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Low temperature is one of the major drawbacks of electric cars in high latitudes. This problem can be addressed using a battery self-preheating system. The existing self-heating systems have problems, such as slow heating rate, complex control system, single thermal management function and poor safety. Herein, we propose a conductive phase change material (cPCM)-based self-preheating system that generates heat simultaneously using the internal resistance of cells and external resistance of the cPCM. This self-preheating system shows a high heating rate of 17.14 °C/min and excellent temperature uniformity (temperature difference of 3.58 °C). The system can preheat the battery safely in the capacity range of 20%–100%. When the battery pack is set in −20 °C, the effective electric energy can be increased by 550% after preheating. An energy conversion model is also built to measure the relationship between the energy improvement of battery and the energy consumption by preheating. This energy conversion model can help the system to make the optimal preheating strategy and obtain the maximum discharge energy. Nevertheless, based on the outcome of this study, this self-preheating strategy can effectively solve the problem of battery in low temperatures and maximize the effective electric energy of the battery. • A battery self-heating system with cPCM as external heating resistance was proposed. • Preheating rate of this system reached 17.14 °C/min under a small current of 9.6 A. • Maximum temperature difference of a 10-cells battery pack was only 3.58 °C. • An energy conversion model of the self-preheating system was developed. • Energy conversion relationship of the battery at low temperatures was explained. [ABSTRACT FROM AUTHOR]

[Display omitted] • The prepared CPCM exhibits excellent flexibility at room temperature. • FCPCM possesses high enthalpy, high thermal conductivity and electrical conductivity. • FCPCM has wide temperature range of flexibility and shape memory properties. • FCPCM is available for fast heating with low current. • For thermal therapy, FCPCM applied to the face and knees, elbows, waist, back, etc. Thermotherapy is often preferable to medication and surgery because of its mild side effects and noninvasiveness. However, current research methods are unable to improve the efficiency of pre-treatment resulting in unportable use, and poor room temperature flexibility has become its critical limitation. Herein, a novel conductive flexible composite phase-change material (CPCM) was prepared for high-performance thermotherapy. Styrene ethylene butylene styrene (SEBS) and expanded graphite (EG) absorb paraffin (PA) and silicone oil onto a three-dimensional network. The CPCM possesses a high energy storage density (100.8–164.5 J/g) and sufficient volume conductivity (383.76 S·m−1) and can improve the thermal conductivity to 1600 % relative to pure PA. Moreover, it is flexible and possesses shape memory at both room temperature and the phase change temperature (44 ℃). The hardness of the material is 44.5 HA at room temperature and can be reduced to 2.0 HA after phase transition. When heated electrically, it heated threefold faster than when heated by hot air. The material was tested by volunteers and was found to maintain a temperature above 40 °C for more than 30 min. The utilization of this CPCM in thermotherapy is a promising low-cost and environmentally friendly strategy. [ABSTRACT FROM AUTHOR]

Abstract: Skyline computation has many applications including multi-criteria decision making. In this paper, we study the problem of efficiently computing the skyline over sliding windows on uncertain data elements against probability thresholds. Firstly, we characterize the properties of elements to be kept in our computation. Then, we show the size of dynamically maintained candidate set and the size of skyline. Novel, efficient techniques are developed to process continuous probabilistic skyline queries over sliding windows. Finally, we extend our techniques to cover the applications where multiple probability thresholds are given, “top-k” skyline data objects are retrieved, or elements have individual life-spans. Our extensive experiments demonstrate that the proposed techniques are very efficient and can handle a high-speed data stream in real time. [Copyright &y& Elsevier]

Histogram techniques have been used in many commercial database management systems to estimate a query result size. Recently, it has been shown that they are very effective to support approximation of query processing especially aggregates. In this paper, we investigate the problem of minimizing average errors of approximate aggregates using histogram techniques. Firstly, we phttp://bkstrm1.epnet.com:8080/ai/images/save.gifSaveresent a novel linear-spline histogram model that is more accurate than the existing models. Secondly, we propose a novel histogram construction technique for minimizing such average errors, which is shown to generate a near optimal histogram. Our experiment results demonstrate that the new histogram construction techniques lead to a great accuracy improvement on the existing techniques. [Copyright &y& Elsevier]

