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Work participation and work facilitation represent basic human rights for everyone. Work represents an important platform for welfare and well-being, but compared to the general workforce in Norway, persons with cognitive disabilities are severely under-represented. When workplaces locked down under the first COVID-19 outbreak spring 2020, some people were made redundant whilst many continued their work from home. The lockdown affected persons with cognitive disabilities through lockdown of workplaces, vocational training centres and even day activity centres. The scheme of working from home was not as obvious or facilitated for this group, as for other employees. When also visits were banned and common areas for socialisation were locked down, the consequences of these lockdowns were exacerbated. In this chapter we have examined and discussed the COVID-19 restrictions in Norway and how they affected the basic human rights of persons with cognitive disabilities, and also how such rights can be promoted through legislation, governance and service provision.

Practitioners have reported a minimal and non-use of building information modelling (BIM), especially in small and medium-sized organisations and BIM infant construction industries. This development calls for a reappraisal of organisations’ strength in capabilities required for BIM uptake towards the target of global construction digitalisation. This study aims to assess the BIM Level 2 uptake capability of organisations in a BIM infant construction industry and identify the underlying interactions between the capability criteria.

The study used a multivariable analysis of fifteen descriptors identified from the people, process, policy, finance and technology domain. Data collection was done in the BIM infant construction industry in Nigeria. Verification of the descriptors and an evaluation of BIM uptake capability in organisations was done. Seventy-three responses were received within the selected context, and data analysis was done with mean weighting and exploratory factor analysis. Maximum Likelihood extraction and Direct Oblimin rotation were used.

Factor analysis revealed three factors that explained 53.28% of the total variance in the BIM Level 2 uptake capability of construction organisations. The factors are workforce capacity and continuous development, an affinity for innovation and strength in physical and operational facilities.

This study provides an overarching and insightful discussion on BIM uptake capability and construction digitalisation with evidence from a BIM-infant construction industry.

The findings of this study are a piece of valuable empirical evidence on Level 2 BIM uptake capability. This empirical situation analysis will inform the advocacy for the advancement of BIM and enhanced utilisation of building information. Evidence on the capability performance of the BIM infant industry has been revealed.

The outcome is expected to stir debate on the preparedness of organisations to further exploit the benefits of BIM in the BIM infant construction industry. Examination of the capability for a particular phase of BIM is scanty in the literature.

This chapter analyzes the semiotic construction of US claims to sovereignty in Hawai‘i. Building on semiotic theories in sociology and theories within critical Indigenous and settler colonial studies, it presents an interpretive analysis of state, military, and academic discursive strategies. The US empire-state attempts to construct colonial narratives of race and sovereignty that rehistoricize the history of Hawaiians and other Indigenous peoples. In order to make claims to sovereignty, settler-colonists construct narratives that build upon false claims to superiority, advancement, and discovery. Colonial resignification is a process by which signs and symbols of Indigenous communities are conscripted into the myths of empire that maintain such sovereign claims. Yet, for this reason, colonial resignification can be undone through reclaiming such signs and symbols from their use within colonial metanarratives. In this case, efforts toward decolonial resignification enacted alternative metanarratives of peoples' relationships to place. This “flip side” of the synecdoche is a process that unravels the ties that bind layered myths by providing new answers to questions that underpin settler colonial sovereignty.

In any critical system, high‐availability of software components like web services has so far been achieved through replication. Three replication strategies known as active, passive, and hybrid, describe for example how many replicas are needed, where to locate replicas, and how replicas interact with the original web service and among themselves if needed. The purpose of this paper is to show how replicates could be substituted with components that are similarly functional to the component that needs back‐up in case of failure.

After examination of the different existing replication strategies, it was decided to test the suitability of the proposed web services high‐availability approach based on communities for each strategy. To this end, the specification of web services using two behaviors, namely control and operational, was deemed appropriate.

The active replication strategy is the only strategy that could support the development of a web services high‐availability approach based on communities of web services.

The proposed approach has been validated in practice by deploying a JXTA‐based testbed. The experimental work has implemented the active replication strategy.

Software component high‐availability could be achieved by components that are similarly functional to this component, which permits the common limitations of existing replication strategies to be addressed.

Public sector has started exploiting artificial intelligence (AI) techniques, however, mainly for operational but much less for tactical or level tasks. The purpose of this study is to exploit AI for the highest strategic-level task of government: to develop an AI-based public sector data analytics methodology for supporting policymaking for one of the most serious and large-scale challenges that governments repeatedly face, the economic crises that lead to economic recessions (though the proposed methodology is of much more general applicability).

A public sector data analytics methodology has been developed, which enables the exploitation of existing public and private sector data, through advanced processing of them using a big data-oriented AI technique, “all-relevant” feature selection, to identify characteristics of firms as well as their external environment that affect (positively or negatively) their resilience to economic crisis.

A first application of the proposed public sector data analytics methodology has been conducted, using Greek firms’ data concerning the economic crisis period 2009–2014, which has led to interesting conclusions and insights, revealing factors affecting the extent of sales revenue decrease in Greek firms during the above crisis period and providing a first validation of the methodology used in this study.

This paper contributes to the advancement of two emerging highly important, for the society, but minimally researched, digital government research domains: public sector data analytics (and especially policy analytics) and government exploitation of AI. It exploits an AI feature selection algorithm, the Boruta “all-relevant” variables identification algorithm, which has been minimally exploited in the past for public sector data analytics, to support the design of public policies for addressing one of the most serious and large-scale economic challenges that governments repeatedly face: the economic crises.

The proposed methodology allows the identification of characteristics of firms as well as their external environment that affect positively or negatively their resilience to economic crisis. This enables a better understanding of the kinds of firms that are more strongly hit by the crisis, which is quite useful for the design of public policies for supporting them; and at the same time reveals firms’ practices, resources, capabilities, etc. that enhance their ability to cope with economic crisis, to design policies for promoting them through educational and support activities.

This methodology can be very useful for the design of more effective public policies for reducing the negative impacts of economic crises on firms, and therefore mitigating their negative consequences for the society, such as unemployment, poverty and social exclusion.

This study develops a novel approach to the exploitation of public and private sector data, based on a minimally exploited, for such purposes, AI technique (“all-relevant” feature selection), to support the design of public policies for addressing one of the most threatening disruptions that modern economies and societies repeatedly face, the economic crises.

This study aims to provide students an opportunity to analyze the financial performance of a publicly listed real estate company and estimate its instinct value by applying appropriate financial models and approaches.

Three major valuation models/approaches generated by financial theory and practice to estimate the intrinsic value of a security: discounting cash-flows valuation (DCF and NPV) – valuation through adjusted net asset and liquidation value (NAV) – relative valuation through price and value multiples (valuation multiple analysis and precedent transactions analysis). Wholly owned subsidiaries versus and joint venture ones.

Analyze financial information of all segments in a multiple-business firm, and apply suitable financial models and approaches among net asset value model (NAV), discounted cash flow (DCF) or net present value (NPV) model, valuation multiple analysis and precedent transactions analysis to estimate the intrinsic value of the whole firm.

This decision-based case allows students to explore the business valuation process for a public listed real estate company, Alexander & Baldwin, Inc. (NYSE: ALEX). Based on financial statements analysis and forward-looking financial expectation on ALEX, this case elevates students' understanding and practice of valuating this multiple-business firms by applying appropriate financial models and approaches among NAV, DCF or NPV, valuation multiple analysis and precedent transactions analysis and enable students to make their investment decisions of buying, holding or selling the company’s stocks.

This case is most appropriate for graduate courses such as corporate finance, investments, personal finance, real estate finance and financial markets and institutes.