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Within the building sector a lack of clarity in terminology does not help designers, clients or researchers. Non-domestic buildings have shown rapid increases in the use of advanced technology and control systems with varying drivers, many of which are labelled as intelligent. The term smart has been used interchangeably with intelligent without any clear distinction between the two. If the term Smart Buildings represented a separate, more advanced grouping, it would provide an opportunity to focus the future progress of non-domestic building development. The paper aims to discuss these issues.

Drawing upon academic and industrial literature and experience, this paper reviews the scope of Intelligent Buildings and the current available definitions of Smart Buildings to form a clear definition of both smart and Intelligent Buildings.

These definitions define the border between the intelligent and the (more advanced) Smart Building. The upper bound of the Smart Building is defined by (the future development of) the predictive building.

This work provides a clear focus which will allow the progression of the non-domestic building sector by providing guidance and aspiration, as well as providing a platform upon which a large amount of technical work can be based.

The purpose of this paper is to discuss the dynamics of the evolution of damage to the residential buildings within the city walls of Norcia during the six-month seismic swarm that hit Central Italy in the period 24th August 2016 to end of January 2019. This is accomplished by comparing the damage recorded by the Italian Civil Protection usability form (AEDES form) during this period after each event.

First, these outputs are compared with a qualitative assessment conducted by means of omnidirectional camera (ODC) imagery collected on site by the authors, to explore the ability of this technology to support post-earthquake damage assessment. The damage level attributed with these two techniques is then further compared with the output of the analytical vulnerability assessment method FaMIVE, which allows to correlate damage to vulnerability. Specifically, the objective is to investigate the efficacy and performance of historic and recent strengthening interventions.

Results show that there is a good correspondence between AeDES and ODC assessments for low to medium damage grades (DG). Discrepancies in higher DGs are discussed in light of the different levels of information that can be recorded by using the two tools. The efficacy of strengthening is also well captured by the FaMIVE method. The procedure estimates a decrease of almost 40, 25 and 20 per cent of the total number of buildings failing out-of-plane, respectively, for the three seismic events considered, when restraining elements are in action.

The analysis conducted in this work make use of deterministic values of Norcia’s masonry fabric characteristics that have been found in literature, thus implying that neither the probabilistic aspects nor the related uncertainties have been properly investigated and addressed. However, this limitation is to be considered within the more general context of the legislation for the preservation of historic buildings which limits substantially any type of semi-destructive tests, hence limiting the reliability of the values available in literature. This in turn affects the decisions informing the design and implementation of strengthening interventions which can be confidently considered reliable and effective.

The paper addresses for the first time a systematic investigation of damage progression in historic masonry structures, part of urban aggregates in heritage cities. The current urban fabric is discussed in view of historic building codes as the basis for determining the present seismic vulnerability of the historic city centre of Norcia. The study provides new data sets for the city of Norcia and develops a statistical correlation between cumulative damage and analytical vulnerability functions for heritage buildings exposed to a swarm of earthquakes. The analytical assessment of the effect of historic strengthening is totally novel.

Despite the fact that research on creativity and cognition have garnered the attention of researchers and practitioners for decades, there is a lack of valid, reliable, and accessible instruments for enhancing and measuring these critical skills. Leveraging research from The LEGO Foundation and in collaboration with BrainPOP and the Learning Economy Foundation, this paper introduces an assessment framework for holistic skills and reports evidence from the 2022 validation study.

The complexities associated with both competencies such as multidimensionality, authenticity, and domain specificity pose a major issue for its measurement and credentialing. This paper provides evidence-based insights on promising ways to measure and provide actionable insights on holistic skills.

Preliminary evidence supports the assessment framework for holistic skills.

Future directions for further development of the assessment are discussed.

The framework should also be of help for practitioners looking for research-based guiding principles for the design of learning-through-play activities aimed to help develop holistic skills across a wide range of digital, physical, and hybrid modalities.

The framework provides new instruments and insights for evaluating the social and educational impact of learning-through-play programs and initiatives.

The learning progressions and formative assessment for holistic skills development are novel and clearly needed in research and practice of learning-through-play.

In the construction industry, it has proven difficult to implement and realize innovation efforts, for example in the development of industrialized construction and use of platform concepts. Thus, the purpose of this study is to characterize the innovation diffusion process in the social system of a large Swedish contractor company. Specifically, the diffusion of three innovative industrialized house-building (IHB) platforms and factors affecting their adoption and implementation (particularly effects of their perceived radicality in relation to the company’s decentralized characteristics) are identified and discussed.

