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The purpose of this paper is to introduce an approach in measuring the shielding gas flow within laser powder bed fusion (L-PBF) machines under near-process conditions (regarding oxygen content and shielding gas flow).

The measurements are made sequentially using a hot-wire anemometer. After a short introduction into the measurement technique, the system which places the measurement probe within the machine is described. Finally, the measured shielding gas flow of a commercial L-PBF machine is presented.

An approach to measure the shielding gas flow within SLM machines has been developed and successfully tested. The use of a thermal anemometer along with an automated probe-placement system enables the space-resolved measurement of the flow speed and its turbulence.

The used single-normal (SN) hot-wire anemometer does not provide the flow vectors’ orientation. Using a probe with two or three hot-films and an improved placement system will provide more information about the flow and less disturbance to it.

A measurement system which allows the measurement of the shielding gas flow within commercial L-PBF machines is presented. This enables the correlation of the shielding gas flow with the resulting parts’ quality.

Previous research has found that individuals with social phobia differ from controls in their processing of emotional faces. For instance, people with social phobia show increased attention to briefly presented threatening faces. However, when exposure times are increased, the direction of this attentional bias is more unclear. Studies investigating eye movements have found both increased as well as decreased attention to threatening faces in socially anxious participants. The current study investigated eye movements to emotional faces in eight patients with social phobia and 34 controls. Three different tasks with different exposure durations were used, which allowed for an investigation of the time course of attention. At the early time interval, patients showed a complex pattern of both vigilance and avoidance of threatening faces. At the longest time interval, patients avoided the eyes of sad, disgust, and neutral faces more than controls, whereas there were no group differences for angry faces.

The complex and occasionally chaotic nature of health care has been previously described in the literature, as has the broadening recognition that different management approaches are required for different types of problems rather than a “one size fits all” approach. The CYNEFIN framework from Snowden outlines a consistent cognitive approach that offers the leader and leadership team an ability to urgently apply the correct actions to a given situation. This paper proposes a variant CYNEFIN approach for healthcare.

Consistent and accurate decision-making within health care is the hallmark of an effective and pragmatic leader and leadership team. An awareness of how one’s cognitive biases and heuristics may adversely impact on this cognitive process is paramount, as is an understanding of the calibration between fast and slow thinking.

The authors propose a variant CYNEFIN approach for health care of “act-probe-sense-respond” to resolve complex and time-critical emergency scenarios, using the differing contexts of a cardiac arrest and an evolving crisis management problem as examples. The variant serves as a pragmatic sense-making framework for the health-care leader and leadership team that can be adopted for many time-critical crisis situations.

The variant serves as a pragmatic sense-making framework for the health-care leader that can be adopted for many crisis situations.

This paper aims to probe into the implementation of internship courses under the five-year cosmetology program in Taiwan from the perspective of Dewey's philosophies in hopes that the analysis can serve as reference for schools in planning their internship courses in the future.

This paper probes into the off-campus internship courses under Taiwan's five-year junior college education program by applying Dewey's empirical philosophy.

The study aims to understand the learning experience acquired by students from learning by doing internship courses as reference for future internship design. The current internship courses comprise the follows: summer, during the semester and for a full academic year. The experience of summer internship is career exploration, the experience for semester internship is career experience and the experience from academic-year internship is career choice. The internship strategies are designed according to the three different internship approaches, so that the internship courses can implement the educational effect of “learning from experience”.

To minimize the discrepancy between the cosmetology program under the five-year junior college education and the employment market, credit-based off-campus internship courses are arranged for students' final years at school.

Through on-site trainings, students accumulate work experience and explore into a related career field. The accumulation of experience and gaining of insights mirror the philosophy of learning by doing, which involves students' reflective thinking.

It is hoped that the analysis can serve as reference for internship courses planning in the future.

To decrease wear and friction, zinc dialkyldithiophosphate (ZDDP) has been used in engine oil for several decades, but the mechanism of the tribofilm formation is still unclear. The purpose of this study is to characterize the chemical details of the tribofilm by using high-resolution approaching.

An ISO VG 100 mineral oil mixed with ZDDP was used in sliding tests on cylindrical roller bearings. Tribofilm formation was observed after 2 h of the sliding test. X-ray photoelectron spectroscopy (XPS) and atom probe tomography (APT) were used for chemical analysis of the tribofilm.

