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Activated red mud (RM) has been proved to be a promising base material for the selective catalysis reduction (SCR) of NOx. The inherent low reducibility and acidity limited its low–temperature activity. In this work, molybdenum oxide, tungsten oxide, and cerium oxide were used to reconfigure the redox sites and acid sites of red mud based catalyst. When activated red mud was reconfigured by cerium-tungsten oxide (Ce–W@RM), the NOx conversion kept above 90% at 219–480 °C. The existence of Ce3+/Ce4+ redox electron pairs provided more surface adsorbed oxygen (Oα) and served as a redox cycle. Positive interactions between Ce, W species and Fe oxide in red mud occurred, which led to the formation of unsaturated chemical bond and promoted the activation of adsorbed NH3 species. WO3 and Ce2(WO4)3 (formed by solid–state reaction between Ce and W species) could provide more Bronsted acid sites (W–O modes of WO3, W O or W–O–W modes of Ce2(WO4)3). CeO2 species could provide more Lewis acid sites. The Langmuir–Hinshelwood (L-H) routes and Eley-Rideal (E-R) routes occurred in the low-temperature SCR reaction on the Ce–W@RM surface. NH4+ species on Bronsted acid sites, NH3 species on Lewis acid sites, bidentate nitrate and bridging nitrate species were key active intermediates species.

The rapid growth of GPS technology and mobile devices has led to a massive accumulation of location data, bringing considerable benefits to individuals and society. One of the major usages of such data is travel time prediction, a typical service provided by GPS navigation devices and apps. Meanwhile, the constant collection and analysis of the individual location data also pose unprecedented privacy threats. We leverage the notion of geo-indistinguishability, an extension of differential privacy to the location privacy setting, and propose a procedure for privacy-preserving travel time prediction without collecting actual individual GPS trace data. We propose new concepts to examine the impact of geo-indistinguishability-based sanitization on the usefulness of GPS traces and provide analytical and experimental utility analysis for privacy-preserving travel time prediction. We also propose new metrics to measure the adversary error in learning individual GPS traces from the collected sanitized data. Our experiment results suggest that the proposed procedure provides travel time prediction with satisfactory accuracy at reasonably small privacy costs.

Network embedding aims to learn the low-dimensional node representations for networks, which has attracted an increasing amount of attention in recent years. Most existing efforts in this field attempt to embed the network based on node similarity, which generally relies on edge existence statistics of the network. Instead of relying on the global edge existence statistics for every node pair, in this article, we utilize the information between a pair of nodes in a local way and propose a model, called node pair information preserving network embedding (NINE), based on adversarial networks. The main idea lies in preserving the node pair information (NI) by means of adversarial networks. The architecture of the proposed NINE model consists of three main components, namely: 1) NI embedder; 2) NI generator; and 3) NI discriminator. In the NI embedder, to avoid the complicated similarity calculation for a pair of nodes, the original NI vector calculated from the direct neighbor information of the two nodes is adopted as features, and the edge existence information is taken as labels to learn the embedded NI vector in a supervised learning manner. The second component is the NI generator, which takes the original node representation vectors of a node pair as input and outputs the generated NI vector. In order to make the generated NI vector follow the same distribution of the corresponding embedded NI vector, the generative adversarial network (GAN) is adopted, resulting in the third component, called the NI discriminator. Extensive experiments are conducted on seven real-world datasets in three downstream tasks, namely: 1) network reconstruction; 2) link prediction; and 3) node classification. Comparison results with seven state-of-the-art models demonstrate the effectiveness, efficiency, and rationality of our model.

In order to satisfy the machining requirements of aero-engine casing in modern aviation industry, this paper investigates three main issues during the design and development process of a five-axis machine tool with high accuracy, stiffness and efficiency, including whole structure design, key components design, and supporting stiffness design. First, an appropriate structure of five-axis machine tool is determined considering the processing characteristics of aero-engine casing. Then, a dual drive swing head and a compact motorized spindle are designed with enough drive capability and stiffness, and related structure, assembly method, cooling technology, and performance simulation are given in detail. Next, a design method of supporting stiffness of guide is proposed through the deformation prediction of the spindle end. Based on above work, a prototype of machine tool is developed, and some experiments are carried out, including performance tests of swing head and motorized spindle, and machining of a simulated workpiece of aero-engine casing. All experimental results show that the machine tool has satisfactory accuracy, stiffness and efficiency, which meets the machining requirements of aero-engine casing. The main work can be used as references for engineers and technicians, which are meaningful in practice.

SrLi2Ti6O14 (SLTO) coated with different amount of ZrO2 was successfully prepared. The as-obtained composites are stacked by a series of particles with a pure phase structure and a good crystallinity. Furthermore, ZrO2 coating not only enhances the structural stability of the materials but also facilitates the diffusion of lithium through the SEI film. As a result, the redox polarization was reduced, and the reversibility of the electrochemical reaction was enhanced. Particularly, SLTO-ZrO2-2 sample delivers a high initial lithiation capacity of 283.6 mA h g−1, and the values maintain at 251.7, 228.0, 207.4, 175.3, and 147.7 mA h g−1 at the current densities of 0.13, 0.26, 0.54, 1.31, and 2.62 A g−1, respectively. Our experiment also confirmed that SLTO materials coated with ZrO2 are suitable for high power density applications, and the lithiation specific energy efficiency of SLTO-ZrO2-2 is 200% as high as that of pure SLTO at a power density of 1257 W kg−1.

Modular Solar-Powered Aircraft (M-SPA) is a kind of High-Altitude Long-Endurance (HALE) aircraft which exploits the mission advantage of swarm UAV and the HALE advantage of large aspect-ratio SPA. M-SPA’s separated mode and combined mode give it the potential to maximize the mission efficiency with limited solar energy. In this paper, firstly, oriented by the mission of maximizing the cruise area, the overall design of the M-SPA is modeled, including the energy model, the aerodynamic model and the flight environment settings. Secondly, by analyzing the energy consumption of the flight modes, we design a multi-phase flight mission strategy. Then, a 24-hour three-dimensional (3D) flight profile of the M-SPA is optimized, including the sub-SPA cooperative path planning in the separation mode. Finally, inspired by the Traveling Salesman Problem (TSP), an improved Ant Colony Algorithm (ACA) is exploited to find the optimal path for each sub-SPA, which is further developed into a dynamic separation and combination scheme for the M-SPA. The simulation results show that the mission performance of the M-SPA outperforms that of the conventional SPA, and explicitly, the mission coverage of the M-SPA is slightly less than a linear increase under comparable simulation conditions.

