bluuebunny/crossref_metadata_2025 huggingface dataset:
GBP5 exacerbates rosacea‐like skin inflammation by skewing macrophage polarization towards M1 phenotype through the NF‐κB signalling pathway
Authors: Zhou Lei , Zhao Han , Zhao He , Meng Xin , Zhao Zhixiang , Xie Hongfu , Li Ji , Tang Yan , Zhang Yiya
Published: November 2022

Background

Rosacea is a chronic inflammatory skin disease with increased macrophage infiltration. However, the molecular mechanism remains unclear.

Objectives

To determine the significance of macrophage infiltration, and the correlation between Guanylate‐binding protein 5 (GBP5) and polarization of macrophages in rosacea‐like inflammation.

Methods

Here we tested the hypothesis that Guanylate‐binding protein 5 (GBP5) aggravates rosacea‐like skin inflammation by promoting the polarization of the M1 macrophages through the NF‐κB signalling pathway. We depleted macrophage by injecting clodronate‐containing liposomes. We next explored the association between GBP5 and macrophage in rosacea tissue through transcriptome analysis and immunofluorescence analysis. We evaluated the severity of rosacea‐like skin inflammation when BALB/c mice were injected with GBP5 siRNA intradermally daily for three consecutive days. At last, to study the causality of knocking down GBP5‐blunted M1 macrophage polarization, THP‐1 cell was treated with GBP5 siRNA.

Results

Macrophage depletion ameliorated rosacea‐like skin inflammation in mice, implying the important role of macrophages in rosacea. Based on the transcriptome analysis, Guanylate‐binding protein 5 (GBP5) was identified as hub gene that was associated with macrophage infiltration in rosacea. Next, we found that GBP5 expression was significantly upregulated in rosacea tissues and positively correlated with macrophage infiltration, the immunofluorescence analysis revealed the co‐localization between GBP5 and macrophages. In vivo, silencing of GBP5 attenuated rosacea‐like skin inflammation in the LL‐37‐induced mouse model and suppressed the expression of M1 signature genes such as IL‐6, iNOS and TNF‐a. In vitro, knocking down GBP5 significantly blunted the polarization of the M1 macrophages partly by repressing the activation of the NF‐κB signalling pathways.

Conclusions

Together, our study revealed the important role of macrophages in rosacea and identified GBP5 as a key regulator of rosacea by inducing M1 macrophage polarization via NF‐κB signalling pathways.
Oviposition behaviour inDrosophila melanogaster: Genetic and behavioural decoupling between oviposition acceptance and preference for natural fruits
Authors: Fanara Juan J. , Beti Maria I. L. , Gandini Luciano , Hasson Esteban
Published: November 2022

In phytophagous insects, oviposition behaviour is an important component of habitat selection and, given the multiplicity of genetic and environmental factors affecting its expression, is defined as a complex character resulting from the sum of interdependent traits. Here, we study two components of egg‐laying behaviour: oviposition acceptance (OA) and oviposition preference (OP) inDrosophila melanogasterusing three natural fruits as resources (grape, tomato and orange) by means of no‐choice and two‐choice experiments, respectively. This experimental design allowed us to show that the results obtained in two‐choice assays (OP) cannot be accounted for by those resulting from no‐choice assays (OA). Since the genomes of all lines used are completely sequenced, we perform a genome‐wide association study to identify and characterize the genetic underpinnings of these oviposition behaviour traits. The analyses revealed different candidate genes affecting natural genetic variation of both OA and OP traits. Moreover, our results suggest behavioural and genetic decoupling between OA and OP and that egg‐laying behaviour is plastic and context‐dependent. Such independence in the genetic architectures of OA and OP variation may influence different aspects of oviposition behaviour, including plasticity, canalization, host shift and maintenance of genetic variability, which contributes to the adoption of adaptive strategies during habitat selection.
Can extreme climatic events induce shifts in adaptive potential? A conceptual framework and empirical test with Anolis lizards
Authors: Simon Monique N. , Rothier Priscila S. , Donihue Colin M. , Herrel Anthony , Kolbe Jason J.
Published: November 2022

