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Urban anomalies may result in loss of life or property if not handled properly. Automatically alerting anomalies in their early stage or even predicting anomalies before happening are of great value for populations. Recently, data-driven urban anomaly analysis frameworks have been forming, which utilize urban big data and machine learning algorithms to detect and predict urban anomalies automatically. In this survey, we make a comprehensive review of the state-of-the-art research on urban anomaly analytics. We first give an overview of four main types of urban anomalies, traffic anomaly, unexpected crowds, environment anomaly, and individual anomaly. Next, we summarize various types of urban datasets obtained from diverse devices, i.e., trajectory, trip records, CDRs, urban sensors, event records, environment data, social media and surveillance cameras. Subsequently, a comprehensive survey of issues on detecting and predicting techniques for urban anomalies is presented. Finally, research challenges and open problems as discussed., Comment: Accepted by IEEE Transactions on Big Data

With the prevalence of smartphones, people have left abundant behavior records in cyberspace. Discovering and understanding individuals' cyber activities can provide useful implications for policymakers, service providers, and app developers. In this paper, we propose a framework to discover daily cyber activity patterns across people's mobile app usage. We first segment app usage traces into small time windows and then design a probabilistic topic model to infer users' cyber activities of each window. By exploring the coherence of users' activity sequences, the daily patterns of individuals are identified. Next, we recognize the common patterns across diverse groups of individuals using a hierarchical clustering algorithm. We then apply our framework on a large-scale and real-world dataset, consisting of 653,092 users with 971,818,946 usage records of 2,000 popular mobile apps. Our analysis shows that people usually obey yesterday's activity patterns, but the patterns tend to deviate as the time-lapse increases. We also discover five common daily cyber activity patterns, including afternoon reading, nightly entertainment, pervasive socializing, commuting, and nightly socializing. Our findings have profound implications on identifying the demographics of users and their lifestyles, habits, service requirements, and further detecting other disrupting trends such as working overtime and addiction to the game and social media.

Oncolytic virus is an emerging anti-cancer strategy. However, extracellular matrix (ECM), as a physical barrier, limits virus spread within the tumor. To overcome the obstacle, we constructed a recombinant Newcastle disease virus (NDV) expressing matrix metalloproteinase (MMP8) (NDV-MMP8) using with reverse genetic technology. In vitro, NDV-MMP8 was identified and verified by WB and ELISA. Cell viability was detected by CCK-8 assay. In vivo, we established two liver cancer xenograft models. NDV-MMP8 was injected into the tumor to observe the tumor volume and survival of mice. The changes in extracellular matrix were observed by Masson's trichrome staining. Virus expression in tumor tissues was detected by immunofluorescence assay. The virus titer in tumor tissues was detected by TCID50. Histopathological changes were detected by hematoxylin and eosin (HE) and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) staining. Intratumoral administration of NDV-MMP8 can effectively degrade ECM, promote the spread of the virus within the tumor, and reduce tumor growth rate. Therefore, the method of increasing intratumoral virus accumulation by degradation of the ECM to enhance the oncolytic effect has great potential for clinical application.

Environment-friendly nano-catalysts capable of activating peroxymonosulfate (PMS) have received increasing attention recently. Nevertheless, traditional nano-catalysts are generally well dispersed and difficult to be separated from reaction system, so it is particularly important to develop nano-catalysts with both good catalytic activity and excellent recycling efficiency. In this work, magnetically recoverable Fe3O4-modified ternary CoFeCu-layered double hydroxides (Fe3O4/CoFeCu-LDHs) was prepared by a simple co-precipitation method and initially applied to activate PMS for the degradation of Rhodamine B (RhB). X-ray diffraction (XRD), fourier transform infrared spectrometer (FT-IR), scanning electron microscope (SEM), transmission electron microscopy (TEM), Brunauer-Emmett-Teller method (BET), and vibrating sample magnetometer (VSM) were applied to characterize morphology, structure, specific surface area and magnetism. In addition, the effects of several key parameters were evaluated. The Fe3O4/CoFeCu-LDHs exhibited high catalytic activity, and RhB degradation efficiency could reach 100% within 20 min by adding 0.2 g/L of catalyst and 1 mmol/L of PMS into 50 mg/L of RhB solution under a wide pH condition (3.0-7.0). Notably, the Fe3O4/CoFeCu-LDHs showed good super-paramagnetism and excellent stability, which could be effectively and quickly recovered under magnetic condition, and the degradation efficiency after ten cycles could still maintain 98.95%. Both radicals quenching tests and electron spin resonance (ESR) identified both HO• and SO4•− were involved and SO4•− played a dominant role on the RhB degradation. Finally, the chemical states of the sample's surface elements were measured by X-ray photoelectron spectroscopy (XPS), and the possible activation mechanism in Fe3O4/CoFeCu-LDHs/PMS system was proposed according to comprehensive analysis.

Temperature-responsive resistance transition behaviors of the melamine sponges wrapped with different graphene oxide derivatives (i.e. nanoribbon, wide-ribbon and sheet) were investigated. Melamine sponge composites coated by three types of GO derivatives were prepared by a simple dip-coating approach. All these composites show good mechanical flexibility and reliability (almost unchanged compressive stress at 70 % strain after 100 cycles), high hydrophobicity (water contact angle >120°), excellent flame resistance (self-extinguishing) and structural stability even after burning, which was used to construct the resistance-based fire alarm/warning sensor. Notably, the different resistance response behaviors of such sensors are strongly dependent on the GO size and network formed on the MF skeleton surface. Typically, at a fixed high temperature of ∼350 ℃, the three fire alarm sensors show different response time (to trigger the alarm light) of 6.3, 8.4 and 11.1 s for nanoribbon, wide-ribbon and sheet at the same concentration, respectively. The structural observation and chemical analysis demonstrated that the discrepancy of temperature-responsive resistance transition behaviors of various GO derivatives was strongly determined by their different thermal reduction degrees during the high-temperature or flame treating process. This work offers a design and development for construction of smart fire alarm device for potential fire prevention and safety applications.

