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Visualizing polymer chain growth is always a hot topic for tailoring structure‐function properties in polymer chemistry. However, current characterization methods are limited in their ability to differentiate the degree of polymerization in real‐time without isolating the samples from the reaction vessel, let alone to detect insoluble polymers. Herein, a reliable relationship is established between polymer chain growth and fluorescence properties through polymerization induced emission. (TPE‐C2)2‐Te is used to realize in situ oxidative polymerization, leading to the aggregation of fluorophores. The relationship between polymerization degree of growing polytelluoxane (PTeO) and fluorescence intensity is constructed, enabling real‐time monitoring of the polymerization reaction. More importantly, this novel method can be further applied to the observation of the polymerization process for growing insoluble polymer via surface polymerization. Therefore, the development of visualization technology will open a new avenue for visualizing polymer chain growth in real‐time, regardless of polymer solubility. [ABSTRACT FROM AUTHOR]

Bromine in waste printed circuit boards has caused serious harm to the environment. How to deal with it safely is a problem. In this paper, the debromination of waste printed circuit boards by microwave gasification pyrolysis was studied, and the properties of pyrolysis products and the migration of bromine were analyzed. The optimum pyrolysis conditions of microwave steam atmosphere were 600 °C, 1000 W, 50 min, 2 mL/min. Compared with conventional pyrolysis and conventional steam pyrolysis, less solid products (60.39 wt %) were obtained, and more metal copper was enriched. The content increased from 27.46 % and 28.35 % to 30.63 %, respectively, and the diffraction peak intensity of copper was also enhanced. There is no obvious change in the type of functional groups of the solid product, and its microscopic morphology is smoother. Compared with conventional pyrolysis, the phenolic compounds in pyrolysis oil decreased, and increased compared with conventional steam pyrolysis. The content of H2 in the pyrolysis gas increased significantly, and the content of CO decreased significantly. Compared with conventional pyrolysis, the bromine content in pyrolysis gas and pyrolysis oil decreased from 94.5 % and 5.5 % to 1 %, respectively, and more bromine (98 %) in the raw material entered the aqueous solution. [ABSTRACT FROM AUTHOR]

Signaling threshold regulating transmembrane adaptor 1 (SIT1) encodes a disulfide-linked homodimeric lymphocyte-specific glycoprotein involved in immune cell activation. However, the relationship between SIT1 and the prognosis of skin cutaneous melanoma (SKCM) and tumor-infiltrating lymphocytes remains elusive. Here, we first compared the differences in SIT1 expression levels between SKCM tissues and adjacent normal tissues. Next, we found that the immune cell infiltration levels and signature pattern of immune infiltration were positively associated with the SIT1 gene mRNA levels. TCGA_SKCM RNA-seq data unveiled that the SIT1 upregulated several immune-associated signaling pathways in GSEA analysis. The high expression of SIT1 was closely related to improved survival in patients with SKCM. A pathway enrichment analysis of SIT1-associated immunomodulators indicated the involvement of the NF-κB signaling pathways. Based on SIT1-associated immunomodulators, we built a 13-gene signature by LASSO Cox regression which served as an independent prognostic factor for the survival of melanoma patients. By using the signature risk score, we achieved a good prediction result for the immunotherapy response and survival of SKCM patients. Our findings provided evidence for SIT1's implication in tumor immunity and survival of SKCM patients. The nominated immune signature is a promising predictive model for prognosis and immunotherapy sensitivity in SKCM patients. [ABSTRACT FROM AUTHOR]

Decision making is the process of making choices by identifying a decision, gathering information, and assessing alternative resolutions. Therefore, in this paper, they proposed an attention-based hierarchical denoised deep clustering (AHDDC) algorithm to solve the problem, which enables GCN to learn multiple layers of hidden information and uses the attention mechanism to strengthen the information. These accepted articles make contributions and novel aspects of different research in social media data scenarios, traffic data scenarios, data security scenarios, medical data scenarios, knowledge graph learning, and the other graph based deep learning models. [Extracted from the article]

The limited efficacy of deep tumor treatments has been considered the "Achilles' heel" of anticancer therapy due to multiple biological barriers. Whether passive diffusion or active transport has difficulties completely overcoming these obstacles. Herein, cation‐π interactions are utilized to construct a tumor drug delivery system integrating the merits of both passive and active transport mechanisms. A cation‐π interaction bridged trimetallic supramolecular drug complex (Cπ‐TMSDC) is constructed based on a drug consisting of one cisplatin molecule linked by K+ (Pt‐COOK) and the other drug with a Ru metal complex containing curcumin (Ru‐Cur). The obtained Cπ‐TMSDC further self‐assembles into cation‐π‐based trimetallic supramolecular drug micelles (Cπ‐TMSDMs) with efficient and stable transportation in vivo due to the strong cation‐π interaction formed between K+ and the curcumin unit in the Cπ‐TMSDC. In acidic tumor microenvironment, the cation‐π interaction smartly dissociates, facilitating the quick release of Pt‐COOK outside Cπ‐TMSDMs to rapidly infiltrate the outer cellular layers by passive diffusion. Meanwhile, the dissociated Ru‐Cur from the core layer of the Cπ‐TMSDMs form secondary self‐assemblies to deeply penetrate inside the solid tumor. Therefore, this strategy results in an efficient tumor drug delivery platform with enhanced deep intratumoral penetration, improved therapeutic effects, and reduced systemic toxicity to normal organs. [ABSTRACT FROM AUTHOR]

Well connected users are generally discovered in communities which is one of the most important tasks for network data analytics and has tremendous real applications. In recent years, community search in attributed graphs has begun to attract attention, which aims to find communities that are both structure and attribute cohesive. Meanwhile, searching a community that is structure cohesive but attribute diversified, denoted as attribute diversified community search, is still at an early stage. In this paper, we introduce our recent effort for discovering attribute diversified community. In fact, for different applications, the needs of attribute diversification for modelling the community are quite different. We introduce three attribute diversified community models in which attribute diversification takes different roles for presenting as an objective and as a constraint. We also discuss major techniques for speeding up the attribute diversified community search. We conduct extensive experiments to show the effectiveness and efficiency of our algorithms for finding attribute diversified communities in various settings. [ABSTRACT FROM AUTHOR]