A case study approach was applied, using empirical material including semi-structured interviews and archival records (research reports from earlier studies at different points in time related to each innovation and annual corporate reports). The material was analyzed using Rogers’ (2003) five-stage innovation process model, acknowledging the importance of social systems’ structures.

Structural characteristics of the social system strongly affect innovation diffusion. In subsystems that had not been involved in initiation of the innovations, they were regarded as radical, which hindered their adoption and implementation.

This study builds upon the recent findings that successful innovation implementation depends on a range of contingencies in the construction context. Although the diffusion of the innovations per se has been traced over a ten-year period, generalizability is limited because the results come from one construction company.

Contractors have invested substantially in the development of industrialized construction and use of platform concepts, but less in their implementation, so they have obtained little gain. How innovations are perceived and implemented in different subsystems affects the success of their implementation in the overarching social system.

This study adheres to previous calls for more research on firm level in the complex social system of construction companies by adopting a ten-year perspective on the diffusion of innovation at a large contractor addressing in particular the impact of the innovations perceived radicality in relation to the decentralized characteristics of the company.

The overall purpose of the paper is to understand the barriers to women's progression to senior positions in universities. It aims to explore similarities and differences between the career experiences and leadership styles of men and women in middle‐ and senior‐level positions at one university. The ultimate aim is to identify interventions to help create a more equal gender balance at senior levels.

A mixed methods approach was adopted. In‐depth interviews were conducted with a quota sample of 53 men and women in order to explore their lived career experiences. In addition, 50 questionnaires were received from the same sample in order to compare factual data about the participants' life histories and biographical circumstances.

The findings show that women's human capital and career progression to date are at least equal to those of men and that this has been achieved without women sacrificing a holistic family life. They also show that there are still some important differences between men and women in the way they plan and manage their careers and the leadership style that they adopt.

A five‐level framework is proposed which sets down the types of intervention that are required to create a more equal gender balance in senior positions. It is argued that this should be used to shape the gender equality schemes developed in universities under the Gender Equality Duty.

The paper provides new evidence about the residual differences between men's and women's career experiences, even in an employment context, which is particularly supportive of women. It also makes a significant contribution to the debate about the gendered nature of leadership.

Compares and contrasts the competitive priorities (manufacturing capabilities) of high‐ and low‐productivity firms, from the perspective of the cumulative model for manufacturing competitiveness by using various statistical tests. Contends that the analysis of 561 firms worldwide demonstrates clearly differences in manufacturing strategy between the two productivity subsets, one of which is that better performing firms are the more likely to address multiple capabilities simultaneously; another is that these firms appear to have the more clearly defined competitive strategies. The high‐productivity firms are thus more supportive than are the low‐productivity firms of the cumulative model. Also discusses other findings.

This paper aims to explore how corporate Australia engages in reconciliation through recognizing and providing pathways for Indigenous Australians' corporate leadership aspirations.

The research design is informed by the prior literature on pathways by minority groups to corporate leadership through the theoretical lens of transformational leadership. The investigation is conducted using textual analysis of reconciliation action plans (RAPs), a contemporary and voluntary practice adopted by Australian listed companies to disclose their commitment to national reconciliation. RAPs are publicly available from the official websites of listed companies.

The analysis of contemporary RAPs highlights organizational initiatives to support Indigenous Australians related to corporate and community leadership. Since the authors’ focus is the former, corporate leadership initiatives are further analyzed. Two initiatives for Indigenous Australians to pursue corporate leadership positions are emerging future leaders' programs and mentoring programs. This is the extent to which the authors observe Australian firms' transformational leadership. While some firms have implemented these initiatives with specific targets, other firms do not have specific initiatives or targets. The paper also conducts longitudinal analysis into the transformational leaders' past RAPs and triangulates to other evidence of reconciliation commitment such as the Uluru Statement from the Heart.

This paper contributes new insights to the research area of board cultural diversity, specifically to the limited literature on Indigenous reconciliation. It provides insights into firms and policymakers to address the ongoing issue of the underrepresentation of Indigenous Australians in corporate leadership. The sample of firms comprises Australian listed firms that have adopted higher-order RAPs, which restricts the generalizability of the findings to other sectors.

