Jie Chen

There is an increasing demand for dynamic systems to become more safe and reliable. This requirement extends beyond the normally accepted safety-critical systems of nuclear reactors and aircraft where

This monograph is a self-contained, coherent presentation of the background and progress of the stability of time-delay systems. Focusing on techniques, tools, and advances in numerical methods and op

PubChem (https://pubchem.ncbi.nlm.nih.gov) is a public repository for information on chemical substances and their biological activities, launched in 2004 as a component of the Molecular Libraries Roadmap Initiatives of the US National Institutes of Health (NIH). For the past 11 years, PubChem has grown to a sizable system, serving as a chemical information resource for the scientific research community. PubChem consists of three inter-linked databases, Substance, Compound and BioAssay. The Substance database contains chemical information deposited by individual data contributors to PubChem, and the Compound database stores unique chemical structures extracted from the Substance database. Biological activity data of chemical substances tested in assay experiments are contained in the BioAssay database. This paper provides an overview of the PubChem Substance and Compound databases, including data sources and contents, data organization, data submission using PubChem Upload, chemical structure standardization, web-based interfaces for textual and non-textual searches, and programmatic access. It also gives a brief description of PubChem3D, a resource derived from theoretical three-dimensional structures of compounds in PubChem, as well as PubChemRDF, Resource Description Framework (RDF)-formatted PubChem data for data sharing, analysis and integration with information contained in other databases.

Abstract PubChem (https://pubchem.ncbi.nlm.nih.gov) is a popular chemical information resource that serves the scientific community as well as the general public, with millions of unique users per month. In the past two years, PubChem made substantial improvements. Data from more than 100 new data sources were added to PubChem, including chemical-literature links from Thieme Chemistry, chemical and physical property links from SpringerMaterials, and patent links from the World Intellectual Properties Organization (WIPO). PubChem's homepage and individual record pages were updated to help users find desired information faster. This update involved a data model change for the data objects used by these pages as well as by programmatic users. Several new services were introduced, including the PubChem Periodic Table and Element pages, Pathway pages, and Knowledge panels. Additionally, in response to the coronavirus disease 2019 (COVID-19) outbreak, PubChem created a special data collection that contains PubChem data related to COVID-19 and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

Abstract Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a powerful strategy for learning feature representations directly from data and have led to remarkable breakthroughs in the field of generic object detection. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field brought about by deep learning techniques. More than 300 research contributions are included in this survey, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics. We finish the survey by identifying promising directions for future research.

We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech--two vastly different languages. Because it replaces entire pipelines of hand-engineered components with neural networks, end-to-end learning allows us to handle a diverse variety of speech including noisy environments, accents and different languages. Key to our approach is our application of HPC techniques, resulting in a 7x speedup over our previous system. Because of this efficiency, experiments that previously took weeks now run in days. This enables us to iterate more quickly to identify superior architectures and algorithms. As a result, in several cases, our system is competitive with the transcription of human workers when benchmarked on standard datasets. Finally, using a technique called Batch Dispatch with GPUs in the data center, we show that our system can be inexpensively deployed in an online setting, delivering low latency when serving users at scale.

In this paper, we develop a new finite-time formation control framework for multi-agent systems with a large population of members. In this framework, we divide the formation information into two independent parts, namely, the global information and the local information. The global formation information decides the geometric pattern of the desired formation. Furthermore, it is assumed that only a small number of agents, which are responsible for the navigation of the whole team, can obtain the global formation information, and the other agents regulate their positions by the local information in a distributed manner. This approach can greatly reduce the data exchange and can easily realize various kinds of complex formations. As a theoretical preparation, we first propose a class of nonlinear consensus protocols, which ensures that the related states of all agents will reach an agreement in a finite time under suitable conditions. And then we apply these consensus protocols to the formation control, including time-invariant formation, time-varying formation and trajectory tracking, respectively. It is shown that all agents will maintain the expected formation in a finite time. Finally, several simulations are worked out to illustrate the effectiveness of our theoretical results.

Solar cells based on inorganic absorbers, such as Si, GaAs, CdTe and Cu(In,Ga)Se2, permit a high device efficiency and stability. The crystals’ three-dimensional structure means that dangling bonds inevitably exist at the grain boundaries (GBs), which significantly degrades the device performance via recombination losses. Thus, the growth of single-crystalline materials or the passivation of defects at the GBs is required to address this problem, which introduces an added processing complexity and cost. Here we report that antimony selenide (Sb2Se3)—a simple, non-toxic and low-cost material with an optimal solar bandgap of ∼1.1 eV—exhibits intrinsically benign GBs because of its one-dimensional crystal structure. Using a simple and fast (∼1 μm min–1) rapid thermal evaporation process, we oriented crystal growth perpendicular to the substrate, and produced Sb2Se3 thin-film solar cells with a certified device efficiency of 5.6%. Our results suggest that the family of one-dimensional crystals, including Sb2Se3, SbSeI and Bi2S3, show promise in photovoltaic applications. Materials with a one-dimensional crystal structure, such as antimony selenide, show considerable potential for making efficient thin-film solar cells.

The sparse representation of a multiple-measurement vector (MMV) is a relatively new problem in sparse representation. Efficient methods have been proposed. Although many theoretical results that are available in a simple case-single-measurement vector (SMV)-the theoretical analysis regarding MMV is lacking. In this paper, some known results of SMV are generalized to MMV. Some of these new results take advantages of additional information in the formulation of MMV. We consider the uniqueness under both an lscr <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm-like criterion and an lscr <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm-like criterion. The consequent equivalence between the lscr <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm approach and the lscr <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm approach indicates a computationally efficient way of finding the sparsest representation in a redundant dictionary. For greedy algorithms, it is proven that under certain conditions, orthogonal matching pursuit (OMP) can find the sparsest representation of an MMV with computational efficiency, just like in SMV. Simulations show that the predictions made by the proved theorems tend to be very conservative; this is consistent with some recent advances in probabilistic analysis based on random matrix theory. The connections will be discussed

Drug manufacturing in a fridge-sized box Commodity chemicals tend to be manufactured in a continuous fashion. However, the preparation of pharmaceuticals still proceeds batch by batch, partly on account of the complexity of their molecular structures. Adamo et al. now present an apparatus roughly the size of a household refrigerator that can synthesize and purify pharmaceuticals under continuous-flow conditions (see the Perspective by Martin). The integrated set of modules can produce hundreds to thousands of accumulated doses in a day, delivered in aqueous solution. Science , this issue p. 61 ; see also p. 44

Chitosan and its oligosaccharides, which are known to possess multiple functional properties, have attracted considerable interest due to their biological activities and potential applications in the food, pharmaceutical, agricultural and environmental industries. Many researchers have focused on chitosan as a potential source of bioactive materials in the past few decades. This review focuses on the biological activities of chitosan and chitooligosaccharides based on our and others’ latest research results, including hypocholesterolemic, antimicrobial, immunostimulating, antitumor and anticancer effects, accelerating calcium and iron absorption, anti-inflammatory, antioxidant and Angiotensin-I-converting enzyme (ACE) inhibitory activities and so on, which are all correlated with their structures and physicochemical properties. The bioactivities summarized here may provide novel insights into the functions of chitosan, its derivatives or oligosaccharides and potentially enable their use as functional-food components and additives.

Bread wheat (Triticum aestivum, AABBDD) is one of the most widely cultivated and consumed food crops in the world. However, the complex polyploid nature of its genome makes genetic and functional analyses extremely challenging. The A genome, as a basic genome of bread wheat and other polyploid wheats, for example, T. turgidum (AABB), T. timopheevii (AAGG) and T. zhukovskyi (AAGGA(m)A(m)), is central to wheat evolution, domestication and genetic improvement. The progenitor species of the A genome is the diploid wild einkorn wheat T. urartu, which resembles cultivated wheat more extensively than do Aegilops speltoides (the ancestor of the B genome) and Ae. tauschii (the donor of the D genome), especially in the morphology and development of spike and seed. Here we present the generation, assembly and analysis of a whole-genome shotgun draft sequence of the T. urartu genome. We identified protein-coding gene models, performed genome structure analyses and assessed its utility for analysing agronomically important genes and for developing molecular markers. Our T. urartu genome assembly provides a diploid reference for analysis of polyploid wheat genomes and is a valuable resource for the genetic improvement of wheat.

About 8,000 years ago in the Fertile Crescent, a spontaneous hybridization of the wild diploid grass Aegilops tauschii (2n = 14; DD) with the cultivated tetraploid wheat Triticum turgidum (2n = 4x = 28; AABB) resulted in hexaploid wheat (T. aestivum; 2n = 6x = 42; AABBDD). Wheat has since become a primary staple crop worldwide as a result of its enhanced adaptability to a wide range of climates and improved grain quality for the production of baker's flour. Here we describe sequencing the Ae. tauschii genome and obtaining a roughly 90-fold depth of short reads from libraries with various insert sizes, to gain a better understanding of this genetically complex plant. The assembled scaffolds represented 83.4% of the genome, of which 65.9% comprised transposable elements. We generated comprehensive RNA-Seq data and used it to identify 43,150 protein-coding genes, of which 30,697 (71.1%) were uniquely anchored to chromosomes with an integrated high-density genetic map. Whole-genome analysis revealed gene family expansion in Ae. tauschii of agronomically relevant gene families that were associated with disease resistance, abiotic stress tolerance and grain quality. This draft genome sequence provides insight into the environmental adaptation of bread wheat and can aid in defining the large and complicated genomes of wheat species.

Graphene quantum dots (GQDs) that are flat 0D nanomaterials have attracted increasing interest because of their exceptional chemicophysical properties and novel applications in energy conversion and storage, electro/photo/chemical catalysis, flexible devices, sensing, display, imaging, and theranostics. The significant advances in the recent years are summarized with comparative and balanced discussion. The differences between GQDs and other nanomaterials, including their nanocarbon cousins, are emphasized, and the unique advantages of GQDs for specific applications are highlighted. The current challenges and outlook of this growing field are also discussed.

Attention mechanisms are widely used in current encoder/decoder frameworks of image captioning, where a weighted average on encoded vectors is generated at each time step to guide the caption decoding process. However, the decoder has little idea of whether or how well the attended vector and the given attention query are related, which could make the decoder give misled results. In this paper, we propose an Attention on Attention (AoA) module, which extends the conventional attention mechanisms to determine the relevance between attention results and queries. AoA first generates an information vector and an attention gate using the attention result and the current context, then adds another attention by applying element-wise multiplication to them and finally obtains the attended information, the expected useful knowledge. We apply AoA to both the encoder and the decoder of our image captioning model, which we name as AoA Network (AoANet). Experiments show that AoANet outperforms all previously published methods and achieves a new state-of-the-art performance of 129.8 CIDEr-D score on MS COCO Karpathy offline test split and 129.6 CIDEr-D (C40) score on the official online testing server. Code is available at https://github.com/husthuaan/AoANet.