The drastically increasing amount of plastic waste is causing an environmental crisis that requires innovative technologies for recycling post-consumer plastics to achieve waste valorization while meeting environmental quality goals. Biocatalytic depolymerization mediated by enzymes has emerged as an efficient and sustainable alternative for plastic treatment and recycling. A variety of plastic-degrading enzymes have been discovered from microbial sources. Meanwhile, protein engineering has been exploited to modify and optimize plastic-degrading enzymes. This review highlights the recent trends and up-to-date advances in mining novel plastic-degrading enzymes through state-of-the-art omics-based techniques and improving the enzyme catalytic efficiency and stability via various protein engineering strategies. Future research prospects and challenges are also discussed.

Prognostics and health management aims to use on-line sensor data to monitor and predict current and future health conditions of degraded systems and components. Nowadays, constructing composite health indices for characterizing current health conditions of a system from multiple degradation-based sensor data has attracted much attention. These kinds of ``data-level'' models show strong ability to provide a better degradation characterization of a degraded system than a model solely depending on data from an individual sensor. Although numerous efforts have been made to propose ``data-level'' fusion methodologies for process data, such as temperature, pressure, speeds, etc., little research has targeted ``data-level'' fusion models for nonprocess data, such as vibration and acoustic signals. In this article, a methodology for constructing a composite health index from fusion of spectrum amplitudes is proposed. Here, each spectrum amplitude can be regarded as ``an individual sensor.'' The goal of this article is that a composite health index generated from fusion of spectrum amplitudes can simultaneously detect incipient faults and provide a monotonically increasing trend for degradation assessment. Our proposed methodology was verified by two illustrative examples including gearbox run-to-failure vibration data and bearing run-to-failure vibration data. Results showed that our proposed methodology is better than popular sparse measures for gear and bearing health monitoring and degradation assessment.

Long-term evolution advanced (LTE-A) heterogeneous networks have been observed to offer reliable and service-differentiated communication, thereby enabling numerous mobile applications such as smart meters, remote sensors, and vehicular applications. This fact envisions the trend of Internet of things (IoT) underlaying heterogeneous small cell networks. On this basis, this paper proposes an energy-efficient framework for such a scenario, where multitier heterogeneous small cell networks provide wireless connection and seamless coverage for mobile users and IoT nodes. In our proposed framework, an elastic cell-zooming algorithm based on the quality of service and traffic loads of end-users is performed by adaptively adjusting the transmission power of small cells in order to reduce energy consumption. In addition, aiming at the high energy efficiency of IoT underlaying small cell networks, a clustering-based IoT structure is used, where a SWIPT-CH selection algorithm is proposed to maximize the average residual energy of IoT nodes and to mitigate resource competition between IoT nodes and mobile users.

The proliferation of online Location-Based Social Networks (LBSN) has offered unprecedented opportunities for understanding fine-grained spatio-temporal behaviors of users and developing new location-aware applications. In this article, we focus on the problem of “Sparse User Check-in Venue Prediction,” where the goal is to predict the next venue LBSN users will visit by exploiting their sparse online check-in traces and the latent decision contexts. While efforts have been made to predict users’ check-in traces on a LBSN, several important challenges still exist. First, check-in traces contributed by LBSN users are often too sparse to provide sufficient evidence for a reliable prediction, especially when the prediction space is huge (e.g., hundreds of thousands of venues in large cities). Second, the user's decision context on which venue to visit next is often latent and has not been incorporated by current venue prediction models. Third, the dynamic and non-deterministic dependency between check-ins is either ignored or replaced by a simplified “consecutiveness” assumption in existing solutions, leading to sub-optimal prediction results. In this article, we develop a Context-aware Sparse Check-in Venue Prediction (CSCVP) scheme inspired by natural language processing techniques to address the above challenges. In particular, CSCVP predicts the venue category information and explores the similarity between users to address data sparsity challenge by significantly reducing the prediction space. It also leverages the Probabilistic Latent Semantic Analysis (PLSA) model to incorporate the user decision context into the prediction model. Finally, we develop a novel Temporal Adaptive Ngram (TA-Ngram) model in CSCVP to capture the dynamic and non-deterministic dependency between check-ins. We evaluate CSCVP using three real-world LBSN datasets. The results show that our scheme significantly improves accuracy (30.9 percent improvement) of the state-of-the-art user check-in venue prediction solutions.

In order to solve the problems of non-uniform gas concentration distribution and serious flooding in the parallel flow field of traditional PEMFC, a stepped flow field of PEMFC bipolar plate is proposed in this paper. The gas concentration distribution, velocity distribution, liquid water distribution and current density distribution of traditional parallel flow field and stepped flow fields are studied by computational fluid dynamics (CFD) simulations. The results suggest that the fuel cells with stepped flow field are superior than the traditional fuel cell with parallel flow field. Compared with fuel cell with parallel flow field, the net power of fuel cell with stepped flow field is increased by 21.5%. Moreover, the stepped flow fields can improve the uniformity of gas concentration and current density distribution, accelerate the discharge of liquid water, alleviate water flooding phenomenon.

The fatigue damage behavior of the nanocrystalline Au films on polyimide substrates was investigated. It was found that the very high-cycle fatigue damage resistance of the Au film was significantly enhanced by at least a factor of ∼2 in supported loading through adding an ultrathin Ti interlayer at the Au film/polyimide interface. Such a better fatigue damage resistance is mainly ascribed to the effective suppression of voiding at the Au film/polyimide interface through modulation of the Au/Ti interface, and thus the propensity of the cyclic strain localization and grain boundary cracking is reduced. The finding may provide a potential strategy for the design of flexible devices with ultra-long fatigue life.

Journal of materials science & technology : JMST 85, 30-43 (2021). doi:10.1016/j.jmst.2020.12.069, Published by Elsevier, Amsterdam

We report a new injectable and biodegradable self-healing hydrogel that shows enhanced anticancer drug release property. The hydrogel was prepared based on biodegradable pectin aldehyde (pectin-CHO) and acylhydrazide functionalized polymer poly(N-isopropylacrylamide-stat-acylhydrazide) P(NIPAM-stat-AH). Due to the dynamic nature of acylhydrazone bonds, the hydrogel exhibits self-healing behavior and its mechanical properties can be regulated by the weight ratio of P(NIPAM-stat-AH) to pectin-CHO. The in vitro and in vivo experiments show the hydrogel has not only good biocompatibility and biodegradability, but also decreases the toxicity of the drugs to living body and exhibits controlled drug release behavior as synergetic anti-tumor drug delivery carriers. The results demonstrate that the pectin-based self-healing hydrogels are injectable, biodegradable, and self-healable that is promising for localized anti-tumor therapy. STATEMENT OF SIGNIFICANCE: Injectable hydrogels with self-healing property and biodegradability are excellent candidates as drug loading and release carrier for biomedical applications. However the pectin as a biobased material is always neglected in self-healing hydrogel preparation. In this study, we fabricated biodegradable self-healing hydrogels from aldehyde group bearing pectin (pectin-CHO) and thermo-responsive copolymer of P(NIPAM-stat-AH). The hydrogel showed sustained drug release behavior, good biocompatibility and biodegradability both in vitro and in vivo. The in vivo experiment shows that the hydrogel with coloaded DOX and CA4 has synergetic therapy to CT26 tumors and this kind of biodegradable hydrogel has great potential application in antitumor therapy.