Multivariate adaptation to climatic shifts may be limited by trait integration that causes genetic variation to be low in the direction of selection. However, strong episodes of selection induced by extreme climatic pressures may facilitate future population-wide responses if selection reduces trait integration and increases adaptive potential (i.e., evolvability). We explain this counter-intuitive framework for extreme climatic events in which directional selection leads to increased evolvability and exemplify its use in a case study. We tested this hypothesis in two populations of the lizard Anolis scriptus that experienced hurricane-induced selection on limb traits. We surveyed populations immediately before and after the hurricane as well as the offspring of post-hurricane survivors, allowing us to estimate both selection and response to selection on key functional traits: forelimb length, hindlimb length, and toepad area. The direct selection was parallel in both islands and strong in several limb traits. Even though overall limb integration did not change after the hurricane, both populations showed a non-significant tendency toward increased evolvability after the hurricane despite the direction of selection not being aligned with the axis of most variance (i.e., body size). The population with comparably lower between-limb integration showed a less constrained response to selection. Hurricane-induced selection, not aligned with the pattern of high trait correlations, likely conflicts with selection occurring during normal ecological conditions that favours functional coordination between limb traits, and would likely need to be very strong and more persistent to elicit a greater change in trait integration and evolvability. Future tests of this hypothesis should use G-matrices in a variety of wild organisms experiencing selection due to extreme climatic events.

Abstract

We surveyed populations of A. scriptus lizards, in two islands, before the hurricane, after the hurricane (estimate of survivors) and the offspring almost two years later. We hypothesized that the direction of hurricane-induced selection would be to reduce between-limb trait correlations, resulting in higher variation in the direction of selection (higher evolvability). We found that selection had a similar direction in both populations, but was likely not strong or persistent enough to change trait correlations. However, the population with lower limb trait correlations showed a response to selection more aligned with the direction of selection. Finally, both populations showed a tendency to increase evolvability after the hurricane.
Effect of an intervention of exercise on sleep and seizure frequency in idiopathic epileptic dogs
Authors: Grady K. , Cameron S. , Kent S. P. , Barnes Heller H. , Barry M. M.
Published: November 2022

Objective

The goal of this study was to compare sleep and seizure frequency between epileptic dogs prescribed a 20% activity increase and epileptic dogs not prescribed an activity increase.

Methods

Sixty‐nine dogs receiving anti‐epileptic drug therapy were enrolled in a 6‐month prospective, randomised, placebo‐controlled clinical trial with an intention‐to‐treat analysis. A canine activity monitoring device was used to measure activity levels and sleep scores.

Results

Using an intention‐to‐treat analysis, the treatment group had an average of 0.381 more seizures per month (95% CI: 0.09 to 0.68) compared with the control group, although the difference in seizure days per month was not statistically significant. In a subgroup analysis of dogs whose activity increased by at least 10%, partial compliers had 0.719 more seizures per month (95% CI: 0.22 to 1.22) and 0.581 seizure days per month (95% CI: 0.001 to 1.16) compared with the control group. Sleep scores increased by 1.2% in the treatment compared with the control group (95% CI: 0.2 to 2.3%).

Conclusions

Seizure frequency and sleep score increased slightly, but significantly, in dogs with idiopathic epilepsy prescribed an increase in activity, compared with a control group.
Thermomorphogenesis goes like clockwork: how the circadian clock fine‐tunes temperature responses through competing transcriptional repressors
Authors: Balcerowicz Martin
Published: November 2022

This article is a Commentary on Li et al. (2023), 237: 177–191.
Professional home care providers' conceptualisations of frailty in the context of home care: A focus group study
Authors: Voie Kristin S. , Blix Bodil H. , Helgesen Ann Karin , Larsen Toril Agnete , Mæhre Kjersti Sunde
Published: November 2022

Background

In Norway, as in many other countries, more people receive health and care services in their homes than before. Home care professionals provide care and support to people with a range of health and care needs. Older home care service users are sometimes referred to as ‘frail’, but the terms ‘frail’ and ‘frailty’ have different meanings in different contexts, and little is known about the meaning ascribed to the terms in the context of home care services. Home care services are crucial for many older persons who have health challenges, and how home care professionals conceptualise frailty might shape clinical encounters.

Objectives

The purpose of this study is to explore how home care professionals conceptualised frailty in the context of home care.

Methods

We conducted four focus group discussions with 14 home care professionals who worked in municipal home care in northern Norway and analysed the data using thematic analysis.