Circular RNAs (circRNAs) comprise a novel class of widespread non-coding RNAs that may regulate gene expression in eukaryotes. However, the characterization and function of circRNAs in human cancer remain elusive. Here we identified at least 5500 distinct circRNA candidates and a series of circRNAs that are differentially expressed in gastric cancer (GC) tissues compared with matched normal tissues. We further characterized one circRNA derived from the PVT1 gene and termed it as circPVT1. The expression of circPVT1 is often upregulated in GC tissues due to the amplification of its genomic locus. circPVT1 may promote cell proliferation by acting as a sponge for members of the miR-125 family. The level of circPVT1 was observed as an independent prognostic marker for overall survival and disease-free survival of patients with GC. Our findings suggest that circPVT1 is a novel proliferative factor and prognostic marker in GC.

A self-powered triboelectric auditory sensor is designed for human-robot interactions.

Uncertainty estimation of climate change impacts has been given a lot of attention in the recent literature. It is generally assumed that the major sources of uncertainty are linked to General Circulation Models (GCMs) and Greenhouse Gases Emissions Scenarios (GGES). However, other sources of uncertainty such as the choice of a downscaling method have been given less attention. This paper focuses on this issue by comparing six downscaling methods to investigate the uncertainties in quantifying the impacts of climate change on the hydrology of a Canadian (Quebec province) river basin. The downscaling methods regroup dynamical and statistical approaches, including the change factor method and a weather generator-based approach. Future (2070–2099, 2085 horizon) hydrological regimes simulated with a hydrological model are compared to the reference period (1970–1999) using the average hydrograph, annual mean discharge, peak discharge and time to peak discharge as criteria. The results show that all downscaling methods suggest temperature increases over the basin for the 2085 horizon. The regression-based statistical methods predict a larger increase in autumn and winter temperatures. Predicted changes in precipitation are not as unequivocal as those of temperatures, they vary depending on the downscaling methods and seasons. There is a general increase in winter discharge (November–April) while decreases in summer discharge are predicted by most methods. Consistently with the large predicted increases in autumn and winter temperature, regression-based statistical methods show severe increases in winter flows and considerable reductions in peak discharge. Across all variables, a large uncertainty envelope was found to be associated with the choice of a downscaling method. This envelope was compared to the envelope originating from the choice of 28 climate change projections from a combination of seven GCMs and three GGES. Both uncertainty envelopes were similar, although the latter was slightly larger. The regression-based statistical downscaling methods contributed significantly to the uncertainty envelope. Overall, results indicate that climate change impact studies based on only one downscaling method should be interpreted with caution.

Abstract The galvanic replacement reaction between a Ag template and HAuCl 4 in an aqueous solution transforms 30–200 nm Ag nanocubes into Au nanoboxes and nanocages (nanoboxes with porous walls). By controlling the molar ratio of Ag to HAuCl 4 , the extinction peak of resultant structures can be continuously tuned from the blue (400 nm) to the near‐infrared (1200 nm) region of the electromagnetic spectrum. These hollow Au nanostructures are characterized by extraordinarily large cross‐sections for both absorption and scattering. Optical coherence tomography measurements indicate that the 36 nm nanocage has a scattering cross‐section of ∼ 0.8 × 10 –15 m 2 and an absorption cross‐section of ∼ 7.3 × 10 –15 m 2 . The absorption cross‐section is more than five orders of magnitude larger than those of conventional organic dyes. Exposure of Au nanocages to a camera flash resulted in the melting and conversion of Au nanocages into spherical particles due to photothermal heating. Discrete‐dipole‐approximation calculations suggest that the magnitudes of both scattering and absorption cross‐sections of Au nanocages can be tailored by controlling their dimensions, as well as the thickness and porosity of their walls. This novel class of hollow nanostructures is expected to find use as both a contrast agent for optical imaging in early stage tumor detection and as a therapeutic agent for photothermal cancer treatment.

This paper is concerned with the stability analysis of linear systems with time-varying delays in a given range. A new type of augmented Lyapunov functional is proposed which contains some triple-integral terms. In the proposed Lyapunov functional, the information on the lower bound of the delay is fully exploited. Some new stability criteria are derived in terms of linear matrix inequalities without introducing any free-weighting matrices. Numerical examples are given to illustrate the effectiveness of the proposed method.

Graph representation learning resurges as a trending research subject owing to the widespread use of deep learning for Euclidean data, which inspire various creative designs of neural networks in the non-Euclidean domain, particularly graphs. With the success of these graph neural networks (GNN) in the static setting, we approach further practical scenarios where the graph dynamically evolves. Existing approaches typically resort to node embeddings and use a recurrent neural network (RNN, broadly speaking) to regulate the embeddings and learn the temporal dynamics. These methods require the knowledge of a node in the full time span (including both training and testing) and are less applicable to the frequent change of the node set. In some extreme scenarios, the node sets at different time steps may completely differ. To resolve this challenge, we propose EvolveGCN, which adapts the graph convolutional network (GCN) model along the temporal dimension without resorting to node embeddings. The proposed approach captures the dynamism of the graph sequence through using an RNN to evolve the GCN parameters. Two architectures are considered for the parameter evolution. We evaluate the proposed approach on tasks including link prediction, edge classification, and node classification. The experimental results indicate a generally higher performance of EvolveGCN compared with related approaches. The code is available at https://github.com/IBM/EvolveGCN.

Abstract Polymers are usually considered as thermal insulators, and their applications are limited by their low thermal conductivity. However, recent studies have shown that certain polymers have surprisingly high thermal conductivity, some of which are comparable to that in poor metals or even silicon. Here, the experimental achievements and theoretical progress of thermal transport in polymers and their nanocomposites are outlined. The open questions and challenges of existing theories are discussed. Special attention is given to the mechanism of thermal transport, the enhancement of thermal conductivity in polymer nanocomposites/fibers, and their potential application as thermal interface materials.

Ultrapermeable benzotriptycene-based PIMs show exceptional gas selectivities that define new positions for the CO₂/CH₄ and CO₂/N₂ Robeson upper bounds.

Abstract A critical challenge in the commercialization of layer‐structured Ni‐rich materials is the fast capacity drop and voltage fading due to the interfacial instability and bulk structural degradation of the cathodes during battery operation. Herein, with the guidance of theoretical calculations of migration energy difference between La and Ti from the surface to the inside of LiNi 0.8 Co 0.1 Mn 0.1 O 2 , for the first time, Ti‐doped and La 4 NiLiO 8 ‐coated LiNi 0.8 Co 0.1 Mn 0.1 O 2 cathodes are rationally designed and prepared, via a simple and convenient dual‐modification strategy of synchronous synthesis and in situ modification. Impressively, the dual modified materials show remarkably improved electrochemical performance and largely suppressed voltage fading, even under exertive operational conditions at elevated temperature and under extended cutoff voltage. Further studies reveal that the nanoscale structural degradation on material surfaces and the appearance of intergranular cracks associated with the inconsistent evolution of structural degradation at the particle level can be effectively suppressed by the synergetic effect of the conductive La 4 NiLiO 8 coating layer and the strong TiO bond. The present work demonstrates that our strategy can simultaneously address the two issues with respect to interfacial instability and bulk structural degradation, and it represents a significant progress in the development of advanced cathode materials for high‐performance lithium‐ion batteries.

To ensure undisrupted business, large Internet companies need to closely monitor various KPIs (e.g., Page Views, number of online users, and number of orders) of its Web applications, to accurately detect anomalies and trigger timely troubleshooting/mitigation. However, anomaly detection for these seasonal KPIs with various patterns and data quality has been a great challenge, especially without labels. In this paper, we proposed Donut, an unsupervised anomaly detection algorithm based on VAE. Thanks to a few of our key techniques, Donut greatly outperforms a state-of-arts supervised ensemble approach and a baseline VAE approach, and its best F-scores range from 0.75 to 0.9 for the studied KPIs from a top global Internet company. We come up with a novel KDE interpretation of reconstruction for Donut, making it the first VAE-based anomaly detection algorithm with solid theoretical explanation.

Transmission electron microscopy was used to study the ordered domain structures in undoped and La/Na‐doped Pb(Mg 1/3 Nb 2/3 )O 3 (PMN), where the compositions of the doped samples were specifically chosen so as to elucidate the ordering mechanism. The results showed that the Mg 2+ ions and Nb 5+ ions are short‐range ordered on the B‐site sublattice in undoped PMN, with a domain size of 2 to 5 nm. This short‐range ordering gives rise to B‐site composition fluctuations occurring on a nanometer scale, and it is this compositional inhomogeneity which is believed to be responsible for the diffuse phase transition behavior. Donor doping with La 2 O 3 can compensate for the local charge imbalance resulting from the short‐range order and thus enhances the degree of ordering. Acceptor doping with Na 2 O, however, increases the charge effect, and hence ordering is suppressed. The effect of Na doping and La doping on the dielectric properties of PMN is also discussed.

Understanding of the triboelectric charge accumulation from the view of materials plays a critical role in enhancing the output performance of triboelectric nanogenerator (TENG). In this paper, we have designed a feasible approach to modify the tribo-material of TENG by filling it with high permittivity nanoparticles and forming pores. The influence of dielectricity and porosity on the output performance is discussed experimentally and theoretically, which indicates that both the surface charge density and the charge transfer quantity have a close relationship with the relative permittivity and porosity of the tribo-material. A high output performance TENG based on a composite sponge PDMS film (CS-TENG) is fabricated by optimizing both the dielectric properties and the porosity of the tribo-material. With the combination of the enhancement of permittivity and production of pores in the PDMS film, the charge density of ∼19 nC cm(-2), open-circuit voltage of 338 V, and power density of 6.47 W m(-2) are obtained at working frequency of 2.5 Hz with the optimized film consisting of 10% SrTiO3 nanoparticles (∼100 nm in size) and 15% pores in volume, which gives over 5-fold power enhancement compared with the nanogenerator based on the pure PDMS film. This work gives a better understanding of the triboelectricity produced by the TENG from the view of materials and provides a new and effective way to enhance the performance of TENG from the material itself, not just its surface modification.

Crystallization is crucial in the pharmaceutical industry as a separation process for intermediates and as the final step in the manufacture of active pharmaceutical ingredients (APIs). In this perspective article to celebrate 10 years of Crystal Growth & Design, we focus on three areas related to crystallization in the pharmaceutical industry: (1) advances in our understanding of the fundamentals of nucleation, (2) production and scale-up of novel solid forms, and (3) continuous processing. While the areas discussed are not new, they are areas, in our opinion, of significant current interest to the community engaged in crystallization in the pharmaceutical industry.