Summary The catalytic hydrogenation of CO2 with renewable H2 is considered as one of the most practical approaches for the reduction of CO2 and the synthesis of valuable chemicals and fuels. Among the value-added products, olefins and aromatics are highly attractive as essential industrial feedstocks. CO2 hydrogenation to olefins and aromatics mainly undergoes a CO2-modified Fischer-Tropsch synthesis (CO2-FTS) route or a methanol-mediated (MeOH) route, but is often limited by low selectivity and catalyst deactivation. In this review, we first overview the reaction mechanisms and key intermediates along the CO2-FTS and MeOH routes. For each route, we summarize the recent progress toward the catalyst development as well as the optimization of reaction conditions. We conclude the review by identifying challenges and opportunities with an emphasis on approaches for exceeding the product distribution limit, suppressing the catalyst deactivation, and improving the feasibility of low-pressure operation by continuous consumption of key intermediates.

Complexing self-assembled DNA nanostructures with various functional guest species is the key to unlocking new and exciting biomedical applications. Cationic guest species not only induce magnesium-free DNA to self-assemble into defined structures but also endow the final complex nanomaterials with new properties. Herein, we propose a novel strategy that employs naturally occurring cationic amino acids to induce DNA self-assembly into defined nanostructures. Natural l-arginine and l-lysine can readily induce the assembly of tile-based DNA nanotubes and DNA origami sheets in a magnesium-free manner. The self-assembly processes are demonstrated to be pH- and concentration-dependent and are achieved at constant temperatures. Moreover, the assembled DNA/amino acid complex nanomaterials are stable at a physiological temperature of 37 °C. Substituting l-arginine with its D form enhances its serum stability. Further preliminary examination of this complex nanomaterial platform for biomedical applications indicates that DNA/amino acids exhibit distinct cellular uptake behaviors compared with their magnesium-assembled counterparts. The nanomaterial mainly clusters around the cell membrane and might be utilized to manipulate molecular events on the membrane. Our study suggests that the properties of DNA nanostructures can be tuned by complexing them with customized guest molecules for a designed application. The strategy proposed herein might be promising to advance the biomedical applications of DNA nanostructures., Graphical abstract Image 1, Highlights • A “noncanonical DNA self-assembly” strategy is proposed. • Cationic amino acids can assemble DNA nanostructures in a magnesium-free system. • The complex nanomaterial exhibited high structural and serum stability. • DNA nanostructures can be engineered with customized guest molecules for multiple applications.

In present study, sodium carbonate (Na2CO3) and ammonium carbonate ((NH4)2CO3) are separately selected as precipitant to synthesize the strontium oxide (SrO) based solid catalyst, namely, SrO–M/Na and SrO–M/NH4 for biodiesel production through transesterification. The influence of precipitant on catalyst physicochemical property, catalytic activity as well as reusability are investigated and compared. To begin with, transesterification parameters of catalyst amount, molar ratio of methanol to oil, reaction time, and reaction temperature are concerned to comprehensively evaluate catalytic performance of the two catalysts. Moreover, catalyst reusability is evaluated under the optimal conditions. With catalyst amount of 4 wt.%, molar ratio of methanol to oil of 9 at 65 ℃ within 30 min, SrO–M/Na exhibits a better catalytic activity in comparison to SrO–M/NH4, whereas, the reusability is in reverse sequence. In order to reveal the reason behind, catalysts are characterized by thermogravimetric/differential thermogravimetric/scanning calorimetry (TG/DTG/DSC), X–ray diffraction (XRD), X–ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), N2–adsorption/desorption, basic strength, basicity and vibrating sample magnetometer (VSM).

The rational design of low-cost catalysts with excellent catalytic activity is one of the most significant topics in heterogeneous catalysis. LaFeO3 (LFO) perovskite was synthesized by the citrate sol-gel method and followed through a selective dissolution method by acid solutions. The surface element percentage, chemical states and pore structure of LFO were tuned. The acid-treated catalyst (LFO-H) showed higher activity in CO oxidation than the raw LFO. In terms of various characterizations, the exposed La ions of the LFO were slightly etched by dilute nitric acid, but the LFO-H still preserved the original perovskite framework. The corresponding surface area, surface oxygen vacancies and reducibility were also improved after acid treatment, accounting for its higher catalytic activity. Moreover, the LFO-H also exhibited excellent thermal stability because the perovskite structure remains after acid treatment.

Switching-frequency vibration caused by pulsewidth modulation (PWM) is a critical shortcoming of permanent magnet synchronous motor (PMSM) drives in certain applications. This article reveals the mechanism for suppressing high-frequency vibration of multiphase surface permanent magnet (SPM) motors via PWM carrier phase shifting (CPS). The article first introduces a 12-phase SPM drive with three different modes of CPS. Then, it presents an analytical method to calculate high-frequency current harmonics as well as flux and force distributions along stator boundaries. By analyzing the calculated current harmonics and flux distributions under different CPS modes and parameters, the article discovers that CPS greatly alters the spatial distribution of high-frequency stator electromagnetic forces and, thus, significantly changes their zero-order components, which are the major contributors to high-frequency motor vibrations. Finally, the optimal CPS mode and parameter are determined analytically and are validated by experimental results.

Non-layered two-dimensional (2D) carbon complexes manifest great potential in energy-related applications owing to their exotic electronic structures, large electrochemically active surface, and intriguing synergistic effects. However, reliable method for mass production and thickness manipulation of 2D carbon complexes remains great challenges. Here, inspired by blowing chewing gum into bubbles, a “tailored gel expanding” strategy is proposed for high-yield synthesis of non-layered 2D carbon complexes with tailored thickness from ~12 nm to ~1 μm, by controllable pyrolysis of metal-polymeric gel with adequate crosslinking degree. The key feature for thickness manipulation is introducing NH4NO3 in sol–gel process, which tailors the expansion behavior of gel precursor during subsequent pyrolysis. Various of 2D sheets with intimately coupled N,O-doped carbon (NOC) and NiCo-based (NiCo, (NiCo)S2, (NiCo)Se2, NiCo2O4, (NiCo)(PO3)2) nanocrystals are obtained on a large scale and without any impurities. Moreover, these 2D products are branched with in-situ grown CNTs on the surface, accelerating electrons transfer and preventing the nanosheets from stacking. As a demonstration, the 2D (NiCo)S2/NOC with optimized thickness manifests excellent lithium storage properties in both half and full cells. This method paves a new path for massive and controlled production of non-layered 2D materials with tailored thickness and robust structure stability for energy-related applications.