Hierarchical graphs are an important class of graphs for modeling many real applications in software and information visualization. In this paper, we investigate area requirements for drawing hierarchically planar graphs regarding two different drawing standards. Firstly, we show an exponential lower bound for the area needed for straight-line drawing of hierarchically planar graphs. The lower bound holds even for s-t hierarchical graphs without transitive arcs, in contrast to the results for upward planar drawing. This motivates our investigation of another drawing standard grid visibility representation, as a relaxation of straight-line drawing. An application of the existing results from upward drawing can guarantee a quadric drawing area for grid visibility representation but does not necessarily guarantee the minimum drawing area. Motivated by this, we will present a new grid visibility drawing algorithm which is efficient and guarantees the minimum drawing area with respect to a given topological embedding. This implies that the area minimization problem is polynomial time solvable restricted to the class of graphs whose planar embeddings are unique. However, we can show that the problem of area minimization of grid visibility for hierarchically planar graphs is generally NP-hard, even restricted to s-t graphs. [Copyright &y& Elsevier]

Although tremendous strides have been made in object recognition over large-scale 3D point clouds, one of the remaining open challenges is detecting tiny objects. The signal and appearance information of tiny objects are generally sparse and insufficient due to their tiny size, which makes the tiny object difficult to be detected with existing studies both in tiny 2D object recognition and general 3D object recognition since 3D topology relations are lost due to feature projection in tiny 2D object recognition and massive background may dominate the feature learner in general 3D object recognition. To the best of our knowledge, we explore tiny object recognition for the first time over large-scale 3D point clouds by designing deep neural network. To cope with this problem, a novel two-stage approach is proposed in this paper, which can effectively learn an informative and discriminative feature representation and efficiently process large-scale point clouds. Specifically, a class-aware filtering strategy is designed to dispense with the redundant background information. After that, we introduce a novel aggregating and sampling scheme in a supervised manner to progressively increase the receptive field for each 3D point, which can adaptively capture useful signals from tiny objects and learn complex geometric structures. Last, we propose a biased loss function that is inversely correlated to the number of tiny object points, thereby effectively handling the sparsity of tiny objects. Extensive experimental results over two real-world data sets validate the effectiveness and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

• Depletion of GW storage over large-scale urban agglomeration was distinct from 2002 to 2017. • Spatial relation of GW storage change was varied along with regional targeted policies of BTH. • Agricultural development of BTH competed for GW use with improving urbanization. • Regional policies need target city-specific characteristics for local GW protection. The overexploitation of groundwater (GW) and the associated environmental consequences are a cause for concern worldwide. Despite widespread unease about replenishing GW through, for example, water diversion schemes, there have been few comprehensive impact assessments, particularly for large urban areas that are undergoing rapid development to achieve regional goals. Using gravity recovery and climate experiment (GRACE) satellite data, we quantified the changes in GW storage from 2002 to 2017 and the reasons for the changes across a large-scale urban agglomeration in China (Beijing-Tianjin-Hebei, BTH) with a serious GW deficit. We found that, at the aggregate scale, from 2002 to 2017, the GW storage over the entire BTH declined at an average annual rate of 1.20 cm, but the rate of decline decreased markedly after 2010. At the city scale, the rates of decline in the GW storage between different cities varied but were strongly spatially correlated. The spatial correlation pattern was inverted from north to south and corresponded with the unbalanced diversion of water between cities from the South-to-North Water Transfer Project and was also affected by the policy of transferring industrial water from Beijing to the central and southern plains of BTH. Human-related factors were the most significant drivers of the changes in GW storage and, of these, agricultural water consumption had the most effect on the changes in GW storage. To protect GW supplies in large-scale urban agglomerations, regionally diverted water allocations should be coordinated with other development strategies, such as the regional industry distribution and transfer policy and agricultural water use management. [ABSTRACT FROM AUTHOR]

Graph Attention Networks (GATs) and Graph Convolutional Neural Networks (GCNs) are two state-of-the-art architectures in Graph Neural Networks (GNNs). It is well known that both models suffer from performance degradation when more GNN layers are stacked, and many works have been devoted to address this problem. We notice that main research efforts in the line focus on the GCN models, and their techniques cannot well fit the GAT models due to the inherent difference between these two architectures. In GAT, the attention mechanism is limited as it ignores the overwhelming propagation from certain nodes as the number of layers increases. To sufficiently utilize the expressive power of GAT, we propose a new version of GAT named L ayer-wise S elf-adaptive GAT (LSGAT), which can effectively alleviate the oversmoothing issue in deep GAT and is strictly more expressive than GAT. We redesign the attention coefficients computation mechanism adaptively adjusted by layer depth, which considers both immediate neighboring and non-adjacent nodes from a global view. The experimental evaluation confirms that LSGAT consistently achieves better results on node classification tasks over relevant counterparts. • We explore and introduce the challenge of overwhelming propagation in deep GATs. • The novel deep GAT model LSGAT is introduced to mitigate the oversmoothing problem. • Extensive experiments demonstrate the superiority of our proposed model. [ABSTRACT FROM AUTHOR]