Tumor hypoxia greatly limits the antitumor therapeutic efficacy of oxygen (O2)‐dependent treatments. The common strategies of doping O2 generating compounds into one "package" may lead to insufficient O2 supply in situ in cancer cells due to the relatively low loading content, limited reproducibility, and uncontrollable release kinetics. Herein, organometallic drug complexes combined with a supramolecular interaction strategy are proposed to realize a stable and efficient O2 self‐supply in situ for overcoming hypoxic tumors. A ruthenium (II)‐coordinated supramolecular metallodrug complex (RuSMDC) is first designed and synthesized via host–guest interactions between two functionalized drug molecules, in which ruthenium is used to catalyze the decomposition of hydrogen peroxide to produce O2. The obtained RuSMDC further self‐assembles into Ru‐containing supramolecular metallodrug micelles (RuSMDMs), which provides sufficient O2 for chemotherapy and photodynamic therapy (PDT) oncotherapy in an anaerobic tumor environment. In vitro and in vivo studies further confirm that RuSMDMs effectively alleviate the tumor hypoxic environment, greatly improve the therapeutic effect of chemotherapy and PDT oncotherapy, and reduce the systemic toxicity to normal organs. [ABSTRACT FROM AUTHOR]

Niclosamide has preclinical activity against a wide range of cancers. In prostate cancer, it inhibits androgen receptor variant 7 and synergizes with abiraterone. The approved niclosamide formulation has poor oral bioavailability. The primary objective of this phase Ib trial was to identify a maximum tolerated dose (MTD) and recommended phase 2 dose (RP2D) of a novel reformulated orally-bioavailable niclosamide/PDMX1001 in combination with abiraterone and prednisone in men with castration-resistant prostate cancer (CRPC). Eligible patients had progressing CRPC, adequate end-organ function, and no prior treatment with abiraterone or ketoconazole. Patients were treated with escalating doses of niclosamide/PDMX1001 and standard doses of abiraterone and prednisone. Peak and trough niclosamide plasma levels were measured. Common Terminology Criteria for Adverse Events (CTCAE) v4.0 and Prostate Cancer Working Group 2 criteria were used to evaluate toxicities and responses. Nine patients with metastatic CRPC were accrued, with no dose-limiting toxicities observed at all dose levels. The recommended Phase II dose of niclosamide/PDMX1001 was 1200 mg orally (PO) three times daily plus abiraterone 1000 mg PO once daily and prednisone 5 mg PO twice daily. Trough and peak niclosamide concentrations exceeded the therapeutic threshold of > 0.2 µM. The combination was well tolerated with most frequent adverse effects of diarrhea. Five out of eight evaluable patients achieved a PSA response; two achieved undetectable PSA and radiographic response. A novel niclosamide/PDMX1001 reformulation achieved targeted plasma levels when combined with abiraterone and prednisone, and was well tolerated. Further study of niclosamide/PDMX1001 with this combination is warranted. [ABSTRACT FROM AUTHOR]

Traffic congestions in urban road traffic networks originate from some crowded road segments with crucial locations, and diffuse towards other parts of the urban road network creating further congestions. This behavior of road networks motivates the need to understand the influence of individual road segments on others in terms of congestion. In this paper, we investigate the problems of global influence ranking and local influence ranking of road segments. We propose an algorithm called RoadRank to compute the global influence scores of each road segment from their traffic measures, and rank them based on their overall influence. To identify the locally influential road segments, we also propose an extension called distributed RoadRank, based on road network partitions. We perform extensive experiments on real SCATS datasets of Melbourne. We found that the segments of Batman Avenue, Footscray Road, Punt Road, La Trobe Street, and Victoria Street, are highly influential in the early morning times, which are well known as congestion hotspots for both the network operators and the commuters. Our promising results and detailed insights demonstrate the efficacy of our method. [ABSTRACT FROM AUTHOR]

Process reuse is one of the important research areas that address efficiency issues in business process modeling. Similar to software reuse, business processes should be able to be componentized and specialized in order to enable flexible process expansion and customization. Current activity/control-flow centric workflow modeling approaches face difficulty in supporting highly flexible process reuse, limited by their procedural nature. In comparison, the emerging artifact-centric workflow modeling approach well fits into these reuse requirements. Beyond the classic class level reuse in existing object-oriented approaches, process reuse faces the challenge of handling synchronization dependencies among artifact lifecycles as parts of a business process. In this article, we propose a theoretical framework for business process specialization that comprises an artifact-centric business process model, a set of methods to design and construct a specialized business process model from a base model, and a set of behavioral consistency criteria to help check the consistency between the two process models. [ABSTRACT FROM AUTHOR]

The efficient identification of social groups with common interests is a key consideration for viral marketing in online social networking platforms. Most existing studies in social groups or community detection either focus on the common attributes of the nodes (users) or rely on only the topological links of the social network graph. The temporal evolution of user activities and interests have not been thoroughly studied to identify their effects on the formation of groups. In this paper, we investigate the problem of discovering and tracking time-sensitive activity driven user groups in dynamic social networks for a given input query consisting a set of topics. The users in these groups have the tendency to be temporally similar in terms of their activities on the topics of interest. To this end, we develop two baseline solutions to discover effective social groups. The first solution uses the network structure, whereas the second one uses the topics of common interest. We further propose an index-based method to incrementally track the evolution of groups with a lower computational cost. Our main idea is based on the observation that the degree of user activeness often degrades or upgrades widely over a period of time. The temporal tendency of user activities is modelled as the freshness of recent activities by tracking the social streams with a fading time window. We conduct extensive experiments on three real data sets to demonstrate the effectiveness and efficiency of the proposed methods. We also report some interesting observations on the temporal evolution of the discovered social groups using case studies. [ABSTRACT FROM AUTHOR]

