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With the continued proliferation of mobile Internet and geo-locating technologies, carpooling as a green transport mode is widely accepted and becoming tremendously popular worldwide. In this paper, we focus on a popular carpooling service called ride hitching, which is typically implemented using a publish/subscribe approach. In a ride hitching service, drivers subscribe ride orders published by riders and continuously receive matching ride orders until one is picked. The current systems (e.g., Didi Hitch) adopt a threshold-based approach to filter out ride orders. That is, a new ride order will be sent to all subscribing drivers whose planned trips can match the ride order within a pre-defined detour threshold. A limitation of this approach is that it is difficult for drivers to specify a reasonable detour threshold in practice. In addressing this problem, we propose a novel top-k subscription query called Top-k Ride Subscription (TkRS) query, which continuously returns the best k ride orders that match drivers' trip plans to them. We propose two efficient algorithms to enable the top-k results maintenance. We also design a novel hybrid index and a two-level buffer to efficiently track the top- $k$ results. Finally, extensive experiments on real-life datasets suggest that our algorithms can achieve desirable performance in practical settings.

Community search that finds query-dependent communities has been studied on various kinds of graphs. As one instance of community search, intimate-core group (community) search over a weighted graph is to find a connected k-core containing all query nodes with the smallest group weight. However, existing state-of-the-art methods start from the maximal k-core to refine an answer, which is practically inefficient for large networks. In this paper, we develop an efficient framework, called local exploration k-core search (LEKS), to find intimate-core groups in graphs. We propose a small-weighted spanning tree to connect query nodes, and then expand the tree to a connected k-core, which is finally refined as an intimate-core group. In addition, to support the intimate group search over large weighted graphs, we develop a weighted-core index (WC-index) and two new WC-index-based algorithms for expansion and refinement phases in LEKS. Specifically, we propose a WC-index-based expansion to efficiently find a candidate graph of intimate-core group, leveraging on a two-level expansion of k-breadth and 1-depth. We propose coarse-grained/fine-grained deletion strategies to achieve high-quality answers in a few iterations. Extensive experiments on real-life networks with ground-truth communities validate the effectiveness and efficiency of our proposed methods.

Sketching is a common approach to visually query time series data. However, a recent study reported that sketching a pattern for querying is "often ineffective on its own" in practice due to lack of "representative objects" to facilitate bottom-up search. In this demonstration, we present a novel data-driven sketch-based visual query interface (VQI) construction system called SENSOR to alleviate this challenge. Given a time series dataset, SENSOR automatically constructs its VQI by populating different components from the underlying data. Specifically, it discovers and exposes a set of representative objects in the form of VST-aware shapelets to facilitate query formulation. Such data-driven construction has several potential benefits such as empowering efficient top-down and bottom-up search and portability of the interface across different application domains and sources.

Network K -function has been the de facto operation for analyzing point patterns in spatial networks, which is widely used in many communities, including geography, ecology, transportation science, social science, and criminology. To analyze a location dataset, domain experts need to generate a network K -function plot that involves computing multiple network K -functions. However, network K -function is a computationally expensive operation that is not feasible to support large-scale datasets, let alone to generate a network K -function plot. To handle this issue, we develop two efficient algorithms, namely count augmentation (CA) and neighbor sharing (NS), which can reduce the worst-case time complexity for computing network K -functions. In addition, we incorporate the advanced shortest path sharing (ASPS) approach into these two methods to further lower the worst-case time complexity for generating network K -function plots. Experiment results on four large-scale location datasets (up to 7.33 million data points) show that our methods can achieve up to 165.85x speedup compared with the state-of-the-art methods.

The service life of refractory brick in the slag tapping hole of the entrained flow gasifier was significantly lower than that in other locations. It was critically important to study the corrosion mechanism of refractory brick in the slag tapping hole for guiding industrial production. Considering the complex flow field in the slag tapping hole, the influence of gas velocity and temperature fluctuation on the service life of refractory brick was investigated in this study. The results showed that the slag flow characteristics periodically changed with the gas temperature and velocity fluctuations. The brick corrosion rate increased with the decrease of fluctuation frequency. Compared with the gas-phase velocity fluctuation, the gas-phase temperature fluctuation had a more significant influence on the brick corrosion rate. When the fluctuation frequency was 0.01 Hz and the gas temperature fluctuation amplitude was 200 K, the corrosion rate of refractory bricks increased by 25%. It could be concluded that the fluctuation of gas-phase temperature was the main cause for the low service life of refractory bricks in the tapping hole.