Magnetic Fe3O4–chitosan nanoparticles were prepared by the covalent binding of chitosan (CTS) onto the surface of magnetic Fe3O4 nanoparticles which were prepared by hydrothermal method using H2O2 as an oxidizer. Transmission electron microscopy (TEM) showed that Fe3O4 particles and Fe3O4–chitosan nanocomposites were regular sphere with a mean diameter of 23 nm and 25 nm, respectively. X-ray diffraction patterns (XRD) indicated that the magnetic Fe3O4 nanoparticles were pure Fe3O4 with a spinel structure and the coating of chitosan did not result in a phase change. The coating of CTS onto the Fe3O4 nanoparticles was also demonstrated by the measurement of thermogravimetric analysis (TGA) and Fourier transform infrared (FTIR) spectra. Magnetic measurement revealed that the saturated magnetization of the Fe3O4–chitosan nanoparticles reached 21.5 emu g−1 and the nanoparticles showed the characteristics of superparamagnetism.

Gold nanoparticles have been conceived as a radiosensitizer in cancer radiation therapy, but one of the important questions for primary drug screening is what size of gold nanoparticles can optimally enhance radiation effects. Herein, we perform in vitro and in vivo radiosensitization studies of 4.8, 12.1, 27.3, and 46.6 nm PEG-coated gold nanoparticles. In vitro results show that all sizes of the PEG-coated gold nanoparticles can cause a significant decrease in cancer cell survival after gamma radiation. 12.1 and 27.3 nm PEG-coated gold nanoparticles have dispersive distributions in the cells and have stronger sensitization effects than 4.8 and 46.6 nm particles by both cell apoptosis and necrosis. Further, in vivo results also show all sizes of the PEG-coated gold nanoparticles can decrease tumor volume and weight after 5 Gy radiations, and 12.1 and 27.3 nm PEG-coated gold nanoparticles have greater sensitization effects than 4.8 and 46.6 nm particles, which can lead to almost complete disappearance of the tumor. In vivo biodistribution confirms that 12.1 and 27.3 nm PEG-coated gold nanoparticles are accumulated in the tumor with high concentrations. The pathology, immune response, and blood biochemistry indicate that the PEG-coated gold nanoparticles do not cause spleen and kidney damages, but give rise to liver damage and gold accumulation. It can be concluded that 12.1 and 27.3 nm PEG-coated gold nanoparticles show high radiosensitivity, and these results have an important indication for possible radiotherapy and drug delivery.

Abstract Performance of triboelectric nanogenerators is limited by low and unstable charge density on tribo-layers. An external-charge pumping method was recently developed and presents a promising and efficient strategy towards high-output triboelectric nanogenerators. However, integratibility and charge accumulation efficiency of the system is rather low. Inspired by the historical development of electromagnetic generators, here, we propose and realize a self-charge excitation triboelectric nanogenerator system towards high and stable output in analogy to the principle of traditional magnetic excitation generators. By rational design of the voltage-multiplying circuits, the completed external and self-charge excitation modes with stable and tailorable output over 1.25 mC m −2 in contact-separation mode have been realized in ambient condition. The realization of the charge excitation system in this work may provide a promising strategy for achieving high-output triboelectric nanogenerators towards practical applications.

The graph convolutional networks (GCN) recently proposed by Kipf and Welling are an effective graph model for semi-supervised learning. This model, however, was originally designed to be learned with the presence of both training and test data. Moreover, the recursive neighborhood expansion across layers poses time and memory challenges for training with large, dense graphs. To relax the requirement of simultaneous availability of test data, we interpret graph convolutions as integral transforms of embedding functions under probability measures. Such an interpretation allows for the use of Monte Carlo approaches to consistently estimate the integrals, which in turn leads to a batched training scheme as we propose in this work---FastGCN. Enhanced with importance sampling, FastGCN not only is efficient for training but also generalizes well for inference. We show a comprehensive set of experiments to demonstrate its effectiveness compared with GCN and related models. In particular, training is orders of magnitude more efficient while predictions remain comparably accurate.

The design problem of networked control systems (NCS) with constant and random network delay in the forward and feedback channels, respectively, is considered in this paper. A novel networked predictive control (NPC) scheme is proposed to overcome the effects of network delay and data dropout. Stability criteria of closed-loop NPC systems are presented. The necessary and sufficient conditions for the stability of closed-loop NCS with constant time delay are given. Furthermore, it is shown that a closed-loop NPC system with bounded random network delay is stable if its corresponding switched system is stable. Both simulation study and practical experiments show the effectiveness of the control scheme

We describe a genetic variation map for the chicken genome containing 2.8 million single-nucleotide polymorphisms (SNPs). This map is based on a comparison of the sequences of three domestic chicken breeds (a broiler, a layer and a Chinese silkie) with that of their wild ancestor, red jungle fowl. Subsequent experiments indicate that at least 90% of the variant sites are true SNPs, and at least 70% are common SNPs that segregate in many domestic breeds. Mean nucleotide diversity is about five SNPs per kilobase for almost every possible comparison between red jungle fowl and domestic lines, between two different domestic lines, and within domestic lines—in contrast to the notion that domestic animals are highly inbred relative to their wild ancestors. In fact, most of the SNPs originated before domestication, and there is little evidence of selective sweeps for adaptive alleles on length scales greater than 100 kilobases.

Radiosensitizers can increase local treatment efficacy under a relatively low and safe radiation dose, thereby facilitating tumor eradication and minimizing side effects. Here, a new class of radiosensitizers is reported, which contain several gold (Au) atoms embedded inside a peptide shell (e.g., Au10-12 (SG)10-12 ) and can achieve ultrahigh tumor uptake (10.86 SUV at 24 h post injection) and targeting specificity, efficient renal clearance, and high radiotherapy enhancement.

Heart rate is an important indicator of people's physiological state. Recently, several papers reported methods to measure heart rate remotely from face videos. Those methods work well on stationary subjects under well controlled conditions, but their performance significantly degrades if the videos are recorded under more challenging conditions, specifically when subjects' motions and illumination variations are involved. We propose a framework which utilizes face tracking and Normalized Least Mean Square adaptive filtering methods to counter their influences. We test our framework on a large difficult and public database MAHNOB-HCI and demonstrate that our method substantially outperforms all previous methods. We also use our method for long term heart rate monitoring in a game evaluation scenario and achieve promising results.

Because of the well-known molecular complexity and heterogeneity of natural organic matter (NOM), an aquatic bulk NOM was fractionated into well-defined polyphenolic-rich and carbohydrate-rich subfractions. These fractions were systematically characterized by fluorescence emission, three dimensional excitation-emission matrices, and synchronous-scan excitation spectroscopy in comparison with those of the reference International Humic Substances Society soil humic acid and Suwannee River fulvic acid. Results indicate that fluorescence spectroscopy can be useful to qualitatively differentiate not only NOM compounds from varying origins but also NOM subcomponents with varying compositions and functional properties. The polyphenolic-rich NOM-PP fraction exhibited a much more intense fluorescence and a red shift of peak position in comparison with the carbohydrate-rich NOM-CH fraction. Results also indicate that synchronous excitation spectra were able to provide improved peak resolution and structural signatures such as peak positioning, shift, and intensity among various NOM components as compared with those of the emission and excitation spectra. In particular, the synchronous spectral peak intensity and its red shift in the region of about 450–480 nm may be used to indicate the presence or absence of high molecular weight and polycondensed humic organic components, or the multicomponent nature of NOM or NOM subcomponents.

The outbreak of the 2019 novel coronavirus disease (COVID-19) has caused more than 100,000 people infected and thousands of deaths. Currently, the number of infections and deaths is still increasing rapidly. COVID-19 seriously threatens human health, production, life, social functioning and international relations. In the fight against COVID-19, Geographic Information Systems (GIS) and big data technologies have played an important role in many aspects, including the rapid aggregation of multi-source big data, rapid visualization of epidemic information, spatial tracking of confirmed cases, prediction of regional transmission, spatial segmentation of the epidemic risk and prevention level, balancing and management of the supply and demand of material resources, and social-emotional guidance and panic elimination, which provided solid spatial information support for decision-making, measures formulation, and effectiveness assessment of COVID-19 prevention and control. GIS has developed and matured relatively quickly and has a complete technological route for data preparation, platform construction, model construction, and map production. However, for the struggle against the widespread epidemic, the main challenge is finding strategies to adjust traditional technical methods and improve speed and accuracy of information provision for social management. At the data level, in the era of big data, data no longer come mainly from the government but are gathered from more diverse enterprises. As a result, the use of GIS faces difficulties in data acquisition and the integration of heterogeneous data, which requires governments, businesses, and academic institutions to jointly promote the formulation of relevant policies. At the technical level, spatial analysis methods for big data are in the ascendancy. Currently and for a long time in the future, the development of GIS should be strengthened to form a data-driven system for rapid knowledge acquisition, which signifies that GIS should be used to reinforce the social operation parameterization of models and methods, especially when providing support for social management.

Pro-carcinogenic bacteria have the potential to initiate and/or promote colon cancer, in part via immune mechanisms that are incompletely understood. Using ApcMin mice colonized with the human pathobiont enterotoxigenic Bacteroides fragilis (ETBF) as a model of microbe-induced colon tumorigenesis, we show that the Bacteroides fragilis toxin (BFT) triggers a pro-carcinogenic, multi-step inflammatory cascade requiring IL-17R, NF-κB, and Stat3 signaling in colonic epithelial cells (CECs). Although necessary, Stat3 activation in CECs is not sufficient to trigger ETBF colon tumorigenesis. Notably, IL-17-dependent NF-κB activation in CECs induces a proximal to distal mucosal gradient of C-X-C chemokines, including CXCL1, that mediates the recruitment of CXCR2-expressing polymorphonuclear immature myeloid cells with parallel onset of ETBF-mediated distal colon tumorigenesis. Thus, BFT induces a pro-carcinogenic signaling relay from the CEC to a mucosal Th17 response that results in selective NF-κB activation in distal colon CECs, which collectively triggers myeloid-cell-dependent distal colon tumorigenesis.

Graphene quantum dots (GQDs), which is the latest addition to the nanocarbon material family, promise a wide spectrum of applications. Herein, we demonstrate two different functionalization strategies to systematically tailor the bandgap structures of GQDs whereby making them snugly suitable for particular applications. Furthermore, the functionalized GQDs with a narrow bandgap and intramolecular Z-scheme structure are employed as the efficient photocatalysts for water splitting and carbon dioxide reduction under visible light. The underlying mechanisms of our observations are studied and discussed.