In many cases, users are not familiar with their exact information needs while searching complicated data sources. This lack of understanding may cause the users to feel dissatisfaction when the system retrieves insufficient results after they issue queries. However, using their original query results, we may recommend additional queries which are highly relevant to the original query. This paper presents XPloreRank to recommend top-l highly relevant keyword queries called "You May Also Like" (YMAL) queries to the users in XML keyword search. To generate such queries, we firstly analyze the original keyword query results content and construct a weighted co-occurring keyword graph. Then, we generate the YMAL queries by traversing the co-occurring keyword graph and rank them based on the following correlation aspects: (a) external correlation, which measures the similarity of the YMAL query to the original query and (b) internal correlation, which measures the capability of the YMAL query keywords in producing meaningful results with respect to the data source. Due to the complexity of generating YMAL queries, we propose a novel A* search-based technique to generate top-l YMAL queries efficiently. We also present a greedy-based approximation for it to improve the performance further. Extensive experiments verify the effectiveness and efficiency of our approach. [ABSTRACT FROM AUTHOR]

Abstract Forecasting the short-term speed of moving vehicles on roads plays a vital role on traffic control and trip planning, which however still remains a challenging task when the high accuracy is required. In this paper, we propose a novel approach to the short-term traffic speed forecasting, which takes into account the influence of different traffic attributes, such as traffic flow, traffic speed, road occupancy and traffic density, of adjacent roads on the traffic speed. In addition, in order to obtain the more accurate relation between traffic speed and traffic attributes, we employ the idea of piecewise correlation function and adopt the Jenks clustering method with dynamic programming to determine the segment intervals of relation. We validate our approach based on the real data collected from Wenzhou and Hangzhou, two large cities located in eastern China. The extensive experimental results show that, compared with the state-of-the-art approaches, our approach has the higher stability and accuracy, especially for 5-minute and 10-minute speed prediction. Highlights • We consider only traffic attributes most relevant to speed and also their weights. • We consider the differences of impacts on speed among adjacent roads. • We employ piecewise correlation function between traffic speed and attributes. • We adopt Jenks clustering with dynamic programming to determine segment intervals. • We validate our approach based on the real data collected from two large cities. [ABSTRACT FROM AUTHOR]

The urban road networks undergo frequent traffic congestions during the peak hours and around the city center. Capturing the spatiotemporal evolution of the congestion scenario in real-time in an urban-scale can aid in developing smart traffic management systems, and guiding commuters in making informed decision about route choice. The congestion scenario is often represented by a set of distinguishable network partitions that have a homogeneous level of congestion inside them but are heterogeneous to others. Due to the dynamic nature of traffic, these partitions evolve with time in terms of their structure and location. In this paper, we propose a comprehensive framework to capture the evolution by incrementally updating the partitions in an efficient manner using a two-layer approach. The physical layer maintains a set of small-sized road network building blocks in a fine granularity, and performs low-level computations to incrementally update them, whereas the logical layer performs high-level computations in order to serve as an interface to query the physical layer about the congested partitions in a coarse granularity. We also propose an in-memory index called Bin that compactly stores the historical sets of building blocks in the main memory with no information loss, and facilitates their efficient retrieval. Our experimental results show that the proposed method is much efficient than the existing re-partitioning methods without significant sacrifice in accuracy. The proposed Bin consumes a minimum space with least redundancy at different time stamps. [ABSTRACT FROM AUTHOR]

Road traffic networks are rapidly growing in size with increasing complexities. To simplify their analysis in order to maintain smooth traffic, a large urban road network can be considered as a set of small sub-networks, which exhibit distinctive traffic flow patterns. In this paper, we propose a robust framework for spatial partitioning of large urban road networks based on traffic measures. For a given urban road network, we aim to identify the different sub-networks or partitions that exhibit homogeneous traffic patterns internally, but heterogeneous patterns to others externally. To this end, we develop a two-stage algorithm (referred as FaDSPa ) within our framework. It first transforms the large road graph into a well-structured and condensed density peak graph (DPG) via density based clustering and link aggregation using traffic density and adjacency connectivity, respectively. Thereafter we apply our spectral theory based graph cut (referred as α -Cut) to partition the DPG and obtain the different sub-networks. Thus the framework applies the locally distributed computations of density based clustering to improve efficiency and the centralized global computations of spectral clustering to improve accuracy. We perform extensive experiments on real as well as synthetic datasets, and compare its performance with that of an existing road network partitioning method. Our results show that the proposed method outperforms the existing normalized cut based method for small road networks and provides impressive results for much larger networks, where other methods may face serious problems of time and space complexities. [ABSTRACT FROM AUTHOR]

Social networks are a vital mechanism to disseminate information to friends and colleagues. In this work, we investigate an important problem—the personalized influential topic search, or PIT-Search in a social network: Given a keyword query $q$ issued by a user $u$ in a social network, a PIT-Search is to find the top-$k$ $q$-related topics that are most influential for the query user $u$. The influence of a topic to a query user depends on the social connection between the query user and the social users containing the topic in the social network. To measure the topics’ influence at the similar granularity scale, we need to extract the social summarization of the social network regarding topics. To make effective topic-aware social summarization, we propose two random-walk based approaches: random clustering and an L-length random walk. Based on the proposed approaches, we can find a small set of representative users with assigned influential scores to simulate the influence of the large number of topic users in the social network with regards to the topic. The selected representative users are denoted as the social summarization of topic-aware influence spread over the social network. And then, we verify the usefulness of the social summarization by applying it to the problem of personalized influential topic search. Finally, we evaluate the performance of our algorithms using real-world datasets, and show the approach is efficient and effective in practice. [ABSTRACT FROM PUBLISHER]

Online trip planning is a popular service that has facilitated a lot of people greatly. However, little attention has been paid to personalized trip planning which is even more useful. In this paper, we define a highly expressive personalized route planning query-the Personalized and Sequenced Route ( PSR) Query which considers both personalization and sequenced constraint, and propose a novel framework to deal with the query. The framework consists of three phases: guessing, crossover and refinement. The guessing phase strives to obtain one high quality route as the baseline to bound the search space into a circular region. The crossover phase heuristically improve the quality of multiple guessed routes via a modified genetic algorithm, which further narrows the radius of the search space. The refinement phase backwardly examines each candidate point and partial route to rule out impossible ones. The combination of these phases can efficiently and effectively narrow our search space via a few iterations. In the experiment part, we firstly show our evaluation results of each phase separately, proving the effectiveness of each phase. Then, we present the evaluation results of the combination of them, which offers insight into the merits of the proposed framework. [ABSTRACT FROM AUTHOR]