In this paper, the control problem for a class of stochastic nonlinear systems with incomplete measurements is investigated based on Luenberger-like nonlinear state observer. The systems are descri...

Crowd counting aims to count the number of pedestrians in an image or a video. Currently, scale variations in crowd counting are inevitable and challenging in practice. Besides, for the crowd counting task, there is only a small amount of annotated data available. It can be seen from previous methods of crowd counting and object detection that the two tasks have similar attention areas. However, existing methods in crowd counting generally ignore the similarities and specialties between the crowd counting task and the object detection task. In this paper, in order to solve the above challenges, we propose an adversarial scale-adaptive neural network (ASANet), consisting of three branches. First, a private branch for the crowd counting task concentrates on generating high-quality density maps. Second, another private branch for the object detection task aims to correctly detect and recognize objects. Third, we design a common branch to learn the similar attention area of the two tasks and assist crowd counting. Experimental results demonstrate an outstanding performance of the ASANet over state-of-the-art methods on three public datasets (ShanghaiTech, UCF_CC_50, and UCF_QNRF).

Nowadays, given the booming demand of blood sugar control from the soaring number of diabetes patients and the insufficient stability of costly enzyme-based glucose sensors in clinic, the development of enzyme-free sensors has been drawing tremendous attraction. Herein, we fabricated a unique structure of Ni/NiO hybrid nanoparticles with mixed-valence states encapsulated in and cross-linked by nitrogen-doped graphene via a facile Ni-MOF-annealing approach and developed its notable feature in efficiently non-enzymatic glucose sensing. The microstructure and valence states, proved vital to the electrochemical activity and sensing performance, were simultaneously regulated by the annealing temperature. The optimized sample obtained at 400 °C displays the superior activation behavior and best glucose sensing performance, offering a high sensitivity of 3.2518 mA mM−1 cm−2, a wide linear range (0.001–3.568 mM), a low detection limit (0.032 μM, S/N = 3) as well as excellent selectivity, good reproducibility, long-term stability, and satisfactory applicability in real sample. By further investigation of the electrochemical kinetics mechanism, the synergistic effect of multivalent system and well-organized microstructure was proved beneficial for charge transfer, activation of reaction sites, and elimination of electrode polarization as well, thus providing a promising strategy in designing non-enzymatic biosensor platform for glucose detection in actual diagnosis.

Fundamental mechanics of gas hydrates is of importance to evaluating geomechanical and geotechnical properties of gas hydrate deposits, but it remains largely unexplored yet due to insufficient direct experimental techniques and high-quality of gas hydrate samples. Here, classic molecular dynamic (MD) simulations are used to study the fracture mechanics of three main methane clathrate hydrates of sI, sII and sH types. The results show that the mechanical properties of those three methane clathrate hydrates are intrinsically different and are degraded by the presence of nanocracks. They show brittle facture and different fracture toughness. In terms of energy release rate, they are ranked as $$\text {sH}\!\!>\!\!\text {sI}>\!\!\text {sII}$$ . Moreover, the three methane clathrate hydrates with nanocracks can be explained by a modified Griffith criterion. Moreover, it is intriguingly identified tip amorphization during the crack propagation process of the three methane clathrate hydrates, and sH methane clathrate hydrate with specific nanocrack exhibits slower crack propagation than other two methane clathrate hydrates.

With the improvement of public security awareness, video anomaly detection has become an indispensable demand in surveillance videos. To improve the accuracy of video anomaly detection, this paper proposes a novel two-stream spatial-temporal architecture called Two-Stream Deep Spatial-Temporal Auto-Encoder (Two-Stream DSTAE), which is composed of a spatial stream DSTAE and a temporal stream DSTAE. Firstly, the spatial stream extracts appearance characteristics whereas the temporal stream extracts the motion patterns, respectively. Then, based on the novel policy joint reconstruction error, this model fuses the spatial stream and the temporal stream to extract spatial-temporal characteristics to detect anomalies. Furthermore, since the optical flow is invariant to appearances such as color or light, we introduce optical flow to enhance the capability of extracting continuity between adjacent frames and inter-frame motion information. We demonstrate the accuracy of the proposed method on the publicly available standard datasets: UCSD, Avenue and UMN datasets. Our experiments demonstrate high accuracy, which is superior to the state-of-the-art methods.

An intra-cavitary left ventricular (LV) thrombus is a relative contraindication to LV assist device (LVAD) implantation based on increased thromboembolic risks. Herein, we present our experience with LVAD patients with or without preoperative diagnosis of LV-thrombus. We retrospectively investigated 563 patients who received LVAD implantation between 2004 and 2018. Diagnosis of LV-thrombus was verified with computed tomography scan, magnetic resonance imaging, echocardiography, or intraoperative LV inspection. The primary endpoint was 30-day survival free of stroke and pump thrombosis. Overall, 72 patients (12.8%) had a diagnosis of LV-thrombus. They were younger (51 years; IQR:41-59), affected by severely reduced ejection fraction (15%; IQR:10-20), more often presenting with dilated cardiomyopathy (61.8%) and INTERMACS profile 1 (33.3%). Preoperative atrial fibrillation was frequent in patients without LV-thrombus (38.9%). Conventional sternotomy was the preferred approach in LV-thrombus patients (77.8%), based on more HMII implantations in these patients (41.7%). Survival free of strokes and pump thrombosis at 30 days was comparable (P = .5751) between patients with (83.3%) or without LV-thrombus (80.9%). LVAD implantation in patients with preoperative LV-thrombus is safe and feasible. When managed through correct diagnostic and intraoperative strategies including accurate inspection of the LV cavity, these patients show similar 30-day outcomes compared to patients without LV-thrombus.

In the human-vehicle-road system of collisions, the human is the most important factor, and digital human models (DHMs) are developed with the aim of preventing or at least reducing human injury. Because most of the relevant literature is focused mainly on collisions in traffic accidents (TAs), only some of the literature reviewed in this paper involves research results on other aspects of collisions. In this review, based on the background of DHMs and the application of DHMs regarding human injury biomechanics in collisions field, research results regarding the development of DHMs are described, the methods for verifying such models are introduced, and the application of the research results is discussed based on the aspect of human injury biomechanics. From the research literature, the development and validation of DHMs and their application in human injury biomechanics are summarized, and future research trends are proposed and discussed.

