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The study, within the Increasing Resilience of Cultural Heritage (ResCult) project, aims to support civil protection to prevent, lessen and mitigate disasters impacts on cultural heritage using a unique standardised-3D geographical information system (GIS), including both heritage and risk and hazard information.

A top-down approach, starting from existing standards (an INSPIRE extension integrated with other parts from the standardised and shared structure), was completed with a bottom-up integration according to current requirements for disaster prevention procedures and risk analyses. The results were validated and tested in case studies (differentiated concerning the hazard and type of protected heritage) and refined during user forums.

Besides the ensuing reusable database structure, the filling with case studies data underlined the tough challenges and allowed proposing a sample of workflows and possible guidelines. The interfaces are provided to use the obtained knowledge base.

The increasing number of natural disasters could severely damage the cultural heritage, causing permanent damage to movable and immovable assets and tangible and intangible heritage. The study provides an original tool properly relating the (spatial) information regarding cultural heritage and the risk factors in a unique archive as a standard-based European tool to cope with these frequent losses, preventing risk.

Microteaching is a teacher training method based on microclasses (groups of four or five students) and microlessons lasting no more than 5–20 min. Since it was first explored in the late 20th century in experiments at Stanford University, microteaching has evolved at the interdisciplinary level. The purpose of this paper is to examine the networks found via an analytical bibliometric study of the scientific output related with microteaching in teacher training, through a study and examination of the Web of Science database.

This research was conducted with the VOSviewer tool for content analysis through data mining and scientific network structure mapping by means of the normalisation technique. This technique is based on the association strength indicator, which is interpreted as a measurement of the similarity of the units of analysis.

Two hundred and nine articles were thus obtained from the Web of Science database. The networks generated and the connections among the various items, co-authorship and co-citation are presented in the results, which clearly indicates that there are significant authors and institutions in the field of microteaching. The largest cluster is made up of institutions such as Australian Catholic University. The most often-cited document is by Rich and Hannafin. Allen (1968), who defines microteaching as a technique based on microclasses and microlessons, is the author most often cited and has the largest number of connections.

This research’s limitations concern either aspects that lie beyond the study’s possibilities or goals that have proved unattainable. The second perspective, which focuses on skill transfer, contains a lower percentage of documents and therefore has a weaker central documentary structure. Lastly, the authors have also had to bear in mind the fact that the scientific output hinges upon a highly specific realm, the appearance and/or liberalisation of digital technologies and access to those technologies in the late 20th century.

This research shows that microteaching is a promising area of research that opens up vast possibilities in higher education teacher training for application in the realm of technologies. This paper could lead to several lines of future research, such as access to and the universal design of learning from the standpoint of different communication and pedagogical models based on microteaching.

Cultural heritage archiving is experiencing an increase in digitalisations of artefacts in the last 15 years. The reason behind this trend is a demand for providing information about the artefact in a more accessible way to the audience, for example, through online delivery or virtual reality. Other reasons might be to simplify and automate the management of artefacts. Having a ‘digital copy’ of artefacts, allows one to search an archive and plan its storage and dissemination in a comprehensive manner. With the increased digitalisation comes an increased use of artificial intelligence [AI] applications. AI can be very beneficial in classifying artefacts automatically through machine learning [ML] and natural language processing [NLP]. For example, an algorithm can identify the source and age of artefacts based on an image and can do this much faster for a large collection of photos than a human. Although AI provides many benefits, it also presents challenges: Sophisticated AI techniques require certain insights on how they work, need specialists to customise a solution, and require an existing large dataset to train an algorithm. Another challenge is that typical AI techniques are regarded as black boxes, which means they decide, but it is not obvious why a decision has been made. This chapter describes a project in collaboration with the South Australian Museum [SAM] on the application of AI to extract material lists from a description of artefacts. A large dataset to train an algorithm did not exist, and hence, a customised approach was required. The outcome of the project was the application of NLP in combination with easy-to-customise rules that can be applied by non-IT specialists. The resulting prototype achieved the extraction of materials from a large list of artefacts within seconds and a flexible solution that can be applied on other collections in the future.

This paper examines the effect of market knowledge on market success of product innovativeness and the moderating role of absorptive capacity. We separated market knowledge into market diversity and market significance components and examined their effects on radical product innovation performance.

This paper adopted the secondary database study. Excluding cases with missing values of main variables, a total of 1,219 Taiwanese manufacturing firms from the Third Taiwan Technology Innovation Survey (TTIS3) database were used to test the hypotheses. A moderated hierarchical regression approach was utilized to analyze the data.

