Search results

The library CD‐ROM database with its enormous storage and retrieving from network capabilities, has been gradually replacing some of its printed counterparts. But one of the disadvantages is that it can only access the full‐text image CD‐ROM database via local area network environment. This paper proposes a new method to solve this problem. The method uses hash function as data structure and image document process technique to perform a practical library full‐text image CD‐ROM database retrieving system. The results of its function can reduce users storage space, allow multi‐users to retrieve the same CD‐ROM database, and allow users to retrieve and print out full text image CD‐ROM database via campus network.

The occurrence and unpredictability of speculative bubbles on financial markets, and their accompanying crashes, have confounded economists and economic historians worldwide. The purpose of this paper is to diagnose and detect the bursting of shipping bubbles ex ante, and to qualify the patterns of shipping price dynamics and the bubble mechanics, so that appropriate counter measures can be taken in advance to reduce side effects arising from bubbles.

Log periodic power law (LPPL) model, developed in the past decade, is used to detect large market falls or “crashes” through modeling of the shipping price dynamics on a selection of three historical shipping bubbles over the period of 1985 to 2016. The method is based on a nonlinear least squares estimation that yields predictions of the most probable time of the regime switching.

It could be concluded that predictions by the LPPL model are quite dependent on the time at which they are conducted. Interestingly, the LPPL model could have predicted the substantial fall in the Baltic Dry Index during the recent global downturn, but not all crashes in the past. It is also found that the key ingredient that sets off an unsustainable growth process for shipping prices is the positive feedback. When the positive feedback starts, the burst of bubbles in shipping would be influenced by both endogenous and exogenous factors, which are crucial for the advanced warning of the market conversion.

The LPPL model has been first applied into the dry bulk shipping market to test a couple of shipping bubbles. The authors not only assess the predictability and robustness of the LPPL model but also expand the understanding of the model and explain patterns of shipping price dynamics and bubble mechanics.

This study aims to propose a theoretical model to characterize the optimal forward freight agreement (FFA) procurement strategies and investigate the determinants of FFA trading activities from a new cross-market perspective.

A two-step model specification is used to empirically test the theoretical results for the Capesize, Panamax and Supramax sectors. It is found that spot demand has a positive relation with FFA trading volume for all three sectors. Moreover, spot demand volatility has a negative relation, while the correlation between spot demand and spot rate has a positive relation with FFA trading volume for the Capesize and Panamax sectors.

The results show that the expected spot demand is scaled by a “quantity premium,” which is the product of a demand covariance term, a demand riskiness term and a demand volatility term. This can be used by the traders in the FFA market to construct their hedging strategies.

Commonly, an organizational information system may have various data types and directory formats. It usually employs different metadata formats to represent the documents. Although the metadata system can cover the different formats of documents, there still exist the integration problems in various metadata systems. This may lower the performance of data processing and impede information sharing. Research focuses on the model of using multi‐XML schema to construct an XML system framework. Through a complete hierarchical tree structure definition of inner elements, the proposed model can settle the weakness of traditional object‐oriented languages in information sharing; it can also eliminate the constraints of storage and management among heterogeneous metadata while processing different metadata information.

The Internet has forced libraries to consider how to assist users to rapidly retrieve information. Such a consideration has accelerated the development of electronic publishing and has positioned the library as mediator between users and providers: archiving information circulation and providing secure copyright clearance through an efficient electronic document delivery and payment mechanism. This work develops an Extensible Markup Language (XML) framework for electronic document delivery that offers a novel electronic document delivery system and also locates publishers who can provide the copyrighted material in an electronic format via the OPAC. The proposed electronic document delivery system has four functions: (1) it enables the electronic document payment; (2) it shortens the time between inquiry and electronic document retrieval; (3) it anticipates the changing role of libraries; and (4) it reduces the printed collection load of libraries.

The integration of information systems must consider various aspects, including the individuals of an organization, cooperating with other vendors, and the connections within this organization. The advantages of integration include shortening the negotiation process among the organization and the cooperating vendors, saving time for the users, and identifying the interface management. However, the greatest difficulties are how to integrate different system platforms and implement technical aspects into a suitable Web‐interface for users to operate. Information technology companies have developed Web Services which rely on the flexibility of the Extended Mark‐up Language. The services not only apply to integrated information systems, but also provide open services in Web environments. This article evaluates the feasibility of Web Services for application in integrated library information systems.