Palladium nanoparticles supported on silica catalysts (Pd/SiO2) were prepared by wet impregnation (WI), dry impregnation (DI), strong electrostatic adsorption (SEA), and charge-enhanced dry impregn...

The economical and societal issues associated with the depletion of fossil feedstocks and severe accumulation of nondegradable plastics in nature have pushed the increasing efforts toward the devel...

Always-on keyword spotting (KWS) that detects wake-up words has been the indispensable module in the voice interaction system. However, the ultra-low-power embedded devices put forward strict requirements on energy consumption, latency, and recognition accuracy of KWS. In this work, we propose a near-sensor processing architecture of feature-configurable distributed network (NS-FDN) for always-on KWS applications. The proposed distributed network adapts to the flexible keywords demands in the actual scene by splitting the conventional single network into distributed sub-networks. We design a channel-independent training framework to improve the recognition accuracy of distributed networks. The speech features are evaluated and the redundancy is reduced in NS-FDN, which can also configure the speech features to further reduce the computing complexity and improve processing speed. For deeper optimization, we implement a 65nm-process prototype chip with near-sensor mixed-signal processing architecture avoiding energy-consuming analog-to-digital converter. By improving the system, algorithm, and hardware designs of the KWS, our co-optimized architecture eliminates the energy consumption bottleneck long-standing in conventional KWS systems and achieves state-of-the-art system performance. The experiment results show that NS-FDN achieves 31.6% energy consumption savings, 1.6 times memory savings, 57 times speedup, and 3.4% higher recognition accuracy compared with the state of the art.

The hydroisomerization of n-hexadecane over Pt-Pd bimetallic catalysts is an effective way to produce clean fuel oil. This work reports a useful preparation method of bimetallic bifunctional catalysts by a co-impregnation or sequential impregnation process. Furthermore, monometallic catalysts with loading either Pt or Pd are also prepared for comparison. The effects of the metal species and impregnation order on the characteristics and catalytic performance of the catalysts are investigated. The catalytic test results indicate that the maximum iso-hexadecane yield over different catalysts increases as follows: Pt/silicoaluminophosphate SAPO-41 < Pd/SAPO-41 < Pt*-Pd/SAPO-41 (prepared by sequential impregnation) < Pt-Pd/SAPO-41 (prepared by co-impregnation). Owing to the synergic effects between Pt and Pd, the Pt-Pd/SAPO-41 catalyst prepared by the co-impregnation method demonstrates the effective promotion of (de)hydrogenation activity. Therefore, this catalyst exhibits the highest iso-hexadecane yield of 89.4% when the n-hexadecane conversion is 96.3%. Additionally, the Pt-Pd/SAPO-41 catalyst also presents the highest catalytic activity and best stability even after 150 h long-term tests.

In this paper, the model of active curtailed wind accommodation based on heat storage electric boiler heating was studied and the curtailed wind accommodation strategy of combined heat storage electric boilers and coal-fired boilers heating was proposed. This is to maximize the curtailed wind accommodation for the ever-increasing curtailed wind problem and environmental problem caused by traditional energy heating in northwest China, northcentral China, and northeast China. Different from the heat storage electric boilers with constant power operation in the past, the multi-level input/exit operation mode of the heat storage electric boilers proposed could accommodate more wind power. The simulation analysis result of a wind power heating project in northeastern China shows that the strategy proposed for active curtailed wind accommodation of heat storage electric boiler heating can effectively improve the wind power accommodation capacity.

Wireless communication systems assisted by unmanned aerial vehicles (UAVs) can well extend the coverage of conventional communication networks and quickly provide service wherein the communication infrastructures are destroyed. However, radio resources are always limited and coupled with complex interference in the orthogonal multiple access framework. Thus, non-orthogonal multiple access (NOMA), which can enhance spectrum efficiency and inherently has capability to mitigate interference, is integrated with UAV communication networks in this article. We first propose a cooperative UAV NOMA network (CUNN) architecture and analyze its technical challenges. In addition, with the goal of cooperatively or competitively utilizing the limited resources effectively in a distributed manner, game theory shows its potential in addressing various technical challenges in wireless networks. Thus, we further survey game theoretical approaches for NOMA networks and UAV-based relay networks, and provide insights on how these game theoretical approaches can be adopted to solve technical challenges in CUNNs. Finally, aiming to achieve high spectrum efficiency and ensure fairness among user equipments, we propose a UAV-iUE (isolated user equipment) association, UAVs' position optimization, and power allocation algorithm in a dynamic cooperative game-theoretic framework. In the numerical results and discussions Section, the effectiveness of the proposed dynamic cooperative game-theoretic framework is shown through multiple simulations.

Next-generation communication networks can provide high capacity, low latency, and massive connections; however, they introduce novel challenges of management complexity, and traditional mathematical methods cannot well characterize the rational behavior of users. In this article, we pay attention to the methods of artificial intelligence (AI) and game theory. We first review the applications of machine learning (ML) and game theory models in wireless communications and summarize their advantages and disadvantages. After surveying the state of the art, in this article we propose a novel framework combining ML and game theory, which explores and exploits the benefits of the two disciplines. Finally, we apply our novel framework to solve the network selection problem in a 5G ultra-dense and heterogeneous network. Simulation results confirm the advantage of our presented framework on reducing the average delay of users.