Large-size flexible biomass carbon membranes have been prepared by using inexhaustible chitosan (CS) as carbon source. The microstructure of carbon membranes depends on the pyrolysis temperature, and the resulting membrane has a porous honeycomb-like architecture with N-doping content of 6.3% after the pyrolysis at 1000 °C. The flexible carbon membranes can be directly used as self-standing electrodes for various batteries, that is, with no current collectors, organic binders, or additional conductive agents. The carbon membranes as anodes for potassium-ion batteries exhibit excellent rate performance and a stable reversible capacity of 146 mAh g−1 at 2 A g−1 after 500 cycles, and its sodium-ion storage remains 236 mAh g−1 after 70 cycles. The kinetic analysis of carbon membranes in batteries shows that the surface-controlled mechanism plays a decisive role in the potassium/sodium storage because the honeycomb structure and high proportion of pyridine N-doping can improve the adsorption of K+/Na+ ions and the conductivity. In addition, the main reasons for the capacity degradation of the carbon membrane under long cycle and different current densities are discussed. The CS-derived flexible carbon membranes show a promising application as cost-effective, binder-free electrodes in flexible batteries.

Pneu-net soft actuators, consisting of pneumatic networks of small chambers embedded in elastomeric structures, are particularly promising candidates in the society of soft robotics. However, there are few studies on the analytical modeling of pneu-net soft actuators, especially in the three-dimensional space. In this article, based on the minimum potential energy method and the continuum rod theory, we propose an analytical model and corresponding design approach for a class of generalized pneu-net soft actuators (gPNSAs) with both bending and twisting deformations by combining the geometric complexity and material elasticity. We experimentally verify our modeling approach and finally investigate the effects of geometric parameters, material properties, and external force on the deformations of gPNSAs, which can be used as a tool for the design of gPNSAs. We further demonstrate that our developed model can predict the deformations of gPNSAs made of multiple materials.

Purpose The purpose of this paper is to analytically predict the on-load field distribution and electromagnetic performance (induced voltage, electromagnetic torque, winding inductances and unbalanced magnetic force) of dual-stator consequent-pole permanent magnet (DSCPPM) machines using subdomain model accounting for tooth-tip effect. The finite element (FE) results are presented to validate the accuracy of this subdomain model. Design/methodology/approach During the preliminary design and optimization of DSCPPM machines, FE method requires numerous computational resources and can be especially time-consuming. Thus, a subdomain model considering the tooth-tip effect is presented in this paper. The whole field domain is divided into four different types of sub-regions, where the analytical solutions of vector potential in each sub-region can be rapidly calculated. The proposed subdomain model can accurately predict the on-load flux density distributions and electromagnetic performance of DSCPPM machines, which is verified by FE method. Findings The radial and tangential components of flux densities in each sub-region of DSCPPM machine can be obtained according to the vector potential distribution, which is calculated based on the boundary and interface conditions using variable separation approach. The tooth-tip effect is investigated as well. Moreover, the phase-induced voltage, winding inductances, electromagnetic torque and X-axis/Y-axis components of unbalanced magnetic forces are calculated and compared by FE analysis, where excellent agreements are consistently exhibited. Originality/value The on-load field distributions and electromagnetic performance of DSCPPM machines are analytically investigated using subdomain method, which can be beneficial in the process of initial design and optimization for such DSCPPM machines.

The migration, transformation and ecological risk of heavy metals (Cr, As, Ni, Cu, Zn, Cd and Pb) in sewage sludge during the microwave-assisted thermal hydrolysis process were investigated under different temperatures (80 °C, 100 °C, 120 °C, 140 °C, and 160 °C). The potential relationship between the bio-availability of heavy metals and the variables of microwave treatment, including pH, ammonium-nitrogen, soluble chemical oxygen demand, pH, soluble protein, soluble polysaccharide and volatile solids, was also explored. The results showed that the migration of heavy metals between solid–liquid phase mainly depended on the temperature. The percentage of all heavy metals (except Cu) in mobile (acid-soluble/exchangeable and reducible) forms decreased after microwave-assisted thermal hydrolysis treatment. The solubilisation of compounds with C = O and O-H accompanied with the generation of organic and inorganic metal halides were also observed in the treated sludge through Fourier transform infrared spectroscopy analysis. NH4+-N showed the highest negative correlation to the bio-availability of most heavy metals (except Cu and Cr) with coefficients (absolute value) over 0.87 (P < 0.05). VS showed a positive correlation to the bio-availability of most heavy metals (except Cu). The total potential ecological risk index (RI) decreased by 46.65% after microwave treatment at 160 °C. HIGHLIGHTS Effect of microwave treatment on heavy metals speciation in sludge was reported.; The metal halides related to organic and inorganic compounds were observed.; NH4+-N showed the greatest correlation to the bio-availability of most heavy metals.; Environmental risk of treated sludge after microwave treatment was alleviated.

In this article, we consider the distributed optimal consensus problem under nominal and nonfragile cases for a class of minimum-phase uncertain nonlinear systems with unity-relative degree and disturbances generated by an external autonomous system. The involved cost function is the sum of all local cost functions associated with each individual agent. Two different edge-based distributed adaptive algorithms utilizing the internal model principle are designed to solve the problem in a fully distributed manner. Graph theory, nonsmooth analysis, convex analysis, and the Lyapunov theory are employed to show that the proposed algorithms converge accurately to the optimal solution of the considered problem. Finally, an example involving the dynamics of a Lorenz-type system is provided to demonstrate the effectiveness of the obtained results. This work was supported in part by the National Natural Science Foundation of China under Grant 61473055 and Grant 61773089, in part by the Fundamental Research Funds for the Central Universities under Grant DUT17ZD227, and in part by the Youth Star of Dalian Science and Technology under Grant 2015R052 and Grant 2016RQ014.

Monthly Precipitation Forecasts (MPF) play a critical role in drought monitoring, hydrological forecasting and water resources management. In this study, we applied two advanced Machine Learning Models (MLM) and latest General Circulation Models (GCM) to generate deterministic MPFs with a resolution of 0.5° across China. Then the Bayesian Joint Probability (BJP) modeling approach is employed to calibrate and generate corresponding ensemble MPFs. Raw and post-processing MPFs were put against gridded observations over the period of 1981–2015. The results indicated that: (1) for deterministic evaluation, the forecasting performance of MLMs was more inclined to generate random forecasts around the mean value, while the GCMs could reflect the increasing or decreasing trend of precipitation to some degree; (2) for probabilistic evaluation, the four BJP calibrated ensemble MPFs were unbiased and reliable. Compared to climatology, reliability and sharpness were all significantly improved. However, in terms of overall accuracy metric, the ensemble MPFs generated from MLMs were similar to climatology. In contrast, the ensemble MPFs generated from GCMs achieved better forecasting skill and were not dependent on forecasting regions and months. Moreover, the post-processing method is necessary to achieve not only bias-free but also reliable as well as skillful ensemble MPFs. HIGHLIGHTS Two advanced Machine Learning Models are employed to generate monthly precipitation forecasts with a resolution of 0.5 degree.; The latest seasonal forecasts of ECMWF are evaluated with the same forecasting grid cells and lead time.; The BJP modeling approach is used to calibrate above four raw forecasts.; A comprehensive comparison is achieved for the raw and post-processing forecasts.