The purpose of this paper, is to predict the various phases and crystal structure from multi-component alloys. Nowadays, the concept and strategies of the development of multi-principal element alloys (MPEAs) significantly increase the count of the potential candidate of alloy systems, which demand proper screening of large number of alloy systems based on the nature of their phase and structure. Experimentally obtained data linking elemental properties and their resulting phases for MPEAs is profused; hence, there is a strong scope for categorization/classification of MPEAs based on structural features of the resultant phase along with distinctive connections between elemental properties and phases.

In this paper, several machine-learning algorithms have been used to recognize the underlying data pattern using data sets to design MPEAs and classify them based on structural features of their resultant phase such as single-phase solid solution, amorphous and intermetallic compounds. Further classification of MPEAs having single-phase solid solution is performed based on crystal structure using an ensemble-based machine-learning algorithm known as random-forest algorithm.

The model developed by implementing random-forest algorithm has resulted in an accuracy of 91 per cent for phase prediction and 93 per cent for crystal structure prediction for single-phase solid solution class of MPEAs. Five input parameters are used in the prediction model namely, valence electron concentration, difference in the pauling negativeness, atomic size difference, mixing enthalpy and mixing entropy. It has been found that the valence electron concentration is the most important feature with respect to prediction of phases. To avoid overfitting problem, fivefold cross-validation has been performed. To understand the comparative performance, different algorithms such as K-nearest Neighbor, support vector machine, logistic regression, naïve-based approach, decision tree and neural network have been used in the data set.

In this paper, the authors described the phase selection and crystal structure prediction mechanism in MPEA data set and have achieved better accuracy using machine learning.

The purpose of this study is to increase the thermal and mechanical properties of the photopolymer by filling with the copper powder for the application of rapid tooling.

In this study, the photopolymer is filled with the different loading of copper powder for investigating the thermal and mechanical properties of the copper/photopolymer composite. The thermal properties of the copper/photopolymer composite are characterized with the degradation temperature and with the thermal conductivity. The mechanical properties of copper/photopolymer composite are performed with the tensile strength and hardness testing. Moreover, the copper/photopolymer composite is imaged by using a scanning electron microscopic with energy dispersive spectroscopy.

The tensile strength of the copper/photopolymer composite is increased over 45 per cent at 20 phr copper loading. The hardness of the photopolymer has a negative correlation with the increasing copper loading and is decreased about 28.5 per cent at 100 phr copper loading. The degradation temperature of the copper/photopolymer composite is increased about 7.2 per cent at 70 phr copper loading. The thermal conductivity of the copper/photopolymer composite is increased over 65 per cent at 100 phr copper loading.

The photopolymer used in rapid prototyping system is generally fragile and has poor thermal properties. This study improves the thermal and mechanical properties of the photopolymer with the copper filling which has been never investigated in the field of rapid prototyping applications.

To promote university education quality, performance measure indicators (PMIs) must be established. These indicators ensure the maintenance of university operating standards, encourage individual universities to work on inadequacies, and promote university competitiveness. The purpose of this paper is to address this issue.

A literature analysis was conducted and European, American and Taiwanese education evaluation indicators were used to develop the PMIs. Data were collected from the following sources: US‐MBNAQ; US News & World Report; two renowned US education journals; UK University Committee Reports; UK‐CVCP/UGC; three renowned UK education evaluation newspapers, opinions from the Ministry of Education and submissions from scholars in Australia and Taiwan. A total of 13 scholars were invited to participate in the literature review and analysis, which employed Delphi methods.

These indicators included 18 measurement dimensions, and 78 PMIs were developed. Applying these PMIs resulted in the creation of key performance indicators and a balanced scorecard (BSC) of performance measures. In addition, BSC represented a self‐evaluation performance indicator that universities could use to achieve the objective of performance management.

The PMI by means of their integration into the BSC, allow management by objectives, a method to build the performance of each department and organization.

Traditional library catalogs have become inefficient and inconvenient in assisting library users. Readers may spend a lot of time searching library materials via printed catalogs. Readers need an intelligent and innovative solution to overcome this problem. The paper seeks to examine data mining technology which is a good approach to fulfill readers' requirements.

Data mining is considered to be the non‐trivial extraction of implicit, previously unknown, and potentially useful information from data. This paper analyzes readers' borrowing records using the techniques of data analysis, building a data warehouse, and data mining.

The paper finds that after mining data, readers can be classified into different groups according to the publications in which they are interested. Some people on the campus also have a greater preference for multimedia data.

The data mining results shows that all readers can be categorized into five clusters, and each cluster has its own characteristics. The frequency with which graduates and associate researchers borrow multimedia data is much higher. This phenomenon shows that these readers have a higher preference for accepting digitized publications. Also, the number of readers borrowing multimedia data has increased over the years. This trend indicates that readers preferences are gradually shifting towards reading digital publications.