In this paper, we investigate the feasibility of using mobility patterns and demographic data to predict hospital visits. We collect mobility traces from two thousand users for around two months. We extract 16 mobility features from these passively collected mobility traces and train an XGBoost model to predict users' hospital visits. We demonstrate that the designed mobility features can significantly improve prediction accuracy (p < 0.01, AUC = 0.79). We further analyze how these mobility features affect the prediction results and measure their importance by using Shapley additive explanation values. We discover that users with less mobility activity, less visit diversity, and few sports facilities, bountiful entertainment around their visited locations are more likely to visit hospitals. Moreover, we conduct predictions on the populations with different demographic features, which achieves meaningful and insightful results, i.e. maintaining a high mobility activity is crucial for older people's health, while fast food store more substantially affects younger people's health; visit patterns can indicate females' health, while the neighborhood environment is more indicative of males, etc. These results shed light on how to use and understand large scale mobility data in health monitoring and other health-related applications in practice.

Radar cross section (RCS) reduction in the working band of antennas, likely leading to radiation performance change or deterioration, is quite difficult for both antenna elements and arrays. Aimed at this problem, this article presents a novel design strategy of microstrip patch antennas with low in-band RCS. This strategy combines metasurface with antenna from the perspectives of both structure and electrical performance. To be specific, anisotropic metasurface is proposed to replace the conventional patch and behave as the radiating structure of the antenna directly, which does not increase the original antenna size. Through proper connection of the metasurface units, the novel antenna has the same radiation performance with conventional microstrip antenna but low in-band RCS for any polarized incidence. The radiation and scattering mechanism of the novel antenna is carefully elaborated, from which a frequency reconfigurable antenna is also derived by loading varactors. Furthermore, different design strategies of low in-band-RCS antenna element and array are clarified. On the basis of which, a $4 \times 4$ array is developed to achieve in-band-RCS reduction with radiation performance kept. Both numerical and experimental results are demonstrated, and the reasonably good agreements verify the effectiveness of the proposed strategy.

The role of elemental partitioning between β and ω phase in embrittling an originally ductile ω-containing Ti–12Mo (wt.%) model alloy was studied using transmission electron microscopy and atom probe tomography. It is revealed that the embrittlement of this alloy already occurs after aging at 400 °C for as short as 10 min, when the size, inter-particle spacing and volume fraction of the ω particles remain almost unchanged. The origin of the aging-induced embrittlement is attributed to the significant rejection of Mo (>5 at.%) from the ω particles during aging, which leads to remarkable increase in the shear modulus (>30 GPa) of the ω particles, promoting intense plastic flow localization and facilitating crack nucleation prior to macroscopic yielding.

The dependence on frequent acknowledgments (ACKs) is an artifact of current transport protocol designs rather than a fundamental requirement. Frequent ACKs waste resources when the overhead of ACKs is nonnegligible. However, reducing the number of ACKs may hurt transport performance. “Tame ACK” is an on-demand ACK mechanism that seeks to minimize ACK frequency, which is exactly what is required by transport.

Cu–Cr/55 vol% diamond composites with 1 wt % (Cu–1Cr/55Dia), 2 wt % (Cu–2Cr/55Dia), and 3 wt % (Cu–3Cr/55Dia) Cr additives, respectively, are fabricated by a hot forging method. The diamond particle surfaces are nearly completely covered by the formed carbides interface for the Cu–3Cr/55Dia composite, and the Cu–3Cr/55Dia has the highest measured thermal conductivity among the three fabricated composites (433 Wm−1K−1). High Resolution Transmission Electron Microscopy analyses suggest that a double-layered interface structure is formed between the copper matrix and the diamond particle in the Cu–3Cr/55Dia, which is composed of 160 nm-thick Cr3C2 and 2 nm-thick Cr23C6. This interface structure, together with high relative density of the composite, high interface coverage of diamond, thin interface layer, and formation of coherent atomic boundaries, significantly contributes to obtaining high thermal conductivity for the Cu–3Cr/55Dia composite. The Diffuse Mismatch Model and Differential Effective Medium model are modified for establishing the quantitative relationship among interface characteristics, interface thermal conductance, and the composite’s thermal conductivity, based on considering the composite’s interface structure and phonon transmission theory. The predicted thermal conductivity value is well matched with the measured value for the Cu–3Cr/diamond (487 Wm−1K−1 vs 433 Wm−1K−1). This helps understand heat transfer behavior in the Cu–Cr/diamond composites.

Bridging the gap between academia and industry is an important issue to promote the practicality of i* framework. Researchers have been dealing with this issue from various perspectives, such as simplifying the meta-models or modeling processes of i* framework. In this paper, we exclusively focus on the scalability issue in laying out large-scale i* models and propose a two-level layout approach to automatically lay out i* models in an efficient and comprehensible manner, contributing to the adoption of i* framework in the industry. The proposed approach is designed by considering the semantics of i* constructs and layout conventions of i* models in order to produce meaningful layouts and can appropriately handle both the SD (Strategic Dependency) view and the SR (Strategic Rationale) view of i* models. We have implemented our approach in an open-access prototype tool, which is able to be integrated with existing iStarML-compatible modeling tools. We have conducted a controlled experiment, a case study, and performance testing to empirically and comprehensively evaluate the utility of our approach, the results of which show that our proposal can efficiently produce meaningful layouts that are as comprehensible as manually laid out models in most cases.

Sarcasm is common in social media, and people use it to express their opinions with stronger emotions indirectly. Although it belongs to a branch of sentiment analysis, traditional sentiment analysis methods cannot identify the rhetoric of irony as it requires a significant amount of background knowledge. Existing sarcasm detection approaches mainly focus on analyzing the text content of sarcasm using various natural language processing techniques. It is argued herein that the essential issue for detecting sarcasm is examining its context, including sentiments of texts that reply to the target text and user’s expression habit. A dual-channel convolutional neural network is proposed that analyzes not only the semantics of the target text, but also its sentimental context. In addition, SenticNet is used to add common sense to the long short-term memory (LSTM) model. The attention mechanism is then applied to take the user’s expression habits into account. A series of experiments were carried out on several public datasets, the results of which show that the proposed approach can significantly improve the performance of sarcasm detection tasks.