The results show that the ZDDP tribofilm consists of the common ZDDP elements along with iron oxides. A considerable amount of zinc and a small amount of sulfur were observed. In particular, an oxide interlayer with sulfur enrichment was revealed by APT between the tribofilm and the steel substrate. The depth profile of the chemical composition was obtained, and a tribofilm of approximately 40 nm thickness was identified by XPS.

A sulfur enrichment at the interface is observed by APT, which is beneath an oxygen enrichment. The clear evidence of the S interlayer confirms the hard and soft acids and bases principle.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2020-0035/

This study aims to find ways to bridge the gap between workplace design and research. Exploring the design process from general design aims to site-specific design makes the process visible to support workplace design research.

Participatory design methods were used to understand employees’ needs and preferences in work-related situations to support the design process. The design process was divided into three phases. The office was temporarily refurbished for the intervention study, and evaluation data was collected with qualitative methods.

Participatory design-generated data revealed typical knowledge work needs, such as the need for privacy, interaction, exposure and preferences for the atmosphere in the workspaces during different situations. The authors identified the following key points to obtain design data: design aims, affordance design and site-specific multidimensional design. An intervention study in a small organisation revealed that lack of activity-supporting spaces created undesirable overlaps for focused work, collaborative work and client communication.

The findings of this paper are explorative and limited to a small knowledge work company. The present approach identifies valuable data collection points in different design phases of workplace design processes. Sharing knowledge from practice to research and vice versa could inform research and improve workplace design.

This study makes the workplace design phases more visible. It supports finding new ways to study the connection between the user-needs and workplaces; and understanding how different design solutions impact workplace experiences, such as satisfaction. This study also brings focus to understanding the versatile needs of small organisations and their workplace design.

Metropolitan areas suffer from frequent road traffic congestion not only during peak hours but also during off-peak periods. Different machine learning methods have been used in travel time prediction, however, such machine learning methods practically face the problem of overfitting. Tree-based ensembles have been applied in various prediction fields, and such approaches usually produce high prediction accuracy by aggregating and averaging individual decision trees. The inherent advantages of these approaches not only get better prediction results but also have a good bias-variance trade-off which can help to avoid overfitting. However, the reality is that the application of tree-based integration algorithms in traffic prediction is still limited. This study aims to improve the accuracy and interpretability of the models by using random forest (RF) to analyze and model the travel time on freeways.

As the traffic conditions often greatly change, the prediction results are often unsatisfactory. To improve the accuracy of short-term travel time prediction in the freeway network, a practically feasible and computationally efficient RF prediction method for real-world freeways by using probe traffic data was generated. In addition, the variables’ relative importance was ranked, which provides an investigation platform to gain a better understanding of how different contributing factors might affect travel time on freeways.

The parameters of the RF model were estimated by using the training sample set. After the parameter tuning process was completed, the proposed RF model was developed. The features’ relative importance showed that the variables (travel time 15 min before) and time of day (TOD) contribute the most to the predicted travel time result. The model performance was also evaluated and compared against the extreme gradient boosting method and the results indicated that the RF always produces more accurate travel time predictions.

This research developed an RF method to predict the freeway travel time by using the probe vehicle-based traffic data and weather data. Detailed information about the input variables and data pre-processing were presented. To measure the effectiveness of proposed travel time prediction algorithms, the mean absolute percentage errors were computed for different observation segments combined with different prediction horizons ranging from 15 to 60 min.

The purpose of this paper is to describe two cycles of learning study (LS) involving eight elementary teachers in British Columbia, Canada. The study explored the teachers’ experiences of learning to plan and teach lessons as informed by recent brain research.

The case study was constructed using data sources including teacher semi-structured interviews (pre-study, post-study and delayed post-study), classroom materials (including student assignments), LS training materials, fieldnotes and recordings of meetings and research lessons; sources were triangulated. Thematic analysis was applied. Contemporary neuroscience perspectives framed the LS discourse and analysis.

The teachers developed theoretical coherence and could better articulate reasons for their pedagogy. They developed understandings of the cognitive architecture underlying functions like learning and memory, allowing them to identify pedagogical actions that are consistent with human biology and understand why these actions are effective in promoting learning.