Hypoxia as one characteristic hallmark of solid tumors has been demonstrated to be involved in cancer metastasis and progression, induce severe resistance to oxygen-dependent therapies, and hamper the transportation of theranostic agents. To address these issues, an oxygen-self-produced sonodynamic therapy (SDT) nanoplatform involving a modified fluorocarbon (FC)-chain-mediated oxygen delivery protocol has been established to realize highly efficient SDT against hypoxic pancreatic cancer. In this nanoplatform, mesopores and FC chains of FC-chain-functionalized hollow mesoporous organosilica nanoparticle carriers can provide sufficient storage capacity and binding sites for sonosensitizers (IR780) and oxygen, respectively. In vitro and in vivo experiments demonstrate the nanoplatform involving this distinctive oxygen delivery protocol indeed breaks the hypoxia-specific transportation barriers, supplies sufficient oxygen to hypoxic PANC-1 cells especially upon exposure to ultrasound irradiation, and relieves hypoxia. Consequently, hypoxia-induced resistance to SDT is inhibited and sufficient highly reactive oxygen species (ROS) are produced to kill PANC-1 cells and shrink hypoxic PANC-1 pancreatic cancer. This distinctive FC-chain-mediated oxygen delivery method provides an avenue to hypoxia oxygenation and holds great potential in mitigating hypoxia-induced resistance to those oxygen-depleted therapies, e.g., photodynamic therapy, radiotherapy, and chemotherapy.

Abstract MXene quantum dots (QDs) are emerging 0D nanomaterials. Here, a new heterostructure is developed based on a 1D photoactive semiconductor and a 0D MXene QD for improved photocatalytic reduction of CO 2 into methanol. Specifically, Ti 3 C 2 QDs are incorporated onto Cu 2 O nanowires (NWs) through a simple self‐assembly strategy. It is demonstrated that Ti 3 C 2 QDs not only significantly improve the stability of Cu 2 O NWs but also greatly improve their photocatatlytic performance by enhancing charge transfer, charge transport, carrier density, light adsorption, as well as by decreasing band bending edge and charge recombination. The energy level diagram derived from both experimental measurements and theoretical calculations provide further insights of such hierarchical photocatalysis system.

To examine racial and ethnic disparities in health care access and utilization after the Affordable Care Act (ACA) health insurance mandate was fully implemented in 2014.Using the 2011-2014 National Health Interview Survey, we examine changes in health care access and utilization for the nonelderly US adult population. Multivariate linear probability models are estimated to adjust for demographic and sociodemographic factors.The implementation of the ACA (year indicator 2014) is associated with significant reductions in the probabilities of being uninsured (coef=-0.03, P<0.001), delaying any necessary care (coef=-0.03, P<0.001), forgoing any necessary care (coef=-0.02, P<0.001), and a significant increase in the probability of having any physician visits (coef=0.02, P<0.001), compared with the reference year 2011. Interaction terms between the 2014 year indicator and race/ethnicity demonstrate that uninsured rates decreased more substantially among non-Latino African Americans (African Americans) (coef=-0.04, P<0.001) and Latinos (coef=-0.03, P<0.001) compared with non-Latino whites (whites). Latinos were less likely than whites to delay (coef=-0.02, P<0.001) or forgo (coef=-0.02, P<0.001) any necessary care and were more likely to have physician visits (coef=0.03, P<0.005) in 2014. The association between year indicator of 2014 and the probability of having any emergency department visits is not significant.Health care access and insurance coverage are major factors that contributed to racial and ethnic disparities before the ACA implementation. Our results demonstrate that racial and ethnic disparities in access have been reduced significantly during the initial years of the ACA implementation that expanded access and mandated that individuals obtain health insurance.

Gabor features have been known to be effective for face recognition. However, only a few approaches utilize phase feature and they usually perform worse than those using magnitude feature. To investigate the potential of Gabor phase and its fusion with magnitude for face recognition, in this paper, we first propose local Gabor XOR patterns (LGXP), which encodes the Gabor phase by using the local XOR pattern (LXP) operator. Then, we introduce block-based Fisher's linear discriminant (BFLD) to reduce the dimensionality of the proposed descriptor and at the same time enhance its discriminative power. Finally, by using BFLD, we fuse local patterns of Gabor magnitude and phase for face recognition. We evaluate our approach on FERET and FRGC 2.0 databases. In particular, we perform comparative experimental studies of different local Gabor patterns. We also make a detailed comparison of their combinations with BFLD, as well as the fusion of different descriptors by using BFLD. Extensive experimental results verify the effectiveness of our LGXP descriptor and also show that our fusion approach outperforms most of the state-of-the-art approaches.

In this paper, we study the consensus problem for second-order multi-agent systems with heterogeneous unknown inertias and control gains under a general directed graph. Unlike the existing consensus algorithms for second-order multi-agent systems in which all agents are assumed to have common unit inertias or share common control gains, we allow the inertias and the control gains to be heterogeneous and time-varying for each agent. We propose fully distributed consensus algorithms over a general directed graph when there exist, respectively, absolute velocity damping and relative velocity damping. Novel integral-type Lyapunov functions are proposed to study the consensus convergence. Moreover, the adaptive σ-modification schemes for the gain adaptation are proposed, which renders smaller control gains and thus requires smaller amplitude on the control input without sacrificing consensus convergence. Furthermore, we show that one proposed algorithm also works for consensus of agents with intrinsic Lipschitz nonlinear dynamics. The control gains are varying and updated by distributed adaptive laws. As a result, the proposed algorithms require no global information and thus can be implemented in a fully distributed manner.

Sb2Se3 is a promising absorber material for photovoltaic cells because of its optimum band gap, strong optical absorption, simple phase and composition, and earth-abundant and nontoxic constituents. However, this material is rarely explored for photovoltaic application. Here we report Sb2Se3 solar cells fabricated from thermal evaporation. The rationale to choose thermal evaporation for Sb2Se3 film deposition was first discussed, followed by detailed characterization of Sb2Se3 film deposited onto FTO with different substrate temperatures. We then studied the optical absorption, photosensitivity, and band position of Sb2Se3 film, and finally a prototype photovoltaic device FTO/Sb2Se3/CdS/ZnO/ZnO:Al/Au was constructed, achieving an encouraging 2.1% solar conversion efficiency.

Sb2Se3 is introduced as the absorber layer for thin film photovoltaics because of its very attractive material, optical, and electrical properties. High quality Sb2Se3 films are produced using a hydrazine solution process, and a heterojunction TiO2/Sb2Se3 solar cell achieving 2.26% efficiency with an impressive high open circuit voltage of 0.52 V is reported.

General circulation models (GCMs) and greenhouse gas emissions scenarios (GGES) are generally considered to be the two major sources of uncertainty in quantifying the climate change impacts on hydrology. Other sources of uncertainty have been given less attention. This study considers overall uncertainty by combining results from an ensemble of two GGES, six GCMs, five GCM initial conditions, four downscaling techniques, three hydrological model structures, and 10 sets of hydrological model parameters. Each climate projection is equally weighted to predict the hydrology on a Canadian watershed for the 2081–2100 horizon. The results show that the choice of GCM is consistently a major contributor to uncertainty. However, other sources of uncertainty, such as the choice of a downscaling method and the GCM initial conditions, also have a comparable or even larger uncertainty for some hydrological variables. Uncertainties linked to GGES and the hydrological model structure are somewhat less than those related to GCMs and downscaling techniques. Uncertainty due to the hydrological model parameter selection has the least important contribution among all the variables considered. Overall, this research underlines the importance of adequately covering all sources of uncertainty. A failure to do so may result in moderately to severely biased climate change impact studies. Results further indicate that the major contributors to uncertainty vary depending on the hydrological variables selected, and that the methodology presented in this paper is successful at identifying the key sources of uncertainty to consider for a climate change impact study.

A new type of metabolizable and efficient radiosensitizers for cancer radiotherapy is presented by combining ultrasmall Au nanoclusters (NCs, <2 nm) with biocompatible coating ligands (glutathione, GSH). The new nanoconstruct (GSH-coated Au25 NCs) inherits attractive features of both the Au core (strong radiosensitizing effect) and GSH shell (good biocompatibility). It can preferentially accumulate in tumor via the improved EPR effect, which leads to strong enhancement for cancer radiotherapy. After the treatment, the small-sized GSH-Au25 NCs can be efficiently cleared by the kidney, minimizing any potential side effects due to the accumulation of Au25 NCs in the body.

Abstract To reveal the molecular mechanisms of oleaginousness in microalgae, transcriptomic and lipidomic dynamics of the oleaginous microalga Nannochloropsis oceanica IMET1 under nitrogen-replete (N+) and N-depleted (N-) conditions were simultaneously tracked. At the transcript level, enhanced triacylglycerol (TAG) synthesis under N- conditions primarily involved upregulation of seven putative diacylglycerol acyltransferase (DGAT) genes and downregulation of six other DGAT genes, with a simultaneous elevation of the other Kennedy pathway genes. Under N- conditions, despite downregulation of most de novo fatty acid synthesis genes, the pathways that shunt carbon precursors from protein and carbohydrate metabolic pathways into glycerolipid synthesis were stimulated at the transcript level. In particular, the genes involved in supplying carbon precursors and energy for de novo fatty acid synthesis, including those encoding components of the pyruvate dehydrogenase complex (PDHC), glycolysis, and PDHC bypass, and suites of specific transporters, were substantially upregulated under N- conditions, resulting in increased overall TAG production. Moreover, genes involved in the citric acid cycle and β-oxidation in mitochondria were greatly enhanced to utilize the carbon skeletons derived from membrane lipids and proteins to produce additional TAG or its precursors. This temporal and spatial regulation model of oil accumulation in microalgae provides a basis for improving our understanding of TAG synthesis in microalgae and will also enable more rational genetic engineering of TAG production.

Gold nanoparticles (GNPs) and modified GNPs having two kinds of functional molecules, cysteamine (AET) and thioglucose (Glu), are synthesized. Cell uptake and radiation cytotoxicity enhancement in a breast-cancer cell line (MCF-7) versus a nonmalignant breast-cell line (MCF-10A) are studied. Transmission electron microscopy (TEM) results show that cancer cells take up functional Glu-GNPs significantly more than naked GNPs. The TEM results also indicate that AET-capped GNPs are mostly bound to the MCF-7 cell membrane, while Glu-GNPs enter the cells and are distributed in the cytoplasm. After MCF-7 cell uptake of Glu-GNPs, or binding of AET-GNPs, the in vitro cytotoxicity effects are observed at 24, 48, and 72 hours. The results show that these functional GNPs have little or no toxicity to these cells. To validate the enhanced killing effect on cancer cells, various forms of radiation are applied such as 200 kVp X-rays and gamma-rays, to the cells, both with and without functional GNPs. By comparison with irradiation alone, the results show that GNPs significantly enhance cancer killing.

