IoT and 5G enabled construction detection model

The Fourth Industrial Revolution (4IR) introduced positive changes to some industries, while for most construction industries it is still enthusiastically anticipated. The 4IR focusing on the construction industry in literature is known as Construction 4.0. The Construction 4.0 concept is invoked to transform the current ways the construction industry operates while ensuring benefits, such as reduced overall construction projects’ costs and duration, improved quality and work safety, etc. Due to the increasing web usage, it is anticipated that the 4IR technologies will achieve full potentials by the uprising of the fifth generation technology standard for broadband cellular networks (i.e. 5G). One of the most important 4IR technologies is found to be the Internet of Things (IoT). In this perspective, a construction monitoring approach, more precisely a model for construction detection and object spatial/time positioning, is presented in this paper. While still in its initial phase, the model was tested and verified in the laboratory environment for small-scale object detection. It was found that the quality of the model will be significantly improved with the use of the 5G network, while the objects’ pool, as big data required for the model’s deep learning, is highly dependent on the IoT.

Research-ready data for multi-cohort analyses: The Dementias Platform UK (DPUK) C-Surv data model

Research-ready data (that curated to a defined standard) increases scientific opportunity and rigour by integrating the data environment. The development of research platforms has highlighted the value of research-ready data, particularly for multi-cohort analyses. Following user consultation, a standard data model (C-Surv), optimised for data discovery, was developed using data from 12 Dementias Platform UK (DPUK) population and clinical cohort studies. The model uses a four-tier nested structure based on 18 data themes selected according to user behaviour or technology. Standard variable naming conventions are applied to uniquely identify variables within the context of longitudinal studies. The data model was used to develop a harmonised dataset for 11 cohorts. This dataset populated the Cohort Explorer data discovery tool for assessing the feasibility of an analysis prior to making a data access request. It was concluded that developing and applying a standard data model (C-Surv) for research cohort data is feasible and useful.

Knowledge-oriented leadership and technology standard innovation: a temporary-team perspective

Purpose This study aims to investigate the relevance of knowledge-oriented leadership (KOL) to technology standard innovation (TSI) from a temporary-team perspective. The mediating and moderating effect on knowledge integration (KI) and leader–member exchange (LMX) is emphasized. Design/methodology/approach The conceptual model is built based on an exhaustive literature review and is empirically tested in terms of a sample of 341 Chinese individuals with TSI experience. Quantitative analysis was performed using a questionnaire with the bootstrapping method used to demonstrate the mediating effect of KI. Findings The empirical results of this study prove that KI mediates the relationship between KOL and TSI. The authors deduce that LMX moderates the positive relation between KOL and KI. Originality/value Few studies have investigated the innovation activities of technology standards from a temporary-team perspective. To the best of the authors’ knowledge, this is the first study to look into the underlying mechanism of KOL in TSI. This research deepens the analysis by introducing LMX’s moderating role in the innovation process of technology standards, thereby providing valuable insights for leaders of innovation activities of technology standards and illuminating new aspects of knowledge-intensive temporary-team management.

Research-ready data for multi-cohort analyses: The Dementias Platform UK (DPUK) C-Surv data model

Research-ready data (that curated to a defined standard) increases scientific opportunity and rigour by integrating the data environment. The development of research platforms has highlighted the value of research-ready data, particularly for multi-cohort analyses. Following user consultation, a standard data model (C-Surv), optimised for data discovery, was developed using data from 12 Dementias Platform UK (DPUK) population and clinical cohort studies. The model uses a four-tier nested structure based on 18 data themes selected according to user behaviour or technology. Standard variable naming conventions are applied to uniquely identify variables within the context of longitudinal studies. The data model was used to develop a harmonised dataset for 11 cohorts. This dataset populated the Cohort Explorer data discovery tool for assessing the feasibility of an analysis prior to making a data access request. It was concluded that developing and applying a standard data model (C-Surv) for research cohort data is feasible and useful.