Abstract: The curse of dimensionality has been a vexatious obstacle in processing queries on multidimensional data. This problem is more serious with uncertain data: an uncertain object''s value may spread extensively in the data space with varying probability distribution. In this paper, we attack this challenging problem and propose a technique called DuoWave for indexing uncertain multidimensional objects under a commonly used data model. We propose efficient algorithms to process range queries, the most popular filtering paradigm for many multidimensional queries on uncertain data. Extensive experiments show that DuoWave significantly outperforms state-of-the-art techniques. Moreover, DuoWave can also be exploited for a number of other query types on uncertain data. [Copyright &y& Elsevier]

Abstract: Many recent applications involve processing and analyzing uncertain data. In this paper, we combine the feature of top-k objects with that of skyline to model the problem of top-k skyline objects against uncertain data. The problem of efficiently computing top-k skyline objects on large uncertain datasets is challenging in both discrete and continuous cases. In this paper, firstly an efficient exact algorithm for computing the top-k skyline objects is developed for discrete cases. To address applications where each object may have a massive set of instances or a continuous probability density function, we also develop an efficient randomized algorithm with an guarantee. Moreover, our algorithms can be immediately extended to efficiently compute p-skyline; that is, retrieving the uncertain objects with skyline probabilities above a given threshold. Our extensive experiments on synthetic and real data demonstrate the efficiency of both algorithms and the randomized algorithm is highly accurate. They also show that our techniques significantly outperform the existing techniques for computing p-skyline. [Copyright &y& Elsevier]

This work investigates a novel m -closest entity (m CE) matching problem over geographic heterogeneous information networks (Geo-HINs). That is, given a Geo-HIN G and m query graphs { q 1 , q 2 , ... , q m } , m CE matching aims to find a group of geographic entities (geo-entities) whose patterns match the query graphs { q 1 , q 2 , ... , q m } correspondingly, for which the maximum distance between any geo-entity pair (i.e., the diameter) in the group is minimized. As a fundamental problem, the m CE matching can be applied for many scenarios, e.g., travel itinerary recommendation and city planning. The existing works have not simultaneously considered the characteristics of patterns matching and spatial search so that they cannot solve our problem, which is computationally expensive. To solve this problem efficiently, we propose a unified framework named F u z z y − E x a c t framework to process entity matching and spatial search comprehensively, in which pruning abilities at non-spatial and spatial layers are cooperatively explored. Two mutually adaptive auxiliary data structures named A r c − T r e e and A r c − F o r e s t are devised to maintain the intermediate search results which are exploited to enhance the search process between non-spatial and spatial layers. Experimental results demonstrate that our algorithm can outperform the baseline methods by 2 orders of magnitude on runtime. • A novel m -closest entity matching problem over HINs is devised. • Pruning abilities at both the non-spatial and spatial layers are explored. • Our approach can achieve much better performance than the baseline methods. [ABSTRACT FROM AUTHOR]

Abstract: A process trace describes the steps taken in a workflow to generate a particular result. Understanding a process trace is critical to be able to verify data, enable its re-use and to make appropriate decisions. Given many process traces, each with a large amount of very low level information, it is a challenge to make process traces meaningful to different users. It is more challenging to compare two complex process traces generated by heterogeneous systems and having different levels of granularity. In this paper, we present a novel notion of multi-granularity process trace that attempts to capture the conceptual abstraction of large process traces at different levels of granularity by leveraging ontology information. Based on this notion, graph matching based algorithms with semantic filtering are developed to efficiently and effectively compute the similarity between two process traces by considering both structural similarity and semantic similarity. Our experiment using both real world and synthetic datasets demonstrates that our techniques provide a practical approach for process trace similarity measurement. [Copyright &y& Elsevier]

Knowledge Graph Question Answering (KGQA) is a major branch of question answering tasks, which can answer fact questions effectively by using the reasonable characteristics of the knowledge graph. Currently, lots of related works combined with a variety of deep learning models are presented for the KGQA task. However, there are still some challenges, such as topic entity recognition under ambiguity expression, semantic level representation of natural language, efficient construction of searching space for answers, etc. In this paper, we propose a comprehensive approach for complex question answering over KG. Firstly, during the stage of topic entity recognition, a deep transition model is constructed to extract topic entities, and an efficient entity linking strategy is presented, which combines character matching and entity disambiguation model. Secondly, for candidate path ranking, a dynamic candidate path generation algorithm is proposed to efficiently create the candidate answer set. And four dedicated similarity calculation models are designed to handle the intricate condition of complex questions with long sequence and diversity expression. Moreover, a fusion policy is proposed to make decision for the final correct answer. We evaluate our approach on CKBQA, a Chinese knowledge base question answering dataset, from CCKS2019 competition. Experimental results demonstrate that the improvements in each process are effective and our approach achieves better performance than the best team in CCKS2019 competition. [ABSTRACT FROM AUTHOR]