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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]

Hypergraphs are instrumental in modeling complex relational systems that encompass a wide spectrum of high-order interactions among components. One prevalent analysis task is the properties estimation of large-scale hypergraphs, which involves selecting a subset of nodes and hyperedges while preserving the characteristics of the entire hypergraph. This paper aims to sample hypergraphs via random walks and is the first to perform unbiased random walks for sampling of nodes and hyperedges simultaneously in large-scale hypergraphs to the best of our knowledge. Initially, we analyze the stationary distributions of nodes and hyperedges for the simple random walk, and show that there is a high bias in both nodes and hyperedges. Subsequently, to eliminate the high bias of the simple random walk, we propose unbiased random walk strategies for nodes and hyperedges, respectively. Finally, a single joint walk schema is developed for sampling nodes and hyperedges simultaneously. To accelerate the convergence process, we employ delayed acceptance and history-aware techniques to assist our algorithm in achieving fast convergence. Extensive experimental results validate our theoretical findings, and the unbiased sampling algorithms for nodes and hyperedges have their complex hypergraph scenarios for which they are applicable. The joint random walk algorithm balanced the sampling applicable to both nodes and hyperedges. [ABSTRACT FROM AUTHOR]

This paper studies batch processing of core maintenance in hypergraph streams. We focus on updating the coreness of each vertex after the hypergraph evolves. Unlike existing works that mainly focus on exact coreness updates for the single hyperedge dynamic or approximate update, we propose the first known batch processing algorithms for exact core maintenance with insertions or deletions of multiple hyperedges. By proposing a hyperedge structure Joint Hyperedge Set, we tackle the challenges of quantifying the range of coreness change and finding potential vertices whose coreness may update. In addition, we accelerate coreness updates even further by finding structures that enable parallel execution. Extensive experiments illustrate the efficiency, scalability, and effectiveness of our batch core maintenance algorithms on real-world hypergraphs. It shows that our algorithms can be faster than the single hyperedge processing approaches by a factor of nearly half the number of hyperedges processed, and our parallel algorithms achieve linear acceleration with the increasing number of threads. [ABSTRACT FROM AUTHOR]

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]

In social networks, the departure of some users can lead to the dropout of others from the community in cascade. Therefore, the engagement of critical users can significantly influence the stability of a network. In the literature, the anchored/collapsed k-core problem is proposed, which aims to enlarge/collapse the community by anchoring/deleting certain nodes. While, in real social networks, nodes are usually associated with different preferences, such as close or conflict interest. Intuitively, a community will be more stable if more nodes share close interest and fewer of them carry conflict interest. However, most existing researches simply treat all users equally, and the inclination property is neglected. To fill the gap, in this paper, we propose two novel problems, named inclined anchored k-core (IAK) problem and minimum detached k-core (MDK) problem, to better characterize the real scenarios. We prove that both problems are NP-hard. To facilitate the computation, novel search strategies are proposed. Comprehensive experiments are conducted on 9 networks to demonstrate the effectiveness and efficiency of the proposed techniques. [ABSTRACT FROM AUTHOR]

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]