S-NPP VIIRS Thermal Emissive Bands 10-Year On-Orbit Calibration and Performance

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership Program (S-NPP) satellite, launched in late 2011, has reached the decade landmark under successful operations. VIIRS has 22 spectral bands, 7 of which are thermal emissive bands (TEB) that cover the 3.70 to 11.84 μm wavelength range. Over the years, VIIRS TEB observations have been used to generate several data products (e.g., surface/cloud/atmospheric temperatures, cloud top altitude, and water vapor properties). The VIIRS TEB calibration uses a quadratic algorithm and is referenced to an on-board blackbody with temperature measurements traceable to the National Institute of Standards and Technology standard. This manuscript provides an overview of the VIIRS instrument operations and TEB calibration activities and algorithms used in the level 1B data and describes the TEB on-orbit performance for S-NPP VIIRS. The 10-year on-orbit performance of the S-NPP VIIRS TEB has generally been stable, and the degradations in the S-NPP TEB detector responses are minor after a decade in orbit. The noise characterization performance repeatedly meets the design requirements for all TEB detectors as well. On-orbit changes in the TEB response-versus-scan-angle, based on pitch maneuver observations, have been demonstrated to be extremely small. Moreover, multiple time series over select ground targets have shown that the sensor’s on-orbit performance is quite stable.

Research-ready data for multi-cohort analyses: The Dementias Platform UK (DPUK) C-Surv data model

Research-ready data (that curated to a defined standard) increases scientific opportunity and rigour by integrating the data environment. The development of research platforms has highlighted the value of research-ready data, particularly for multi-cohort analyses. Following user consultation, a standard data model (C-Surv), optimised for data discovery, was developed using data from 12 population and clinical cohort studies. The model uses a four-tier nested structure based on 18 data themes and 137 domains selected according to user behaviour or technology. Standard variable naming conventions are applied to uniquely identify variables within the context of longitudinal studies. The model was used to develop a harmonised dataset for 11 cohorts. This dataset populated the Cohort Explorer data discovery tool for assessing the feasibility of an analysis prior to making a data access request. It was concluded that developing and applying a standard data model (C-Surv) for research cohort data is feasible and useful.

The Dialectics of Technology Standardization

Technology standardization unfolds as a dialectic process marked by paradoxical tensions. However, standardization research has yet to provide a dialectic analysis of how tensions and management responses interact recursively over time, and with what effect. In this paper, we apply dialectics to analyze an action research study of a Swedish initiative that developed and diffused a technology standard to facilitate the integration of disparate IT systems in road haulage firms. Drawing on the technology standardization literature and our empirical analysis, we engage in midrange theorizing to capture the recursive dynamics through which standard-setters construct and respond to manifestations of three latent tensions: development versus diffusion activities, private versus public interests, and local versus global solutions. Our resulting dialectic theorizing explicates how standard-setters bring these latent tensions into being; how they construct salient tensions through the oppositional logics of polarization, complementarity, and mutuality; how they manage these tensions through splitting, integrating, and suspension responses; and how consequential functional, architectural, and organizational standardization outcomes produce a new social order in which new tensions emerge. These theoretical insights contribute to both the technology standardization and dialectics literatures.

Towards a Closer Look at the Pipes and Joints of Educational Data Infrastructures

More recently, scholars in the field Critical Data Studies have turned attention to the infrastructures by means of which educational data is produced, processed, circulated, and consumed. While respective studies have rightly emphasized the social, cultural, political, and economic factors that are shaping these infrastructures, the technical dimension of these developments has remained largely unexplored. As a consequence, analyses are easily deemed irrelevant by technologists and designers engaged in educational datamining and learning analytics. This paper therefore aims to broaden the analytic scope of Critical Data Studies in education and to engage more closely with the technical dimension of the emerging educational data infrastructures. Towards this end, the paper outlines a technogenetic account of (digital) infrastructures and standards, and provides a case-study to illustrate how this account can be leveraged to unravel assumptions and perspectives implied in an educational technology standard such as the Experience API. The results of the case study indicate that while the Experience API is highly abstract and generic nature, it lends itself to a rather restricted idea of learning and education.