Explicit method to emulate neutron radiation effects on key ceramic materials is lacking, with most literatures stressing direct comparison between radiation parameters while underestimating the defect-property correlation. Herein, the evolutionary correlation between defects of displaced atoms and properties in 6H-SiC was systematically investigated, aiming to found the basis for performance assessment of neutron irradiated material. The point defect accumulation, defect cluster growth, and homogeneous crystalline-amorphous (c-a) transition were recognized, with different stages of Young’s modulus reduction and optical absorption enhancement accordingly triggered. Specifically, the relaxation process accompanying the c-a transition was believed to induce the final reduction stage of Young’s modulus and account for the conflict between existing research results. Average lattice disorder, regarded as the average density of displaced atoms, was proved to be the success reference in the property assessment approach, paving the way for the extension into high temperature radiation where more defect types coexist.

This paper is dedicated to solving the economic dispatch problem in smart grids, which aims at minimizing the total generation cost while satisfying the power supply-demand balance and generation capacity constraints. A distributed algorithm is proposed to solve the economic dispatch problem over directed communication topologies that are time-varying unbalanced. Its features are that the proposed algorithm is push-based, and the model of line losses is integrated into the problem of our interest to reduce the power losses. Resorting to geometric graph theory and convex analysis, it is proved that the incremental costs achieve consensus at a convergence rate $O({{1 / {\sqrt {t} }}})$ with $t$ being the number of iterations. Furthermore, robustness of the proposed algorithm is investigated solving the EDP when the communication information undergoes arbitrary large but bounded constant delays and the local gradients involve observation noises with zero-mean and bounded variance. Finally, three case studies implemented on an islanded microgrid, the IEEE 30-bus system and the IEEE 118-bus system are tested to demonstrate the effectiveness of the proposed algorithm.

Our extensive first-principles calculations reveal that the chemical activities of monolayer transition metal dichalcogenides (TMDs) MX2 (M = Mo or W, and X = Te, Se, or S) for water splitting and hydrogen evolution are modified and promoted on their grain boundaries (GBs) when in-plane tensile loadings are applied. Compared with monolayer TMDs without GBs, the flexoelectricity induced by nonuniform deformation and strain gradient significantly enhances the charge polarizations of X and M atoms at the GB sites of monolayer TMDs, which facilitates the dissociation of water molecules on the GB sites and reduces the reaction barrier of hydrogen evolution reaction. The energy barriers of splitting water molecules and hydrogen adsorption free energies on the GB sites decrease with increasing the flexoelectric effect. These results highlight an attractive way of utilizing the flexoelectric effect of GB-containing TMDs to improve their surface catalytic capability for hydrogen generation.

Pressure is a unique thermodynamic variable to explore the phase competitions and novel phases inaccessible at ambient conditions. The resistive switching material GaTa4Se8 displays several quantum phases under pressure, such as a Jeff = 3/2 Mott insulator, a correlated quantum magnetic metal, and d-wave topological superconductivity, which has recently drawn considerable interest. Using high-pressure Raman spectroscopy, X-ray diffraction, extended X-ray absorption, transport measurements, and theoretical calculations, we reveal a complex phase diagram for GaTa4Se8 at pressures exceeding 50 GPa. In this previously unattained pressure regime, GaTa4Se8 ranges from a Mott insulator to a metallic phase and exhibits superconducting phases. In contrast to previous studies, we unveil a hidden correlation between the structural distortion and band gap prior to the insulator-to-metal transition, and the metallic phase shows superconductivity with structural and magnetic properties that are distinctive from the lower-pressure phase. These discoveries highlight that GaTa4Se8 is a unique material to probe novel quantum phases from a structural, metallicity, magnetism, and superconductivity perspective.

The issue of impulse noise detection and reduction is a critical problem for image processing application systems. In order to detect impulse noises in corrupted images, a statistic named local consensus index (LCI) is proposed for quantitatively evaluating how noise free a pixel is, and then an impulse noise detection scheme based on LCI is introduced. First, the similarity between arbitrary two pixels in an image is quantified based on both their geometric distance and intensity difference, and the LCI of arbitrary pixel is calculated by summing all the similarity values of pixels in its neighborhood. As a new statistic, the value of LCI indicates the local consensus of the concerned pixel regarding its neighbors and could also tell whether a pixel is noise free or impulsive. Therefore, LCI can be directly used as an efficient indicator of impulse noise. Furthermore, to improve the performance of impulse noise detection, different strategies are applied to the pixels at flat regions and the ones with complex textures, since distributions of LCI value within those regions are totally different. As for impulse noise filtering, a hybrid graph Laplacian regularization (HGLR) method is introduced to restore the intensities of those pixels degraded by impulse noise. We conduct extensive experiments to verify the effectiveness of our impulsive noise detection and reduction method, and the results show that the proposed method outperforms the state-of-the-art techniques in terms of impulse detection and noise removal.

When a penetrator with enhanced lateral effect (PELE) impacts on a reinforced concrete (RC) target, the target is damaged with a large opening. An understanding of how PELE projectile parameters affect the opening dimension, is essential for effective design of the PELE projectile. In this study, under the condition that the impact velocity and target parameters (strength and thickness) were fixed values, the important influence factors of the PELE (jacket wall thickness B, jacket material strength Y 1 , filling material strength Y 2 and angle of monolithic jacket θ) were determined by a dimensional analysis. Tests and simulations of the PELE penetrating the RC target were conducted to analyze the influence of these factors on opening diameter ( D ¯ , an equivalent diameter under relative kinetic energy). Based on the test and simulation results, it is found that the influence of these factors B, Y1 and θ on the deformation mode of the jacket shows a similar trend: as values of the three factors decrease, the jacket deforms from small bending deformation to large one, and then to curling deformation. This causes the opening diameter to first increase with the decrease of these three factors, and then decreases. It is well known that the bending resistance of the jacket is related to these factors B, Y1 and θ. Therefore, a plastic limit bending moment (M0) of the jacket was quoted to characterize the influence of these factors on the bending deformation of the jacket and the opening diameter of the target. The influence factor Y 2 causes D ¯ to first increase with the increase of Y 2 , and then decreases. A formula was developed to predict the opening diameter, whose influence parameters were considered in a dimensionless way. It has been shown that the dimensionless opening diameter D ¯ / d 1 is dependent on two dimensionless parameters Q = d 1 3 f c M 0 and G = f c Y 2 , where d 1 and f c are the outer diameter of the projectile and the compressive strength of the target, respectively.