Results

Our analysis resulted in five themes: ‘“Frail” – a term which is too imprecise to be useful’, ‘Frailty as a consequence of ageing’, ‘Frailty as lack of engagement and possibilities for engagement’, ‘Frailty as a contextual phenomenon’ and ‘Frailty as potentially affected by care’. The home care professionals conceptualised frailty as an individual trait but also as resulting from the interplay between individual and environmental factors. Moreover, their conceptualisations of frailty represented a continuum between frailty as related to prevention and management (‘cure’) and frailty as related to ageing as natural decline (‘care’).

Conclusion

The home care professionals conceptualised frailty diversely, as moving along a continuum between cure and care. Diverse conceptualisations of frailty might be necessary if nurses are to meet the changing and varying care needs of older persons who live in their own homes and need health and care services.
Intelligent video surveillance using enhanced deep belief based multilayered convolution neural network classification techniques
Authors: Kaliappan Nattar Kannan , Thapasimuthu Rajeswari Saravanan , Dakshinamoorthy Prabakar , Sundararaju Nirmalraj , Sundar Ramesh
Published: November 2022

Video surveillance has undergone numerous changes in the past few years and several pieces of research have been carried out in this field. Object tracking is the significant task in such systems, and hence it is essential to review the standard approaches dealing with object detection, classification and tracking. This work proposes a novel classification technique for a detected object of a moving scene from a video dataset. Initially the dataset has been processed and prepared for data training based on neural networks. The data has been classified using the proposed enhanced deep belief based multilayered convolutional neural network (EBMCNN). The major focus is on deep learning applications involved in estimating the count, total persons involved and the activities in a crowd where all criteria are taken into consideration, thereby achieving security through video analysis. Identifying theft and the detection of violence are some security measures where video is converted to frames which are then processed to analyse the individuals along with their activities. The classification is performed through comparative analysis of a real‐time dataset. Experimental results show the accuracy 97%, precision 93.8%, recall 87.7% and F‐1 score 87.5%.
Emplacement age of the Markagunt gravity slide in southwestern Utah, USA
Authors: Holliday McKenna E. , Rivera Tiffany , Jicha Brian , Trayler Robin B. , Biek Robert F. , Braunagel Michael J. , Griffith W. Ashley , Hacker David B. , Malone David H. , Mayback Danika F.
Published: November 2022

The Markagunt gravity slide (MGS) is a large‐volume landslide in southwestern Utah that originated within the Oligocene‐Miocene Marysvale volcanic field. Gravity slides are single emplacement events with long runout distances and are now recognized as a new class of volcanic hazard. Accumulation of volcanic material on a structurally weak substrate along with voluminous shallow intrusive events led to collapse. Here, 40Ar/39Ar data for landslide‐generated pseudotachylyte, the landslide‐capping Haycock Mountain Tuff and the deformed Osiris Tuff are combined with a Bayesian age model to determine an emplacement age of 23.05 + 0.22/−0.20 Ma for the MGS. The results suggest a lag time of <200 kyr between the caldera‐forming eruption of the Osiris Tuff, additional buildup of the unstable volcanic pile and subsequent mass movement.
Using a teach‐back intervention significantly improves knowledge, perceptions, and satisfaction of patients with Nurses' discharge medication education
Authors: Marks Lisa , O'Sullivan Laura , Pytel Kathy , Parkosewich Janet A.
Published: November 2022

Background

Evidence indicates that patients desire medication information, but clinicians often fail to teach this information in an understandable way. Teach‐Back promotes effective clinician‐patient communication and enhances medication adherence, satisfaction with education, and hospital experiences.

Aims

This study examined effects of a nurse‐led Teach‐Back intervention, Teaching Important Medication Effects (TIME), compared with usual care on patients' knowledge, experiences, and satisfaction with medication education before and after discharge. The aims were to examine patients' priority learning needs, group differences in new medication knowledge before and after discharge, and post‐discharge experience and satisfaction with medication instruction.

Methods

A longitudinal pretest/post‐test, 2‐group design was used with 107 randomly selected medical‐surgical patients from an academic hospital. After receiving medication instructions, patients were interviewed before and within 72 h after discharge. Bivariate analyses were used to assess group differences in demographic and outcome variables.

