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The purpose of this paper is to present a case of an office building in England and show how the technology in energy efficiency in building will contribute to energy conservation.

The paper presents a case of an office building. The building is then modelled and analysed using IES Virtual Environment VE to estimate the Target Emissions Rate (TER) and the Building Emissions Rate (BER) to see how the building could satisfy Part L of the Building Regulations.

The building in case use various sustainable solutions such as limiting the heat loss and gain through the fabric, ventilation system with a good high heat recovery system, increasing the availability of daylight and good lighting control system. The office building in the case study is in full compliance with Part L of the Building Regulations. The sustainable technology in the building will assist the compliance with Part L of the Building Regulations.

This is a single case study building; more case studies for buildings of this nature are required.

The paper demonstrates various feasible solutions of sustainable technology in buildings that might help comply with the regulation.

The case study building is a real case taken directly from one of the author's projects when he was working as a building services engineer. This case study building and its sustainable features have not been presented before in an academic journal.

This article outlines the theoretical foundations of the research contributions of this edited collection about “Diversity and Discrimination in Research Organizations.” First, the sociological understanding of the basic concepts of diversity and discrimination is described and the current state of research is introduced. Second, national and organizational contextual conditions and risk factors that shape discrimination experiences and the management of diversity in research teams and organizations are presented. Third, the questions and research approaches of the individual contributions to this edited collection are presented.

The COVID-19 pandemic has sparked a remarkable volume of research literature, and scientists are increasingly in need of intelligent tools to cut through the noise and uncover relevant research directions. As a response, the authors propose a novel framework. In this framework, the authors develop a novel weighted semantic graph model to compress the research studies efficiently. Also, the authors present two analyses on this graph to propose alternative ways to uncover additional aspects of COVID-19 research.

The authors construct the semantic graph using state-of-the-art natural language processing (NLP) techniques on COVID-19 publication texts (>100,000 texts). Next, the authors conduct an evolutionary analysis to capture the changes in COVID-19 research across time. Finally, the authors apply a link prediction study to detect novel COVID-19 research directions that are so far undiscovered.

Findings reveal the success of the semantic graph in capturing scientific knowledge and its evolution. Meanwhile, the prediction experiments provide 79% accuracy on returning intelligible links, showing the reliability of the methods for predicting novel connections that could help scientists discover potential new directions.

To the authors’ knowledge, this is the first study to propose a holistic framework that includes encoding the scientific knowledge in a semantic graph, demonstrates an evolutionary examination of past and ongoing research and offers scientists with tools to generate new hypotheses and research directions through predictive modeling and deep machine learning techniques.

This paper aims to present a methodology by which future knowledge flow can be predicted by predicting co-citations of patents within a technology domain using a link prediction algorithm applied to a co-citation network.

Several methods and approaches are used: a dynamic analysis of a patent citation network to identify technology life cycle phases, patent co-citation network mapping from the patent citation network and the application of link prediction algorithms to the patent co-citation network.

The results of the presented study indicate that future knowledge flow within a technology domain can be predicted by predicting patent co-citations using the preferential attachment link prediction algorithm. Furthermore, they indicate that the patent – co-citations occurring between the end of the growth life cycle phase and the start of the maturation life cycle phase contribute the most to the precision of the knowledge flow prediction. Finally, it is demonstrated that most of the predicted knowledge flow occurs in a time period closely following the application of the link – prediction algorithm.

By having insight into future potential co-citations of patents, a firm can leverage its existing patent portfolio or asses the acquisition value of patents or the companies owning them.

It is demonstrated that the flow of knowledge in patent co-citation networks follows a rich get richer intuition. Moreover, it is show that the knowledge contained in younger patents has a greater chance of being cited again. Finally, it is demonstrated that these co-citations can be predicted in the short term when the preferential attachment algorithm is applied to a patent co-citation network.

With the recent development of science and technology, research on information diffusion has become increasingly important.