Graph query autocompletion (GQAC) takes a user’s graph query as input and generates top-k query suggestions as output, to help alleviate the verbose and error-prone graph query formulation process in a visual interface. To compose a target query with GQAC, the user may iteratively adopt suggestions or manually add edges to augment the existing query. The current state-of-the-art of GQAC, however, focuses on a large collection of small- or medium-sized graphs only. The subgraph features exploited by existing GQAC are either too small or too scarce in large graphs. In this paper, we present Flexible graph query autocompletion for LArge Graphs, called FLAG. We are the first to propose wildcard labels in the context of GQAC, which summarizes query structures that have different labels. FLAG allows augmenting users’ queries with subgraph increments with wildcard labels to form suggestions. To support wildcard-enabled suggestions, a new suggestion ranking function is proposed. We propose an efficient ranking algorithm and extend an index to further optimize the online suggestion ranking. We have conducted a user study and a set of large-scale simulations to verify both the effectiveness and efficiency of FLAG. The results show that the query suggestions saved roughly 50% of mouse clicks and FLAG returns suggestions in few seconds.

Time-series shapelets are discriminative subsequences, recently found effective for time series classification (tsc). It is evident that the quality of shapelets is crucial to the accuracy of tsc. However, major research has focused on building accurate models from some shapelet candidates. To determine such candidates, existing studies are surprisingly simple, e.g., enumerating subsequences of some fixed lengths, or randomly selecting some subsequences as shapelet candidates. The major bulk of computation is then on building the model from the candidates. In this paper, we propose a novel efficient shapelet discovery method, called bspcover, to discover a set of high-quality shapelet candidates for model building. Specifically, bspcover generates abundant candidates via Symbolic Aggregate approXimation with sliding window, then prunes identical and highly similar candidates via Bloom filters, and similarity matching, respectively. We next propose a $p$p-Cover algorithm to efficiently determine discriminative shapelet candidates that maximally represent each time-series class. Finally, any existing shapelet learning method can be adopted to build a classification model. We have conducted extensive experiments with well-known time-series datasets and representative state-of-the-art methods. Results show that bspcover speeds up the state-of-the-art methods by more than 70 times, and the accuracy is often comparable to or higher than existing works.

The optimal sequenced route (OSR) query, as a popular problem in route planning for smart cities, searches for a minimum-distance route passing through several POIs in a specific order from a starting position. In reality, POIs are usually rated, which helps users in making decisions. Existing OSR queries neglect the fact that the POIs in the same category could have different scores, which may affect users’ route choices. In this paper, we study a novel variant of OSR query, namely Rating Constrained Optimal Sequenced Route query (RCOSR), in which the rating score of each POI in the optimal sequenced route should exceed the query threshold. To efficiently process RCOSR queries, we first extend the existing TD-OSR algorithm to propose a baseline method, called MTDOSR. To tackle the shortcomings of MTDOSR, we try to design a new RCOSR algorithm, namely Optimal Subroute Expansion (OSE) Algorithm. To enhance the OSE algorithm, we propose a Reference Node Inverted Index (RNII) to accelerate the distance computation of POI pairs in OSE and quickly retrieve the POIs of each category. To make full use of the OSE and RNII, we further propose a new efficient RCOSR algorithm, called Recurrent Optimal Subroute Expansion (ROSE), which recurrently utilizes OSE to compute the current optimal route as the guiding path and update the distance of POI pairs to guide the expansion. Then, we extend our techniques to handle a variation of RCOSR query, namely RCkOSR query. The experimental results demonstrate that the proposed algorithm significantly outperforms the existing approaches.

Advanced artificial intelligence techniques, such as the federated learning, has been applied to broad areas, e.g., image classification, speech recognition, smart city, and healthcare. Despite intensive research on the federated learning, existing schemes are vulnerable to attacks and cannot meet the security requirement for real applications. The problem of designing a secure federated learning framework to ensure the correctness of training procedure has not been sufficiently studied and remains open. In this paper, we propose VFChain, a verifiable and auditable federated learning framework based on the blockchain system. First, to provide the verifiability, a committee is selected through the blockchain to collectively aggregate models and record verifiable proofs in the blockchain. Then, to provide the auditability, a novel authenticated data structure is proposed for blockchain to improve the search efficiency of verifiable proofs and support a secure rotation of committee. Finally, to further improve the efficiency, an optimization scheme is proposed to support multiple-model learning tasks. We implement VFChain and conduct extensive experiments by utilizing the popular deep learning model and the public real-world dataset. The evaluation results demonstrate the effectiveness of our proposed VFChain system.