This paper explores the under researched phenomenon of Indigenous people's pathways to corporate leadership. The research design is informed by transformational leadership theory through considering institutional actions for reconciliation. This research provides evidence of the extent to which corporate Australia has taken action on the issue of the under-representation of Australian Indigenous people in corporate leadership.

Data literacy – the ability to read, analyze, interpret, evaluate and argue with data and data visualizations – is an essential competency in social studies. This study aims to examine the degree to which US state standards require teachers to teach data literacy in social studies, addressing the questions: to what extent are US social studies teachers required to teach data literacy? If they are required to teach it, are they provided with guidance about competencies to address at each school or grade level and with respect to particular content?

The study used content analysis, using a variety of priori and emergent codes, to review social studies standards documents from all 50 states and the District of Columbia.

Findings indicate that although state standards suggest that data visualizations should play a role in social studies instruction, they provide poor guidance for a coherent, progressive and critical approach across grade levels.

Researchers currently know little about if and how teachers address data literacy in social studies education. This study provides a snapshot of guidance teachers across states are given for teaching data literacy, and by extension, the quality of data literacy instruction recommended for students across the USA.

This paper reports an investigation of the performance of a sample of office buildings in Hong Kong which were marketed as being intelligent. A summary review of literature charting the development of intelligent buildings and perspectives of what intelligence means when applied to office buildings provides a basis for the study. Adopting a fitness‐for‐purpose perspective, as the approach adopted most widely for evaluations, data on the buildings’ designs (incorporation of intelligence features), users’ requirements and occupants’ views of performance of their workplaces were collected via questionnaire surveys. Analysis followed the building ranking method adopted by Harrison. Results indicate that only a minority of the office buildings marketed as “intelligent” achieve the level of performance to match users’ and occupants’ requirements and so constitute business value intelligent buildings; the majority are underachieving.

The rapid urbanization of Indian cities and the population surge in cities has steered a massive demand for energy, thereby increasing the carbon emissions in the environment. Information and technology advancements, aided by predictive tools, can optimize this energy demand and help reduce harmful carbon emissions. Out of the multiple factors governing the energy consumption and comfort of buildings, indoor room temperature is a critical one, as it envisages the need for regulating the temperature. This paper aims to propose a mathematical model for short-term forecasting of indoor room temperature in the Indian context to optimize energy consumption and reduce carbon emissions in the environment.

A study is conducted to forecast the indoor room temperature of an Indian corporate building structure, based upon various external environmental factors: temperature and rainfall and internal factors like cooling control, occupancy behavior and building characteristics. Expert insight and principal component analysis are applied for appropriate variables selection. The machine learning approach using Box–Jenkins time series models is used for the forecasting of indoor room temperature.

ARIMAX model, with lagged forecasted and explanatory variables, is found to be the best-fit model. A predictive short-term hourly temperature forecasting model is developed based upon ARIMAX model, which yields fairly accurate results for data set pertaining to the building conditions and climatic parameters in the Indian context. Results also investigate the relationships between the forecasted and individual explanatory variables, which are validated using theoretical proofs.

The models considered in this research are Box–Jenkins models, which are linear time series models. There are non-linear models, such as artificial neural network models and deep learning models, which can be a part of this study. The study of hybrid models including combined forecasting techniques comprising linear and non-linear methods is another important area for future scope of study. As this study is based on a single corporate entity, the models developed need to be tested further for robustness and reliability.

Forecasting of indoor room temperature provides essential practical information about meeting the in-future energy demand, that is, how much energy resources would be needed to maintain the equilibrium between energy consumption and building comfort. In addition, this forecast provides information about the prospective peak usage of air-conditioning controls within the building indoor control management system through a feedback control loop. The resultant model developed can be adopted for smart buildings within Indian context.

This study has been conducted in India, which has seen a rapid surge in population growth and urbanization. Being a developing country, India needs to channelize its energy needs judiciously by minimizing the energy wastage and reducing carbon emissions. This study proposes certain pre-emptive measures that help in minimizing the consumption of available energy resources as well as reducing carbon emissions that have significant impact on the society and environment at large.

A large number of factors affecting the indoor room temperature present a research challenge for model building. The paper statistically identifies the parameters influencing the indoor room temperature forecasting and their relationship with the forecasted model. Considering Indian climatic, geographical and building structure conditions, the paper presents a systematic mathematical model to forecast hourly indoor room temperature for next 120 h with fair degree of accuracy.