As an essential issue in wind energy industry, wind speed forecasting plays a vital role in optimal scheduling and control of wind energy generation and conversion. In this paper, a novel method called EnsemLSTM is proposed by using nonlinear-learning ensemble of deep learning time series prediction based on LSTMs (Long Short Term Memory neural networks), SVRM (support vector regression machine) and EO (extremal optimization algorithm). First, in order to avert the drawback of weak generalization capability and robustness of a single deep learning approach when facing diversiform data, a cluster of LSTMs with diverse hidden layers and neurons are employed to explore and exploit the implicit information of wind speed time series. Then predictions of LSTMs are aggregated into a nonlinear-learning regression top-layer composed of SVRM and the EO is introduced to optimize the parameters of the top-layer. Lastly, the final ensemble prediction for wind speed is given by the fine-turning top-layer. The proposed EnsemLSTM is applied on two case studies data collected from a wind farm in Inner Mongolia, China, to perform ten-minute ahead utmost short term wind speed forecasting and one-hour ahead short term wind speed forecasting. Statistical tests of experimental results compared with other popular prediction models demonstrated the proposed EnsemLSTM can achieve a better forecasting performance.

Poly (vinylidene fluoride) (PVDF)/graphene oxide (GO) ultrafiltration (UF) membranes are prepared via immersion precipitation phase inversion process. Raman spectra results indicate the existence of GO in PVDF/GO UF membranes. SEM pictures show that the PVDF/GO UF membranes present developed finger-like pore substructure along with the increased porosity and mean pore size. As revealed by FT-IR spectra, large amount of OH groups are appeared due to the introduction of GO nanosheets that improve the surface hydrophilicity of the modified membrane. In permeation experiment, the water flux is improved after blending GO. With 2 wt% GO content, the pure water flux and permeation flux reach peak values of 26.49 L/m2 h and 14.21 L/m2 h, increasing 79% and 99% respectively. Furthermore, the flux recovery ratio (FRR) and the fouling resistance results suggest that PVDF/GO UF membranes have better antifouling properties than pure PVDF due to the changes of surface hydrophilicity and membrane morphologies. AFM images show that UF membranes have a smoother surface with a higher efficient filtration area, which would enhance antifouling properties.

People have been suffering from air pollution for a decade in China, especially from PM2.5 (particulate matter with a diameter of less than 2.5 μm). Accurate prediction of air quality has great practical significance. In this paper, we propose a data-driven model, called as long short-term memory - fully connected (LSTM-FC) neural network, to predict PM2.5 contamination of a specific air quality monitoring station over 48 h using historical air quality data, meteorological data, weather forecast data, and the day of the week. Our predictive model consists of two components: (1) Using a long short-term memory (LSTM)-based temporal simulator to model the local variation of PM2.5 contamination and (2) Using a neural network-based spatial combinatory to capture spatial dependencies between the PM2.5 contamination of central station and that of neighbor stations. We evaluate our model on a dataset containing records of 36 air quality monitoring stations in Beijing from 2014/05/01 to 2015/04/30 and compare it with artificial neural network (ANN) and long short-term memory (LSTM) models on the same dataset. The results show that our LSTM-FC neural network model gives a better predictive performance.

Therapeutic applications of microRNA (miRNA) in KRAS-driven pancreatic cancers might be valuable, but few studies have explored this area. Here, we report that miR-96 directly targets the KRAS oncogene and functions as a tumor-suppressing miRNA in pancreatic cancer cells. Ectopic expression of miR-96 through a synthetic miRNA precursor inhibited KRAS, dampened Akt signaling, and triggered apoptosis in cells. In human clinical specimens, miR-96 was downregulated or deleted where an association with KRAS elevations was observed. In vitro and in vivo assays established that miR-96 decreased cancer cell invasion and migration and slowed tumor growth in a manner associated with KRAS downregulation. Our findings identify miR-96 as a potent regulator of KRAS, which may provide a novel therapeutic strategy for treatment of pancreatic cancer and other KRAS-driven cancers.

The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOTST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on "real-time" shortterm tracking in RGB, (iii) VOT-LT2019 focused on longterm tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard shortterm, long-term tracking and tracking with multi-channel imagery. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website.

The in-vitro, three dimensional kinematic characteristics of the human ankle and subtalar joint were investigated in this study. The main goals of this investigation were: 1) To determine the range of motion of the foot-shank complex and the associated range of motion of the ankle and subtalar joints; 2) To determine the kinematic coupling characteristics of the foot-shank complex, and 3) To identify the relationship between movements at the ankle and subtalar joints and the resulting motion produced between the foot and the shank. The tests were conducted on fifteen fresh amputated lower limbs and consisted of incrementally displacing the foot with respect to the shank while the motion of the articulating bones was measured through a three dimensional position data acquisition system. The kinematic analysis was based on the helical axis parameters describing the incremental displacements between any two of the three articulating bones and on a joint coordinate system used to describe the relative position between the bones. From the results of this investigation it was concluded that: 1) The range of motion of the foot-shank complex in any direction (dorsiflexion/plantarflexion, inversion/eversion and internal rotation/external rotation) is larger than that of either the ankle joint or the subtalar joint.; 2) Large kinematic coupling values are present at the foot-shank complex in inversion/eversion and in internal rotation/external rotation. However, only a slight amount of coupling was observed to occur in dorsiflexion/plantarflexion.; 3) Neither the ankle joint nor the subtalar joint are acting as ideal hinge joints with a fixed axis of rotation.; 4) Motion of the foot-shank complex in any direction is the result of rotations at both the ankle and the subtalar joints. However, the contribution of the ankle joint to dorsiflexion/plantarflexion of the foot-shank complex is larger than that of the subtalar joint and the contribution of the subtalar joint to inversion/eversion is larger than that of the ankle joint.; 5) The ankle and the subtalar joints have an approximately equal contribution to internal rotation/external rotation movements of the foot-shank complex.

Abstract This paper is concerned with the problem of stability and stabilization of neutral time‐delay systems. A new delay‐dependent stability condition is derived in terms of linear matrix inequality by constructing a new Lyapunov functional and using some integral inequalities without introducing any free‐weighting matrices. On the basis of the obtained stability condition, a stabilizing method is also proposed. Using an iterative algorithm, the state feedback controller can be obtained. Numerical examples illustrate that the proposed methods are effective and lead to less conservative results. Copyright © 2008 John Wiley &amp; Sons, Ltd.

We present the analytical solution of the wave function and energy dispersion of armchair graphene nanoribbons (GNRs) based on the tight-binding approximation. By imposing the hard-wall boundary condition, we find that the wave vector in the confined direction is discretized. This discrete wave vector serves as the index of different subbands. Our analytical solutions of wave function and associated energy dispersion reproduce the results of numerical tight-binding and the solutions based on the $\mathbf{k}∙\mathbf{p}$ approximation. In addition, we also find that all armchair GNRs with edge deformation have energy gaps, which agrees with recently reported first-principles calculations.

Texture is a fundamental characteristic of many types of images, and texture representation is one of the essential and challenging problems in computer vision and pattern recognition which has attracted extensive research attention over several decades. Since 2000, texture representations based on Bag of Words and on Convolutional Neural Networks have been extensively studied with impressive performance. Given this period of remarkable evolution, this paper aims to present a comprehensive survey of advances in texture representation over the last two decades. More than 250 major publications are cited in this survey covering different aspects of the research, including benchmark datasets and state of the art results. In retrospect of what has been achieved so far, the survey discusses open challenges and directions for future research.

Abstract The etiology of dental caries remains elusive because of our limited understanding of the complex oral microbiomes. The current methodologies have been limited by insufficient depth and breadth of microbial sampling, paucity of data for diseased hosts particularly at the population level, inconsistency of sampled sites and the inability to distinguish the underlying microbial factors. By cross-validating 16S rRNA gene amplicon-based and whole-genome-based deep-sequencing technologies, we report the most in-depth, comprehensive and collaborated view to date of the adult saliva microbiomes in pilot populations of 19 caries-active and 26 healthy human hosts. We found that: first, saliva microbiomes in human population were featured by a vast phylogenetic diversity yet a minimal organismal core; second, caries microbiomes were significantly more variable in community structure whereas the healthy ones were relatively conserved; third, abundance changes of certain taxa such as overabundance of Prevotella Genus distinguished caries microbiota from healthy ones, and furthermore, caries-active and normal individuals carried different arrays of Prevotella species; and finally, no ‘caries-specific’ operational taxonomic units (OTUs) were detected, yet 147 OTUs were ‘caries associated’, that is, differentially distributed yet present in both healthy and caries-active populations. These findings underscored the necessity of species- and strain-level resolution for caries prognosis, and were consistent with the ecological hypothesis where the shifts in community structure, instead of the presence or absence of particular groups of microbes, underlie the cariogenesis.

DNA phylogenetic comparisons have shown that morphology-based species recognition often underestimates fungal diversity. Therefore, the need for accurate DNA sequence data, tied to both correct taxonomic names and clearly annotated specimen data, has never been greater. Furthermore, the growing number of molecular ecology and microbiome projects using high-throughput sequencing require fast and effective methods for en masse species assignments. In this article, we focus on selecting and re-annotating a set of marker reference sequences that represent each currently accepted order of Fungi. The particular focus is on sequences from the internal transcribed spacer region in the nuclear ribosomal cistron, derived from type specimens and/or ex-type cultures. Re-annotated and verified sequences were deposited in a curated public database at the National Center for Biotechnology Information (NCBI), namely the RefSeq Targeted Loci (RTL) database, and will be visible during routine sequence similarity searches with NR_prefixed accession numbers. A set of standards and protocols is proposed to improve the data quality of new sequences, and we suggest how type and other reference sequences can be used to improve identification of Fungi. Database URL: http://www.ncbi.nlm.nih.gov/bioproject/PRJNA177353.

Glucose-capped gold nanoparticles (Glu-GNPs) have been used to improve cellular targeting and radio-sensitization. In this study, we explored the mechanism of Glu-GNP enhanced radiation sensitivity in radiation-resistant human prostate cancer cells. Cell survival and proliferation were measured using MTT and clonogenic assay. Flow cytometry with staining by propidium iodide (PI) was performed to study the cell cycle changes induced by Glu-GNPs, and western blotting was used to determine the expression of p53 and cyclin proteins that correlated to cell cycle regulation. With 2 Gy of ortho-voltage irradiation, Glu-GNP showed a 1.5-2.0 fold enhancement in growth inhibition when compared to x-rays alone. Comparing the cell cycle change, Glu-GNPs induced acceleration in the G0/G1 phase and accumulation of cells in the G2/M phase at 29.8% versus 18.4% for controls at 24 h. G2/M arrest was accompanied by decreased expression of p53 and cyclin A, and increased expression of cyclin B1 and cyclin E. In conclusion, Glu-GNPs trigger activation of the CDK kinases leading to cell cycle acceleration in the G0/G1 phase and accumulation in the G2/M phase. This activation is accompanied by a striking sensitization to ionizing radiation, which may have clinical implications.