Adaptive control methods are suitable for offshore steel structures subject to harmful vibrations, as they employ reference models to adapt to coastal and nearshore physics. To decrease the dependence on the accurate characteristics of the offshore platform, a compensating measure containing the ocean environment is proposed in the adaptive control scheme. With incomplete states as the driving input, external loads are approximated using a wavelet neural network frame. Numerical experiments are conducted on a platform model with varying parameters to test the performance of the proposed adaptive controller. It is shown that the adaptive weights derived from the chosen Lyapunov function are qualified both theoretically and practically. The system-output-based adaptive controller overcomes the disadvantage of state loss. The compensated disturbance environment guarantees the reliability of the restored reference system based on mismatched physics. The designed estimator as a part of the adaptive controller compensates for the deviations of the environment between the reference and the practical, resulting in a desirable reduction in the excessive vibration.

The recent years have witnessed the growing popularity of indoor location-based services (LBS) in practice and research. Among others, indoor shortest path query (ISPQ) is of fundamental importance for indoor LBS. However, existing works on ISPQ ignore indoor temporal variations, e.g., the open and close times associated with entities like doors and rooms. In this paper, we define a new type of query called Indoor Temporal-variation aware Shortest Path Query (ITSPQ). It returns the valid shortest path based on the up-to-date indoor topology at the query time. A set of techniques is designed to answer ITSPQ efficiently. We design a graph structure (IT-Graph) that captures indoor temporal variations. To process ITSPQ using IT-Graph, we design two algorithms that check a door's accessibility synchronously and asynchronously. Furthermore, we propose a novel index structure (IT-Index) that extends the state-of-the-art index significantly by storing dynamic door-to-door distances in a compact distance cube associated with tree nodes. When processing ITSPQ using IT-Index, we make use of the distance cube to avoid time-consuming indoor distance computation on-the-fly. We evaluate the proposed techniques using extensive experiments on synthetic and real data. The results show that our IT-Index based method is the most efficient for processing ITSPQ at a modest cost of index memory consumption.

Spatial-temporal kernel density visualization (STKDV) has been extensively used in a wide range of applications, e.g., disease outbreak analysis, traffic accident hotspot detection, and crime hotspot detection. While STKDV can provide accurate and comprehensive data visualization, computing STKDV is time-consuming, which is not scalable to large-scale datasets. To address this issue, we develop a new sliding-window-based solution (SWS), which theoretically reduces the time complexity for generating STKDV, without increasing the space complexity. Moreover, we incorporate SWS with the progressive visualization framework, which can continuously output partial visualization results to users (from coarse to fine), until users satisfy the visualization. Our experimental studies on five large-scale datasets show that SWS achieves 1.71x to 24x speedup compared with the state-of-the-art methods.

A counter-current downer is proposed as a new structure to improve the flash ironmaking technology (FIT). A computational fluid dynamics (CFD) model was established to predict the dilute gas–particle reacting flow, and a core–annulus structure was observed. Furthermore, counter-current and cocurrent downers have been compared in similar conditions. As a result, reduced iron of the counter-current downer had a higher reduction degree (99.8%) than that of the cocurrent downer (70.1%). The first two reaction steps (Fe2O3 → Fe3O4 and F3O4 → FeO) were swift, while the subsequent step (FeO → Fe) in the counter-current downer showed a significant difference due to the preponderant reduction potential and temperature. Different gas velocities (0.167–0.667 m/s) were also investigated, and the high-speed gas flow brought more particles with decreasing metal yield from 89.3% to 27.5%. However, more gas amounts always led to a higher reduction degree separately of captured and escaped particles.

Kernel density visualization (KDV) has been the de facto method in many spatial analysis tasks, including ecological modeling, crime hotspot detection, traffic accident hotspot detection, and disease outbreak detection. In these tasks, domain experts usually generate multiple KDVs with different bandwidth values. However, generating a single KDV, let alone multiple KDVs, is time-consuming. In this paper, we develop a share-and-aggregate framework, namely SAFE, to reduce the time complexity of generating multiple KDVs given a set of bandwidth values. On the other hand, domain experts can specify bandwidth values on the fly. To tackle this issue, we further extend SAFE and develop the exact method SAFE all and the 2-approximation method SAFE exp which reduce the time complexity under this setting. Experimental results on four large-scale datasets (up to 4.33M data points) show that these three methods achieve at least one-order-of-magnitude speedup for generating multiple KDVs in most of the cases without degrading the visualization quality.

