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The graphical representation of a finite element model (undirected graphs) imposes some constraints on the choice of storage techniques and data structures; first, the storage structure must deal efficiently with sparse matrices; second, the retrieval method of an edge, of a finite element model, around selected nodes must minimize the multiple occurrences of the same edge if plotting efficiency is to be achieved; and third, the insertion and extraction of edges in a data structure must be independent of the selected nodes identification scheme. This paper evaluates the relative merit of elementary storage methods and data structures in terms of the time and space costs required to satisfy the above constraints. The theoretical costs are derived and the experimental costs are evaluated and compared. Depending on the homogeneity of the degree of the nodes, a static data structure or a linked list data structure using listed or sectioned hashing techniques are shown to yield the minimum time and space costs.

The purpose of this study is to make a prediction of China's energy consumption structure from the perspective of compositional data and construct a novel grey model for forecasting compositional data.

Due to the existing grey prediction model based on compositional data cannot effectively excavate the evolution law of correlation dimension sequence of compositional data. Thus, the adaptive discrete grey prediction model with innovation term based on compositional data is proposed to forecast the integral structure of China's energy consumption. The prediction results from the new model are then compared with three existing approaches and the comparison results indicate that the proposed model generally outperforms existing methods. A further prediction of China's energy consumption structure is conducted into a future horizon from 2021 to 2035 by using the model.

China's energy structure will change significantly in the medium and long term and China's energy consumption structure can reach the long-term goal. Besides, the proposed model can better mine and predict the development trend of single time series after the transformation of compositional data.

The paper considers the dynamic change of grey action quantity, the characteristics of compositional data and the impact of new information about the system itself on the current system development trend and proposes a novel adaptive discrete grey prediction model with innovation term based on compositional data, which fills the gap in previous studies.

Items of information that have been stored in a computer normally need to be accessed via their contents. In principle this is always possible by doing an exhaustive scan of the entire file of information, but to achieve the access efficiently we use some sort of organizing principle, a file organization or file structure, to reduce the amount anning. Typically the items retrieved are a response to a request which fully or partially specifies their contents. Often the file organization requires pre‐processing of the body of information so that a secondary body of information (an index or directory) may be created which in some sense reveals the contents of the file. So, ultimately file structures are time saving devices, where we pay for the time saved by extra storage. They enable us quickly to find items of information by completely or partially specifying their contents.

Efficient operation of supply chain management (SCM) software is highly dependent on performance of its data structures that are used for data storage and retrieval. Each module in the software should use data structures that are appropriate for the types of operations performed in that module. The purpose of this paper is to develop and introduce an efficient data structure for storage and retrieval of data related to capacity of resources.

A major aim of supply management systems is timely production and delivery of products. This paper reviews data structures and designs an efficient data structure for storage and retrieval of data that is used in the scheduling module of SCM software.

This paper introduces a new data structure and search and update algorithms. This data structure can be used in SCM software to record the availability of non‐storable resources.

This is the first paper that discusses the role of data structures in SCM software and develops a data structure that can be used in the scheduling routine of SCM systems. Scheduling is one of the complex modules of SCM software. Some of the special characteristics related to capacity of resources to develop a data structure that can be efficiently searched and updated as part of scheduling routines were used in the new data structure. This data structure is a modified version of threaded height‐balanced binary search tree. Each node in the proposed tree has one more key than a node in the ordinary threaded height‐balanced binary search tree. The available algorithms in the literature on search and update operations on height‐balanced binary search trees are modified to suit the proposed tree.

The use of sequence and structure databanks is examined in relation to their application in some of the main branches of protein studies. Also the question of availability is addressed by means of presenting some information on current sequence and structure databanks. Increasingly research in molecular science requires joint access to both sequence and structure databases, and the reasons for this development, together with some of the methods for integrated access, are analysed.

Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.

Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.

The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.

This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.