Keyword search is integrated in many applications on account of the convenience to convey users' query intention. Most existing works in keyword search on graphs modeled the query results as individual minimal connected trees or connected graphs that contain the keywords. We observe that significant overlap may exist among those query results, which would affect the result diversification. Besides, most solutions required accessing graph data and pre-built indexes in memory, which is not suitable to process big dataset. In this paper, we define the smallest k-compact tree set as the keyword query result, where no shared graph node exists between any two compact trees. We then develop a progressive A* based scalable solution using MapReduce to compute the smallest k-compact tree set, where the computation process could be stopped once the generated compact tree set is sufficient to compute the keyword query result. We conduct experiments to show the efficiency of our proposed algorithm. [ABSTRACT FROM AUTHOR]

A Web database typically responds to a query with a Web page, which encodes the query results into semi-structured data objects using HTML tags. We call such data objects Web data records or data records . Mining Web data records is very important for many applications, e.g., meta search, comparative shopping, etc. This paper proposes a new effective approach called AutoRM, which mines data records from single Web page automatically. AutoRM involves three major steps: (1) constructing the DOM tree of the given Web page; (2) mining all sets of adjacent similar C-Records ( C andidate data Records ) from the constructed DOM tree; (3) mining actual data records from C-Records. In many Web pages, similar data records are distributed in bigger and adjacent similar objects. Existing approaches typically identify such objects as data records. Conversely, AutoRM views such objects as C-Records, and mines actual data records from them. One key issue for mining similar data records is the boundary detection of each data record. Existing approaches typically make some brittle assumptions for handling this issue. By making more robust assumptions, AutoRM tends to detect data record boundaries more accurately. Experimental results show that AutoRM is highly effective, and outperforms state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Answering why-not questions in databases is promised to have wide application prospect in many areas and thereby, has attracted recent attention in the database research community. This paper addresses the problem of answering these so-called why-not questions in similar graph matching for graph databases. Given a set of answer graphs of an initial query graph $q$ and a set of missing ( why-not) graphs, we aim to modify $q$ into a new query graph $q^*$ such that the missing graphs are included in the new answer set of $q^*$ . We present an approximate solution to address the above as the optimal solution is NP-hard to compute. In our approach, we first compute the bounded search space and the distance to be minimized for $q^*$ . Then, we present a two-phase algorithm to find the new query $q^*$ . In the first phase, we generate a set of candidate edges to be added/deleted into/from the initial query $q$ within the bounded search space and in the second phase, we select a subset of candidate edges generated in the first phase to minimize the distance for $q^*$ . We also demonstrate the effectiveness and efficiency of our approach by conducting extensive experiments on two real datasets. [ABSTRACT FROM PUBLISHER]

Entity alignment is an essential process in knowledge graph (KG) fusion, which aims to link entities representing the same real-world object in different KGs, to achieve entity expansion and graph fusion. Recently, embedding-based entity pair similarity evaluation has become mainstream in entity alignment research. However, these methods heavily rely on labelled entity pairs, which are often unavailable. Some self-supervised methods exploit features of KGs regardless of noise when generating aligned entity pairs. To resolve this issue, we propose a generative adversarial entity alignment method, which is more robust to noise data. The proposed method then exploits both attribute and structure information in the KGs and applies a BERT-based contrastive loss function to embed entities in KGs. Experimental results on several benchmark datasets demonstrate the superiority of our framework compared with most existing state-of-the-art entity alignment methods. [ABSTRACT FROM AUTHOR]

While keyword query empowers ordinary users to search vast amount of data, the ambiguity of keyword query makes it difficult to effectively answer keyword queries, especially for short and vague keyword queries. To address this challenging problem, in this paper we propose an approach that automatically diversifies XML keyword search based on its different contexts in the XML data. Given a short and vague keyword query and XML data to be searched, we first derive keyword search candidates of the query by a simple feature selection model. And then, we design an effective XML keyword search diversification model to measure the quality of each candidate. After that, two efficient algorithms are proposed to incrementally compute top-$k$ qualified query candidates as the diversified search intentions. Two selection criteria are targeted: the $k$ selected query candidates are most relevant to the given query while they have to cover maximal number of distinct results. At last, a comprehensive evaluation on real and synthetic data sets demonstrates the effectiveness of our proposed diversification model and the efficiency of our algorithms. [ABSTRACT FROM PUBLISHER]

Given a keyword query q and a large structured, traditional keyword search may return a large number of relevant results to users, which leads to a frustrating procedure for the users to select their interesting results. To help users understand the data to be searched, in this work we investigate the problem of frequent co-occurring terms (FCTs) in large relational data. By returning a set of most FCTs with the given keywords, we can provide a chance for users to see a big picture of relevant data information. The investigation of FCT problem is also one of the fundamental building blocks of data mining because the discovered FCTs can be employed to analyze the topics or contexts of user interest. Although the problem of FCTs computation was proposed and investigated in Tao and Yu [(2009) Finding Frequent Co-Occurring Terms in Relational Keyword Search. 12th Int. Conf. Extending Database Technology EDBT, Saint-Petersburg, Russia, March 23–26, pp. 839–850. ACM, New York, USA], further investigation is needed to improve the performance because FCT computation is very expensive. Especially for the increasing volume of data, the centralized approach in Tao and Yu [(2009) Finding Frequent Co-Occurring Terms in Relational Keyword Search. 12th Int. Conf. Extending Database Technology EDBT, Saint-Petersburg, Russia, March 23–26, pp. 839–850. ACM, New York, USA] may incur a big challenge on the efficiency of performing an FCT computation. To do this, we investigate how to perform parallel FCT computation using MapReduce which is a well-accepted framework for data-intensive applications over clusters of computers. We design an effective mapping mechanism that exploits the approximately maximal workload of FCT computation for balancing the computational cost of each processor, while reducing the shuffling cost and avoiding the data-skewness. Analytical and experimental evaluations demonstrate the efficiency and scalability of our proposed approach using TPC-H benchmark datasets with different sizes. [ABSTRACT FROM PUBLISHER]