For CVaR (conditional value-at-risk) portfolio nonsmooth optimization problem, we propose an infeasible incremental bundle method on the basis of the improvement function and the main idea of incremental method for solving convex finite min-max problems. The presented algorithm only employs the information of the objective function and one component function of constraint functions to form the approximate model for improvement function. By introducing the aggregate technique, we keep the information of previous iterate points that may be deleted from bundle to overcome the difficulty of numerical computation and storage. Our algorithm does not enforce the feasibility of iterate points and the monotonicity of objective function, and the global convergence of the algorithm is established under mild conditions. Compared with the available results, our method loosens the requirements of computing the whole constraint function, which makes the algorithm easier to implement.

CO2 electroreduction to value-added chemicals by virtue of renewable electricity is significant for carbon emission abatement and renewable energy conversion/storage. Conventional CO2 electroreduction occurring in aqueous solution or ionic liquid suffers from insufficient CO2 solubility and a separation dilemma of the soluble products. In this work, we report that when using Sn–Cu hollow fiber electrodes, gas-phase CO2 can be directly electroreduced into various multicarbon oxygenates. The faradaic efficiencies of acetaldehyde and acetone over a 0.3 wt% Sn–Cu hollow fiber electrode are 10 and 12% at the cell voltage of −1.4 V. The presence of an appropriate amount of SnO2 nanoparticles decorated on a Cu hollow fiber surface not only facilitates the reaction kinetics with elevated current densities, but also improves the C–C coupling of intermediates, promoting the formation of multicarbon oxygenates.

The defects including halide vacancies and uncoordinated Pb in perovskite films caused by the cracking of Pb–I bonds in the thermal annealing process are responsible for the degradation of the photovoltaic performance and long-term stability of perovskite solar cells (PSCs). Passivation of halide vacancies and uncoordinated Pb defects and construction of high quality perovskite films are vital to promote carrier lifetime and reduce non-radiative recombination losses. Herein, we develop a facile and broadly applicable surface passivation strategy for various compositional perovskite films, in which cadmium bromide (CdBr2) is selected and adopted to passivate the halide vacancies and uncoordinated Pb defects of the perovskite films owing to the stronger bond energies of Cd–I and Pb–Br than that of Pb–I. CdBr2 modification is found to be able to prolong carrier lifetime and decrease defect amount together with improving film quality of the typical compositional perovskites i.e. MAPbI3, Cs0.15FA0.85PbI3 and CsPbI2Br, as evidenced by spectral and photoelectrochemical measurements. As a result, the photovoltaic performance and ambient, photo and thermal stability of perovskite devices are greatly promoted, with a high efficiency of 16.05% achieved by the CdBr2-modified PSCs with the architecture of ITO/SnO2/MAPbI3/carbon, exceeding the previous highest PCE record for carbon-based MAPbI3 PSCs.

Recently, crowd counting has drawn widespread attention in computer vision, but it is extremely challenging because of the varying scales and densities. Many existing methods focus on improving the multi-scale representation by utilizing multi-column or multi-branch architectures with different kernel sizes. However, such networks cannot extract the feature maps with large receptive fields due to limitation of depth. In addition, the importance of utilizing the multi-level feature information in a deep network is ignored. In this paper, we propose a multi-scale and multi-level features aggregation network (MFANet) for accurate and efficient crowd counting, and it can be trained by end-to-end. A vital component of the network is the scale and level aggregation module (SLAM), which can extract multi-scale features and make full use of multi-level feature information for more accurate estimation. When six SLAMs are stacked together and applied to our network, our method can achieve the best performance. Furthermore, we introduce a new loss function called normalized Euclidean loss (NEL) to balance the contribution of all samples to network training. To demonstrate the performance of the proposed method, extensive experiments are conducted on four benchmark crowd counting datasets, including ShanghaiTec Part A/B, UCF-CC-50, Mall, and UCF-QNRF. Experimental results show that our MFANet achieves state-of-the-art performance in crowd counting and crowd localization.

Structural health monitoring (SHM) systems have been extensively used to ensure the operational safety of long-span bridges. Large-scale bridge structural response and loading data observed from various sensors show obvious big data characteristics. However, serious “data island” problems, which exist in the conventional SHM solutions, inevitably limit the effects of sensory data analysis and information sharing. A unified bridge SHM semantic representation model is much in demand. By taking the advantages of Semantic Web technologies, this article presents a novel model, called the bridge structure and health monitoring ontology, to achieve fine-grained modeling of bridge structures, SHM systems, sensors, and sensory data from multiple perspectives. A bridge SHM big data platform is used to demonstrate the usefulness. Several representative data accessing and rule-based reasoning scenarios are employed as to illustrate the advantages of the proposed manner.

The simple frigate shape (SFS) as defined by The Technical Co-operative Program (TTCP), is a simplified model of the frigate, which helps to investigate the basic flow fields of a frigate. In this paper, the flow fields of the different modified SFS models, consisting of a bluff body superstructure and the deck, were numerically studied. A parametric study was conducted by varying both the superstructure length L and width B to investigate the recirculation zone behind the hangar. The size and the position of the recirculation zones were compared between different models. The numerical simulation results show that the size and the location of the recirculation zone are significantly affected by the superstructure length and width. The results obtained by Reynolds-averaged Navier-Stokes method were also compared well with both the time averaged Improved Delayed Detached-Eddy Simulation results and the experimental data. In addition, by varying the model size and inflow velocity, various flow fields were numerically studied, which indicated that the changing of Reynolds number has tiny effect on the variation of the dimensionless size of the recirculation zone. The results in this study have certain reference value for the design of the frigate superstructure.

The microstructure of advanced high-strength steels (AHSSs) is usually designed via adjusting austenite decomposition behavior upon cooling, while relatively less attention was paid to austenite formation upon heating. Here we explore the potentials of flash heating in tuning the microstructure and mechanical behavior of Quenching & Partitioning (Q&P) steels with an emphasis on the role of chemical heterogeneity. Besides substantially refining intercritical austenite grains (e.g. austenite formed during intercritical annealing), it was interestingly found that flash heating can also allow intercritical austenite to inherit Mn heterogeneity in the original pearlite-ferrite microstructure due to the kinetic mismatch between the sluggish diffusion of Mn and the rapid austenite formation. Chemical heterogeneity can to a large extent alter the decomposition of intercritical austenite and carbon partitioning upon cooling, and plays a notable role in enhancing thermal stability of austenite. The role of chemical heterogeneity in austenite decomposition and carbon partitioning behavior was explained via phase field simulations. The flash treated Q&P (FQP) steels have a broad range of tensile strength (from 980 MPa to 1180 MPa) and good ductility, which outperforms the conventional Q&P (CQP) steels. The current study demonstrates that flash heating opens alternative routes to create unique microstructures and improve the mechanical performance of AHSSs.

