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The aim of this study is to simulate the self-organized evolution mechanism of inner innovation of large-scale construction enterprises (LSCE) under different contexts. Based on NK stochastic combinatorial optimization model with two parameters (N, K), this study proposes three modules—ambidexterity, punctuated equilibrium, and automation—to simulate the change behaviors of the inner innovation system. Furthermore, this study analyses the influence of factor correlation on inner innovation performance under discusses nine different combination situations. The research results show that (a) regardless of how the relationship strength changes, fitness increases with an increasing K value; and (b) the comparison of optimization modes show that the punctuated equilibrium mode can guarantee the long- and short-term inner innovation performance LSCE. This study therefore reveals the mutual effects of the factors in the inner innovation system in LSCE and provides an effective model for internal systems analyses in the construction industry and in other sectors.

Platform-based companies are rapidly emerging and occupy major positions in the global market. In addition, the business model that relies on digital technology makes platform-based companies sustainable. This paper empirically examines the impact of internationalization breadth on corporate performance and explores the moderating role of the platform enterprise network effect and innovation investment. Annual panel data from listed Chinese platform-based companies between 2012 to 2020 were used to also verify the scale of overseas companies’ association and social networks and the moderating effect of centrality and innovation research and development (R&D) investment on these two factors. The results indicate that the irregular internationalization rhythm of platform-based companies was negatively correlated with corporate performance, and the association network and the social network centrality of overseas companies had a positive moderating effect. Innovative R&D investment could help platform-based companies expand overseas to improve their corporate performance, and its moderating effect was more significant for platform companies in the early stages of internationalization. In terms of the internationalization pace, overseas network construction, and innovative R&D investment strategies, this paper provides valuable suggestions for platform-based companies to expand internationally and improve their corporate performance.

Cloud computing and sustainability have become part of a core strategy in organizations globally and locally, since their characteristics assist both businesses and individuals to become unique and exclusive in their work and study. Businesses and individuals should integrate sustainability in their strategy and to include cloud computing technology as a tool for sustainable work, especially in the Information Technology (IT) departments to cut costs and increase efficiencies and productivity. This paper examines European organizations' awareness of cloud computing and sustainability opportunities and risks, via an online survey targeting 56 Information Technology managers in Europe. A Cloud Computing Conceptual model was developed using structural equation modeling (SEM) to evaluate the survey results. The study results confirmed that cloud computing technology opportunities, including sustainability in the organization's strategy, will enhance their job performance and job satisfaction, use and awareness; however, security, privacy and risks are still a major concern.

Although considered a current highlight, the concept of intelligent buildings (IB) has not been adopted as quickly and widely as expected. One of the reasons for this is the lack of information and knowledge support to all professionals involved at the design stage of a project. This paper provides a brief overview on the new advances of IB technologies and discusses ways of supplementing the decision making process by adopting two methods for economical and technical aspects of IB applications. It goes on to introduce an on-going research which aims to utilise knowledge based systems to provide comprehensive evaluations to alternatives and decision support to retrofit building projects, when they are being conceptualised.

Introduction Increases in recent times in electricity costs and in associated emissions of greenhouse gases are having an impact on societies to adopt business and lifestyle strategies based on sustainability practices. The emergence of the smart grid (Xinghuo et al., 2011) facilitates both suppliers and consumers of electricity in reducing carbon footprint and improving the reliability and efficiency of electricity generation, distribution and utilization. The smart grid unifies recent developments in the electrical power area with those in information and communication technologies (ICT) to bring to bear changes to business practices and life styles of consumers. The smart grid recognizes the distributed nature of electricity industry and the unifying power of the ICT. Traditional power grids consist of (i) large-scale electricity generators that are located within easy reach of energy resources, (ii) highvoltage transmission lines to bring bulk electricity to load centres that are close to loads, such as industries, cities, townships etc., and (iii) lower voltage distribution networks which in turn distribute electrical power to smaller consumers of electricity. Unlike such traditional power grids, smart grids have distributed energy generation that encompasses both centrally-located large-scale generators with ratings of 100's of megawatts (MWs) and many geographically distributed smaller generators of widely varying sizes from 10's of MWs that use fossil fuels and renewables to a few kilo watts (kW) that may be solar photo voltaic (PV) panels mounted on the roof of a small house. [FIGURE 1 OMITTED] Several geographically distributed power generators need to be integrated into the smart grid, recognizing the varying capacities, characteristics and technologies associated with generators (Figure 1). Electricity generated using renewable energy sources, such as photovoltaic (PV) solar panels and wind turbines, is variable depending upon the season, weather conditions and the period of any given day. This variability has a strong influence on the delivery of reliable power to consumers. Storage of electrical energy to dampen the effects of variability in the power from renewables is therefore an important aspect of the smart grid. Various types of energy storage: pumped hydro storage, batteries, fuel cells, flywheels etc. need to be integrated into a smart grid. Such distributed energy storages in the grid may serve different networks within the grid so that they continue to operate as self-powered islands during outages resulting from natural causes or system faults (Nourai & Keane, 2010). The electricity generated by solar PV panels and by some wind generators is in the form of direct current (DC). This DC power must be converted (or inverted) to make alternating current (AC) power to enable connection to an AC smart grid. Smart grids need smart inverters with controls to maximise renewable power utilization, and to supply power to either the local load and/or the grid (Xinghuo et al., 2011). The smart grid needs to integrate the action of generators, energy suppliers and customers. The smart grid must provide suitable multi-way communication of relevant information between various business actors associated with the grid. The smart grid includes even a small household as a business actor into its business model because a household contributes towards sustainable business outcomes. There are many research papers on smart grids with a focus on large power systems (Brown, 2008; Ipakchi & Albuyeh, 2009; and Farhangi, 2010) that emphasize the importance of pervasive control and monitoring requirements in a smart grid. They point out the convergence of ICT with power system engineering. There are also many publications (Guinard, 2011; Kamilaris, et al., 2011) that deal with energy management at household levels using the ICT strategy, such as Web of things. …