We review the problem of finding contained rewritings (CRs) for XPath queries using XPath views. CR is proposed to cater for data integration scenarios, where views are unlikely to be complete due to the limited coverage of data sources, and hence equivalent rewritings are impossible to be found. As a result, we are usually required to find a maximal contained rewriting (MCR) for a query to provide the best possible answers. An MCR is a set of CRs, and may contain redundant CRs. Obviously, evaluating redundant CRs on materialized views is unnecessary. In this paper, we first address how to find the irredundant maximal contained rewriting (IMCR), i.e. all the irredundant CRs. We show that the existing approach ignores a type of situation, and turns out to be not sufficient. As a result, the only safe solution is a brute-force pairwise containment check for all the CRs. We then propose some heuristics to speed up the brute-force comparisons. When a materialized view is given, we propose how to evaluate the IMCR on the materialized view, which, to our knowledge, is the first work on optimizing the evaluation of a set of produced CRs on the materialized view by considering the inherent structural characteristics of the CRs. Our experiments show the effectiveness and efficiency of our algorithms. [ABSTRACT FROM AUTHOR]

Under external stimuli, the bilayer hydrogels can change their shapes or move mechanically with fast, sensitive and adjustable motion characteristics. However, the main methods for preparing bilayer hydrogels have many limitations, such as they involve complicated synthesis processes, lead to fragile bilayer interface and structure, and result in single responsiveness and deformation, which hinders the practical applications of bilayer hydrogel in intelligent bionics. Here, we used the reaction-diffusion of sodium alginate (SA) and chitosan (CS) combined with the photopolymerization of N-isopropylacrylamide (NIPAM) to prepare (PNIPAM/SA)/(SA/CS) bilayer hydrogel film. The bilayer hydrogel film can well respond to the alteration of temperature, pH, salt concentration and thickness ratio with good interlayer bonding. This strategy can be applied to intelligent drive device to promote its practical application. [ABSTRACT FROM AUTHOR]

Supramolecular self-assembly of molecules into highly ordered architectures is attractive for developing various advanced functional materials. Compared to the assemblies of one single building block, supramolecular coassembly (SCA) of multiple component systems has recently emerged as a promising approach for generating highly functional and complex structures. The assembly and integration of multiple building blocks at the molecular level are of paramount importance for constructing SCA systems with sophisticated architectures and diverse functions. This feature article highlights the recent advances and future trends in SCAs, ranging from their synthetic strategies, morphological control, to functional applications. The monomer pairs used to synthesize SCAs are rationalized into two classes including structural monomer pairs and functional monomer pairs. The assembly behaviors are then discussed according to the dimensionality of the coassembled morphologies from zero to three dimensions. Finally, the emergent functions and applications of SCAs are highlighted such as adsorption, catalysis, optoelectronics, and biomedicines. [ABSTRACT FROM AUTHOR]

Mobile computing over intelligent mobile is affecting human's habits of obtaining information over Internet, especially keyword search. Most of previous keyword search works are mainly focused on traditional web data sources, in which the performance can be improved by adding more computing power and/or building more offline-computed index. However, it is very challenging to apply the traditional keyword search methods to mobile web-based keyword search because mobile computing has many different features, e.g., frequent disconnections, variety of bandwidths, limited power of mobile devices, limited data size to be downloaded, etc.. To address this challenge, in this paper we design an adaptive mobile-based XML keyword search approach, called XBridge-Mobile, that can derive the semantics of a keyword query and generate a set of effective structured patterns by analyzing the given keyword query and the schemas of XML data sources. Each structured pattern represents one of user's possible search intentions. The patterns will be firstly sent to the mobile client from web server. And then, the mobile client can select some interested patterns to load the results. By doing this, we can reduce the communication cost a lot between web server and mobile client because only the derived patterns and a few results need to be transferred, not all the keyword search results, by which we can save lots of expenses when the downloaded data is priced. In addition, we can economically maintain the frequent structured pattern queries in the mobile device, which can further reduce the expense of downloading data. At last, we analyze and propose a ranking function to measure the quality of keyword search results, design a set of algorithms to optimize mobile keyword search based on the maintained structured patterns, and present the experimental study of XBridge-Mobile with real XML datasets. [ABSTRACT FROM AUTHOR]

The probabilistic threshold query is one of the most common queries in uncertain databases, where a result satisfying the query must be also with probability meeting the threshold requirement. In this paper, we investigate probabilistic threshold keyword queries (PrTKQ)over XML data, which is not studied before. We first introduce the notion of quasi-SLCA and use it to represent results for a PrTKQ with the consideration of possible world semantics. Then we design a probabilistic inverted (PI)index that can be used to quickly return the qualified answers and filter out the unqualified ones based on our proposed lower/upper bounds. After that, we propose two efficient and comparable algorithms: Baseline Algorithm and PI index-based Algorithm. To accelerate the performance of algorithms, we also utilize probability density function. An empirical study using real and synthetic data sets has verified the effectiveness and the efficiency of our approaches. [ABSTRACT FROM AUTHOR]

Keyword search enables inexperienced users to easily search XML database with no specific knowledge of complex structured query languages and XML data schemas. Existing work has addressed the problem of selecting data nodes that match keywords and connecting them in a meaningful way, e.g., SLCA and ELCA. However, it is time-consuming and unnecessary to serve all the connected subtrees to the users because in general the users are only interested in part of the relevant results. In this paper, we propose a new keyword search approach which basically utilizes the statistics of underlying XML data to decide the promising result types and then quickly retrieves the corresponding results with the help of selected promising result types. To guarantee the quality of the selected promising result types, we measure the correlations between result types and a keyword query by analyzing the distribution of relevant keywords and their structures within the XML data to be searched. In addition, relevant result types can be efficiently computed without keyword query evaluation and any schema information. To directly return top-k keyword search results that conform to the suggested promising result types, we design two new algorithms to adapt to the structural sensitivity of the keyword nodes over the keyword search results. Lastly, we implement all proposed approaches and present the relevant experimental results to show the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Abstract: The current business environment changes rapidly, dictated by user requirements and market opportunities. Organisations are therefore driven to continuously adapt their business processes to new conditions. Thus, management of business process schema evolution, particularly process version control, is in great demand to capture the dynamics of business process schema changes. This paper aims to facilitate version control for business process schema evolution, with an emphasis on version compatibility, co-existence of multiple versions and dynamic version shifts. A multi-level versioning approach is established to specify dependency between business process schema evolutions, and a novel version preserving graph model is proposed to record business process schema evolutions. A set of business process schema updating operations is devised to support the entire set of process change patterns. By maintaining sufficient and necessary schema and version information, our approach provides comprehensive support for navigating process instance executions of different and changing versions, and deriving the process schema of a certain version. A prototype is also implemented for the proof-of-concept purpose. [Copyright &y& Elsevier]

