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Since library storage has been increasing day by day, it is difficult for readers to find the books which interest them as well as representative booklists. How to utilize meaningful information effectively to improve the service quality of the digital library appears to be very important. The purpose of this paper is to provide a recommendation system architecture to promote digital library services in electronic libraries.

In the proposed architecture, a two‐phase data mining process used by association rule and clustering methods is designed to generate a recommendation system. The process considers not only the relationship of a cluster of users but also the associations among the information accessed.

The process considered not only the relationship of a cluster of users but also the associations among the information accessed. With the advanced filter, the recommendation supported by the proposed system architecture would be closely served to meet users' needs.

This paper not only constructs a recommendation service for readers to search books from the web but takes the initiative in finding the most suitable books for readers as well. Furthermore, library managers are expected to purchase core and hot books from a limited budget to maintain and satisfy the requirements of readers along with promoting digital library services.

Purpose – Traditional library catalogs have become inefficient and inconvenient in assisting library users. Readers may spend much time in searching library materials via printed catalogs. Readers need an intelligent and innovative solution to overcome this problem. The purpose of this paper is to illustrate how data mining technology is a good approach to fulfill readers' requirements. Design/methodology/approach – Data mining is considered to be the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. This paper analyzes the readers' borrowing records by using the following techniques: data analysis, building data warehouse and data mining. Findings – The mining results show that all readers can be categorized into five clusters, and each cluster has its own characteristics. It was also found that the frequency for graduates and associate researchers to borrow multimedia data is much higher. This phenomenon shows that these readers have a higher preference for accepting digitized publication. Besides, we notice that more readers borrow multimedia data rise in years. This up trend indicates that readers are gradually shifting their preference in reading digital publications. Originality/value – The paper proposes a technique to discover clusters by using ant colony methods.

The purpose of this paper is to show how rapidly evolving information technology has dramatically changed the knowledge dissemination process. However, many of them lack a generic evaluation process to verify the system's performance. In an attempt to solve this problem, this study seeks to propose an agent‐based model to provide a dynamic, flexible framework for performance evaluations of e‐learning projects.

The paper proposes an agent‐based model which comprises a learning model, balanced scorecard and option‐pricing approach to evaluate the performance of an e‐learning project.

E‐learning could be the paradigm shift of traditional education. Thus, the paper provides organizations with a methodology to deliberately evaluate their e‐learning projects by treating it as a continuous improvement process.

The original contributions in this paper are: application of a balance scorecard to weigh different perspectives; application of a real options approach for risk management of e‐learning projects; construction of an agent‐based system for autonomous qualitative/quantitative information gathering.

This study aims to investigate whether and how different sentiments affect the stock market’s reaction to the American Customer Satisfaction Index (ACSI) information.

The portfolio approach, with time-varying risk factor loadings and the asset-pricing models, is borrowed from the finance literature to investigate the ACSI-performance relationship. A direct sentiment index is used to examine how investors’ optimistic, neutral and pessimistic sentiments affect the aforementioned relation.

This paper finds that customer satisfaction is a valuable intangible asset that generates positive abnormal returns. On average, investing in the Strong-ACSI Portfolio is superior to investing in the market index. Even when the stock market holds pessimistic beliefs, investors can beat the market by investing in firms that score well on customer satisfaction. The out-performance of our zero-cost, long–short ACSI strategy also confirms the mispricing of ACSI information in pessimistic periods.

Findings are limited to firms covered by the ACSI data.

Finance research has further documented evidence of the stock market under-reacting to intangible information. For example, firms with higher research and development expenditures, advertising, patent citations and employee satisfaction all earn superior returns. Literature also proves that investors efficiently react to tangible information, whereas they undervalue intangible information. In summary, combining our results and those reported in the literature, customer satisfaction is value-relevant for both investors and firm management, particularly in pessimistic periods.

This study is the first to investigate how sentiment affects the positive ACSI-performance relationship, while considering the time-varying property of risk factors. This study is also the first to show that ACSI plays a more important role during pessimistic periods. This study contributes to the growing literature on the marketing–finance interface by providing better understanding of how investor emotional states affect their perceptions and valuations of customer satisfaction.