Multi-layer network community detection has drawn an increasing amount of attention recently. Despite success, the existing methods mainly focus on the lower-order connectivity structure at the level of individual nodes and edges. And the higher-order connectivity structure has been largely ignored, which contains better signature of community compared with edges. The main challenges in utilizing higher-order structure for multi-layer network community detection are that the most representative higher-order structure may vary from one layer to another and the connectivity structure formed by the same node subset may exhibit different higher-order connectivity patterns in different layers. To this end, this paper proposes a novel higher-order structure, termed harmonic motif, which is a dense subgraph having on average the largest statistical significance in each layer. Based on the harmonic motif, a primary layer is constructed by integrating higher-order structural information from all layers. Additionally, the higher-order structural information of each individual layer is taken as the auxiliary information. A coupling is established between the primary layer and each auxiliary layer. Accordingly, a harmonic motif modularity is designed to generate the community structure. Extensive experiments on eleven real-world multi-layer network datasets have been conducted to confirm the effectiveness of the proposed method.

15 vol.% SiC/Al-6.5Zn-2.8 Mg-1.7Cu (wt%) composites with varying particle sizes (3.5, 7.0, 14 and 20 μm), i.e., C-3.5, C-7.0, C-14, and C-20, respectively, were fabricated by powder metallurgy (PM) method and subjected to microstructural examination. The effect of particle size on mechanical properties and fracture behaviors of the T6-treated composites was revealed and analyzed in detail. Element distribution and precipitates variations in the composites with varying particle sizes were emphatically considered. Results indicated that both tensile strength and plasticity of the T6-treated composites increased first and then decreased with particle size decreasing. The C-7.0 composite simultaneously exhibited the highest ultimate tensile strength (UTS) of 686 MPa and best elongation (El.) of 3.1%. The smaller-sized SiC particle would introduce more oxide impurities, which would react with the alloying element in the matrix to cause Mg segregation and depletion. According to strengthening mechanism analysis, the weakening of precipitation strengthening in the T6-treated C-3.5 composite was the main cause of the lower tensile strength. Additionally, the larger SiC particle, the more likely to fracture, especially in the composites with high yield strength. For the T6-treated C-20 composites, more than 75% SiC particles were broken up, resulting in the lowest plasticity. As decreasing particle size, the fracture behaviors of the T6-treated composites would change from particle fracture to matrix alloy fracture gradually.

Lumbar fixator has been widely used, which can stabilize the lumbar spine and improve the fusion outcomes, but also lead to many complications. The effects of the internal fixator on biomechanical properties of the fused lumbar spine have been widely concerned for many years. However, most studies only considered the static loads and did not consider the effect of the fixator on the properties of the human lumbar spine under whole-body vibration (WBV). The purpose of this study is to investigate how the fixation system affects the biomechanical characteristics of the lumbar spine, fusion outcomes, and complications under WBV based on the finite element analysis. A three-dimensional nonlinear osteoligamentous finite element model of the intact L1-sacrum spine with muscles was established. A 5-Hz, 40-N sinusoidal vertical load supplemented with a 400-N preload was applied at L1 to simulate the vibration of the human body. For the adjacent segments, the fixation system may increase the risk of the adjacent segment disease under WBV. For the fused segments, the fixation system may decrease the risk of subsidence and cage failure including fatigue failure under WBV. The fixation system may provide a more stable and suitable environment for vertebral cell growth under WBV and lead to better fusion outcomes. This study reveals insights into the effect of the fixation system on the vibration characteristics of the lumbar and provides new information on the fixation system, fusion outcomes, complications, clinical evaluation, and selection of fixation system.

In linear Fresnel collectors (LFCs), a receiver composed of CPC secondary reflector and evacuated tube need to be highly coupled with the mirror field. An optimized design method of the LFC is proposed in this paper. Firstly, a mathematical model of the CPC for the LFC is established, with the effects of the choice of half-acceptance angle and truncation ratio on the geometrical optics efficiency of the CPC studied by using the ray tracing method according to the characteristics of the LFC. Secondly, a method for designing the mirror field without shading is presented by using the half-acceptance angle of the CPC to limit its height-width ratio. Finally, a scheme for designing the mirror field is proposed, considering the ground utilization ratio and the mean shading and blocking efficiency. The results indicate that the optimized CPC at half-acceptance angle 50° and truncation ratio 0.4 for the LFC can achieve the geometrical optics efficiency of 84.52%. The designed mirror field can achieve the ground utilization ratio of 73.08% and the mean shading and blocking efficiency of 97.56%.

The microstructural evolution in Re-containing Ni-based single crystal superalloys with different Tantalum (Ta) content (2Ta, 5Ta and 8Ta in wt %) was investigated. Ta addition significantly affected the γ′ precipitate morphology, γ/γ′ lattice misfit and microstructural stability during long-term aging. Results showed that the partitioning behaviors of solutes were enhanced by Ta addition, meanwhile, the reversal partitioning behavior of W was triggered which partitioned from γ′ precipitate to γ matrix. The elemental concentration redistribution caused variations in lattice misfit from positive to negative, the values of lattice misfit were measured to be 0.16 % for 2Ta alloy, then decreased to −0.07 % for 5Ta alloy and negatively increased to −0.23 % for 8Ta alloy. These variations in the lattice misfit were reflected on the transition of γ′ morphology from round-cornered cuboidal shape to cuboidal with sharp corners, accomplished with increasing shape parameter ratio η. Consequently, the optimal γ′ shape could be obtained at lattice misfit of approximately 0.3 %. The γ′ coarsening investigation at 900 °C (up to 2000 h) indicated that Ta addition was beneficial for improving the microstructural stability by reducing the coarsening rate and interfacial energy, accompanied by the enhanced capability of resisting γ′ coalescence. By incorporating the calculated interfacial energy, computational modeling, Thermo-Calc and PrecipiCalc, were employed to elucidate the γ′ kinetic pathways, the simulation results agreed with experiments, indicating that the model and parameters were reasonable. Additionally, it was found that there was no overlap between γ′ nucleation and coarsening when the γ/γ′ interfacial energy increased to a critical value.