The results revealed that the relationship between market diversity and radical product innovation performance is a predominantly positive concave downward curve. In contrast, the relationship between market significance and radical product innovation performance is a predominantly negative concave downward curve. Furthermore, the results also indicated that absorptive capacity has different moderating effects on the relationships between market diversity/significance and radical product innovation performance. Absorptive capacity enhances the negative effect of market significance but suppresses the positive effect of market diversity on radical product innovation performance.

This paper is the first research which contributes to examining the relationship between market knowledge and radical product innovation sale performance.

Fish quarantine is a measure to prevent the entry and spread of quarantine fish pests and diseases abroad and from one area to another within Indonesia's territory. Based on these backgrounds, this study aims to identify the knowledge, knowledge management (KM) processes and knowledge management system (KMS) priority needs for quarantine fish and other fishery products measures (QMFFP) and then develop a classification model and web-based decision support system (DSS) for QMFFP decisions.

This research methodology uses combination approaches, namely, contingency factor analysis (CFA), the cross-industry standard process for data mining (CRISP-DM) and knowledge management system development life cycle (KMSDLC). The CFA for KM solution design is performed by identifying KM processes and KMS priorities. The CRISP-DM for decision classification model is done by using a decision tree algorithm. The KMSDLC is used to develop a web-based DSS.

The highest priority requirements of KM technology for QMFFP are data mining and DSS with predictive features. The main finding of this study is to show that web-based DSS (functions and outputs) can support and accelerate QMFFP decisions by regulations and field practice needs. The DSS was developed using the CTree algorithm model, which has six main attributes and eight rules.

This study proposes a novel comprehensive framework for developing DSS (combination of CFA, CRISP-DM and KMSDLC), a novel classification model resulting from comparing two decision tree algorithms and a novel web-based DSS for QMFFP.

The purpose of this study is to design a wearable medical device as a human care platform and to introduce the design details, key technologies and practical implementation methods of the system.

A multi-channel data acquisition scheme based on PCI-E (rapid interconnection of peripheral components) was proposed. The flexible biosensor is integrated with the flexible data acquisition card with monitoring capability, and the embedded (device that can operate independently) chip STM32F103VET6 is used to realize the simultaneous processing of multi-channel human health parameters. The human health parameters were transferred to the upper computer LabVIEW by intelligent clothing through USB or wireless Bluetooth to complete the transmission and processing of clinical data, which facilitates the analysis of medical data.

The smart clothing provides a mobile medical cloud platform for wearable medical through cloud computing, which can continuously monitor the body's wrist movement, body temperature and perspiration for 24 h. The result shows that each channel is completely accurate to the top computer display, which can meet the expected requirements, and the wearable instant care system can be applied to healthcare.

The smart clothing in this study is based on the monitoring and diagnosis of textiles, and the electronic communication devices can cooperate and interact to form a wearable textile system that provides medical monitoring and prevention services to individuals in the fastest and most accurate way. Each channel of the system is precisely matched to the display screen of the host computer and meets the expected requirements. As a real-time human health protection platform technology, continuous monitoring of human vital signs can complete the application of human motion detection, medical health monitoring and human–computer interaction. Ultimately, such an intelligent garment will become an integral part of our everyday clothing.

Delay in housing adaptation is a major problem, especially in assessing if homes are suitable for the occupants and in determining if the occupants are qualified for the Disabled Facilities Grant (DFG). This paper describes the development of two self-administered intelligent integrated assessment tools from the DFG Adapt-ABLE system: (1) The Home Suitability Assessment Platform, which is a preventive mechanism that allows assessment of the suitability of homes based on occupants’ mobility status and (2) an indicative assessment platform that determines if the applicants are qualified for the DFG to prevent lengthy delays.

The adopted method aligned with a development study approach: a grounded literature review, a severity measurement approach, two stakeholder engagement workshops, four brainstorming sessions and four focus group exercises. The system development relied on Entity–Relationship Diagram (ERD) technique for data structures and database systems design. It uses DFG context sensitivity with alignment with DFG guidance, interlinkages and interoperability between the assessment tools and other platforms of the integrated Adapt-ABLE system.

The assessment tools are client-level outcomes related to accessibility, usability and activity based on the assessment process. The home suitability platform shows the percentage of the suitability of a home with assessment results that suggest appropriate action plans based on individual mobility status. The indicative assessment combines the function of referral, allocation, assessment and test of resources into an integrated platform. This enables timely assessment, decision-making and case-escalation by Occupational Therapists based on needs criteria and the eligibility threshold.