Results

Usual care (n = 52) and TIME (n = 55) groups' characteristics were equivalent. Knowing new medication names and side effects were top learning priorities. Medication side effect knowledge was better in TIME versus usual care groups at discharge (94.3% vs. 72.5%, p = .003) and follow‐up (93.9% vs.75.8%, p = .04). TIME positively influenced patients' medication education experiences; specifically, nurses always explained why a new medication was needed and its side effects. TIME was associated with patients being very satisfied with nurses' education versus usual care (97% vs. 46.9%, p < .001).

Linking Evidence to Action

TIME was significantly and positively associated with knowledge of new medication side effects over time and experiences and satisfaction with nurses' medication education. Teach‐Back was effective in educating patients about new medications. It is an essential approach for patient education, requiring clinicians to confirm patients' retention and understanding of new information with each encounter.
Large Eddy Simulations of Turbulent Gas-Liquid Flows in a Diverging Horizontal Channel Using a Hybrid Multiphase Approach
Authors: Nguyen Bich-Diep , Popp Sebastian , Hundshagen Markus , Skoda Romuald , Mansour Michael , Thévenin Dominique , Hasse Christian
Published: November 2022

Centrifugal pumps conveying gas–liquid flows are often designed based on pure liquid flows due to limited understanding of gas formations within a gas–liquid flow. Computational fluid dynamics (CFD) can provide insights into the flow characteristics, yet standard multiphase models are not able to describe a wide range of spatial scales. In this study, a hybrid multiphase approach for turbulent flows based on an Eulerian-Eulerian solver with volume-of-fluid (VOF) enhancements is applied, which can handle multiple flow regimes (continuous gas/liquid, disperse bubbles) at once. Large eddy simulations (LES) of gas–liquid-flows through a diverging horizontal channel (diffuser) are performed to evaluate the possibilities and limitations of this approach. Three representative cases, each containing different flow characteristics, are computed using a fixed bubble size approach. The results show that the hybrid approach is able to capture all flow characteristics and predict the gas void size and position precisely. However, it can be observed that the performance of the approach significantly depends on the specified bubble size.
Theoretical Solution for Fully Developed Laminar Flow in a Noncircular Duct
Authors: Jing Haixiao , Li Pengfeng , Zhu Lei , Yang Zhendong
Published: November 2022

In this study, the friction factor of a fully developed laminar flow in a noncircular duct, which is used in an automotive cooler system, is investigated. The Poisson equation of velocity with homogeneous boundary conditions is solved by using the method of separation of variables and analytical solutions of velocity, wall shear stress, and friction factor for arbitrary symmetric noncircular ducts are obtained. The analytical solutions are validated by comparing against analytical and experimental data available for rectangular and triangular ducts. Cross-sectional distribution of velocity, wall shear stress along the duct boundary, and friction factor are analyzed in depth. It is obtained that the friction factor for this specific duct is λ=95Re, with Re defined with the equivalent diameter as length scale. Moreover, the applicability of a general equation of friction factor proposed by Muzychka and Yovanovich is validated for this specific duct by comparing with the analytical solution. The result shows that the general equation is accurate enough and can be used for the evaluation of flow resistance in the design of the cooler system.
Numerical Modeling of Dust Deposition Rate on Ground-Mounted Solar Photovoltaic Panels
Authors: Kaiss El-Cheikh Amer , Hassan Noha M.
Published: November 2022

Despite the growth in the global cumulative installed photovoltaic (PV) capacity, the efficiency of PV panels is greatly reduced due to dust accumulation and soiling. To enhance this efficiency, consideration must be given to the factors that affect dust deposition ranging from panel configuration to weather conditions. This research aims to determine which of those factors contribute significantly to dust accumulation and model this behavior. Numerical experiments were performed to study those factors based on a planned Design of Experiments (DOE). Dust particle size, dust amount, wind speed, wind direction, and the solar panel tilt angle are the five factors examined using computational fluid dynamics (CFD) simulations. Statistical and regression analyses were then used to determine the most significant factors and model their effect on the deposition rate. Results revealed that the dust diameter, panel tilt angle, and wind speed influence the deposition rate the most. Dust diameter is positively correlated to the dust deposition rate. Larger dust particles have a lower deposition rate as the wind velocity increases. In addition, smaller dust particles will always give the lowest dust deposition rate irrespective of the tilt angle. It was also seen that the maximum dust deposition rate occurs at a panel’s tilt angle of approximately 50 deg regardless of the wind speed or the dust particle size. The developed mathematical model shows the factors contributing to soiling and panel efficiency reduction over exposure time. This model can be used further to optimize panel cleaning frequency.
Manufacturing Fixation in Design: Exploring the Effects of Manufacturing Fixation During Idea Generation
Authors: Brennan Jennifer Bracken , Miney William B. , Simpson Timothy W. , Jablokow Kathryn W. , McComb Christopher
Published: November 2022