Brightly red fluorescent carbon dots are synthesized hydrothermally and dissolved in diluted hydrochloric acid solution. Such carbon dots exhibit excitation-independent emission at about 620 nm with quantum yield over 10%, which is visible in daylight. After the carbon dots solution is sprayed to the fingerprints on various solid substrates and dried in air, clear fingerprints can be seen under an ultraviolet lamp and stay stable for 1 day. Detailed characterizations suggest that during the drying process, the coffee-ring effect and the electrostatic interactions between the carbon dots and the fingerprint residues prevent the typical aggregation-induced fluorescence quenching of carbon dots.

Statistical and dynamical downscaling techniques have been proposed to bridge the gaps between coarse-scale and generally biased climate model outputs and the point-scale requirements of impact model inputs. Amongst the various statistical approaches, empirical downscaling methods are the most commonly used due to their ease of implementation. Several empirical downscaling approaches have been proposed and need to be assessed as to which method contributes (or not) to the overall climate change uncertainty. Accordingly, this work aims at assessing the uncertainty of six empirical downscaling methods in quantifying the hydrological impact of climate change over two North American river basins under different climate conditions. The six empirical downscaling methods are grouped into change factor (two methods) and bias correction (four methods) approaches. The uncertainty related to the choice of an empirical downscaling method is compared to that associated with the choice of climate simulation, through the use of two Regional Climate Models (RCMs) driven by three different General Circulation Models (GCMs), totaling four RCM simulations, taken from the NARCCAP inter-comparison project. The future (2041–2065) hydrological regimes simulated with an empirical lumped hydrology model (HSAMI) are compared to the reference period (1971–1995) using a set of hydrology criteria which includes statistics of both mean and extreme values. The results show a large uncertainty envelope associated with the choice of a given empirical downscaling method, as well as for the choice of an RCM simulation. The uncertainty due to empirical downscaling and RCM simulation was more significant in projecting extreme streamflow than in projecting mean flows. Comparing the uncertainty envelope of empirical downscaling methods to the envelope resulting from four RCM simulations indicates that both are similar, even though the latter was slightly larger for some statistics. Finally, the uncertainty linked to the choice of an empirical downscaling approach (change factor vs. bias correction) was much larger than within each type. Overall, this work emphasizes the importance of using several climate projections and empirical downscaling approaches to delineate uncertainty when assessing the climate change impacts on hydrology.

We present the design, analysis and measurements of a polarization-insensitive tunable metamaterial absorber with varactor diodes embedded between metamaterial units. The basic unit shows excellent absorptivity in the designed frequency band over a wide range of incident angles. By regulating the reverse bias voltage on the varactor diode, the absorption frequency of the designed unit can be controlled continuously. The absorption mechanism is interpreted using the electromagnetic-wave interference theory. When the metamaterial units are placed along two orthogonal directions, the absorber is insensitive to the polarization of incident waves. The tunability of the absorber has been verified by experimental results with the measured bandwidth of 1.5 GHz (or relative bandwidth of 30%).

This paper investigates whether female independent directors are more likely to impose high dividend payouts. We find evidence that firms with a larger fraction of female directors on their board have greater dividend payouts. This finding is robust to alternative econometric specifications, and alternative measures of dividend payouts and female board representation. The positive effect of board gender composition on dividends remains when we employ propensity score matching, the instrumental variable approach, and difference-in-differences approach to address potential endogeneity concerns. Furthermore, we find that board gender composition significantly increases the dividend payout only for firms with weak governance, suggesting that female directors use dividend payouts as a governance device.

Uniform Au@CeO2 core–shell submicrospheres, in which a Au nanoparticle core is coated with a shell composed of CeO2 nanoparticles, are easily synthesized by using hydrothermal and calcinating processes. When the Au@CeO2 core–shell submicrospheres are used for catalytic oxidation of CO to CO2, the full conversion temperature is decreased from over 300 °C to 155 °C with respect to the conventional supported Au–CeO2 catalysts. Furthermore, the Au@CeO2 core–shell submicrospheres show superior catalytic stability, and no deactivation occurs after 72 h reaction. The mechanisms of the growth and the high catalytic performance of Au@CeO2 core–shell submicrospheres are discussed in detail.

Doping of low-dimensional graphitic materials, including graphene, graphene quantum dots and single-wall carbon nanotubes with nitrogen, sulfur or boron can significantly change their properties. We report that simple fluorination followed by annealing in a dopant source can superdope low-dimensional graphitic materials with a high level of N, S or B. The superdoping results in the following doping levels: (i) for graphene, 29.82, 17.55 and 10.79 at% for N-, S- and B-doping, respectively; (ii) for graphene quantum dots, 36.38 at% for N-doping; and (iii) for single-wall carbon nanotubes, 7.79 and 10.66 at% for N- and S-doping, respectively. As an example, the N-superdoping of graphene can greatly increase the capacitive energy storage, increase the efficiency of the oxygen reduction reaction and induce ferromagnetism. Furthermore, by changing the degree of fluorination, the doping level can be tuned over a wide range, which is important for optimizing the performance of doped low-dimensional graphitic materials.

Replacing conventional dialkylamino substituents with a three-membered aziridine ring in naphthalimide leads to significantly enhanced brightness and photostability by effectively suppressing twisted intramolecular charge transfer formation. This replacement is generalizable in other chemical families of fluorophores, such as coumarin, phthalimide, and nitrobenzoxadiazole dyes. In highly polar fluorophores, we show that aziridinyl dyes even outperform their azetidinyl analogues in aqueous solution. We also proposed one simple mechanism that can explain the vulnerability of quantum yield to hydrogen bond interactions in protonic solvents in various fluorophore families. Such knowledge is a critical step toward developing high-performance fluorophores for advanced fluorescence imaging.

Cystic echinococcosis (hydatid disease), caused by the tapeworm E. granulosus, is responsible for considerable human morbidity and mortality. This cosmopolitan disease is difficult to diagnose, treat and control. We present a draft genomic sequence for the worm comprising 151.6 Mb encoding 11,325 genes. Comparisons with the genome sequences from other taxa show that E. granulosus has acquired a spectrum of genes, including the EgAgB family, whose products are secreted by the parasite to interact and redirect host immune responses. We also find that genes in bile salt pathways may control the bidirectional development of E. granulosus, and sequence differences in the calcium channel subunit EgCavβ1 may be associated with praziquantel sensitivity. Our study offers insights into host interaction, nutrient acquisition, strobilization, reproduction, immune evasion and maturation in the parasite and provides a platform to facilitate the development of new, effective treatments and interventions for echinococcosis control.

Design of an effective and efficient fractional order PID (FOPID) controller, as a generalization of a standard PID controller based on fractional order calculus, for an industrial control system to obtain high-quality performances is of great theoretical and practical significance. From the perspective of multi-objective optimization, this paper presents a novel FOPID controller design method based on an improved multi-objective extremal optimization (MOEO) algorithm for an automatic regulator voltage (AVR) system. The problem of designing FOPID controller for AVR is firstly formulated as a multi-objective optimization problem with three objective functions including minimization of integral of absolute error (IAE), absolute steady-state error, and settling time. Then, an improved MOEO algorithm is proposed to solve this problem by adopting individual-based iterated optimization mechanism and polynomial mutation (PLM). From the perspective of algorithm design, the proposed MOEO algorithm is relatively simpler than NSGA-II and single-objective evolutionary algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO), chaotic anti swarm (CAS) due to its fewer adjustable parameters. Furthermore, the superiority of proposed MOEO-FOPID controller to NSGA-II-based FOPID, single-objective evolutionary algorithms-based FOPID controllers, MOEO-based and NSGA-II-based PID controllers is demonstrated by extensive experimental results on an AVR system in terms of accuracy and robustness.

Abstract. Zoning which is to divide the study area into different zones according to their geographical differences at the global, national or regional level, includes natural division, economic division, geographical zoning of departments, comprehensive zoning and so on. Zoning is of important practical significance, for example, knowing regional differences and characteristics, regional research and regional development planning, understanding the favorable and unfavorable conditions of the regional development etc. Geographical environment is arising from the geographical position linkages. Geographical environment unit division is also a type of zoning. The geographical environment indicators are deeply studied and summed up in the article, including the background, the associated and the potential. The background indicators are divided into four categories, such as the socio-economic, the political and military, the strategic resources and the ecological environment, which can be divided into more sub-indexes. While the sub-indexes can be integrated to comprehensive index system by weighted stacking method. The Jenks natural breaks classification method, also called the Jenks optimization method, is a data classification method designed to determine the best arrangement of values into different classes. This is done by seeking to minimize each class’s average deviation from the class mean, while maximizing each class’s deviation from the means of the other groups. In this paper, the experiment of Chinese surrounding geographical environment unit division has been done based on the natural breaks (jenks) method, the geographical environment index system and the weighted stacking method, taking South Asia as an example. The result indicates that natural breaks (jenks) method is of good adaptability and high accuracy on the geographical environment unit division. The geographical environment research was originated in the geopolitics and flourished in the geo-economics. The main representatives of the geopolitics are German geographer Friedrich Ratzel, British geographer Mackinder and American geographical politician Nicholas John Spykman etc. The main representative of the geo-economics is American geographical economist Edward Luttwak. China has the most neighboring countries in the world, and its geographical environment is extremely complex. With the continuous development of globalization, China's relations with neighboring countries have become more complex and more closely. So it is very meaningful to have depth research on geographical environment unit division of China.

Sandwich-structured polymer nanocomposites that provide a pathway to overcome the paradox between permittivity and breakdown strength ever existing in dielectric materials are receiving increasing attentions for their superior energy storage performance. Despite certain advances obtained in previous effort, further enhancement of the energy density by structure optimizing is still a challenge. Herein, we present a newly designed sandwich-structured barium titanate/poly(vinylidene fluoride-co-hexafluoropropylene) (BaTiO3/P(VDF-HFP)) nanocomposite via layer-by-layer tape casting process, where high contents of BaTiO3 nanoparticles are dispersed in the middle layer to offer high permittivity, while two outer layers containing small amounts of BaTiO3 provide favorable breakdown strength. The solution-processed nanocomposites with an optimal composition exhibits an ultrahigh discharged energy density of 26.4 J cm−3 and a superior discharged efficiency of 72%, which are by far the highest values ever achieved in sandwich-structured dielectric polymer composites. It is revealed that the designed structure can enhance the breakdown strength and discharged efficiency by preventing the charge injection from electrodes and impeding the development of electrical tress during breakdown process, as confirmed by the leakage current and thermally stimulated depolarization current measurements, as well as the finite element simulations. This work represents a new design paradigm to exploit advanced dielectric materials for electrical energy storage applications.