In data engineering, view formalisms are used to provide flexibility to users and user applications by allowing them to extract and elaborate data from the stored data sources. Conversely, since the introduction of EXtensible Markup Language (XML), it is fast emerging as the dominant standard for storing, describing, and interchanging data among various web and heterogeneous data sources. In combination with XML Schema, XML provides rich facilities for defining and constraining user‐defined data semantics and properties, a feature that is unique to XML. In this context, it is interesting to investigate traditional database features, such as view models and view design techniques for XML. However, traditional view formalisms are strongly coupled to the data language and its syntax, thus it proves to be a difficult task to support views in the case of semi‐structured data models. Therefore, in this paper we propose a Layered View Model (LVM) for XML with conceptual and schemata extensions. Here our work is three‐fold; first we propose an approach to separate the implementation and conceptual aspects of the views that provides a clear separation of concerns, thus, allowing analysis and design of views to be separated from their implementation. Secondly, we define representations to express and construct these views at the conceptual level. Thirdly, we define a view transformation methodology for XML views in the LVM, which carries out automated transformation to a view schema and a view query expression in an appropriate query language. Also, to validate and apply the LVM concepts, methods and transformations developed, we propose a viewdriven application development framework with the flexibility to develop web and database applications for XML, at varying levels of abstraction.

Two ideal types of data can be distinguished in housing research: structured and less-structured data. Questionnaires and official statistics are examples of structured data, while less-structured data arise for instance from open interviews and documents. Structured data are sometimes labelled quantitative, while less-structured data are called qualitative. In this paper structured and less-structured data are considered from the perspective of measurement and analysis. Structured data arise when the researcher has an a priori category system or measurement scale available for collecting the data. When such an a priori system or scale is not available the data are called less-structured. It will be argued that these less-structured observations can only be used for any further analysis when they contain some minimum level of structure called a category system, which is equivalent to a nominal measurement scale. Once this becomes evident, one realizes that through the necessary process of categorization less-structured data can be analyzed in much the same way as structured data, and that the difference between the two types of data is one of degree and not of kind. In the second part of the paper these ideas are illustrated with examples from my own research on the meaning of preferences for dwelling features in which the concept of a meaning structure plays a central part. Until now these meaning structures have been determined by means of semi-structured interviews which, even with small samples, result in large amounts of less-structured data.

The plain High Dimensional Model Representation (HDMR) method needs Dirac delta type weights to partition the given multivariate data set for modelling an interpolation problem. Dirac delta type weight imposes a different importance level to each node of this set during the partitioning procedure which directly effects the performance of HDMR. The purpose of this paper is to develop a new method by using fluctuation free integration and HDMR methods to obtain optimized weight factors needed for identifying these importance levels for the multivariate data partitioning and modelling procedure.

A common problem in multivariate interpolation problems where the sought function values are given at the nodes of a rectangular prismatic grid is to determine an analytical structure for the function under consideration. As the multivariance of an interpolation problem increases, incompletenesses appear in standard numerical methods and memory limitations in computer‐based applications. To overcome the multivariance problems, it is better to deal with less‐variate structures. HDMR methods which are based on divide‐and‐conquer philosophy can be used for this purpose. This corresponds to multivariate data partitioning in which at most univariate components of the Plain HDMR are taken into consideration. To obtain these components there exist a number of integrals to be evaluated and the Fluctuation Free Integration method is used to obtain the results of these integrals. This new form of HDMR integrated with Fluctuation Free Integration also allows the Dirac delta type weight usage in multivariate data partitioning to be discarded and to optimize the weight factors corresponding to the importance level of each node of the given set.

The method developed in this study is applied to the six numerical examples in which there exist different structures and very encouraging results were obtained. In addition, the new method is compared with the other methods which include Dirac delta type weight function and the obtained results are given in the numerical implementations section.

The authors' new method allows an optimized weight structure in modelling to be determined in the given problem, instead of imposing the use of a certain weight function such as Dirac delta type weight. This allows the HDMR philosophy to have the chance of a flexible weight utilization in multivariate data modelling problems.