In this letter, a wide-beam (at two main planes) antenna is proposed. To broaden the 3 dB beamwidth of the original antenna, a pair of parasitic patches with metallic vias is creatively arranged on either side of the original radiation patches. Based on the proposed antenna element, two linear phased arrays are fabricated and measured. Measured results reveal that the wide-angle scanning capability of both H -plane and E -plane arrays can be obtained. The scanning beam of the former can cover territories ranging from −90° to 90°, with gain loss within 3 dB (8 GHz–11 GHz). The scanning beam of the latter can cover territories ranging from −70° to 70°, with gain loss of 3–5 dB (8 GHz–10 GHz).

Distributed and pervasive web services have become a major platform for sharing information. However, the hypertext transfer protocol secure (HTTPS), which is a crucial web encryption technology for protecting the information security of users, creates a supervisory burden for network management (e.g., quality-of-service guarantees and traffic engineering). Identifying various types of encrypted traffic is crucial for cyber security and network management. In this paper, we propose a novel deep learning model called BGRUA to identify the web services running on HTTPS connections accurately. BGRUA utilizes a bidirectional gated recurrent unit (GRU) and attention mechanism to improve the accuracy of HTTPS traffic classification. The bidirectional GRU is used to extract the forward and backward features of the byte sequences in a session. The attention mechanism is adopted to assign weights to features according to their contributions to classification. Additionally, we investigate the effects of different hyperparameters on the performance of BGRUA and present a set of optimal values that can serve as a basis for future relevant studies. Comparisons to existing methods based on three typical datasets demonstrate that BGRUA outperforms state-of-the-art encrypted traffic classification approaches in terms of accuracy, precision, recall, and F1-score.

Under irradiation conditions, excess point defects interact strongly with the solute atoms in fusion reactor materials. The solutes can bind with point defects and form clusters such as solute–vacancy complexes. The evolution of these complexes at long timescale significantly influences the mechanical properties of the irradiated materials. In this study, we mainly use climbing image nudged elastic band method to investigate the evolution and migration of titanium–vacancy complexes in a vanadium-based alloy. The transition pathways associated with atomic trajectories of complex evolution in bulk and in grain boundary vicinity are calculated. Results show that both basin energies and transition barriers are affected when a complex is in grain boundary vicinity. The evolution is revealed to be driven by multiple titanium–vacancy interactions synergistically. The migration is supposed to be induced by two mechanisms: the vacancy reorientation jumps and complex dissociation–reformation which are both based on titanium–vacancy interactions. This research contributes to the understanding of solute diffusion and early-stage radiation defects in vanadium-based alloys.

This article presents a switched-transformer-based polar Doherty digital power amplifier (DPA) for deep back-off efficiency enhancement. A single-transformer-footprint parallel-combining transformer (PCT) power combiner is introduced for 1.3–3.5-GHz wide frequency coverage, dynamic load modulation at 0-/6-/12-/18-dB power back-off (PBO) levels, and an ultra-compact die size. Implemented in 40-nm CMOS, the DPA is powered by a 1.1-V supply and only occupies 0.7 $\times $ 1.15 mm2 chip area. It achieves 21.4-dBm peak power with the power added efficiencies (PAEs) of 31.3%, 27.7%, 16.6%, and 7.7% for 0-/6-/12-/18-dB PBOs at 1.5 GHz. With 20-MHz 64QAM long term evolution (LTE) signal, the DPA achieves ${P}_{{\text {avg}}}$ of 15.2 dBm and the average PAE of 25.3% with −32.5-dB error vector magnitude (EVM) at 1.5 GHz. Moreover, for 20-MHz 64 QAM WLAN signal, ${P}_{{\text {avg}}}$ of 14.7 dBm with the average PAE of 20.1% is obtained with − 25-dB EVM at 2.4 GHz.

In recent years, the amount of user check-in data has significantly increased on social network platforms. Such data is an ideal source for characterizing user behaviors and identifying similar users, contributing to many research areas (e.g. user-based collaborative filtering). However, existing trajectory-based user similarity analysis approaches do not distinguish the effects of geographical factors at a fine-grained level, and thus are not able to unleash the full power of semantic information that is hidden in the trajectory. In this paper, we have proposed an effective graph embedding approach to identify similar users based on their check-in data. Specifically, we firstly identify meaningful concepts of user check-in data, based on which we design two metagraphs for representing features of similar user behaviors. Then we characterize each user with a sequence of nodes that are derived through a metagraph-guided random walk strategy. Such sequences are embedded to generate meaningful user vectors for measuring user similarity and eventually identifying similar users. We have evaluated our proposal on three public datasets, the results of which show that our approach is 4% higher than the best existing approach in terms of F1-measure.

Literature on the air travel activities of patients supported by permanent mechanical assist devices is rare. To the best of our knowledge, no air travel guidelines or fitness prerequisites exist on whether and when ventricular assist device (VAD) patients are allowed to travel by plane after device implantation. In this study, we evaluated the topic of air travel after VAD implantation. This working group aimed to produce a report on air travel passengers supported by VADs, regarding their fitness to fly. Fifty left ventricular assist device (LVAD) patients were surveyed in a worldwide multicenter study. The single survey was performed with a multimethod design, including interviews conducted face‐to‐face, online, and on phone. Out of 50 patients, 97% described their traveling by aircraft as perfect and uneventful during the flight. Eighty‐five percent of the study participants consulted their medical practitioner before the flight. No patient reported the occurrence of a severe condition associated with flying. LVAD alarms, especially low flow alarms, did not occur in any of the devices. Thirty‐five percent of the surveyed patients, however, stated a major problem pertaining to the security check procedures at the airport. The results of this study suggest that commercial air travel is safe for stable patients on permanent VAD support and traveling can be resumed securely after VAD implantation. Conscientious preparation by packing necessary devices, fluids, medications, and careful preparation for the airport security check is recommended.