Capitalizing on any new or unfamiliar manufacturing technology requires an ability to look beyond the manufacturing limitations that have constrained one's design ideas in the past. For advanced manufacturing technology with unique capabilities such as additive manufacturing, this becomes critical for designing effective geometric features and parts. However, cognitive bias or fixation on familiar manufacturing processes may make it challenging for designers to determine how to take advantage of new manufacturing technology. For example, a person that is used to generating design ideas for subtractive manufacturing may struggle when asked to use additive manufacturing. This research is the first to provide evidence that fixation on certain manufacturing types can impact designers’ ability to utilize new, and less familiar, technologies during idea generation. We call this new form of design fixation Manufacturing Fixation in Design (MFD), which we define as unconscious and often unintentional adherence to a limited set of manufacturing processes and/or constraints and capabilities during the design ideation process. A workshop-based study with industry practitioners confirms that MFD exists and introduces a method to assess its impact. Specifically, a Design for Additive Manufacturing workshop given at an aerospace technology company with professional engineering designers explores how MFD manifests and how it can be measured. Implications of the results on practice and education are discussed along with future studies to delineate the drivers of MFD in design professionals.
Material Prediction for Design Automation Using Graph Representation Learning
Authors: Bian Shijie , Grandi Daniele , Hassani Kaveh , Sadler Elliot , Borijin Bodia , Fernandes Axel , Wang Andrew , Lu Thomas , Otis Richard , Ho Nhut , Li Bingbing
Published: November 2022

Successful material selection is critical in designing and manufacturing products for design automation. Designers leverage their knowledge and experience to create high-quality designs by selecting the most appropriate materials through performance, manufacturability, and sustainability evaluation. Intelligent tools can help designers with varying expertise by providing recommendations learned from prior designs. To enable this, we introduce a graph representation learning framework that supports the material prediction of bodies in assemblies. We formulate the material selection task as a node-level prediction task over the assembly graph representation of CAD models and tackle it using Graph Neural Networks (GNNs). Evaluations over three experimental protocols performed on the Fusion 360 Gallery dataset indicate the feasibility of our approach, achieving a 0.75 top-3 micro-F1 score. The proposed framework can scale to large datasets and incorporate designers’ knowledge into the learning process. These capabilities allow the framework to serve as a recommendation system for design automation and a baseline for future work, narrowing the gap between human designers and intelligent design agents.
Computational Patterns of Team Interactions and Associations With Conflict Within New Product Development Teams
Authors: Sitarama Shankaran , Agogino Alice M.
Published: November 2022

New Product Development involves team members coming together and working in teams to come up with innovative solutions to problems resulting in new products. They need to be creative as a team, debate extensively and come up with a breadth of ideas to solve the given customer need and problem. They also need to be very efficient in their teamwork and work cohesively. These two traits require the teams to have a divergent and a convergent thinking simultaneously. There needs to be a good balance. The team dynamics invariably result in conflicts among team members. While some amount of conflict (ideational conflict) is desirable in NPD teams to be creative as a group, relational conflicts (or discords among members) could be detrimental to teamwork. Team communication truly reflects these tensions and team dynamics. In this research study and paper, we explore the social network analysis of team communication to define and identify computational patterns of interaction and compare them with team’s assessed conflict level.