Despite several efforts during the last years, the web model and semantic web technologies have not yet been successfully applied to empower Ubiquitous Computing architectures in order to create knowledge‐rich environments populated by interconnected smart devices. In this paper we point out some problems of these previous initiatives and introduce SoaM (Smart Objects Awareness and Adaptation Model), an architecture for designing and seamlessly deploying web‐powered context‐aware semantic gadgets. Implementation and evaluation details of SoaM are also provided in order to identify future research challenges.

The use of mobile devices in handling our daily activities that involve the storage or access of sensitive data (e.g. on-line banking, paperless prescription services, etc.) is becoming very common. These mobile electronic services typically use a knowledge-based authentication method to authenticate a user (claimed identity). However, this authentication method is vulnerable to several security attacks. To counter the attacks and to make the authentication process more secure, this paper aims to investigate the use of touch dynamics biometrics in conjunction with a personal identification number (PIN)-based authentication method, and demonstrate its benefits in terms of strengthening the security of authentication services for mobile devices.

The investigation has made use of three light-weighted matching functions and a comprehensive reference data set collected from 150 subjects.

The investigative results show that, with this multi-factor authentication approach, even when the PIN is exposed, as much as nine out of ten impersonation attempts can be successfully identified. It has also been discovered that the accuracy performance can be increased by combining different feature data types and by increasing the input string length.

The novel contributions of this paper are twofold. Firstly, it describes how a comprehensive experiment is set up to collect touch dynamics biometrics data, and the set of collected data is being made publically available, which may facilitate further research in the problem domain. Secondly, the paper demonstrates how the data set may be used to strengthen the protection of resources that are accessible via mobile devices.

The present study aims to find out the best polymer/polymer pair in electrical insulating applications. Moreover, the effects of different polymer counterpart and applied load on the friction and wear behaviour of PA 46 + 30%GFR and unfilled PA 66 thermoplastic polymers are to be studied.

Friction and wear tests vs PA 46 + 30%GFR and PPS + 30%GFR polymer composites were carried out on a pin‐on‐disc arrangement and at a dry sliding conditions. Tribological tests were performed at room temperature under 20, 40 and 60 N loads and at 0.5 m/s sliding speed.

The results showed that, the coefficient of friction decreases with the increasing of load (up to 40 N) for PA 46 + 30%GFR composite and polyamide (PA) 66 polymer used in this study. However, above 40 N applied load the coefficient of friction increases. The specific wear rate for PA 46 + 30%GFR and PA 66 against PPS + 30%GFR polymer composite counterpart are about in the order of 10⁻¹³ m²/N while the specific wear rate for PA 46 + 30%GFR and PA 66 against PA 46 + 30%GFR polymer composite counterpart are in the order of 10⁻¹⁴ m²/N. For PA 46 + 30%GFR composite and unfilled PA 66 polymers tested the specific wear rate values increased with the increment of load. The highest specific wear rate is for unfilled PA 66 against PPS + 30%GFR with a value of 2.81 × 10⁻¹³ m²/N followed by PA 66 against PA 46 + 30%GFR with a value of 2.26 × 10⁻¹³ m²/N. The lowest wear rate is PA 46 + 30%GFR polymer composite against PA 46 + 30%GFR polymer composite counterpart with a value of 3.19 × 10⁻¹⁴ m²/N. The average specific wear rates for unfilled PA 66 against PA 46 + 30%GFR is 80 times higher than PA 46 + 30%GFR wear rate while specific wear rates for unfilled PA 66 against PPS + 30%GFR is 100 times higher than that of PA 46 + 30%GFR wear rate. From point view of tribological performance, PA 46 + 30%GFR is a more suitable engineering thermoplastic composite materials for electrical contact breaker applications.

In the present work, tribological tests were performed only at room temperature under three different loads and a sliding speed. This is the limitation of the work.

This work is easily used for industrial polyamides to check their tribological behaviours.

This is an original and experimental study and it will be useful both for academicians and for industrial sides.