The flexibility of XML data model allows a more natural representation of uncertain data compared with the relational model. Matching twig pattern against XML data is a fundamental problem in querying information from XML documents. For a probabilistic XML document, each twig answer has a probabilistic value because of the uncertainty of data. The twig answers that have small probabilistic value are useless to the users, and usually users only want to get the answers with the k largest probabilistic values. To this end, existing algorithms for ordinary XML documents cannot be directly applicable due to the need for handling probability distributional nodes and efficient calculation of top-k probabilities of answers in probabilistic XML. In this paper, we address the problem of finding twig answers with top-k probabilistic values against probabilistic XML documents directly. We propose a new encoding scheme called PEDewey for probabilistic XML in this paper. Based on this encoding scheme, we then design two algorithms for finding answers of top-k probabilities for twig queries. One is called ProTJFast, to process probabilistic XML data based on element streams in document order, and the other is called PTopKTwig, based on the element streams ordered by the path probability values. Experiments have been conducted to study the performance of these algorithms. [ABSTRACT FROM AUTHOR]

As probabilistic data management is becoming one of the main research focuses and keyword search is turning into a more popular query means, it is natural to think how to support keyword queries on probabilistic XML data. With regards to keyword query on deterministic XML documents, ELCA (Exclusive Lowest Common Ancestor) semantics allows more relevant fragments rooted at the ELCAs to appear as results and is more popular compared with other keyword query result semantics (such as SLCAs). In this paper, we investigate how to evaluate ELCA results for keyword queries on probabilistic XML documents. After defining probabilistic ELCA semantics in terms of possible world semantics, we propose an approach to compute ELCA probabilities without generating possible worlds. Then we develop an efficient stack-based algorithm that can find all probabilistic ELCA results and their ELCA probabilities for a given keyword query on a probabilistic XML document. Finally, we experimentally evaluate the proposed ELCA algorithm and compare it with its SLCA counterpart in aspects of result probability, time and space efficiency, and scalability. [ABSTRACT FROM AUTHOR]

Database users may be frustrated by no answers returned when they pose a query on the database. In this paper, we study the problem of relaxing queries on RDF databases in order to acquire approximate answers. We address two problems in efficient query relaxation. First, to ensure the quality of answers, we compute the similarities between relaxed queries with regard to the user query and use them to score the potential relevant answers. Second, for obtaining top-k answers, we develop two algorithms. One is based on the best-first strategy and relaxed queries are executed in the ranking order. The batch based algorithm executes the relaxed queries as a batch and avoids unnecessary execution cost. At last, we implement and experimentally evaluate our approaches. [ABSTRACT FROM AUTHOR]

Web service and business process technologies are widely adopted to facilitate business automation and collaboration. Given the complexity of business processes, it is a sought-after feature to show a business process with different views to cater for the diverse interests, authority levels, etc., of different users. Aiming to implement such flexible process views in the Web service environment, this paper presents a novel framework named FlexView to support view abstraction and concretisation of WS-BPEL processes. In the FlexView framework, a rigorous view model is proposed to specify the dependency and correlation between structural components of process views with emphasis on the characteristics of WS-BPEL, and a set of rules are defined to guarantee the structural consistency between process views during transformations. A set of algorithms are developed to shift the abstraction and concretisation operations to the operational level. A prototype is also implemented for the proof-of-concept purpose. [ABSTRACT FROM AUTHOR]

Abstract: Searching XML data with a structured XML query can improve the precision of results compared with a keyword search. However, the structural heterogeneity of the large number of XML data sources makes it difficult to answer the structured query exactly. As such, query relaxation is necessary. Previous work on XML query relaxation poses the problem of unnecessary computation of a big number of unqualified relaxed queries. To address this issue, we propose an adaptive relaxation approach which relaxes a query against different data sources differently based on their conformed schemas. In this paper, we present a set of techniques that supports this approach, which includes schema-aware relaxation rules for relaxing a query adaptively, a weighted model for ranking relaxed queries, and algorithms for adaptive relaxation of a query and top-k query processing. We discuss results from a comprehensive set of experiments that show the effectiveness and the efficiency of our approach. [Copyright &y& Elsevier]

The execution of a business process usually involves the cooperation of many resources (actors) performing various tasks (activities). Generally speaking, the cooperation among actors could significantly influence the efficiency of process execution. The better cooperation among actors, the higher performance the process instance would achieve. The task allocation is, therefore, expected to ensure that the actors are compatible well with each other when performing tasks. However, the decision of task allocation is always ad-hoc or performed manually in many organizations. As for some automatic methods, they usually serve only for sequentially structured simple processes. In addition, the existing studies only concentrate on the cooperation between two actors and fail to consider the effects from the context of performing different tasks by these two actors especially in the business process with many-to-many relationships among the resources and activities. To address these issues, we handle the process with complex block structures including iteration, choice and parallelism, and propose an effective entity-wise method to measure the cooperation by combining both actors and activities together. Based on them, we present two approaches, one greedy-based and the other A*-based, to assign actors for maximum cooperation in the complex structured business process with many-to-many relationships among the resources and activities. The experimental results on both public released and synthetic datasets demonstrate the effectiveness and efficiency of our approaches. [ABSTRACT FROM AUTHOR]