Data from trials investigating the effects and risks of endovascular thrombectomy for the treatment of stroke due to basilar-artery occlusion are limited.We conducted a multicenter, prospective, randomized, controlled trial of endovascular thrombectomy for basilar-artery occlusion at 36 centers in China. Patients were assigned, in a 2:1 ratio, within 12 hours after the estimated time of basilar-artery occlusion to receive endovascular thrombectomy or best medical care (control). The primary outcome was good functional status, defined as a score of 0 to 3 on the modified Rankin scale (range, 0 [no symptoms] to 6 [death]), at 90 days. Secondary outcomes included a modified Rankin scale score of 0 to 2, distribution across the modified Rankin scale score categories, and quality of life. Safety outcomes included symptomatic intracranial hemorrhage at 24 to 72 hours, 90-day mortality, and procedural complications.Of the 507 patients who underwent screening, 340 were in the intention-to-treat population, with 226 assigned to the thrombectomy group and 114 to the control group. Intravenous thrombolysis was used in 31% of the patients in the thrombectomy group and in 34% of those in the control group. Good functional status at 90 days occurred in 104 patients (46%) in the thrombectomy group and in 26 (23%) in the control group (adjusted rate ratio, 2.06; 95% confidence interval [CI], 1.46 to 2.91, P<0.001). Symptomatic intracranial hemorrhage occurred in 12 patients (5%) in the thrombectomy group and in none in the control group. Results for the secondary clinical and imaging outcomes were generally in the same direction as those for the primary outcome. Mortality at 90 days was 37% in the thrombectomy group and 55% in the control group (adjusted risk ratio, 0.66; 95% CI, 0.52 to 0.82). Procedural complications occurred in 14% of the patients in the thrombectomy group, including one death due to arterial perforation.In a trial involving Chinese patients with basilar-artery occlusion, approximately one third of whom received intravenous thrombolysis, endovascular thrombectomy within 12 hours after stroke onset led to better functional outcomes at 90 days than best medical care but was associated with procedural complications and intracerebral hemorrhage. (Funded by the Program for Innovative Research Team of the First Affiliated Hospital of USTC and others; ATTENTION ClinicalTrials.gov number, NCT04751708.).

Adaptive networks are suitable for decentralized inference tasks. Recent works have intensively studied distributed optimization problems in the case where the nodes have to estimate a single optimum parameter vector collaboratively. However, there are many important applications that are multitask-oriented in the sense that there are multiple optimum parameter vectors to be inferred simultaneously, in a collaborative manner, over the area covered by the network. In this paper, we employ diffusion strategies to develop distributed algorithms that address multitask problems by minimizing an appropriate mean-square error criterion with l2-regularization. The stability and performance of the algorithm in the mean and mean-square error sense are analyzed. Simulations are conducted to verify the theoretical findings, and to illustrate how the distributed strategy can be used in several useful applications related to target localization and hyperspectral data unmixing.

Bi, a high atom number element, has a high photoelectric absorption coefficient, and Se element has anticancer activity. Hence, their compound chalcogenide (Bi2Se3) deserves a thorough investigation for biomedical applications. This study reveals that Bi2Se3 nanoplates (54 nm wide) protected with poly(vinylpyrollidone) (PVP) are biocompatible and have low toxicity even at a high dose of 20 mg/kg in mice. This conclusion was made through the studies on the biodistribution and 90-day long term in vivo clearance of the nanoplates. Liver and spleen were dominant organs for the nanoplates accumulation which was mainly due to RES absorption, but 93 % the nanoplates were cleared after 90 days treatment. Concentrations of Bi and Se in tumor tissue continuously increased until 72 h after intraperitoneal injection into mice. Such selective accumulation of Bi was utilized to enhance the contrast of X-ray CT images. The Bi elements concentrated in a tumor led to damage on the tumor cells when exposed to gamma radiation. Growth of the tumor significantly delayed and stopped after 16 days after the tumor was treated with the Bi2Se3 nanoplates and radiation. This work clearly shows that the Bi2Se3 nanoplates may be used for cancer radiation therapy and CT imaging. They deserve further studies for biological and medical applications.

This paper presents a method for identifying a set of dense subgraphs of a given sparse graph. Within the main applications of this “dense subgraph problem,” the dense subgraphs are interpreted as communities, as in, e.g., social networks. The problem of identifying dense subgraphs helps analyze graph structures and complex networks and it is known to be challenging. It bears some similarities with the problem of reordering/blocking matrices in sparse matrix techniques. We exploit this link and adapt the idea of recognizing matrix column similarities, in order to compute a partial clustering of the vertices in a graph, where each cluster represents a dense subgraph. In contrast to existing subgraph extraction techniques which are based on a complete clustering of the graph nodes, the proposed algorithm takes into account the fact that not every participating node in the network needs to belong to a community. Another advantage is that the method does not require to specify the number of clusters; this number is usually not known in advance and is difficult to estimate. The computational process is very efficient, and the effectiveness of the proposed method is demonstrated in a few real-life examples.

Thermal rectification is a fundamental phenomenon for active heat flow control. Significant thermal rectification is expected to exist in the asymmetric nanostructures, such as nanowires and thin films. As a one-atom-thick membrane, graphene has attracted much attention for realizing thermal rectification as shown by many molecular dynamics simulations. Here, we experimentally demonstrate thermal rectification in various asymmetric monolayer graphene nanostructures. A large thermal rectification factor of 26% is achieved in a defect-engineered monolayer graphene with nanopores on one side. A thermal rectification factor of 10% is achieved in a pristine monolayer graphene with nanoparticles deposited on one side or with a tapered width. The results indicate that the monolayer graphene has great potential to be used for designing high-performance thermal rectifiers for heat flow control and energy harvesting.

SARS-CoV-2 infection is benign in most individuals but, in around 10% of cases, it triggers hypoxaemic COVID-19 pneumonia, which leads to critical illness in around 3% of cases. The ensuing risk of death (approximately 1% across age and gender) doubles every five years from childhood onwards and is around 1.5 times greater in men than in women. Here we review the molecular and cellular determinants of critical COVID-19 pneumonia. Inborn errors of type I interferons (IFNs), including autosomal TLR3 and X-chromosome-linked TLR7 deficiencies, are found in around 1-5% of patients with critical pneumonia under 60 years old, and a lower proportion in older patients. Pre-existing auto-antibodies neutralizing IFNα, IFNβ and/or IFNω, which are more common in men than in women, are found in approximately 15-20% of patients with critical pneumonia over 70 years old, and a lower proportion in younger patients. Thus, at least 15% of cases of critical COVID-19 pneumonia can be explained. The TLR3- and TLR7-dependent production of type I IFNs by respiratory epithelial cells and plasmacytoid dendritic cells, respectively, is essential for host defence against SARS-CoV-2. In ways that can depend on age and sex, insufficient type I IFN immunity in the respiratory tract during the first few days of infection may account for the spread of the virus, leading to pulmonary and systemic inflammation.

Of the two cultivated species of allopolyploid cotton, Gossypium barbadense produces extra-long fibers for the production of superior textiles. We sequenced its genome (AD)2 and performed a comparative analysis. We identified three bursts of retrotransposons from 20 million years ago (Mya) and a genome-wide uneven pseudogenization peak at 11-20 Mya, which likely contributed to genomic divergences. Among the 2,483 genes preferentially expressed in fiber, a cell elongation regulator, PRE1, is strikingly At biased and fiber specific, echoing the A-genome origin of spinnable fiber. The expansion of the PRE members implies a genetic factor that underlies fiber elongation. Mature cotton fiber consists of nearly pure cellulose. G. barbadense and G. hirsutum contain 29 and 30 cellulose synthase (CesA) genes, respectively; whereas most of these genes (>25) are expressed in fiber, genes for secondary cell wall biosynthesis exhibited a delayed and higher degree of up-regulation in G. barbadense compared with G. hirsutum, conferring an extended elongation stage and highly active secondary wall deposition during extra-long fiber development. The rapid diversification of sesquiterpene synthase genes in the gossypol pathway exemplifies the chemical diversity of lineage-specific secondary metabolites. The G. barbadense genome advances our understanding of allopolyploidy, which will help improve cotton fiber quality.

The small intestine and pancreas are among the most frequent abdominal sites of origin of neuroendocrine tumours. Distinctive features of these forms are represented by the relatively low incidence and the wide heterogeneity in biological behaviour. In this light, it is difficult to standardize indications for surgery and the most appropriate approach. It would be helpful for surgeons managing patients with these tumours to have guidelines for surgical treatment of small intestinal neuroendocrine tumours and pancreatic neuroendocrine tumours. The proposed guidelines represent a consensus of the working group of the European Neuroendocrine Tumor Society (ENETS).

Domain adaptive person re-identification (re-ID) is a challenging task, especially when person identities in target domains are unknown. Existing methods attempt to address this challenge by transferring image styles or aligning feature distributions across domains, whereas the rich unlabeled samples in target domains are not sufficiently exploited. This paper presents a novel augmented discriminative clustering (AD-Cluster) technique that estimates and augments person clusters in target domains and enforces the discrimination ability of re-ID models with the augmented clusters. AD-Cluster is trained by iterative density-based clustering, adaptive sample augmentation, and discriminative feature learning. It learns an image generator and a feature encoder which aim to maximize the intra-cluster diversity in the sample space and minimize the intra-cluster distance in the feature space in an adversarial min-max manner. Finally, AD-Cluster increases the diversity of sample clusters and improves the discrimination capability of re-ID models greatly. Extensive experiments over Market-1501 and DukeMTMC-reID show that AD-Cluster outperforms the state-of-the-art with large margins.

Defect passivation and surface modification of hybrid perovskite films are essential to achieving high power conversion efficiency (PCE) and stable perovskite photovoltaics. Here, we demonstrate a facile strategy that combines high PCE with high stability in CH3NH3PbI3 (MAPbI3) solar cells. The strategy utilizes inorganic perovskite quantum dots (QDs) to distribute elemental dopants uniformly across the MAPbI3 film and attach ligands to the film’s surface. Compared with pristine MAPbI3 films, MAPbI3 films processed with QDs show a reduction in tail states, smaller trap-state density, and an increase in carrier recombination lifetime. This strategy results in reduced voltage losses and an improvement in PCE from 18.3% to 21.5%, which is among the highest efficiencies for MAPbI3 devices. Ligands introduced with the aid of the QDs render the perovskite film’s surface hydrophobic—inhibiting moisture penetration. The devices maintain 80% of their initial PCE under 1-sun continuous illumination for 500 h and show improved thermal stability.