TJP2/ZO-2-inactivating mutations in humans cause progressive cholestatic liver disease. Liver-specific deletion of Tjp2 in the mouse (Tjp2 cKO mice) leads to mild progressive cholestasis without an overt degradation of the bile-blood barrier (BBB). These mice are more susceptible to cholic acid (CA) induced liver injury. Interestingly, while initially also more susceptible, Tjp2 cKO mice develop tolerance to a DDC-supplemented diet. The DDC diet induces an exacerbated ductular reaction in Tjp2 cKO mice, which arises from the transdifferentiation of hepatocytes to cholangiocytes. Consequently, this transdifferentiation is only observed if Tjp2 is inactivated in hepatocytes, but not if deleted in cholangiocytes. The DDC-diet-induced hepatocyte transdifferentiation in Tjp2 cKO mice requires Yap and Wwtr1/Taz, whose protein expression is upregulated in hepatocytes lacking Tjp2, but is independent of Notch2. Although inactivating Tjp2 is sufficient for the upregulation of Yap and Wwtr1/Taz protein, efficient transdifferentiation requires the DDC-diet insult. Thus, Tjp2 negatively regulates Yap/Taz-mediated transdifferentiation of hepatocytes to cholangiocytes in response to DDC-diet-induced liver injury. Furthermore, transdifferentiation is regulated at multiple levels and the type of injury inflicted on the Tjp2 deficient liver plays an important role in the resulting pathophysiology.

Opposed multi-burner (OMB) gasification technology is the first large-scale gasification technology developed in China with completely independent intellectual property rights. It has been widely used around the world, involving synthetic ammonia, methanol, ethylene glycol, coal liquefaction, hydrogen production and other fields. This paper summarizes the research and development process of OMB gasification technology from the perspective of the cold model experiment and process simulation, pilot-scale study and industrial demonstration. The latest progress of fundamental research in nozzle atomization and dispersion, mixing enhancement of impinging flow, multiscale reaction of different carbonaceous feedstocks, spectral characteristic of impinging flame and particle characteristics inside gasifier, and comprehensive gasification model are reviewed. The latest industrial application progress of ultra-large-scale OMB gasifier and radiant syngas cooler (RSC) combined with quenching chamber OMB gasifier are introduced, and the prospects for the future technical development are proposed as well.

The high-temperature syngas and molten slag droplets discharged from entrained-flow coal gasifiers contain a large amount of heat energy, which can be efficiently recovered by radiant syngas coolers (RSCs). However, it is hard to know the solidification degree of molten slag droplets at the outlet of an RSC during industrial operations. In this work, the industrial-scale RSC and molten slag droplet models are established to predict the solidification degree of slag droplets at the outlet of the RSC. Then, the effects of slag diameter, syngas flow field, slag initial temperature, slag porosity, and slag pore structure are investigated by numerical simulations, and residence time as well as complete solidification time are calculated by coupling of a discrete-phase model and a solidification model. The results indicate that as the slag droplet diameter increases, the residence time of the slag droplet shortens, but the complete solidification time increases. When the slag droplet diameter is greater than or equal to 3.0 mm, the complete solidification time is larger than the residence time, and the slag droplet cannot solidify completely at the outlet of the RSC. The solidification degree in the windward zone is greater than that in the leeward zone. Although the slag initial temperature has little effect on the solidification, a lower slag initial temperature is still conducive to a greater solidification degree. Additionally, the pore structure facilitates solidification, and the promoting effect of penetrated pores is more remarkable than that of closed pores. A larger porosity is also beneficial to accelerate the solidification of molten slag droplets and increase the solidification degree.

Blockchain technology has emerged as the cornerstone of many decentralized applications operating among otherwise untrusted peers. However, it is well known that existing blockchain systems do not scale well. Transactions are often executed and committed sequentially in order to maintain the same view of the total order. Furthermore, it is necessary to duplicate both transaction data and their executions in every node in the blockchain network for integrity assurance. Such storage and computation requirements put significant burdens on the blockchain system, not only limiting system scalability but also undermining system security and robustness by making the network more centralized. To tackle these problems, in this paper, we propose SlimChain, a novel blockchain system that scales transactions through off-chain storage and parallel processing. Advocating a stateless design, SlimChain maintains only the short commitments of ledger states on-chain while dedicating transaction executions and data storage to off-chain nodes. To realize SlimChain, we propose new schemes for off-chain smart contract execution, on-chain transaction validation, and state commitment. We also propose optimizations to reduce network transmissions and a new sharding technique to improve system scalability further. Extensive experiments are conducted to validate the performance of the proposed SlimChain system. Compared with the existing systems, SlimChain reduces the on-chain storage requirements by 97% ~ 99%, while also improving the peak throughput by 1.4× ~ 15.6×.

