models are carried out to observe the change of authentication accuracy over time. The result shows that, for employees with stable positions and business content, the characteristics of their behavior patterns will change when the number of interactions increases. Moreover, the consequences of the initial detection model fluctuate to different degrees, reducing the accuracy of authentication. Therefore, in future we need to grasp the rules of user behavior and continue to optimize the existing authentication methods of information systems.