Abstract Afterglow imaging with long-lasting luminescence after cessation of light excitation provides opportunities for ultrasensitive molecular imaging; however, the lack of biologically compatible afterglow agents has impeded exploitation in clinical settings. This study presents a generic approach to transforming ordinary optical agents (including fluorescent polymers, dyes, and inorganic semiconductors) into afterglow luminescent nanoparticles (ALNPs). This approach integrates a cascade photoreaction into a single-particle entity, enabling ALNPs to chemically store photoenergy and spontaneously decay it in an energy-relay process. Not only can the afterglow profiles of ALNPs be finetuned to afford emission from visible to near-infrared (NIR) region, but also their intensities can be predicted by a mathematical model. The representative NIR ALNPs permit rapid detection of tumors in living mice with a signal-to-background ratio that is more than three orders of magnitude higher than that of NIR fluorescence. The biodegradability of the ALNPs further heightens their potential for ultrasensitive in vivo imaging.

International Journal of Robust and Nonlinear ControlVolume 27, Issue 1 p. 3-14 Research Article Bipartite consensus of multi-agent systems over signed graphs: State feedback and output feedback control approaches Hongwei Zhang, Corresponding Author Hongwei Zhang hwzhang@swjtu.edu.cn School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan, 610031 China Correspondence to: H. Zhang, School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan 610031, China. E-mail: hwzhang@swjtu.edu.cnSearch for more papers by this authorJie Chen, Jie Chen Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong KongSearch for more papers by this author Hongwei Zhang, Corresponding Author Hongwei Zhang hwzhang@swjtu.edu.cn School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan, 610031 China Correspondence to: H. Zhang, School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan 610031, China. E-mail: hwzhang@swjtu.edu.cnSearch for more papers by this authorJie Chen, Jie Chen Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong KongSearch for more papers by this author First published: 14 April 2016 https://doi.org/10.1002/rnc.3552Citations: 195Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Summary This paper studies bipartite consensus problems for continuous-time multi-agent system over signed directed graphs. We consider general linear agents and design both state feedback and dynamic output feedback control laws for the agents to achieve bipartite consensus. Via establishing an equivalence between bipartite consensus problems and the conventional consensus problems under both state feedback and output feedback control approaches, we make direct application of existing state feedback and output feedback consensus algorithms to solve bipartite consensus problems. Moreover, we propose a systematical approach to design bipartite consensus control laws. Copyright © 2016 John Wiley & Sons, Ltd. Citing Literature Volume27, Issue110 January 2017Pages 3-14 RelatedInformation

Wearable human-machine interfaces (HMIs) are an important class of devices that enable human and machine interaction and teaming. Recent advances in electronics, materials, and mechanical designs have offered avenues toward wearable HMI devices. However, existing wearable HMI devices are uncomfortable to use and restrict the human body's motion, show slow response times, or are challenging to realize with multiple functions. Here, we report sol-gel-on-polymer-processed indium zinc oxide semiconductor nanomembrane-based ultrathin stretchable electronics with advantages of multifunctionality, simple manufacturing, imperceptible wearing, and robust interfacing. Multifunctional wearable HMI devices range from resistive random-access memory for data storage to field-effect transistors for interfacing and switching circuits, to various sensors for health and body motion sensing, and to microheaters for temperature delivery. The HMI devices can be not only seamlessly worn by humans but also implemented as prosthetic skin for robotics, which offer intelligent feedback, resulting in a closed-loop HMI system.

Semiconducting photocatalytic solar-hydrogen conversion (SHC) from water is a great challenge for renewable fuel production. Organic semiconductors hold great promise for SHC in an economical and environmentally benign manner. However, organic semiconductors available for SHC are scarce and less efficient than most inorganic ones, largely due to their intrinsic Frenkel excitons with high binding energy. In this study the authors report polymer heterojunction (PHJ) photocatalysts consisting of polyfluorene family polymers and graphitic carbon nitride (g-C3 N4 ) for efficient SHC. A molecular design strategy is executed to further promote the exciton dissociation or light harvesting ability of these PHJs via alternative approaches. It is revealed that copolymerizing electron-donating carbazole unit into the poly(9,9-dioctylfluorene) backbone promotes exciton dissociation within the poly(N-decanyl-2,7-carbazole-alt-9,9-dioctylfluorene) (PCzF)/g-C3 N4 PHJ, achieving an enhanced apparent quantum yield (AQY) of 27% at 440 nm over PCzF/g-C3 N4 . Alternatively, copolymerizing electron-accepting benzothiadiazole unit extended the visible light response of the obtained poly(9,9-dioctylfluorene-alt-benzothiadiazole)/g-C3 N4 PHJ, leading to an AQY of 13% at 500 nm. The present study highlights that constructing PHJs and adapting a rational molecular design of PHJs are effective strategies to exploit more of the potential of organic semiconductors for efficient solar energy conversion.

Electrochemical conversion of CO2 into fuels using electricity generated from renewable sources helps to create an artificial carbon cycle. However, the low efficiency and poor stability hinder the practical use of most conventional electrocatalysts. In this work, a 2D hierarchical Pd/SnO2 structure, ultrathin Pd nanosheets partially capped by SnO2 nanoparticles, is designed to enable multi-electron transfer for selective electroreduction of CO2 into CH3 OH. Such a structure design not only enhances the adsorption of CO2 on SnO2 , but also weakens the binding strength of CO on Pd due to the as-built Pd-O-Sn interfaces, which is demonstrated to be critical to improve the electrocatalytic selectivity and stability of Pd catalysts. This work provides a new strategy to improve electrochemical performance of metal-based catalysts by creating metal oxide interfaces for selective electroreduction of CO2 .

The reduced dimensionality makes low-dimensional nanomaterials possessing diverse unusual size-dependent transport properties, due to the distinct quantum confinement, surface and interfacial scatterings for electron, photon and phonon at the nanoscale. In this review, we summarize the state-of-the-art studies on the topic of size-dependent phononic thermal transport in low-dimensional nanomaterials, including both theoretical and experimental reports. First, the length-dependent thermal transport in quasi-one-dimensional (quasi-1D) and two-dimensional (2D) nanomaterials are discussed, in which the underlying fundamental physics are correspondingly summarized. Then, we review the various effects of transverse dimensions on the thermal conductivity, including the diameter effect in nanowires, and the thickness and width effects in 2D sheets and nanoribbons. Finally, considering the significant importance of interfacial thermal resistance in nanoscale devices due to the increased density of interface, the size effect on the interfacial thermal resistance and thermal rectification is also discussed. The basic concept of phononic engineering to control the interfacial thermal resistance and also the detailed phonon scattering mechanisms are summarized. This perspective review would provide basic and advanced knowledge to understand and utilize the size-dependent thermal transport in nanomaterials, which will be beneficial to the further understanding of energy transport and conversion in the low-dimensional quantum devices.

Radiotherapy is often the most straightforward first line cancer treatment for solid tumors. While it is highly effective against tumors, there is also collateral damage to healthy proximal tissues especially with high doses. The use of radiosensitizers is an effective way to boost the killing efficacy of radiotherapy against the tumor while drastically limiting the received dose and reducing the possible damage to normal tissues. Here, we report the design and application of a good radiosensitizer by using ultrasmall gold nanoclusters with a naturally occurring peptide (e.g., glutathione or GSH) as the protecting shell. The GSH coated gold nanoclusters can escape the RES absorption, leading to a good tumor uptake (8.1% ID/g at 24 h post injection). As a result, the as-designed Au nanoclusters led to a strong enhancement for radiotherapy, as well as a negligible damage to normal tissues. After the treatment, the ultrasmall gold nanoclusters can be efficiently cleared by the kidney, thereby avoiding potential long term side effects caused by the accumulation of gold atoms in the body. Our data suggest that the ultrasmall peptide protected Au nanoclusters are a promising radiosensitizer for cancer radiotherapy.

Gene therapy is a promising strategy for specific treatment of numerous gene-associated human diseases by intentionally altering the gene expression in pathological cells. A successful clinical application of gene-based therapy depends on an efficient gene delivery system. Many efforts have been attempted to improve the safety and efficiency of gene-based therapies. Nanoparticles have been proved to be the most promising vehicles for clinical gene therapy due to their tunable size, shape, surface, and biological behaviors. In this review, the clinical development of nanoparticles for gene delivery will be particularly highlighted. Several promising candidates, which are closest to clinical applications, will be briefly reviewed. Then, the recent developments of nanoparticles for clinical gene therapy will be identified and summarized. Finally, the development of nanoparticles for clinical gene delivery in future will be prospected. Gene therapy is a promising strategy for specific treatment of numerous gene-associated human diseases by intentionally altering the gene expression in pathological cells. A successful clinical application of gene-based therapy depends on an efficient gene delivery system. Many efforts have been attempted to improve the safety and efficiency of gene-based therapies. Nanoparticles have been proved to be the most promising vehicles for clinical gene therapy due to their tunable size, shape, surface, and biological behaviors. In this review, the clinical development of nanoparticles for gene delivery will be particularly highlighted. Several promising candidates, which are closest to clinical applications, will be briefly reviewed. Then, the recent developments of nanoparticles for clinical gene therapy will be identified and summarized. Finally, the development of nanoparticles for clinical gene delivery in future will be prospected.

Abstract Atomically thin 2D carbon nitride sheets (CNS) are promising materials for photocatalytic applications due to their large surface area and very short charge‐carrier diffusion distance from the bulk to the surface. However, compared to their bulk counterpart, CNS' applications always suffer from an enlarged bandgap and thus narrowed solar absorption range. Here, an approach to significantly increase solar absorption of the atomically thin CNS via fluorination followed by thermal defluorination is reported. This approach can greatly increase the visible‐light absorption of CNS by extending the absorption edge up to 578 nm. The modulated CNS loaded with Pt cocatalyst as a photocatalyst shows a superior photocatalytic hydrogen production activity under visible‐light irradiation to Pt‐CNS. Combining experimental characterization with theoretical calculations shows that this approach can introduce cyano groups into the framework of CNS as well as the accompanied nitrogen vacancies at the edges, which leads to both narrowing the bandgap and changing the charge distribution. This study will provide an effective strategy to increase solar absorption of carbon‐nitride‐based photocatalysts for solar energy conversion applications.

Light-matter interactions are ubiquitous, and underpin a wide range of basic research fields and applied technologies. Although optical interactions have been intensively studied, their microscopic details are often poorly understood and have so far not been directly measurable. X-ray and optical wave mixing was proposed nearly half a century ago as an atomic-scale probe of optical interactions but has not yet been observed owing to a lack of sufficiently intense X-ray sources. Here we use an X-ray laser to demonstrate X-ray and optical sum-frequency generation. The underlying nonlinearity is a reciprocal-space probe of the optically induced charges and associated microscopic fields that arise in an illuminated material. To within the experimental errors, the measured efficiency is consistent with first-principles calculations of microscopic optical polarization in diamond. The ability to probe optical interactions on the atomic scale offers new opportunities in both basic and applied areas of science.