Due to the unstructuredness and the lack of schema information of knowledge graphs, social networks and RDF graphs, keyword search has been proposed for querying such graphs/networks. Recently, various keyword search semantics have been designed. In this paper, we propose a generic ontology-based indexing framework for keyword search, called Bisimulation of Generalized Graph Index ( $\mathsf {BiG\hbox{-}index}$ BiG - index ), to enhance the search performance. The novelties of $\mathsf {BiG\hbox{-}index}$ BiG - index reside in using an ontology graph $G_{Ont}$ G O n t to summarize and index a data graph $G$ G iteratively, to form a hierarchical index structure $\mathbb {G}$ G . $\mathsf {BiG\hbox{-}index}$ BiG - index is generic since it only requires keyword search algorithms to generate query answers from summary graphs having two simple properties. Regarding query evaluation, we transform a keyword search $q$ q into $\mathbb {Q}$ Q according to $G_{Ont}$ G O n t in runtime. The transformed query is searched on the summary graphs in $\mathbb {G}$ G . The efficiency is due to the small sizes of the summary graphs and the early pruning of semantically irrelevant subgraphs. To illustrate $\mathsf {BiG\hbox{-}index}$ BiG - index 's applicability, we show popular indexing techniques for keyword search (e.g., $\mathsf {Blinks}$ Blinks and $\mathsf {r\hbox{-}clique}$ r - clique ) can be easily implemented on top of $\mathsf {BiG\hbox{-}index}$ BiG - index . Our extensive experiments show that $\mathsf {BiG\hbox{-}index}$ BiG - index reduced the runtimes of popular keyword search work $\mathsf {Blinks}$ Blinks by 50.5 percent and $\mathsf {r\hbox{-}clique}$ r - clique by 29.5 percent.

Time series shapelets are short discriminative subsequences that recently have been found not only to be accurate but also interpretable for the classification problem of univariate time series (UTS). However, existing work on shapelets selection cannot be applied to multivariate time series classification (MTSC) since the candidate shapelets of MTSC may come from different variables of different lengths and thus cannot be directly compared. To address this challenge, in this paper, we propose a novel model called ShapeNet, which embeds shapelet candidates of different lengths into a unified space for shapelet selection. The network is trained using cluster-wise triplet loss, which considers the distance between anchor and multiple positive (negative) samples and the distance between positive (negative) samples, which are important for convergence. We compute representative and diversified final shapelets rather than directly using all the embeddings for model building to avoid a large fraction of non-discriminative shapelet candidates. We have conducted experiments on ShapeNet with competitive state-of-the-art and benchmark methods using UEA MTS datasets. The results show that the accuracy of ShapeNet is the best of all the methods compared. Furthermore, we illustrate the shapelets’ interpretability with two case studies.

Motivated by location-based social networks which allow people to access location-based services as a group, we study a novel variant of optimal sequenced route (OSR) queries, optimal sequenced route for group meetup (OSR-G) queries. OSR-G query aims to find the optimal meeting POI (point of interest) such that the maximum users’ route distance to the meeting POI is minimized after each user visits a number of POIs of specific categories (e.g., gas stations, restaurants, and shopping malls) in a particular order. To process OSR-G queries, we first propose an OSR-Based (OSRB) algorithm as our baseline, which examines every POI in the meeting category and utilizes existing OSR (called E-OSR) algorithm to compute the optimal route for each user to the meeting POI. To address the shortcomings (i.e., requiring to examine every POI in the meeting category) of OSRB, we propose an upper bound based filtering algorithm, called circle filtering (CF) algorithm, which exploits the circle property to filter the unpromising meeting POIs. In addition, we propose a lower bound based pruning (LBP) algorithm, namely LBP-SP which exploits a shortest path lower bound to prune the unqualified meeting POIs to reduce the search space. Furthermore, we develop an approximate algorithm, namely APS, to accelerate OSR-G queries with a good approximation ratio. Finally the experimental results show that both CF and LBP-SP outperform the OSRB algorithm and have high pruning rates. Moreover, the proposed approximate algorithm runs faster than the exact OSR-G algorithms and has a good approximation ratio.