To analyze the process of information diffusion, researchers have proposed a framework with graphical evolutionary game theory (EGT) according to the theory of biological evolution.

Through this method, one can study and even predict information diffusion.

This paper summarizes three existing works using graphical EGT to discuss how to obtain the static state and the dynamics of information diffusion in social network.

The purpose of this paper is to have a review on the analysis of information diffusion based on evolutionary game theory. People now get used to interact over social networks, and one of the most important functions of social networks is information sharing. Understanding the mechanisms of the information diffusion over social networks is critical to various applications including online advertisement and rumor control.

It has been shown that the graphical evolutionary game theory (EGT) is a very efficient method to study this problem.

By applying EGT to information diffusion, the authors could predict every small change in the process, get the detailed dynamics and finally foretell the stable states.

In this paper, the authors provide a general review on the evolutionary game-theoretic framework for information diffusion over social network by summarizing the results and conclusions of works using graphical EGT.

The purpose of this paper is to focus on understanding the factors which affect the social interaction in the case of Facebook. Many authors point out the great potential of these networks for social interaction and as conduits of information. However, studies show that the topology of the network is disconnected, consisting of small sub-networks that make Facebook unsuitable for disseminating information. This situation has created the need to introduce exogenous factors, aimed at boosting and providing cohesion to the network structure. In this context, the authors test the following question: how exogenous and endogenous factors contribute to encouraging social interaction on Facebook.

For the analysis of social interaction on Facebook, a population consisting of all the followers of the walls of ten corporate social networks was used. From the total 269,424 users analyzed, a stratified sample of 132 followers was obtained and networks were built for each of them. The authors then proceeded to search for each follower’s friends and friends of friends to build the social network up to the fourth level, obtaining a total of 132 subnets with 1,628,074 links between them. To determine the impact of both exogenous and endogenous factors in the interaction of the network the authors performed a causal analysis.

The results obtained from this study provide empirical evidence on the adequacy of companies’ dynamization measures used and how exogenous and endogenous factors influence the social interaction on Facebook. Thus, the results show that exogenous factors, such as the activity of the community manager and the digital marketing investment in the network, do not have a significant effect on the interaction. On the other hand, endogenous factors, such as network density and clustering, have a positive effect on the trigger of social interaction between the followers. Therefore, companies must consider the importance of the structural factors that characterize network followers, such as density or clustering coefficient, to be able to interpret and optimize them to obtain higher levels of social interaction.

This is one of a few papers that examine interactions in social network sites (SNS), particularly in corporate network sites in Facebook. The results expose the importance for organizations to have reliable information on the patterns of interaction to properly manage the resources allocated for this purpose in SNS.

“Becoming” a doctor involves the acquisition of medical knowledge, skills and professional identity. Medical professional identity formation (MPIF) is complex, multi-factorial and closely linked to societal expectations, personal and social identity. Increasingly, doctors are required to engage in leadership/management involving significant identity shift. This paper aims to explore medical professional identity (MPI) and MPIF in relation to doctors as leaders. Selected identity theories are used to enrich the understanding of challenges facing doctors in leadership situations and two concepts are introduced: medical leader identity (MLI) and medical leader identity formation (MLIF) and consideration given to how they can be nurtured within medical practice.

A rapid conceptual review of relevant literature was carried out to identify a set of relevant concepts and theories that could be used to develop a new conceptual framework for MLI and MLIF.

MLIF is crucial for doctors to develop as medical leaders, and, like MPIF, the process begins before medical school with both identities influenced, shaped and challenged throughout doctors’ careers. Individuals require support in developing awareness that their identities are multiple, nested, interconnected and change over time.

This paper draws on concepts from wider literature on professional identity, in relation to how doctors might develop their MLI alongside their MPI. It offers a new perspective on MPI in the light of calls on doctors to “become and be healthcare leaders” and introduces the new concepts of MLI and MLIF.