Sliding wear is a common phenomenon in the iron ore handling industry. Large-scale handling of iron ore bulk-solids causes a high amount of volume loss from the surfaces of bulk-solids-handling equipment. Predicting the sliding wear volume from equipment surfaces is beneficial for efficient maintenance of worn equipment. Recently, the discrete element method (DEM) simulations have been utilised to predict the wear by bulk-solids. However, the sensitivity of wear prediction subjected to DEM parameters has not been systemically investigated at single particle level. To ensure the wear predictions by DEM are accurate and stable, this study aims to conduct the sensitivity analysis at the single particle level.

In this research, pin-on-disc wear tests are modelled to predict the sliding wear by individual iron ore particles. The Hertz–Mindlin (no slip) contact model is implemented to simulate interactions between particle (pin) and geometry (disc). To quantify the wear from geometry surface, a sliding wear equation derived from Archard’s wear model is adopted in the DEM simulations. The accuracy of the pin-on-disc wear test simulation is assessed by comparing the predicted wear volume with that of the theoretical calculation. The stability is evaluated by repetitive tests of a reference case. At the steady-state wear, the sensitivity analysis is done by predicting sliding wear volumes using the parameter values determined by iron ore-handling conditions. This research is carried out using the software EDEM^® 2.7.1.

Numerical errors occur when a particle passes a joint side of geometry meshes. However, this influence is negligible compared to total wear volume of a wear revolution. A reference case study demonstrates that accurate and stable results of sliding wear volume can be achieved. For the sliding wear at steady state, increasing particle density or radius causes more wear, whereas, by contrast, particle Poisson’s ratio, particle shear modulus, geometry mesh size, rotating speed, coefficient of restitution and time step have no impact on wear volume. As expected, increasing indentation force results in a proportional increase. For maintaining wear characteristic and reducing simulation time, the geometry mesh size is recommended. To further reduce simulation time, it is inappropriate using lower particle shear modulus. However, the maximum time step can be increased to 187% T_R without compromising simulation accuracy.

The applied coefficient of sliding wear is determined based on theoretical and experimental studies of a spherical head of iron ore particle. To predict realistic volume loss in the iron ore-handling industry, this coefficient should be experimentally determined by taking into account the non-spherical shapes of iron ore particles.

The effects of DEM parameters on sliding wear are revealed, enabling the selections of adequate values to predict sliding wear in the iron ore-handling industry.

The accuracy and stability to predict sliding wear by using EDEM^® 2.7.1 are verified. Besides, this research accelerates the calibration of sliding wear prediction by DEM.

This paper aims to study the tribocorrosion and the surface repassivation behaviors of Monel 400 alloy in artificial seawater.

In this study, the tribocorrosion behavior of Monel 400 alloy was studied under different applied loads in artificial seawater by using a pin-on-disk tribometer equipped with an electrochemical workstation. The applied loads were selected ranging from 50 to 200 N. The surface repassivation behavior of Monel 400 alloy was studied by X-ray photoelectron spectroscopy.

It was demonstrated that mass loss was determined by the combined effect of mechanical wear and chemical corrosion. The surface repassivation mechanism of the alloy is that layer corrosion product film formed on the surface of Monel 400 alloy, which can protect metal matrix from future corrosion.

This research adds original content in revealing the tribocorrosion and surface repassivation behaviors of Monel 400 alloy under different loads, which offer a theoretical basis for the application under the corrosion and wear environment of Monel 400.

This manuscript aims to deal with the tribological property variations of engine oil (SAE15W40) by the addition of copper oxide (CuO) nanoparticles on weight percentage basis.

Experimental studies on the influence of CuO nanoparticles utilised as an additive in lubricating oil (SAE15W40) under boundary lubrication conditions have been carried out using a pin-on-disc machine in accordance with ASTM G-99 standard. The variation of viscosity, coefficient of friction, wear and settling of nanoparticles has been studied as a function of particle concentration in the lubricant.

Results show that the frictional force and specific wear rate decrease with an increase in concentration of nanoparticles comes to a minimum at a specific concentration and then increases, showing the presence of an optimum concentration. With the increase in concentration of nanoparticles, the kinematic and dynamic viscosities, and the flash and fire points are found to increase.

The use of CuO nanoparticles as additives to a moderate level is a very efficient means of improving the tribological properties of lubricating oils.