This study aims at investigating the influence of turbulence modulation caused by a thread structure on coaxial air-blast atomization by means of high-speed flow visualizations and droplet particle size techniques. The medium in the central channel of an atomizer is water while the annular channel is airflow. The results show that the thread structure added to the inner surface of an annular channel plays an important role in atomization effect. The generated liquid ligaments on the jet present more structures, which grow shorter and breakup faster than that without thread. To compare the difference in jet breakup length and droplet diameter caused by the thread structure, we establish a new breakup length model and then use the ratio of structure factors to describe the change in the droplet diameter. The results in this experiment confirm the improvement of atomization performance brought by optimization design of the thread structure.

To date severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has infected over 100 million individuals resulting in over two million deaths. Many vaccines are being deployed to prevent coronavirus disease 2019 (COVID-19) including two novel mRNA-based vaccines1,2. These vaccines elicit neutralizing antibodies and appear to be safe and effective, but the precise nature of the elicited antibodies is not known3–6. Here we report on the antibody and memory B cell responses in a cohort of 20 volunteers who received either the Moderna (mRNA-1273) or Pfizer-BioNTech (BNT162b2) vaccines. Consistent with prior reports, 8 weeks after the second vaccine injection volunteers showed high levels of IgM, and IgG anti-SARS-CoV-2 spike protein (S) and receptor binding domain (RBD) binding titers3,5,6. Moreover, the plasma neutralizing activity, and the relative numbers of RBD-specific memory B cells were equivalent to individuals who recovered from natural infection7,8. However, activity against SARS-CoV-2 variants encoding E484K or N501Y or the K417N:E484K:N501Y combination was reduced by a small but significant margin. Consistent with these findings, vaccine-elicited monoclonal antibodies (mAbs) potently neutralize SARS-CoV-2, targeting a number of different RBD epitopes in common with mAbs isolated from infected donors. Structural analyses of mAbs complexed with S trimer suggest that vaccine- and virus-encoded S adopts similar conformations to induce equivalent anti-RBD antibodies. However, neutralization by 14 of the 17 most potent mAbs tested was reduced or abolished by either K417N, or E484K, or N501Y mutations. Notably, the same mutations were selected when recombinant vesicular stomatitis virus (rVSV)/SARS-CoV-2 S was cultured in the presence of the vaccine elicited mAbs. Taken together the results suggest that the monoclonal antibodies in clinical use should be tested against newly arising variants, and that mRNA vaccines may need to be updated periodically to avoid potential loss of clinical efficacy.

With the emergence and growing popularity of and location-based service (LBS) technologies, the continuous k nearest neighbor (CO k NN) query in obstructed space is becoming a very important service. In this article, we study the CO k NN in obstructed space, which retrieves k obstructed nearest neighbors (ONNs) for every point on a query segment q̄ . The state-of-the-art approach, called Euclidean based CONN (E-CONN), exploits an R-tree to traverse the dataset P in ascending order of their Euclidean distances to q̄ . Taking a different point of view, in this article, we explore the idea of Voronoi diagram to define the notion of obstructed Voronoi diagram (OVD) . The Voronoi cells with obstacles are divided into visible and invisible regions for quickly answering nearest neighbor queries. To facilitate efficient retrieval of Voronoi cells and processing of continuous nearest neighbor (CONN) queries, we propose a new grid-based index, called Voronoi diagram with Obstacles in Grid (VO-Grid) , which indexes Voronoi cells and associated obstacle information with a grid file. Based on VO-Grid, we propose an efficient algorithm, called CONN with VO-Grid Acceleration (CONN-VOA), to accelerate the CONN query processing. Moreover, we extend CONN-VOA to the CO k NN query, which also explores effective filtering and early termination for reducing redundant access of data objects. A comprehensive performance evaluation using both real and synthetic datasets is conducted to validate the proposed ideas and demonstrate the efficiency of our algorithms. The experimental results show that the CONN-VOA algorithm substantially outperforms E-CONN algorithm.

The flowability of granular material is commonly determined from shear cell tests, but in the real world, the granular flow is quite different in many cases. For this paper, the flowabilities in a narrow channel of five different granular materials were investigated. The flow parameters of the granular flows were determined to classify the flowabilities. Granular flow surface velocities and flow heights and thicknesses were measured, and it was found that the mean shear rates in the linear portions of the velocity profiles increase with both flow rates and inclination. A universal parameter, the ratio of the sine of the inclination to the shear rate in the linear part of the velocity profile, is proposed as a constant. This constant is independent of particle properties. In addition, a flowability parameter, the ratio of the mean shear rate to the cosine of the inclination, is proposed to characterize the friction between the particles in the flowing granular material.