Abstract A key challenge for heavy industry in emerging economies is how to meet international greenhouse gas (GHG) emission standards since they are often based on the conditions and capacities of manufacturing in advanced countries. Firms in developing nations are typically cost-driven and reliant on older, less efficient technology: very few have achieved the relevant targets. Cement making underscores the point: no study to date has specifically quantified, in technical and financial terms, the gap between existing firm performance and global GHG emission standards. We examine Indonesia's largest cement manufacturing facility to investigate what needs to be done to overcome the discrepancy. The article starts by reviewing key contextual issues such as the facility's location, scale, organisational configuration, available materials, energy use, and technological capacities. The plant's direct emission intensity is 0.69 t CO2e/t cement, higher than the global target for 2030 (0.55 t CO2e/t). Analysis reveals six potential emissions reduction activities: (1) utilizing fly ash as a clinker substitute; (2) employing limestone as a clinker substitute; (3) using biomass from rice husks as an alternative fuel; (4) adding pre-heating stages in kilns; (5) waste heat recovery for power generation; and (6) using refused-derived fuel from municipal solid waste as an alternative fuel. These measures, if adopted in full, could reduce GHGs at the facility by up to 33%, or a total of 34,145,190 t CO2e over a 10-year timeframe (2020–2030). This abatement action would leave the facility's direct emissions intensity to 0.48 t CO2e/t cement. In present values, assuming a 10% discount rate, they would result in savings of US$415 million for a US$94 million outlay. Despite the apparent technical and financial advantages, all measures together are unlikely to be adopted, since the plant studied is well advanced in its lifecycle and the parent company is experiencing financial constraints common to those in developing nations.

Cyber-physical attacks are the most significant threat facing the utilisation and development of the various smart grid technologies. Among these attacks, false data injection attacks (FDIAs) represent a major category, with a wide variety of types and effects. There has been extensive reporting on FDIAs recently. Several detection algorithms have been developed over the past few years to address this threat. In Chapter 2, this thesis starts by providing a deep analysis of the literature on these algorithms. The concluding remarks of this chapter present the main criteria that should be considered in developing future detection algorithms for FDIAs in different systems of smart grids. Following that, this dissertation proposes FDIA detection algorithms in the major systems in smart grids that are the most susceptible and vulnerable towards FDIAs. In wide-area monitoring systems, being able to promptly differentiate FDIA from normal grid contingencies is crucial for a grid operator to decide the correct response and reduce FDIA false alarms. In Chapter 3, two FDIA characterisation algorithms are developed to address this issue. The automatic generation control (AGC) is paramount in maintaining the stability and operation of power grids. FDIAs are particularly difficult to detect and represent a major threat to AGC systems. Chapter 4 proposes a novel spatio-temporal learning algorithm that can learn the normal dynamics of the power grid with AGC systems. It then utilises this unsupervised learned model in detecting FDIA affecting the AGC system. The utilisation of distributed generation units in power distribution systems has increased the complexity of system monitoring and operation. Numerous information and communication technologies have been adopted recently to overcome the associated challenges, but they have created wide opportunities for energy theft and other types of cyber-physical attacks. Chapter 5 utilises the developed spatio-temporal learning algorithm in Chapter 4 in detecting the various possibilities of FDIA affecting the distribution systems by evaluating the reconstruction error of each measurement sample. The proposed algorithm is data-driven, which makes it resilient against distribution systems’ uncertainties and nonlinearities. The collected results indicate a superior detection performance of the proposed detection algorithms compared to those in the literature.

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