Using Information Filtering in Web Data Mining Process

Web service-oriented Grid is becoming a standard for achieving loosely coupled distributed computing. Grid services could easily be specified with web-service based interfaces. In this paper we first envisage a realistic Grid market with players such as end-users, brokers and service providers participating co-operatively with an aim to meet requirements and earn profit. End-users wish to use functionality of Grid services by paying the minimum possible price or price confined within a specified budget, brokers aim to maximise profit whilst establishing a SLA (Service Level Agreement) and satisfying end-user needs and at the same time resisting the volatility of service execution time and availability. Service providers aim to develop price models based on end-user or broker demands that will maximise their profit. In this paper we focus on developing stochastic approaches to end-user workflow scheduling that provides QoS guarantees by establishing a SLA. We also develop a novel 2-stage stochastic programming technique that aims at establishing a SLA with end-users regarding satisfying their workflow QoS requirements. We develop a scheduling (workload allocation) technique based on linear programming that embeds the negotiated workflow QoS into the program and model Grid services as generalised queues. This technique is shown to outperform existing scheduling techniques that don't rely on real-time performance information

Crepe cake structured layered double hydroxide/sulfur/graphene as a positive electrode material for Li-S batteries

This work was financially supported by National Natural Science Foundation of China (Nos. 21975030 and 11674005), and the Ministry of Science and Technology of China (No. 2016YFB0700600 (National Materials Genome Project)).Solving the polysulfide shuttle problem is one of the core challenges for the industrialization of lithium–sulfur batteries. In this work, a triphasic composite of LDH/sulfur/rGO (LDH: layered double hydroxide, rGO: reduced graphene oxide) with a crepe cake like structure is designed and fabricated as a positive electrode material for lithium–sulfur batteries. Sulfur nanoparticles are embedded in the interlayer space of the composite and thus are well protected physically via three-dimensional wrapping and chemically via strong interaction of LDH nanoflakes with lithium polysulfides, such as ionic bonds and S···H hydrogen bonds. In addition, the flexible lamellar structure of the composite with soft graphene layers can tolerate the volume expansion of sulfur during lithiation as well as facilitate ionic permeability and electron transport, which is favorable for the redox reactions of polysulfide. The present work sheds light on the future development and industrialization of lithium–sulfur batteries.PostprintPeer reviewe

Utilizing Search Intent in Topic Ontology-based User Profile for Web Mining

It is well known that taking the Web user profiles into account can enhance the effectiveness of Web mining systems. However, due to the dynamic and complex nature of Web users, automatically acquiring worthwhile user profiles was found to be very challenging. Ontology-based user profile can possess more accurate user information. This research emphasizes on acquiring search intentions information. This paper presents a new approach of developing user profile for Web searching. The model considers the user's search intentions by the process of PTM (Pattern-Taxonomy Model). Initial experiments show that the user profile based on search intention is more useful than the generic PTM user profile. Developing user profile that contains user search intentions is essential for effective Web search and retrieval

Sequential Pattern Mining and Nonmonotonic Reasoning for Intelligent Information Agents

With the explosive growth of information available on the Internet, more effective data mining and data reasoning mechanism is required to process the sheer volume of information. Belief revision logic offers the expressive power to represent information retrieval contexts, and it also provides a sound inference mechanism to model the nonmonotonicity arising in changing retrieval contexts. Contextual knowledge for information retrieval can be extracted via efficient sequential pattern mining. We present a pattern taxonomy extraction model which efficiently performs the task of discovering descriptive frequent sequential patterns by pruning the noisy associations. This paper illustrates a novel approach of integrating the sequential data mining method into the belief revision based adaptive information agents to improve the agents' learning autonomy and prediction power. Initial experiments show that our belief revision logic and sequential pattern mining based intelligent information agents outperform the vector space model based information agents. Our work opens the door to the development of next generation of intelligent information agents to alleviate the information overload problem.17 page(s

Automatic Pattern-Taxonomy Extraction for Web Mining

In this paper, we propose a model for discovering frequent sequential patterns, phrases, which can be used as profile descriptors of documents. It is indubitable that we can obtain numerous phrases using data mining algorithms. However, it is difficult to use these phrases effectively for answering what users want. Therefore, we present a pattern taxonomy extraction model which performs the task of extracting descriptive frequent sequential patterns by pruning the meaningless ones. The model then is extended and tested by applying it to the information filtering system. The results of the experiment show that pattern-based methods outperform the keyword-based methods. The results also indicate that removal of meaningless patterns not only reduces the cost of computation but also improves the effectiveness of the system. 1

The Molecular Mechanism of Human Voltage-Dependent Anion Channel 1 Blockade by the Metallofullerenol Gd@C82(OH)22: An In Silico Study

The endohedral metallofullerenol Gd@C82(OH)22 has been identified as a possible antineoplastic agent that can inhibit both the growth and metastasis of cancer cells. Despite these potentially important effects, our understanding of the interactions between Gd@C82(OH)22 and biomacromolecules remains incomplete. Here, we study the interaction between Gd@C82(OH)22 and the human voltage-dependent anion channel 1 (hVDAC1), the most abundant porin embedded in the mitochondrial outer membrane (MOM), and a potential druggable target for novel anticancer therapeutics. Using in silico approaches, we observe that Gd@C82(OH)22 molecules can permeate and form stable interactions with the pore of hVDAC1. Further, this penetration can occur from either side of the MOM to elicit blockage of the pore. The binding between Gd@C82(OH)22 and hVDAC1 is largely driven by long-range electrostatic interactions. Analysis of the binding free energies indicates that it is thermodynamically more favorable for Gd@C82(OH)22 to bind to the hVDAC1 pore when it enters the channel from inside the membrane rather than from the cytoplasmic side of the protein. Multiple factors contribute to the preferential penetration, including the surface electrostatic landscape of hVDAC1 and the unique physicochemical properties of Gd@C82(OH)22. Our findings provide insights into the potential molecular interactions of macromolecular biological systems with the Gd@C82(OH)22 nanodrug