Fractures have significantly impacted the preservation, enrichment, and productivity of shale gas in the Longmaxi Formation in the complex structural area of the southern Sichuan Basin. Based on outcrop, core, imaging logging, inclusion analysis, acoustic emission experiments, and burial and thermal evolution history data, the formation stage, evolution and preservation conditions of structural fractures were comprehensively studied. Shear fractures are the main features of tectonic origin and are characterized by small widths, wide spacing, and high filling degrees. The structural fractures of the Longmaxi Formation can be divided into three groups in terms of their formation stage. The first-stage fractures were formed in the middle-late period of the Yanshanian tectonic movement (86.2–68.5 Ma), when the palaeotectonic stress was 68.33–71.82 MPa; and the homogenization temperature of the fluid inclusions in the corresponding fracture fillings is 150–178°C. The second-stage fractures were formed at the end of the Yanshanian -early Himalayan tectonic movement (68.5–35.2 Ma), when the palaeotectonic stress was 85.2–100.5 MPa; the homogenization temperature of the corresponding fluid inclusions is 123–148°C. The third-stage tectonic fractures formed during the middle-late period of the Himalayan tectonic movement (35.2–0 Ma), when the palaeotectonic stress was 55.6–63.8 MPa; fillings are scarce in these fractures, but the measured homogenization temperature of the corresponding fluid inclusions is 90–110°C. Shale gas preservation conditions were affected by the fracture formation stage, maximum horizontal crustal stress, fault scale, structural burial depth, formation pressure coefficient, etc. Three types of favourable preservation conditions were identified. The Type I target area is the low slope area in the middle of the syncline, which occurs more than 2.0 km from the fault. The research results have guiding significance for the evaluation of the preservation conditions of deep shale gas in the study area and similar areas.

In this paper, an adaptive learning H∞ controller is proposed to attenuate the wave-wind-induced vibration of a jacket offshore platform with varying control delay and structured uncertainty. A novel scheme is proposed regarding bounded H∞ performance and nonlinear uncertainty approximation online, incorporated with adaptive and fixed parts respectively. The adaptive part is self-adjusting based on neural network updating laws, and the fixed part is derived through minimizing the generalized H∞ disturbance attenuation index. In the presented scheme, accurate values of real-time control delay and upper-bounds of uncertainties are not necessarily required. The stability of controller is proved by Lyapunov functions following the simulation results. Comparable experiments efficiently illustrate feasibility and vibration-attenuation effectiveness of the proposed approach in both shallow and deep water.

For a more in-depth analysis on the marine chemical condition during the earliest Cambrian, mechanism of organic matter enrichment and exploration potential of shale gas resources, based on the petrology, organic geochemistry, and elemental characteristics of the W207 Well, this study discuss the Early Cambrian paleo-ocean environment and factors controlling of organic matter enrichment during the deposition of black strata Qiongzhusi Formation in the upslope areas, southwestern Sichuan Basin, Yangtze Block, China. The sedimentary cycles show that during the depositon of fine-grained Qiongzhusi Fm, interactive cycles of multiple deep-water and shallow-water shelf developed, and were controlled by the rise and fall in sea level. In particular, the slope turbidite (fan) and gravity flow sediments indicate that the shallow-water shelf facies were dominant in the upslope area (SW Sichuan Basin), where not in deep water for a long time, and the sedimentary thickness of the organic rich black shale was limited. Evidence from organic geochemistry indicates that the organic matter of the Qiongzhusi Fm from the W207 Well was mainly of type I kerogen, with a high degree of thermal evolution, little residual hydrocarbon, and low capacity for hydrocarbon generation. The redox parameters indicate that the marine environment on the upslope was medium restrictive, with some upwelling. The seawater had undergone a transformation according to anoxic–oxidic. Therefore, paleo-ocean productivity in the upslope area was generally low, and exhibited a prominent downward trend from bottom to top. Given its high risk of exploration, the authors suggest that the direction of shale gas resource evaluation should change to the intracratonic sag (downslope) area, which is characterized by deep-water shelf facies.