Paleozoic granites can provide important insights of crustal differentiation and collision process of the North Qinling terrane. The Dafanggou leucogranites that intruded in the Qinling Complex is a Paleozoic granite with zircon U-Pb age of 404±6.4 Ma (MSWD=0.13, n=9). The leucogranites display moderate SiO2 (68.4 wt.% to 71.7 wt.%) content and high Na2O/K2O (1.07 to 2.74) and Sr/Y (42 to 65) ratios, and low A/CNK (1.04 to 1.08) and Rb/Sr (0.09 to 0.16) ratios. Combined with negative eNd(t) (−10.4) and eHf(t) (− 9.35 to −0.25) values and ancient TDM2 ages (1.2 to 1.7 Ga), suggesting the leucogranite are derived from ancient amphibolitic crust in the Qinling Group. In addition, the absence of coeval mafic igneous rocks or mafic enclaves occurred in the Dafanggou pluton and the low TZrn (∼700 °C) of leucogranites preclude that the leucogranites derived from high temperature dehydration melting of amphibolite. Thus, we propose that the occurrence of the Dafanggou leucogranite indicates that water-flux melting of middle-lower crust in postcollision setting may be a potential model for the genesis of granites in collisional orogenic belt.

Multifunctional nanocoatings with mechanical robustness, high transparence, antifogging and self-cleaning have attracted significant attention because of their wide applications in glass-related fields. However, it is still very difficult to construct this kind of multifunctional nanocoatings due to the requirement of their comprehensive structure parameters. In this work, we successfully fabricated robust transparent antifogging self-cleaning nanocoatings by employing dendritic porous silica nanoparticles (DPSNs) evenly loaded with 2–3 nm of small TiO2 nanoparticles (NPs) as a building block. A series of DPSNs@X% TiO2 nanocomposites with tunable weight ratios (X%) of TiO2/DPSNs from 10% to 60% were firstly prepared by controlling the growth of TiO2 on the heterogeneous interface of center-radial large pores of DPSNs, followed by calcination. Noteworthily, DPSNs@10% TiO2 exhibited highest photocatalytic and antibacterial performance mainly due to uniform distribution of TiO2 NPs, their small sizes of 2–3 nm and center-radial pore. Therefore, DPSNs@10% TiO2 was chosen as an optimized building block and combined with acid-catalyzed silica sol (ACSS) to develop an excellent suspension for multifunctional nanocoatings. The obtained glass slide with the optimal nanocoating showed photocatalytic self-cleaning behavior, high transparence, hydrophilic (WCA = 6.2°) antifogging, and high mechanical robustness, which can withstand 4B tape adhesion test and 3H pencil scratching test. This work provides an important exploration for developing multifunctional nanocoatings.

A high-performance nickel cathode material HPMo/Ni(OH)2 for the Ni–Zn cell is developed by using phosphomolybdic acid (HPMo) as a structure-directing agent. Its three-dimensional porous flower-like structure makes the active material possess the advantages of high specific surface areas, rich active sites, easy electrolyte accesses, and fast electron transfer. Combined with these advantages, the Ni–Zn cell of HPMo/Ni(OH)2 as the cathode, ZnO as the anode, exhibits a high specific capacity of 277.5 mAh g− 1 (cut off 1.5 V), and even up to 346.9 mAh g− 1 during deep discharge (cut off 0.5 V) at 2.5 A g− 1. When the ZnO/graphene as the anode, the cycle life of HPMo/Ni(OH)2 can extend to 10,000 at 5 A g− 1. Besides, this is the first time that the three-dimensional nickel hydroxide is produced in large quantities with simple preparation process using phosphomolybdic acid as a structure directing agent. This also provides a new synthesis strategy for the self-assembly of 3D nanomaterials, and presents new prospects for the development of more advanced energy storage materials.

With energy and environmental issues becoming increasingly prominent, electric vehicles (EVs) have become the important transportation means in the logistics distribution. In the real-world urban road network, there often exist multiple paths between any two locations (depot, customer, and charging station) since the time-dependent travel times. That is, the travel speed of an EV on each path may be different during different time periods, and thus, this paper explicitly considers path selection between two locations in the time-dependent electric vehicle routing problem with time windows, denoted as path flexibility. Therefore, the integrated decision-making should include not only the routing plan but also the path selection, and the interested problem of this paper is a time-dependent electric vehicle routing problem with time windows and path flexibility (TDEVRP-PF). In order to determine the optimal path between any two locations, an optimization model is established with the goal of minimizing the distance and the battery energy consumption associated with travel speed and cargo load. On the basis of the optimal path model, a 0-1 mixed-integer programming model is then formulated to minimize the total travel distance. Hereinafter, an improved version of the variable neighborhood search (VNS) algorithm is utilized to solve the proposed models, in which multithreading technique is adopted to improve the solution efficiency significantly. Ultimately, several numerical experiments are carried out to test the performance of VNS with a view to the conclusion that the improved VNS is effective in finding high-quality distribution schemes consisted of the distribution routes, traveling paths, and charging plans, which are of practical significance to select and arrange EVs for logistics enterprises.

Porous Si can be synthesized from diverse silica (SiO2) via magnesiothermic reduction technology and widely employed as potential anode material in lithium ion batteries. However, concerns regarding the influence of residual silicon oxide (SiOx) component on resulted Si anode after reduction are still lacked. In this work, we intentionally fabricate a cauliflower-like silicon/silicon oxide (CF-Si/SiOx) particles from highly porous SiO2 spheres through insufficient magnesiothermic reduction, where residual SiOx component and internal space play an important role in preventing the structural deformation of secondary bulk and restraining the expansion of Si phase. Moreover, the hierarchically structured CF-Si/SiOx exhibits uniformly-dispersed channels, which can improve ion transport and accommodate large volume expansion, simultaneously. As a result, the CF-Si/SiOx-700 anode shows excellent electrochemical performance with a specific capacity of ~1,400 mA·h·g−1 and a capacity retention of 98% after 100 cycles at the current of 0.2 A·g−1.