• The application of Self-supervised learning reduces the time and effort of manual labeling. • Contrastive learning frameworks can generate powerful feature extractors to improve generalization. • Apply microseismic dataset from different practical engineering monitoring systems for generalization testing. The identification of valid microseismic events is a crucial stage in the processing of microseismic data and plays a central role in subsequent monitoring and early warning of rockburst disasters. However, obtaining microseismic data is difficult, and manually identifying effective microseismic signals is even more challenging. Therefore, exploring high-precision intelligent classification methods with limited labeled data is necessary. In this study, we applied a self-supervised learning method based on contrastive learning to classify small samples of labeled data. Firstly, use large unlabeled data to pre-train SimCLR (a simple contrastive learning framework) for transfer learning to classify small sample labeled data. The classification accuracy was 87.14%, an increase of 11.3% compared to the supervised-deep-learning method. Furthermore, we directly applied the pre-trained SimCLR to the microseismic data of various projects, achieving a classification accuracy of 86.70%. This demonstrates strong classification performance and generalization and provides valuable technical support for the subsequent monitoring and warning of rockburst disasters. [ABSTRACT FROM AUTHOR]

Intelligent early warning of rockburst hazards is critical for ensuring safe and efficient coal mining operations. The utilization of monitoring techniques, such as microseismic (MS), acoustic emission (AE), and electromagnetic radiation (EMR), has become standard practice for monitoring dynamic hazards in mining environments. However, the inherent complexity and unpredictability of the signals generated by these monitoring systems present significant challenges. While the application of deep-learning methods has gained traction in the field of coal-rock dynamic disaster management, their reliance on vast amounts of data and susceptibility to subjective labeling and poor generalization have hindered the achievement of timely, efficient, accurate, and comprehensive warning of rockburst hazards. In response to these challenges, this study applied an unsupervised learning method based on long short-term memory and an autoencoder to identify precursors of rockburst hazards and predict signals. The robustness and universality of the model were evaluated using MS, AE, and EMR data from the mine site. Then, the entropy method was used to comprehensively process the MS, AE, and EMR signals and conduct risk assessment. Finally, impressive results were achieved: the accuracy of precursor recognition reached 99.18% and the fitting rate of signal prediction reached 93%. Through on-site verification, the efficacy of this approach is evidenced by its synchronization with field records, enabling proactive responses to potential rockburst risks. This method is expected to enhance intelligent warning systems and ensure the safety of coal mine activities. [ABSTRACT FROM AUTHOR]

3,4-Seco-derivatives of myricerone were prepared by the Baeyer-Villiger oxidation of myricerone, and their structures were elucidated based on NMR and MS analyses. The synthesized compounds were evaluated as endothelin receptor antagonists and exhibited significant binding affinity for the ET receptor. [ABSTRACT FROM AUTHOR]

An introduction is presented which discusses various reports within the issue on topics including the distributed processing approach for sliding-window-based k-nearest neighbor (k-NN) queries, indexing and querying Web pages model, and HML keyword.

With the rapid development of transportation systems, traffic data have been largely produced in daily lives. Finding the insights of all these complex data is of great significance to vehicle dispatching and public safety. In this work, we propose a multitask deep learning model called Multitask Recurrent Graph Convolutional Network (MRGCN) for accurately predicting traffic flows in the city. Specifically, we design a multitask framework consisting of four components: a region-flow encoder for modeling region-flow dynamics, a transition-flow encoder for exploring transition-flow correlations, a context modeling component for contextualized fusion of two types of traffic flows and a task-specific decoder for predicting traffic flows. Particularly, we introduce Dual-attention Graph Convolutional Gated Recurrent Units (DGCGRU) to simultaneously capture spatial and temporal dependencies, which integrate graph convolution and recurrent model as a whole. Extensive experiments are carried out on two real-world datasets and the results demonstrate that our proposed method outperforms several existing approaches. [ABSTRACT FROM AUTHOR]

Service-based systems (SBSs) reuse existing loosely coupled Web services to provide value-added composite ones, which brings about much flexibility when the business changes frequently. The advent of automatic Web service composition technology allows system designers to quickly build SBSs without having to manually create process models. Despite the large number of strategies proposed so far, most of them compose Web services through the user-provided initial inputs and expected target outputs, which is not convenient for users to express their functional requirements. To address this issue, we allow users to employ keywords to represent key tasks of the composed Web services. To automatically compose Web services based on the given keywords, we study a new problem of keyword search in the AND/OR graph constructed through semantically matching input-output interfaces of existing related Web services. Due to the complexity of the problem, we propose a heuristic search approach, called UP-DFS. To improve the performance of UP-DFS, we further design two types of pruning strategies. The empirical study shows that our approach can efficiently generate a semantic input-output-based Web service composition that contains all the key tasks in the right order required by users while minimizing the number of services in the composition. [ABSTRACT FROM AUTHOR]

Simple Summary: Castration-resistant prostate cancer (CRPC) treatments include next-generation anti-androgen therapies (NGATs), taxane therapy, and PARP inhibitors (PARPi). However, resistance often occurs across and within therapeutic classes, which can complicate sequential treatment options. We developed acquired resistant models to study therapeutic resistance. Our findings indicate that while NGAT-resistant cells are cross-resistant to other NGATs, they remain sensitive to taxanes and olaparib. Cells resistant to docetaxel display cross-resistance to cabazitaxel and olaparib but respond to NGATs. Olaparib-resistant cells are cross-resistant to other PARPi but still sensitive to NGATs and docetaxel. Our research underscores the significance of rationale drug sequencing in CRPC treatment and underlying mechanisms of resistance. Current common treatments for castration-resistant prostate cancer (CRPC) typically belong to one of three major categories: next-generation anti-androgen therapies (NGAT) including enzalutamide, abiraterone acetate, apalutamide, and darolutamide; taxane therapy represented by docetaxel; and PARP inhibitors (PARPi) like olaparib. Although these treatments have shown efficacy and have improved outcomes for many patients, some do not survive due to the emergence of therapeutic resistance. The clinical landscape is further complicated by limited knowledge about how the sequence of treatments impacts the development of therapeutic cross-resistance in CRPC. We have developed multiple CRPC models of acquired therapeutic resistance cell sublines from C4-2B cells. These include C4-2B MDVR, C4-2B AbiR, C4-2B ApaR, C4-2B DaroR, TaxR, and 2B-olapR, which are resistant to enzalutamide, abiraterone, apalutamide, darolutamide, docetaxel, and olaparib, respectively. These models are instrumental for analyzing gene expression and assessing responses to various treatments. Our findings reveal distinct cross-resistance characteristics among NGAT-resistant cell sublines. Specifically, resistance to enzalutamide induces resistance to abiraterone and vice versa, while maintaining sensitivity to taxanes and olaparib. Conversely, cells with acquired resistance to docetaxel exhibit cross-resistance to both cabazitaxel and olaparib but retain sensitivity to NGATs like enzalutamide and abiraterone. OlapR cells, significantly resistant to olaparib compared to parental cells, are still responsive to NGATs and docetaxel. Moreover, OlapR models display cross-resistance to other clinically relevant PARP inhibitors, including rucaparib, niraparib, and talazoparib. RNA-sequencing analyses have revealed a complex network of altered gene expressions that influence signaling pathways, energy metabolism, and apoptotic signaling, pivotal to cancer's evolution and progression. The data indicate that resistance mechanisms are distinct among different drug classes. Notably, NGAT-resistant sublines exhibited a significant downregulation of androgen-regulated genes, contrasting to the stable expression noted in olaparib and docetaxel-resistant sublines. These results may have clinical implications by showing that treatments of one class can be sequenced with those from another class, but caution should be taken when sequencing drugs of the same class. [ABSTRACT FROM AUTHOR]

