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Algorithmic and computational thinking are necessary skills for designers in an increasingly digital world. Parametric design, a method to construct designs based on algorithmic logic and rules, has become widely used in architecture practice and incorporated in the curricula of architecture schools. However, there are few studies proposing strategies for teaching parametric design into architecture students, tackling software literacy while promoting the development of algorithmic thinking.

A descriptive study and a prescriptive study are conducted. The descriptive study reviews the literature on parametric design education. The prescriptive study is centered on proposing the incomplete recipe as instructional material and a new approach to teaching parametric design.

The literature on parametric design education has mostly focused on curricular discussions, descriptions of case studies or studio-long approaches; day-to-day instructional methods, however, are rarely discussed. A pedagogical strategy to teach parametric design is introduced: the incomplete recipe. The instructional method proposed provides students with incomplete recipes for parametric scripts that are increasingly pared down as the students become expert users.

The article contributes to the existing literature by proposing the incomplete recipe as a strategy for teaching parametric design. The recipe as a pedagogical tool provides a means for both software skill acquisition and the development of algorithmic thinking.

Building permitting is mostly a manual, labor intensive and time-consuming process. Initiatives for streamlining the process are not always helpful since they often fail to address the core problems within the process. A framework for modeling the permitting process can be useful to identify bottlenecks, core challenges and best practices. Hence, the authors aim to demonstrate and validate a previously suggested workflow for permit process modeling using the permitting process in Israel as a test case.

The authors implement qualitative expert interviews for data acquisition. The collected data are then processed for a qualitative data analysis. The results of the analysis are then validated using a focus group workshop in the field of building permits. In the test case the focus group consisted of Israeli experts.

The authors present a detailed overview of the as-is building permit process in Israel and the existing challenges. Through this test case, the authors found that the framework is applicable in different countries and that it can provide valuable insights into the core problems within the process. In addition, application of the same framework in different countries can provide comparable results that would allow the authors to identify best practices.

The major contribution of this work is the development and validation of a framework for building permitting process modeling which can be used to identify existing challenges and bottlenecks in the process. Implementing a structured and unified approach provides an opportunity to easily compare processes in different countries to identify best practices.

The purpose of this study was to examine the experiences of multiple campus teams as they engaged in the assessment of their science, technology, engineering and mathematics (STEM) mentoring ecosystems within a peer assessment dialogue exercise.

This project utilized a qualitative multicase study method involving six campus teams, drawing upon completed inventory and visual mapping artefacts, session observations and debriefing interviews. The campuses included research universities, small colleges and minority-serving institutions (MSIs) across the United States of America. The authors analysed which features of the peer assessment dialogue exercise scaffolded participants' learning about ecosystem synergies and threats.

The results illustrated the benefit of instructor modelling, intra-team process time and multiple rounds of peer assessment. Participants gained new insights into their own campuses and an increased sense of possibility by dialoguing with peer campuses.

This project involved teams from a small set of institutions, relying on observational and self-reported debriefing data. Future research could centre perspectives of institutional leaders.

The authors recommend dedicating time to the institutional assessment of mentoring ecosystems. Investing in a campus-wide mentoring infrastructure could align with campus equity goals.

In contrast to studies that have focussed solely on programmatic outcomes of mentoring, this study explored strategies to strengthen institutional mentoring ecosystems in higher education, with a focus on peer assessment, dialogue and learning exercises.

The purpose of this paper is to develop a software-library in the R programming language that implements the concepts of the interpretive coordinate, interpretive axis and interpretive plane. This allows for the automatic and reliable interpretation of results from the multiple correspondence analysis (MCA) as previously proposed and published. Consequently, the users can seamlessly apply these concepts to their data, both via R commands and a corresponding graphical interface.

Within the context of this study, and through extensive literature review, the advantages of developing software using the Shiny library were examined. This library allows for the development of full-stack applications for R users without the need for knowledge of the corresponding technologies required for the development of complex applications. Additionally, the structural components of a Shiny application were presented, leading ultimately to the proposed software application.