With the advance of next generation sequencing technologies, researchers now routinely obtain a collection of microbial sequences with complex phylogenetic relationships. It is often of interest to analyze the association between certain environmental factors and characteristics of the microbial collection. Though methods have been developed to test for association between the microbial composition with environmental factors as well as between coevolving traits, a flexible model that can provide a comprehensive picture of the relationship between microbial community characteristics and environmental variables will be tremendously beneficial. We developed a Bayesian approach for association analysis while incorporating the phylogenetic structure to account for the dependence between observations. To overcome the computational difficulty related to the phylogenetic tree, a variational algorithm was developed to evaluate the posterior distribution. As the posterior distribution can be readily obtained for parameters of interest and any derived variables, the association relationship can be examined comprehensively. With two application examples, we demonstrated that the Bayesian approach can uncover nuanced details of the microbial assemblage with regard to the environmental factor. The proposed Bayesian approach and variational algorithm can be extended for other problems involving dependence over tree-like structures.

Helical supramolecular architectures play important structural and functional roles in biological systems. Although their occurrence is widely perceived to correlate to fundamental chiral units including l-amino acids and d-sugars, the detailed relationship between molecular and supramolecular handedness is still unclear. At the same time, although achiral units are practically always in close proximity to chiral ones by covalent linkage along a polymeric chain, their effect on supramolecular handedness has received relatively less attention. Here, we designed a set of short amphiphilic peptides, in which an achiral glycine residue was incorporated at the interface between the hydrophobic and hydrophilic segments. We observed that glycine incorporation caused dramatic variations in suprastructural handedness in self-assembled peptide nanofibrils, and the effect of the hydrophilic charged residue at the C-terminus on supramolecular handedness was demolished, leading to chiral truncation. Furthermore, molecular dynamics simulations and quantum chemistry calculations revealed that the unanticipated role of the glycine residue in regulating supramolecular handedness originated from its effect on the conformational preference of single β-strands. Importantly, reduced density gradient analyses on single β-strands indicated that, due to the lack of a side chain in glycine, intricate noncovalent interactions were produced among the neighboring amino acid side chains of the incorporated glycine and its local backbone, resulting in diverse β-strand conformations.

View-based 3D model classification and retrieval are increasingly important in various fields. High classification accuracy and retrieval precision are urgently needed in the related applications. However, these two topics are always considered separately and very few works give an in-depth analysis of their relations. We would like to argue that although the classification and retrieval focus on different characteristics of embedding features, they are compatible rather than opposed to each other. Inspired by the recent deep metric learning approaches in this field, we propose a novelty loss named center-push loss for joint feature learning. The proposed loss can drive the convolutional neural network to learn object features that distributed compact in intra-class while separated in inter-class effectively. It avoids the annoying triplet sampling operation which always needs a delicately designed sampling strategy for efficient network optimization. The new loss is simple in structure and fast in training and achieves the best performance in classification and retrieval compared with many states of the arts.

The moisture conditions and temperature changes are two key factors affecting wound management. Thermosensitive hydrogels are ideal dressing materials since they can maintain a moist environment at the wound site and release additional bioactive molecules to treat the wound when the area is infected. In this study, we developed a novel thermosensitive dressing based on cotton fabric as a matrix to provide robust mechanical properties. Inclusion complexes between cyclodextrins (HP-β-CD, DM-β-CD, and β-CDP) and curcumin were prepared and loaded into the thermosensitive dressing to obtain drug-release properties. The cotton coated with a thermosensitive polymer (CS-g-PVCL) that was crosslinked by citric acid to form a hydrogel was loaded with the appropriate inclusion complex chosen based on phase solubility studies. This composite dressing was investigated to characterize its wound management properties, such as the water vapor permeability and drug release behavior in response to the temperature. The resulting cytotoxicity, antibacterial, and antioxidant properties suggest the potential use of this thermosensitive-hydrogel-functionalized textile as an active wound dressing.

Multi-view subspace clustering has been an important and powerful tool for partitioning multi-view data, especially multi-view high-dimensional data. Despite great success, most of the existing multi-view subspace clustering methods still suffer from three limitations. First, they often recover the subspace structure in the original space, which can not guarantee the robustness when handling multi-view data with nonlinear structure. Second, these methods mostly regard subspace clustering and affinity matrix learning as two independent steps, which may not well discover the latent relationships among data samples. Third, many of them ignore the different importance of multiple views, whose performance may be badly affected by the low-quality views in multi-view data. To overcome these three limitations, this paper develops a novel subspace clustering method for multi-view data, termed Kernelized Multi-view Subspace Clustering via Auto-weighted Graph Learning (KMSC-AGL). Specifically, the proposed method implicitly maps the multi-view data from linear space into nonlinear space via kernel-induced functions, so as to exploit the nonlinear structure hidden in data. Furthermore, our method aims to enhance the clustering performance by learning a set of view-specific representations and their affinity matrix in a general framework. By integrating the view weighting strategy into this framework, our method can automatically assign the weights to different views, while learning an optimal affinity matrix that is well-adapted to the subsequent spectral clustering. Extensive experiments are conducted on a variety of multi-view data sets, which have demonstrated the superiority of the proposed method.

This work investigates the unified coding and identification of smart grid IoT devices, as more and more IoT devices in smart grid need to be managed and controlled. We combine blockchain technology with 5G MEC to realize the connection of massive power IoT devices at the edge of 5G network. Due to blockchain’s distributed storage and credibility, it is used to identify and register IoT devices in smart grid, ensuring the reliability and accuracy of smart grid IoT devices management. In this paper, we propose a hybrid blockchain mechanism based on 5G MEC smart grid, where both public blockchain and private blockchain are deployed on the MEC gateway/server. To facilitate the data searching and extracting, we endeavor to build a blockchain explorer indexed by IoT device identifier. After that, we study the typical consensus algorithms in the blockchain such as PoW, PoS, DPoS, PDFT, and discuss their feasibility in the hybrid blockchain. Finally, we analyzed and compared the performance of different consensus algorithms from the perspective of average computing time and average time to agreement.

The improvement in heat transfer efficiency between the hot wall and sewage sludge was a critical issue to enhance the conductive drying performance. The drying behavior of thin-film dewatered sewage sludge was investigated based on a conductive dryer assisted with mechanical compression at hot wall temperatures of 120–210 °C. The heat and mass transfer behavior of the sludge in the conductive drying process alone was compared to those in the mechanical compression assisted conductive drying process at three external mechanical loads of 25, 100, and 200 kPa. The average drying rates with mechanical compression were higher than those without mechanical load and were enhanced with the increase of mechanical loads at 210 °C. The extrusion of interfacial vapor film and the reduction of sludge surface roughness was responsible for the enhanced interfacial heat transfer efficiency under mechanical compression. The effective moisture diffusivity, mass transfer coefficient, and effective thermal conductivity were enhanced by mechanical compression. The improved moisture transfer inside sludge and on the open surface, and the decreased heat transfer resistance of sludge was due to the generated pressure-driven flow and the reduced gas cavities in sludge, resulting in the higher drying rates. Additionally, this finding provided reference data for developing a new sludge drying method.