The sensory responses of cortical neuronal populations following training have been extensively studied. However, the spike firing properties of individual cortical neurons following training remain unknown. Here, we have combined two-photon Ca2+ imaging and single-cell electrophysiology in awake behaving mice following auditory associative training. We find a sparse set (~5%) of layer 2/3 neurons in the primary auditory cortex, each of which reliably exhibits high-rate prolonged burst firing responses to the trained sound. Such bursts are largely absent in the auditory cortex of untrained mice. Strikingly, in mice trained with different multitone chords, we discover distinct subsets of neurons that exhibit bursting responses specifically to a chord but neither to any constituent tone nor to the other chord. Thus, our results demonstrate an integrated representation of learned complex sounds in a small subset of cortical neurons., Using a combination of two-photon imaging and single-cell electrophysiology, the authors discover that associative learning induces the emergence of a unique subset of neurons in the auditory cortex, exhibiting high-rate bursting responses to the learned complex sounds but not to any of the constituents.

Global demand for rare earth elements (REEs), which are central to many modern high-tech industries, is increasing rapidly. For the first time, we discovered and determined the REEs concentrations ...

High areal capacity and stable Coulombic efficiency (CE) in lithium metal anodes (LMAs) play pivotal roles in developing high-energy-density rechargeable batteries. However, few reported LMAs delivering high and stable CE (>50 cycles) under ultrahigh areal capacity (>10 mA h cm-2). We demonstrated that the simultaneous homogenization of electric field and Li ion flux by using self-supported and surface-oxidized 3D hollow porous copper fibers (3D-HPCFs) can greatly increase both the areal capacity and reversibility of Li deposition. Li can be easily confined inside the hollow porous fibers and within the interspaces among fibers without uncontrollable Li dendrites. The 3D-HPCF-based anode can be deeply cycled at high capacity of 15 mA h cm-2 with average CE of 98.87% for 53 cycles, enabling a practical cell to realize high capacity retentions at a surplus Li of 10%. This work provides a novel Li deposition-regulation technology in LMAs targeting for next generation high-energy-density batteries.

Skeleton designs are widely seen in the robotics industry and multimedia applications such as animated films and computer games. The design of skeletons is mental-labor intensive, especially axes directions of joints are difficult even for the most experienced designers to select. In the existing works, there are auto creation of skeletons from meshes, and skeleton axes optimizations from a predefined set of actions. In this work, we extend automatic construction of skeletons by proposing skeleton axes design from an objective task. A two-layered framework is proposed to optimize randomly initialized axes based on auto generated actions. First, the limit of skeleton scales that can be automatically designed is discussed. Second, a self-adaptive actions discretizer implemented by neural networks is proposed to reduce the optimization complexity. Third, actions and axes are scored by physics engine simulation, and optimizations on the score generate axes that have the best performance. Experiments include robotic designs and an animation application. Comparisons show that the proposed framework outperforms other mainstream optimizers both in speed and effectiveness.

A cyber–physical system is a mechanism controlled or monitored by computer-based algorithms, tightly integrated with the internet and its users. Cyber–physical systems such as smart grid, autonomous automobile systems, medical monitoring, process control systems, robotics systems, and automatic pilot avionics will use physical sensors to produce and collect data. Most of the data contains personal information, which is so called privacy, should be carefully protected. How to protect privacy is now a hot-topic not only in academia but also in industry. Differential privacy has been accepted as the privacy concept due to its concise definition and its simple implementation. However, the interactive model cannot achieve differential privacy without data provider’s timely answers, which means data provider should always be attachable. It is unrealistic to keep data provider online due to the risk of data provider be broken will grow rapidly as time goes by. With today’s differential privacy technology, a non-interactive model remains an open problem. To find an alternative, we consider implant whole dataset into a cloud server to provide all the functions instead of data provider. Nonetheless, once the server is compromised, the privacy of the data cannot be guaranteed. It appears that there should be a strong definition, the cloud server is completely trustworthy, before differential privacy can actually be implemented. An intuitive thought to improve this situation is to only upload encrypted datasets. Then, the server could be semi-honest or even fully malicious. Homomorphic encryption can make the encrypted dataset operable, but it requires considerable storage space and bandwidth, which are impractical. We realized that order-preserving encryption is a tradeoff between data utility and practicability. Thus, we propose a novel outsourcing differential privacy data release scheme in cyber–physical system. The proposed scheme allows data providers to outsource their datasets to a cloud service provider with low communication cost. Let the cloud service provider be the host that answers the queries from the data evaluator with noisy results. The data providers can go offline after uploading their encrypted datasets, which is one of the critical requirements for a practical system. In this paper, we present a detailed theoretical analysis, including proofs of differential privacy and security. We also report an experimental evaluation on real datasets.

Despite the remarkable advances in mitigating ice formation and accretion, however, no engineered anti-icing surfaces today can durably prevent frost formation, droplet freezing, and ice accretion in an economical and ecofriendly way. Herein, sustainable and low-cost electrolyte hydrogel (EH) surfaces are developed by infusing salted water into a hydrogel matrix for avoiding icing. The EH surfaces can both prevent ice/frost formation for an extremely long time and reduce ice adhesion strength to ultralow value (Pa-level) at a tunable temperature window down to -48.4 °C. Furthermore, ice can self-remove from the tilted EH surface within 10 s at -10 °C by self-gravity. As demonstrated by both molecular dynamic simulations and experiments, these extreme performances are attributed to the diffusion of ions to the interface between EH and ice. The sustainable anti-icing properties of EH can be maintained by replenishing in real-time with available ion sources, indicating the promising applications in offshore platforms and ships.