Software utilizing the Shiny library enables nonexpert developers to rapidly develop specialized applications, either to present or to assist in the understanding of objects or concepts that are scientifically intriguing and complex. Specifically, with this proposed application, the users can promptly and effectively apply the scientific concepts addressed in this study to their data. Additionally, they can dynamically generate charts and reports that are readily available for download and sharing.

The proposed package is an implementation of the fundamental concepts of the exploratory MCA method. In the next step, discoveries from the geometric data analysis will be added as features to provide more comprehensive information to the users.

The practical implications of this work include the dissemination of the method’s use to a broader audience. Additionally, the decision to implement it with open-source code will result in the integration of the package’s functions by other third-party user packages.

The proposed software introduces the initial implementation of concepts such as interpretive coordination, the interpretive axis and the interpretive plane. This package aims to broaden and simplify the application of these concepts to benefit stakeholders in scientific research. The software can be accessed for free in a code repository, the link to which is provided in the full text of the study.

This paper aims to introduce a model using a digital twin concept in a cold heading manufacturing and develop a digital visual management (VM) system using Lean overall equipment effectiveness (OEE) tool to enhance the process performance and establish Fourth Industrial Revolution (I4.0) platform in small and medium enterprises (SMEs).

This work utilised plan, do, check, act Lean methodology to create a digital twin of each machine in a smart manufacturing facility by taking the Lean tool OEE and digitally transforming it in the context of I4.0. To demonstrate the effectiveness of process digitisation, a case study was carried out at a manufacturing department to provide the data to the model and later validate synergy between Lean and I4.0 platform.

The OEE parameter can be increased by 10% using a proposed digital twin model with the introduction of a Level 0 into VM platform to clearly define the purpose of each data point gathered further replicate in projects across the value stream.

The findings suggest that researchers should look beyond conversion of stored data into visualisations and predictive analytics to improve the model connectivity. The development of strong big data analytics capabilities in SMEs can be achieved by shortening the time between data gathering and impact on the model performance.

The novelty of this study is the application of OEE Lean tool in the smart manufacturing sector to allow SME organisations to introduce digitalisation on the back of structured and streamlined principles with well-defined end goals to reach the optimal OEE.

Cyber security has never been more important than it is today in an ever more connected and pervasive digital world. However, frequently reported shortages of suitably skilled and trained information system (IS)/cyber security professionals elevate the importance of delivering effective Security Education,Training and Awareness (SETA) programmes within organisations. Therefore, the purpose of this study is the questionable effectiveness of SETA programmes at changing employee behaviour and an absence of empirical studies on the critical success factors (CSFs) for SETA programme effectiveness.

This exploratory study follows a three-stage research design to give voice to practitioners with SETA programme expertise. Data is gathered in Stage 1 using semi-structured interviews with 20 key informants (the emergence of the CSFs), in Stage 2 from 65 respondents to a short online survey (the ranking of the CSFs) and in Stage 3 using semi-structured interviews with nine IS/cyber security practitioners (the emergence of the guiding principles). Using a multi-stage research design allows the authors to propose and evaluate the 11 CSFs for SETA programme effectiveness.

This study conducted a mean score analysis to evaluate the level of importance of each CSF within two independent groups of IS/cyber security professionals. This multi-stage analysis produces a ranked list of 11 CSFs for SETA programme effectiveness, while the difference in the rankings leads to the emergence of five CSF-specific guiding principles (to increase the likelihood of delivering an effective SETA programme within an organisational context). This analysis also reveals that most of the contradictions/differences in CSF rankings between IS/cyber security practitioners are linked to the design phase of the SETA programme life cycle. While two CSFs, “maintain quarterly evaluation of employee performance” (CSF-DS6) and “build security awareness campaigns” (CSF-EV1), represent the most significant contradiction in this study.

The 11 CSFs for SETA programme effectiveness, along with the five CSF-specific guiding principles, provide a greater depth of knowledge contributing to both theory and practice and lays the foundation for future studies. Therefore, the outputs of this study provide valuable insights on the areas that practice needs to get right to deliver effective SETA programmes.

At present, there is no reference to Attention Autism (AA) as a framework and therapeutic tool with autistic children in occupational therapy (OT) literature. By way of introducing AA as a potential intervention to the OT community, this study aims to investigate the extent to which participation in a two-day AA training could contribute to increasing confidence and inspire changes in practice for Irish occupational therapists (OTs) supporting autistic children.