The reconfigurable intelligent surface (RIS) is envisioned to create ultra-secure wireless networks. Previous works on the RIS-assisted security provisioning techniques assumed wiretap channel information at the transmitter, which is practically unavailable in a passive eavesdropping scenario. In this article, we consider a point-to-point anti-eavesdropping system in which a RIS is used to enable the secure transmission from a multi-antenna transmitter to a multi-antenna legitimate receiver. A passive eavesdropper, whose channel state information is completely unknown, attempts to decode the secret messages. We propose a security approach by using the reflection at the RIS as multiplicative randomness against the wiretapper. Specifically, the reflection coefficients in terms of amplitude and phase are updated in each transmission and kept private at the RIS. Through the reflection designs, the effective channel matrix is diagonalized at the legitimate receiver. In contrast, the eavesdropper receives coupled signals with the weights of the randomness at RIS. The main contributions of this article are three reflection designs and correspondingly three secure transmission schemes, which fulfills diverse requirements of the balance amongst performance metrics including the degrees of randomness, spectral efficiency, and reliability. The main benefits of the proposed secure transmission schemes are four-fold. First, the transmitter does not need to know the eavesdropper’s channel states. Second, closed-form solutions for the reflection coefficients are provided. Third, the legitimate receiver has a linear decoding complexity. Fourth, the unauthorized wiretapper is unable to cancel out the multiplicative randomness and thus cannot extract much useful information. Numerical results show that exploiting the RIS as a source of multiplicative randomness provides a new perspective to improve the security of the wireless networks.

Funder: Ministry of Science and Technology of the People’s Republic of China (Chinese Ministry of Science and Technology), The cornerstones of emerging high-performance organic photovoltaic devices are bulk heterojunctions, which usually contain both structure disorders and bicontinuous interpenetrating grain boundaries with interfacial defects. This feature complicates fundamental understanding of their working mechanism. Highly-ordered crystalline organic p–n heterojunctions with well-defined interface and tailored layer thickness, are highly desirable to understand the nature of organic heterojunctions. However, direct growth of such a crystalline organic p–n heterojunction remains a huge challenge. In this work, we report a design rationale to fabricate monolayer molecular crystals based p–n heterojunctions. In an organic field-effect transistor configuration, we achieved a well-balanced ambipolar charge transport, comparable to single component monolayer molecular crystals devices, demonstrating the high-quality interface in the heterojunctions. In an organic solar cell device based on the p–n junction, we show the device exhibits gate-tunable open-circuit voltage up to 1.04 V, a record-high value in organic single crystalline photovoltaics.

Super-resolution imaging has been revolutionizing technical analysis in various fields from biological to physical sciences. However, many objects are hidden by strongly scattering media such as biological tissues that scramble light paths, create speckle patterns and hinder object’s visualization, let alone super-resolution imaging. Here, we demonstrate non-invasive super-resolution imaging through scattering media based on a stochastic optical scattering localization imaging (SOSLI) technique. After capturing multiple speckle patterns of photo-switchable point sources, our computational approach utilizes the speckle correlation property of scattering media to retrieve an image with a 100-nm resolution, an eight-fold enhancement compared to the diffraction limit. More importantly, we demonstrate our SOSLI to do non-invasive super-resolution imaging through not only static scattering media, but also dynamic scattering media with strong decorrelation such as biological tissues. Our approach paves the way to non-invasively visualize various samples behind scattering media at nanometer levels of detail., The authors introduce stochastic optical scattering localization imaging (SOSLI) for non-invasive super-resolution imaging through scattering media. They capture multiple speckle patterns of photo-switchable point sources and use the speckle correlation to retrieve images with 100 nm resolution.

Based on the research results and practical engineering experience pertaining to the protection and reinforcement of the cliff of the Mogao Grottoes in Dunhuang, China, this paper presents a method that is mainly based on the analytic hierarchy process (AHP) to evaluate the preservation state and risk of the Mogao cliff, a means that numerical simulation was conducted to quantitatively evaluate the stability and effectiveness of protective measures for the Mogao cliff, a set of reinforcement methods which integrate the key protection techniques based on propping, anchoring, grouting, and anti-weathering and the quality control measures based on assessing their effectiveness for surrounding rocks of the grottoes, and a set of methods for monitoring and warning based on risk theory throughout the entire reinforcement process. The four above-mentioned techniques complement and support with each other, and every stage is based on research. Additionally, the protection and reinforcement concepts implemented at the Mogao cliff are summarized in this paper. Finally, preventive protection and reinforcement techniques for sandy conglomerate grottoes were established based on the research, evaluation, calculation, and monitoring. The techniques presented in this paper can be used as a theoretical foundation and provide technical guidance for the protection and reinforcement of similar cultural heritage sites all over the world.

Weakly-supervised video object localization is a challenging yet important task. The system should spatially localize the object of interest in videos, where only the descriptive sentences and their corresponding video segments are given in the training stage. Recent efforts propose to apply image-based Multiple Instance Learning (MIL) theory in this video task, and propagate the supervision from the video into frames by applying different frame-weighting strategies. Despite their promising progress, the spatio-temporal correlation between different object regions in videos has been largely ignored. To fill the research gap, in this work we introduce a simple but effective feature expression and aggregation framework, which utilizes the self-attention mechanism to capture the latent spatio-temporal correlation between multimodal object features and design a multimodal interaction module to model the similarity between the semantic query in sentences and the object regions in videos. We conduct extensive experimental evaluation on the YouCookII and ActivityNet-Entities datasets, which demonstrates significant improvements over multiple competitive baselines.

Coalescence-induced droplet jumping on superhydrophobic surfaces has been observed at microscale and even nanoscale. The enhancement in jumping velocity of coalescing droplets is crucial for condensation heat transfer enhancement, anti-icing, self-cleaning, and so forth. However, the research on how to acquire a higher jumping velocity is really very limited. In this paper, we use molecular dynamics simulations to study the coalescence-induced jumping of two equally sized nanodroplets on chemically heterogeneous surfaces composed of alternating stripes with different hydrophobicity. We show that the jumping velocity is closely related to the stripe width and wettability contrast, and it can even exceed that on an ideal superhydrophobic surface with 180° contact angle when the striped surfaces are properly designed. We also demonstrate that there is always an optimal stripe width yielding the maximum jumping velocity, whereas its value is independent of the wettability contrast. We reveal that the dominant factor to determine the jumping velocity is the apparent contact angle of equilibrated droplets over heterogeneous surfaces for small stripe widths; it changes to the time of liquid bridges impacting surfaces for moderate stripe widths and to the contact area between equilibrated droplets and relatively hydrophobic stripes for large stripe widths. We believe the present simulations can provide useful guidance to design self-jumping superhydrophobic surfaces.