With Bitcoin being universally recognized as the most popular cryptocurrency, more Bitcoin transactions are expected to be populated to the Bitcoin blockchain system. As a result, many transactions can encounter different confirmation delays. Concerned about this, it becomes vital to help a user understand (if possible) how long it may take for a transaction to be confirmed in the Bitcoin blockchain. In this work, we address the issue of predicting confirmation time within a block interval rather than pinpointing a specific timestamp. After dividing the future into a set of block intervals (i.e., classes), the prediction of a transaction's confirmation is treated as a classification problem. To solve it, we propose a framework, Hybrid Confirmation Time Estimation Network (Hybrid-CTEN), based on neural networks and XGBoost to predict transaction confirmation time in the Bitcoin blockchain system using three different sources of information: historical transactions in the blockchain, unconfirmed transactions in the mempool, as well as the estimated transaction itself. Finally, experiments on real-world blockchain data demonstrate that, other than XGBoost excelling in the binary classification case (to predict whether a transaction will be confirmed in the next generated block), our proposed framework Hybrid-CTEN outperforms state-of-the-art methods on precision, recall and f1-score on all the multiclass classification cases (4-class, 6-class and 8-class) to predict in which future block interval a transaction will be confirmed. [ABSTRACT FROM AUTHOR]

Real social networks contain many communities, where members within each community are densely connected with each other, while they are sparsely connected with the members outside of the community. Since each member can join multiple communities simultaneously, communities in social networks are usually overlapping with each other. How to efficiently and effectively identify overlapping communities in a large social network becomes a fundamental problem in the big data era. Most existing studies on community finding focused on non-overlapping communities based on several well-known community fitness metrics. However, recent investigations have shown that these fitness metrics may suffer free rider and separation effects where the overlapping region of two communities always belongs to the denser one, rather to both of them. In this paper, we study the overlapping community detection problem in social networks that not only takes the quality of the found overlapping communities but also incorporate both free rider and separation effects on the found communities into consideration. Specifically, in this paper, we first propose a novel community fitness metric - triangle based fitness metric, for overlapping community detection that can minimize the free rider and separation effects on found overlapping communities, and show that the problem is NP-hard. We then propose an efficient yet scalable algorithm for the problem that can deliver a feasible solution. We finally validate the effectiveness of the proposed fitness metric and evaluate the performance of the proposed algorithm, through conducting extensive experiments on real-world datasets with over 100 million vertices and edges. Experimental results demonstrate that the proposed algorithm is very promising. [ABSTRACT FROM AUTHOR]

Users are rarely familiar with the content of a data source they are querying, and therefore cannot avoid using keywords that do not exist in the data source. Traditional systems may respond with an empty result, causing dissatisfaction, while the data source in effect holds semantically related content. In this paper we study this no-but-semantic-match problem on XML keyword search and propose a solution which enables us to present the top-k semantically related results to the user. Our solution involves two steps: (a) extracting semantically related candidate queries from the original query and (b) processing candidate queries and retrieving the top-k semantically related results. Candidate queries are generated by replacement of non-mapped keywords with candidate keywords obtained from an ontological knowledge base. Candidate results are scored using their cohesiveness and their similarity to the original query. Since the number of queries to process can be large, with each result having to be analyzed, we propose pruning techniques to retrieve the top-k results efficiently. We develop two query processing algorithms based on our pruning techniques. Further, we exploit a property of the candidate queries to propose a technique for processing multiple queries in batch, which improves the performance substantially. Extensive experiments on two real datasets verify the effectiveness and efficiency of the proposed approaches. [ABSTRACT FROM AUTHOR]

Objective:Aldehyde dehydrogenase (ALDH) has recently been reported as a marker of cancer stem-like cells in ovarian cancer. However, the prognostic role of ALDH in ovarian cancer still remains controversial. In this study, we aimed to evaluate the association between the expression of ALDH and the outcome of ovarian cancer patients by performing a meta-analysis. Methods:We systematically searched for studies investigating the relationships between ALDH expression and outcome of ovarian cancer patients. Only articles in which ALDH expression was detected by immunohistochemical staining were included. A meta-analysis was performed to generate combined hazard ratios (HRs) with 95% confidence intervals (CIs) for overall survival (OS) and disease-free survival (DFS). Results:A total of 1,258 patients from 7 studies (6 articles) were included in the analysis. Our results showed that high ALDH expression in patients with ovarian cancer was associated with poor prognosis in terms of Os (HR, 1.25; 95% CI, 1.07-1.47; P = 0.005) and DFS (HR, 1.58; 95% CI, 1.00-2.49; P = 0.052), though the difference for DFS was not statistically significant. In addition, there was no evidence of publication bias as suggested by Begg’s and Egger’s tests (Begg’s test, P = 0.707; Egger’s test, P = 0.355). Conclusion:The present meta-analysis indicated that elevated ALDH expression was associated with poor prognosis in patients with ovarian cancer. [ABSTRACT FROM AUTHOR]