These assessment tools are useful for understanding occupants’ perception of their physical housing environment in terms of accessibility, suitability and usability based on basic activities of daily living and their mobility status. The indicative self-assessment tool will substantially cut down the application journey. The developed tools have been recommended for use in the CSJ Disability Commission report and the UK government Guidance on DFGs for local authorities in England.

The compliance checking of Building Information Modeling (BIM) models is crucial throughout the lifecycle of construction. The increasing amount and complexity of information carried by BIM models have made compliance checking more challenging, and manual methods are prone to errors. Therefore, this study aims to propose an integrative conceptual framework for automated compliance checking of BIM models, allowing for the identification of errors within BIM models.

This study first analyzed the typical building standards in the field of architecture and fire protection, and then the ontology of these elements is developed. Based on this, a building standard corpus is built, and deep learning models are trained to automatically label the building standard texts. The Neo4j is utilized for knowledge graph construction and storage, and a data extraction method based on the Dynamo is designed to obtain checking data files. After that, a matching algorithm is devised to express the logical rules of knowledge graph triples, resulting in automated compliance checking for BIM models.

Case validation results showed that this theoretical framework can achieve the automatic construction of domain knowledge graphs and automatic checking of BIM model compliance. Compared with traditional methods, this method has a higher degree of automation and portability.

This study introduces knowledge graphs and natural language processing technology into the field of BIM model checking and completes the automated process of constructing domain knowledge graphs and checking BIM model data. The validation of its functionality and usability through two case studies on a self-developed BIM checking platform.

This paper aims to facilitate researchers, practitioners and policymakers in understanding and managing the impact of the black swan event – COVID-19 on work from home in the social science subject area through bibliometric analysis. For this purpose, the authors analyzed publications from the Scopus database.

In this paper, the authors conducted bibliometric analysis based on two major techniques: performance analysis and science mapping. The authors applied VOSviewer and Biblioshiny to address the research questions of present study. The study explored the hot trend topics and summarized them with discussions and implications.

Based on the analysis of 500 publications, the authors present an overview of performance and science mapping from the perspective of different aspects such as publication output and authors. Also, authors visualized the text mining by co-word analysis forming nine clusters as well as mapping trend topics. The existing publications were divided into ten clusters according to different keyword analyses: Leadership, Mental health, Technology, Crisis Management, Gender, Challenges, well-being and Work-life balance.

Sample from the Scopus database is not exhaustive, and the dataset may be skewed due to the adoption of the selection criteria. The authors’ concentration was on academic publications in English that excludes potentially intriguing and pioneering studies done in other languages. The study area was limited to social science only.

The paramount lesson is that the COVID-19 quandary is multifaceted, necessitating not simply adaptations to current strategies but also an understanding and analysis of advancements in the economy, commerce and society. According to the analysis presented above, to overcome the COVID-19 “black swan event”, managers must think ahead. The analysis gives leaders and decision-makers a range of useful information on work from home (WFH) difficulties in COVID-19, as well as initiatives and revisions that must be implemented at the economic, social and scientific aspects when dealing with such uncertainties. The findings also aid managers in forecasting the need for sophisticated technology, organizational agility and resilience to attain the desired direction of progress.

With a focus on addressing WFH during COVID-19 from social science perspective and to synthesize its future research directions systematically, the authors performed Bibliometric analysis both with VOSviewer and Biblioshiny, in order to enhance the overall analysis for higher accuracy and more reliable results that is unique value addition and contribution to the existing literature.

This paper aims to identify the different system approach using Building Information Modelling (BIM) technology that is equipped with decision making processes. Maintenance planning and management are integral components of the construction sector, serving the broader purpose of post-construction activities and processes. However, as Precast Concrete (PC) construction projects increase in scale and complexity, the interconnections among these activities and processes become apparent, leading to planning and performance management challenges. These challenges specifically affect the monitoring of façade components for corrective and preventive maintenance actions.

The concept of maintenance planning for façades, along with the main features of information and communication technology tools and techniques using building information modeling technology, is grounded in the analysis of numerous literature reviews in PC building scenarios.

This research focuses on an integrated system designed to analyze information and support decision-making in maintenance planning for PC buildings. It is based on robust data collection regarding concrete façades' failures and causes. The system aims to provide appropriate planning decisions and minimize the risk of façade failures throughout the building's lifetime.

The study concludes that implementing a research framework to develop such a system can significantly enhance the effectiveness of maintenance planning for façade design, construction and maintenance operations.