A pilot study design with mixed qualitative and quantitative methods was used to evaluate the impact of a two-day AA training on six OTs. The OTs support autistic children throughout Ireland across public, private and voluntary sectors. They completed brief, non-standardised questionnaires 2 weeks before the training (Time 1) and again 12 weeks post (Time 2) training session. At Time 2, additional exploratory questions were answered by OTs regarding their use of AA in practice.

This explorative study’s quantitative findings presented percentage change increases within three areas of confidence for all OTs. These include establishing attention, motivating and developing functional skill goals with autistic children. One of the participants did not score any change in confidence in a fourth area, building rapport, however, the five other participants scored percentage change increases. Qualitative data provided by participants showed that they were implementing AA in practice since attending the training. Five of the participants reported positive experiences of using AA and one participant reported the programme was not suitable for her caseload because of their level of understanding and need.

This was a small, exploratory, practice-based study. As this is the first study exploring this area of practice for OTs, to the best of the authors’ knowledge, there were no standardised methods of assessment available, therefore a self-designed survey was used by the author which had a limited number of open-ended questions and four Likert scale questions. This study was also limited in that there was one main researcher who also delivered the two-day AA training. The sample data set was small which resulted in the limitation of the choice of methods used to analyse the quantitative data. Percentage changes were used as the only available and reliable method for a small data set.

Findings of this study, despite their preliminary nature, indicate that AA training may be a useful professional development consideration for OTs who provide a service for autistic children. Further AA research in OT is required including larger and more rigorous studies. An alternative training option of The Curiosity Programme may be considered for OTs supporting children who may not yet be ready to participate in AA.

This paper aims to explore how music festival organisers negotiate diversity and inclusion in marketing and promotion practices through symbolic and social boundaries.

Based on semi-structured interviews with 18 festival organisers in Rotterdam and participant observation with six festival photographers I show that symbolic and social boundaries are employed in three areas: (1) boundaries in festival format (i.e. [partially] free or ticketed), (2) boundaries in distribution partners and technologies and (3) boundaries in promotional content.

Symbolic and social boundaries are intentionally used by festival organisers to build and delineate festival audiences. Implications are drawn on current understandings of the accessibility of music festival spaces, arguing that festival research should move beyond within-space dynamics to grasp the negotiation of diversity and inclusion at festivals more fully.

While music festivals are often marketed as celebratory spaces that are “welcoming to everyone”, few studies have investigated diversity and inclusion nor marketing and promotion practices at music festivals. This study shows how festival audiences are shaped through marketing and promotion practices.

This article proposes an architecture of an intelligent and autonomous recommendation system to be applied to any virtual learning environment, with the objective of efficiently recommending digital resources. The paper presents the architectural details of the intelligent and autonomous dimensions of the recommendation system. The paper describes a hybrid recommendation model that orchestrates and manages the available information and the specific recommendation needs, in order to determine the recommendation algorithms to be used. The hybrid model allows the integration of the approaches based on collaborative filter, content or knowledge. In the architecture, information is extracted from four sources: the context, the students, the course and the digital resources, identifying variables, such as individual learning styles, socioeconomic information, connection characteristics, location, etc. Tests were carried out for the creation of an academic course, in order to analyse the intelligent and autonomous capabilities of the architecture.

This paper aims to discuss the development of a defect classification system that can be used to detect and classify powder bed surface defects from captured layer images without the need for specialised computational hardware. The idea is to develop this system by making use of more traditional machine learning (ML) models instead of using computationally intensive deep learning (DL) models.

The approach that is used by this study is to use traditional image processing and classification techniques that can be applied to captured layer images to detect and classify defects without the need for DL algorithms.

The study proved that a defect classification algorithm could be developed by making use of traditional ML models with a high degree of accuracy and the images could be processed at higher speeds than typically reported in literature when making use of DL models.

This paper addresses a need that has been identified for a high-speed defect classification algorithm that can detect and classify defects without the need for specialised hardware that is typically used when making use of DL technologies. This is because when developing closed-loop feedback systems for these additive manufacturing machines, it is important to detect and classify defects without inducing additional delays to the control system.