The present study was to evaluated the clinical value of anatomical thermal ablation to treat hepatocellular carcinoma compared with routine thermal ablation.Hepatocellular carcinoma patients with tumor diameter ≤50 mm treated by thermal ablation at our center were retrospectively enrolled from October 2015 to December 2018. Enrolled patients were grouped into the anatomical ablation group and routine ablation group, respectively. To minimize the effects of potential confounders from selection bias, a propensity score matching was carried out. Technical efficacy, recurrence and survivals rates were compared.Altogether 101 patients (119 lesions) were grouped into the anatomical ablation group and 101 patients (131 lesions) into the routine ablation group. The ablation zone volume of the anatomical ablation group was 36.8 (2.5-176.9) ml, significantly larger than that of the routine ablation group (28.5 [28.5 (2.8-184.3) ml] (p = 0.005)). Adjusted with propensity score matching, The 1-, 2-, and 3-year local recurrence rates were 0.0%, 0.0%, and 0.0% for the anatomical ablation group and 6.9%, 10.1%, and 10.1% for the routine ablation group, respectively (p = 0.013). The cumulative 1-, 2-, and 3-year progression-free survival rates were 93.4%, 82.7%, and 79.0% for the anatomical ablation group, 74.2%, 56.9%, and 51.6% for the routine ablation group (p = 0.001).Anatomical ablation could be a favorable ablation strategy to improve therapeutic effect of thermal ablation for HCC with visible feeding vessels and reserved liver function.

Structural diversity of a user in a social network is the number of social contexts in his/her contact neighborhood. The problem of structural diversity search is to find the top-k vertices with the largest structural diversity in a graph. However, when identifying distinct social contexts, existing structural diversity models (e.g., t-sized component, t-core, and t-brace) are sensitive to an input parameter of t. To address this drawback, we propose a parameter-free structural diversity model. Specifically, we propose a novel notation of discriminative core, which automatically models various kinds of social contexts without parameter t. Leveraging on discriminative cores and h-index, the structural diversity score for a vertex is calculated. We study the problem of parameter-free structural diversity search in this paper. An efficient top-k search algorithm with a well-designed upper bound for pruning is proposed. To further speed up the computation, we design a novel parallel algorithm for efficient top-k search over large graphs. The parallel algorithm computes diversity scores for a batch of vertices simultaneously using multi-threads. Extensive experiment results demonstrate the parameter sensitivity of existing t-core based model and verify the superiority of our methods.

With the help of Aspen Plus, a two-dimensional unsteady CFD model is developed to simulate the coal gasification process in a fixed bed gasifier. A developed and validated two dimensional CFD model for coal gasification has been used to predict and assess the viability of the syngas generation from coal gasification employing the updraft fixed bed gasifier. The process rate model and the sub-model of gas generation are determined. The particle size variation and char burning during gasification are also taken into account. In order to verify the model and increase the understanding of gasification characteristics, a set of experiments and numerical comparisons have been carried out. The simulated results in the bed are used to predict the composition of syngas and the conversion of carbon. The model proposed in this paper is a promising tool for simulating the coal gasification process in a fixed bed gasifier.

Hierarchical directed acyclic graph (HDAG) is an essential graph model to represent terminology relationships in a hierarchy, such as Disease Ontology, Gene Ontology, and Wikipedia. However, due to massive terminologies and complex structures in a HDAG, an end user might feel difficult to explore and summarize the whole graph, which is practically useful but less studied in the literature. In this demo, we develop an interactive system of HDAG-Explorer to help users summarize HDAG with highly important and diverse vertices. Our HDAG-Explorer system exhibits several useful features including summarized visualization, interactive exploration, and structural statistics report. All these features facilitate in-depth understanding of the HDAG data. We showcase the usability of the HDAG-Explorer through two real-world applications of summarized topic recommendation and visual data exploration.

Massive amounts of data that contain spatial, textual, and temporal information are being generated at a rapid pace. With streams of such data, which includes check-ins and geo-tagged tweets, available, users may be interested in being kept up-to-date on which terms are popular in the streams in a particular region of space. To enable this functionality, we aim at efficiently processing two types of general top-k term subscriptions over streams of spatio-temporal documents: region-based top-k spatial-temporal term (RST) subscriptions and similarity-based top-k spatio-temporal term (SST) subscriptions. RST subscriptions continuously maintain the top-k most popular trending terms within a user-defined region. SST subscriptions free users from defining a region and maintain top-k locally popular terms based on a ranking function that combines term frequency, term recency, and term proximity. To solve the problem, we propose solutions that are capable of supporting real-life location-based publish/subscribe applications that process large numbers of SST and RST subscriptions over a realistic stream of spatio-temporal documents. The performance of our proposed solutions is studied in extensive experiments using two spatio-temporal datasets.