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In this paper we discuss an area of information systems management and development that appears to have been practised by many major European and US organisations: the area of corporate data modelling. However, rather suprisingly perhaps, it is clear that there is little theoretical or empirical literature devoted to this important issue. There is little empirical evidence to indicate the actual scale of adoption of this practice, and there is certainly little analytical material devoted to questions of the efficacy of this activity or considerations of good practice in this area. The main aim of this paper is to begin to offer some early empirical and analytical material on corporate data modelling. We have been conducting a study of a number of organisation’s experience of corporate data modelling in the UK. We would hope that an examination of the current corporate experience of corporate data modelling will stimulate a clearer discussion of the purposes and practices of this important area of modern information systems planning.

The Greehouse Gas (GHG) in the shipping industry has attracted increasing attention. One potential method to reduce the GHG mitigation cost is the Clean Development Mechanism (CDM). The purpose of this paper is to identify factors that may increase or hinder the CDM in the shipping industry and provide policy implications.

The paper is an extension and application of the methodology first published by Wang and Firestone in Energy for Sustainable Development. The gravity model in international trade theory is used. The econometric model is employed for the analysis.

Larger project endowment, higher government efficiency, high‐quality expertise and infrastructure may play roles in increasing CDM in the shipping industry. The promotion of small‐scale projects, upgrade of the infrastructure, assistance of technologies and knowledge overseas can help the shipping industry and small countries to attract more CDM.

The paper is among the first work to describe and analyze potential barriers for the international shipping industry to use the CDM. It also suggests a set of measures to address the policy options to promote CDM in the shipping industry and small developing countries.

This paper aims to describe how building information model (BIM) and big data can be combined in the same interface for providing new value to stakeholders, such as the property owner and user, as well as property service and workplace service companies. The research presents a new concept, which shows how the BIM can be exploited efficiently during maintenance.

Initially, existing facility management (FM) processes were investigated to find out how to digitize them and identify bottlenecks. Second, BIM’s data content was explored to identify the information that could be used to streamline FM processes. Third, the potential of the active data measured in the building was evaluated. Finally, research was undertaken to find out how constantly fluctuating information can be combined with BIM objects and what kind of added value that combination could offer. The literature review was used to support the primary contribution. In addition, the research problems were described and the basics of the research were obtained by interviews. The author has interviewed 27 professionals from several stakeholders.

The first finding is that the BIM can serve as a platform for building use, various services and management when it has been adequately generated during the planning and construction phases and enriched before being commissioned. The other essential finding is the theory of conditions data model (CDM), which is a technical environment that combines active data with BIM. The most important advantages of BIM in FM are as follows: • Building owner attains better user satisfaction, acquires better quality and smarter services, saves energy, ensures better indoor conditions and improves building profitability. • Service providers can develop and offer new services, speed up operations, save resources and generate more profits. • The occupant gets a better user experience, faster and higher quality services and better indoor conditions.

The CDM enables to generate for the real estate and construction (RE&C) sector a novel BIM-based ecosystem with standard rules, instead of every individual operator developing his/her own unique solution for BIM use in FM. This will have an impact on the entire RE&C sector’s operating methods and will have significant financial implications in the near future. Application of this research is limited to office buildings where indoor condition measuring is undertaken continuously and where the knowledge of the use cases of spaces is available. In addition, the proper BIM in the Industry Foundation Classes format must exist. The evaluation of the validity of big data is not discussed in this article. Visualization of data and content of user interfaces will be the topic of another article by the author. This article does not deal with intricate technical details, but crucial issues are defined.

The article presents a unique method for BIM use in FM. The theory of CDM (how to combine active data with BIM) is completely new and a similar solution has not been presented earlier. The theory of the presented method will be the crucial key for BIM use and will lead worldwide commissioning. Currently, the theory is under test in the practical pilot project. The results of the project will be published in the next article.

Chronic diseases are considered as one of the serious concerns and threats to public health across the globe. Diseases such as chronic diabetes mellitus (CDM), cardio vasculardisease (CVD) and chronic kidney disease (CKD) are major chronic diseases responsible for millions of death. Each of these diseases is considered as a risk factor for the other two diseases. Therefore, noteworthy attention is being paid to reduce the risk of these diseases. A gigantic amount of medical data is generated in digital form from smart healthcare appliances in the current era. Although numerous machine learning (ML) algorithms are proposed for the early prediction of chronic diseases, these algorithmic models are neither generalized nor adaptive when the model is imposed on new disease datasets. Hence, these algorithms have to process a huge amount of disease data iteratively until the model converges. This limitation may make it difficult for ML models to fit and produce imprecise results. A single algorithm may not yield accurate results. Nonetheless, an ensemble of classifiers built from multiple models, that works based on a voting principle has been successfully applied to solve many classification tasks. The purpose of this paper is to make early prediction of chronic diseases using hybrid generative regression based deep intelligence network (HGRDIN) model.