LS is shown to be an effective professional development (PD) model where theoretical knowledge, like neuroscience, could be employed and tested in classroom settings to provide depth to support teachers’ praxis. This teaching–research nexus supports exploration of fruitful connections between theoretical knowledge and education to advance the science of learning and the science of instruction.

Findings demonstrated how LS could be employed with alternative theoretical perspectives to promote teacher PD, thus extending beyond the dominant use of variation theory. Also, illustrated is the potential use of LS to bridge the knowledge gap between neuroscience and education.

This paper proposes a solution for recognizing human faces under mask-wearing. The lower part of human face is occluded and could not be used in the learning process of face recognition. So, the proposed solution is developed to recognize human faces on any available facial components which could be varied depending on wearing or not wearing a mask.

The proposed solution is developed based on the FaceNet framework, aiming to modify the existing facial recognition model to improve the performance of both scenarios of mask-wearing and without mask-wearing. Then, simulated masked-face images are computed on top of the original face images, to be used in the learning process of face recognition. In addition, feature heatmaps are also drawn out to visualize majority of parts of facial images that are significant in recognizing faces under mask-wearing.

The proposed method is validated using several scenarios of experiments. The result shows an outstanding accuracy of 99.2% on a scenario of mask-wearing faces. The feature heatmaps also show that non-occluded components including eyes and nose become more significant for recognizing human faces, when compared with the lower part of human faces which could be occluded under masks.

The convolutional neural network based solution is tuned up for recognizing human faces under a scenario of mask-wearing. The simulated masks on original face images are augmented for training the face recognition model. The heatmaps are then computed to prove that features generated from the top half of face images are correctly chosen for the face recognition.

The objective of this paper is to study the efficacy of the ASPECTSS Design Index's concepts as drivers of design intervention for educational environments for students on the autism spectrum. Based on the seven principles of acoustics, spatial sequencing, escape spaces, compartmentalization, transitions, sensory zoning and safety, ASPECTSS formed the basis for a preliminary post-occupancy evaluation (POE) and survey of an existing school environment.

Concepts drawn from the review of other strategies for autism spectrum disorder (ASD) friendly design were integrated with the seven ASPECTSS principles to create a design framework and consequent design retro-fit for a Pre-K-12th grade public school for students on the autism spectrum. The following design interventions were proposed: colour-coding based navigation; acoustical treatments in key circulation spaces; introduction of transition alcoves; classroom reorganisation using compartmentalization principles and the introduction of escape spaces for de-escalation. Specifically, a classroom template of modules of ASPECTSS-compliant layouts was provided to all staff. The efficacy and impact of these interventions were assessed using a whole campus online staff survey with further probing using classroom observations and subsequent interviews.

The results show alignment between the implementation of the ASPECTSS informed design interventions and responses to nine of the Likert scale items were all significantly lower than the middle response, indicating a high degree of satisfaction from survey respondents. These questions and responses related to the colour scheme facilitating ease of navigation for visitors of the school, the acoustics of the building successfully mitigating sound magnification and subsequently student distractibility, the organisation of the classrooms enhancing learning and the de-escalation zones allowing improved management of disruptive behaviours in the classroom.

This study focuses primarily on the Autism ASPECTSS Design Index as a framework for assessing classroom efficacy. Other tools and frameworks may produce different insights. A single school site was studied. Validation of these findings in other school environments is necessary before generalising these strategies at scale. The use of qualitative tools, primarily teacher and staff surveys, provides one lens into the efficacy of these design strategies. Further research using measurable biometric indicators such as heart-rate and stress levels measured through wearable technology could provide a first step towards the triangulation of these findings.

These findings could help provide more standardised best practices for designing learning environments for autism, potentially providing supportive strategies with real impact on learning quality, skill development and knowledge acquisition in school environments. This could potentially have economic implications by supporting more efficient progress for autistic students through their school curriculum.

Similar to economic impact, if validated and generalised, these findings could help with sense of accomplishment, general mental health improvement, alleviation of family stress and potential reduction of stigma in the autism community.

There is a slowly emerging field of design guidance for autism schools, but very little empirical evidence on the measurable efficacy of these strategies. This research provides one type of such evidence, as measured by the perceived impact from the point of view of staff and teachers at the school.