In the proposed paper generative regression (GR) model is used in combination with deep neural network (DNN) for the early prediction of chronic disease. The GR model will obtain prior knowledge about the labelled data by analyzing the correlation between features and class labels. Hence, the weight assignment process of DNN is influenced by the relationship between attributes rather than random assignment. The knowledge obtained through these processes is passed as input to the DNN network for further prediction. Since the inference about the input data instances is drawn at the DNN through the GR model, the model is named as hybrid generative regression-based deep intelligence network (HGRDIN).

The credibility of the implemented approach is rigorously validated using various parameters such as accuracy, precision, recall, F score and area under the curve (AUC) score. During the training phase, the proposed algorithm is constantly regularized using the elastic net regularization technique and also hyper-tuned using the various parameters such as momentum and learning rate to minimize the misprediction rate. The experimental results illustrate that the proposed approach predicted the chronic disease with a minimal error by avoiding the possible overfitting and local minima problems. The result obtained with the proposed approach is also compared with the various traditional approaches.

Usually, the diagnostic data are multi-dimension in nature where the performance of the ML algorithm will degrade due to the data overfitting, curse of dimensionality issues. The result obtained through the experiment has achieved an average accuracy of 95%. Hence, analysis can be made further to improve predictive accuracy by overcoming the curse of dimensionality issues.

The proposed ML model can mimic the behavior of the doctor's brain. These algorithms have the capability to replace clinical tasks. The accurate result obtained through the innovative algorithms can free the physician from the mundane care and practices so that the physician can focus more on the complex issues.

Utilizing the proposed predictive model at the decision-making level for the early prediction of the disease is considered as a promising change towards the healthcare sector. The global burden of chronic disease can be reduced at an exceptional level through these approaches.

In the proposed HGRDIN model, the concept of transfer learning approach is used where the knowledge acquired through the GR process is applied on DNN that identified the possible relationship between the dependent and independent feature variables by mapping the chronic data instances to its corresponding target class before it is being passed as input to the DNN network. Hence, the result of the experiments illustrated that the proposed approach obtained superior performance in terms of various validation parameters than the existing conventional techniques.

Innovation has been acknowledged as the key for modern industries. However, the construction industry is criticised for being poor in innovation performance compared to other industry sectors. Large construction firms are the main contributor to technological innovation in the construction industry, but the driving process of their technological innovation has not yet been fully investigated in previous studies. The purpose of this paper is to provide quantitative analysis of the technological innovation driving process of large construction firms.

An extended crépon, duguet and mairesse (CDM) model has been developed to analyse the key influencing factors for technological innovation in construction firms. The sample data are selected from the world’s largest construction market, China, and include 129 listed construction firms.

The results show significant positive correlation between R&D investment and innovation output and also between innovation output and performance. The effect of influencing factors on the R&D investment, innovation output and performance are also revealed by the empirical study. The underlying reasons are discussed and suggestions are given for the construction industry to improve the technological innovation capacity of construction firms.

This research contributes to the literature of construction innovation and benefits practitioners by providing a quantitative approach to demonstrate the driving process of innovation in construction firms.

Flights are often delayed owing to emergencies. This paper proposes a cooperative slot secondary assignment (CSSA) model based on a collaborative decision-making (CDM) mechanism, and the operation mode of flight waves designs an improved intelligent algorithm to solve the optimal flight plan and minimize the total delay of passenger time.

Taking passenger delays, transfer delays and flight cancellation delays into account comprehensively, the total delay time is minimized as the objective function. The model is verified by a linear solver and compared with the first come first service (FCFS) method to prove the effectiveness of the method. An improved adaptive partheno-genetic algorithm (IAPGA) using hierarchical serial number coding was designed, combining elite and roulette strategies to find pareto solutions.

Comparing and analyzing the experimental results of various scale examples, the optimization model in this paper is greatly optimized compared to the FCFS method in terms of total delay time, and the IAPGA algorithm is better than the algorithm before in terms of solution performance and solution set quality.

Based on the actual situation, this paper considers the operation mode of flight waves. In addition, the flight plan solved by the model can be guaranteed in terms of feasibility and effectiveness, which can provide airlines with reasonable decision-making opinions when reassigning slot resources.

This paper aims to analyse the impact of innovation on the productivity of firms in India using the data from the World Enterprise Survey. This paper first classifies three different types of innovation measures then further analyses their relation with the productivity of the firms.

The methodology used for this study has incorporated the structural Crépon-Douget-Mairesse (CDM) model wherein productivity is measured using both the innovation inputs and the innovation outputs. Three main equations have been used to quantify this relation includes the knowledge intensity function, innovation function and the productivity equation.

Findings indicate that decision to invest in research and development (R&D) is influenced negatively by financial obstacles and trade obstacles and positively influenced by telecommunication obstacles, government obstacles and the size of the firm in India. Similarly, financial obstacles and the size of the firm are affecting the firm’s research expenditure per employee. Also, financial obstacles seem to hinder the research intensity and larger firms seem to have higher research intensity. The size of the firm contributes significantly to product innovation. However, R&D spending seems to be negatively related to the innovation outcome. The findings relating to productivity shows neither product nor process innovation outputs, independently are not contributing significantly to the productivity of firms. However, product and process innovation, together serve as innovation outputs is a significant contributor to firm productivity. On the other hand, organisational innovation contributes significantly to the productivity of the firms in a negative manner.

The findings relating to productivity shows neither product nor process innovation outputs, independently are not contributing significantly to the productivity of firms (which has been measured by sales per worker is impacted by the capital and the labour inputs). However, product and process innovation, together serve as innovation outputs is a significant contributor to firm productivity. On the other hand, organisational innovation contributes significantly to the productivity of the firms in a negative manner. The reason could be due to the fact that the definition of organisational innovation incorporates both dissolutions and mergers.

The purpose of this paper is to present a new simplified local remeshing procedure for the study of discrete crack propagation in finite element (FE) mesh. The proposed technique accounts for the generation and propagation of crack‐like failure within an FE‐model. Beside crack propagation, the technique enables the analysis of fragmentation of initially intact continuum. The capability of modelling fragmentation is essential in various structure‐structure interaction analyses such as projectile impact analysis and ice‐structure interaction analysis.

The procedure combines continuum damage mechanics (CDM), fictitious crack approach and a new local remeshing procedure. In the approach a fictitious crack is replaced by a discrete crack by applying delete‐and‐fill local remeshing. The proposed method is independent of mesh topology unlike the traditional discrete crack approach. The procedure is implemented for 3‐D solid elements in commercial finite element software Abaqus/Explicit using Python scripting. The procedure is completely automated, such that crack initiation and propagation analyses do not require user intervention. A relatively simple constitutive model was implemented strictly for demonstrative purposes.

Well known examples were simulated to verify the applicability of the method. The simulations revealed the capabilities of the method and reasonable correspondence with reference results was obtained. Material fragmentation was successfully simulated in ice‐structure interaction analysis.

The procedure for modelling discrete crack propagation and fragmentation of initially intact quasi‐brittle materials based on local remeshing has not been presented previously. The procedure is well suited for simulation of fragmentation and is implemented in a commercial FE‐software.

The purpose of this study is to analyze the performance of indigenous innovation in developing countries in the era of trade liberalization. It analyzes indigenous innovation from research and development (R&D) investments to innovation output and its effect on economic growth.

The sample for this study includes 20 middle-income countries across five continents for the period between 1994 and 2018. The study employs the Crepon Duguet and Mairessec CDM model in a panel data setting to do a multistage analysis of the innovation process. A vector error correction model VECM is employed to test for Granger causality between the variables investigated.

The results show that imports and foreign direct investments (FDI) have generally have short-run and long-run causal effects on domestic R&D investments. In regions where imports and FDI do not have individual causal effects on innovation output, a joint increase in each of them and R&D have both short-run and long-run causal effects. Indigenous innovation is a significant contributor to economic growth when a country can produce and export novel products.

The sample is only limited to developing economies, and due to the unavailability of data, only 20 countries were captured.

Imported products and FDI are critical to the innovation drive when such activities are targeted at enhancing indigenous innovation from R&D to the production of new products. Hence, policy formulation should encourage the absorption of foreign technologies that serve as inputs to indigenous innovation.

This paper focuses specifically on indigenous innovation and analyses the influence of foreign technologies in this effort. It tests the moderating roles of imports and FDI in the relationship between R&D and innovation output, concluding that both variables enhance the effect of R&D on innovation output.

The purpose of this study is to provide a general review of the existing academic and practitioner literatures, pertaining to entrepreneurial selling with a view to articulate major entrepreneurial selling practices, patterns and principles that lead to entrepreneurial success and to propose two four-quadrant matrices.

The paper explores commonalities and distinctions in the entrepreneurial selling concepts articulated by Deutsch and Wortmann and Onyemah and Rivera-Pesquera – and relevant writings by Blank as well as Sarasvathy – are explored and analyzed.

It was found that the early stage entrepreneurial selling activities of founders – as a means of gleaning prospective customer feedback for product prototyping – form the core of contemporary entrepreneurial selling conceptualizations. Two provisional four-quadrant entrepreneurial selling matrices are proposed corresponding to the literature reviewed.

It is hoped that the two four-quadrant matrices might serve as a springboard for future researchers interested in exploring entrepreneurial selling. The notion of preliminary selling as a valuable form of marketing research is also worthy of future research.

Given the extent to which the perspectives of entrepreneurship practitioners, clinical professors and consultants are cited and explored, manifold aspects of entrepreneurial selling are put forth. The various approaches to preliminary selling that are explored are of especially high value to practitioners.

This is the first paper to fully explore the commonalities and distinctions across the entrepreneurial selling conceptualizations developed by Deutsch and Wortmann, as well as by Onyemah and Rivera-Pesquera, and the first to propose a conceptual framework focused specifically on entrepreneurial selling.