We’ve identified and defined four distinctive patterns of interaction (Central member pattern, Subgroup and Aloof member pattern, Isolate member pattern and Pendant pattern). These patterns are then correlated to the level of conflict in teams. The patterns are found to capture the signatures of high and low to medium conflict in teams with a high level of accuracy.
A Proposal for a Prototyping Method Focused on Communication That Increases Economies of Prototyping
Authors: Mitomi Keita , Ikenoue Tomoko , Takizawa Keita , Mitsuhashi Toshiharu
Published: November 2022

In recent years, studies on efficient strategies in prototyping have been accelerated in number. Among them, studies on the economies of prototyping, which analyze the relationship between the gained value of prototyping and its fidelity, are being conducted. However, the types of prototyping that improve the economies of prototyping are yet to be revealed. Also, it has been indicated that generating communication in prototyping can lead to performance improvement. Therefore, this study focused on communication during the prototyping process and analyzed the economies of prototyping at a private company. First, the economies of prototyping were explored using 27 prototyping and their gained values and fidelity for the private company. Next, each prototyping was categorized using a prototyping category centered on communication to indicate trends in economies of prototyping in each category. Furthermore, Pearson’s product-moment correlation coefficient was calculated to grasp the data relationship between the economies of prototyping and communication. Considering the results of the above analyses, we proposed a prototyping method that improves the economies of prototyping. Specifically, we proposed the technique of “prototyping that involves external stakeholders early on in the development stage and shortens expended time” as this leads to enhanced economies of prototyping. This study suggests the possibility of improving the economies of prototyping by consciously implementing “prototyping in a way that reduces the time required while involving external stakeholders at an early stage.”
Modal Synthesis Method for Inter-Blade Dry-Friction Surface Angle Design of Turbine Wheel for Vibration Suppression
Authors: Pesek Ludek , Snabl Pavel , Prasad Chandra Shekar
Published: November 2022

New calculation approach based on modal synthesis method is proposed for evaluation of structural and dry-friction damping effect on self-excited vibrations due to aero-elastic instability in the bladed turbine wheels. The aerodynamic excitation arises from the spatially periodical flow of steam through the stator blade cascade. The self-excited aero-elastic forces of blades are described by Van der Pol model. The proposed method as reduced order method (ROM) simplified approach is computationally efficient solution allowing to estimate effect of many important blade cascade parameters of such a complex non-linear mechanical system. The paper is aimed at the narrow frequency range of nozzle excitation and on the case when a slip motion is prevailing in the contacts. The method is applied herein to an industrial turbine wheel design with 66 blades. For evaluation of damping effect, the tie-boss and shroud couplings are applied. Therefore, neighboring blades are interconnected by rigid arms that are on one side fixed to one blade and are in friction contact on their free side with the other blade. Static normal contact forces are prescribed in contact point pairs at the initial state. Due to relative normal motions in contacts, the prescribed contact forces vary in time. Friction forces in contacts are driven by the modified Coulomb friction law. The effect of the angles of inter-blade contact surfaces on the wheel dynamics and on level of friction damping is discussed.
Metareasoning Approaches for Thermal Management During Image Processing
Authors: Dawson Michael K. , Herrmann Jeffrey W.
Published: November 2022

Resource-constrained electronic systems are present in many semi- and fully-autonomous systems and are tasked with computationally heavy tasks such as neural network image processing. Without sufficient cooling, these tasks often increase device temperature up to a predetermined maximum, beyond which the task is slowed by the device firmware to maintain the maximum. This is done to avoid decreased processor lifespan due to thermal fatigue or catastrophic processor failure due to thermal overstress. This paper describes a study that evaluated how well metareasoning can manage a Raspberry Pi 4B’s central processing unit (CPU) temperature while it is performing image processing (object detection and classification) on the Common Objects in Context (COCO) dataset. We developed and tested two metareasoning approaches: the first maintains constant image throughput, and the second maintains constant expected detection accuracy. The first approach switched between the InceptionV2 and MobileNetV2 image classification networks with a Single Shot Multibox Detector (SSD) attached. The second approach was tested on each network for a range of parameter values. The study also considered cases that used the system’s built-in throttling method to control the temperature. Both metarea-soning approaches were able to stabilize the device temperature without relying on throttling.
Identifying Potentials of Product Personalization
Authors: Kuhl Juliane , Krause Dieter
Published: November 2022

Adapting components and modules within the context of product personalization promises exact fulfillment of individual customer requirements. However, personalization should only be used where it is valuable and manageable. The design of product architectures with personalizable modules has so far received little methodological support. Therefore, a method for personalization-oriented product design is to be developed in the long term. In a first step, it is important to systematically derive potentials for personalization, which can then be examined in terms of value and workload.

The aim of this paper is to identify indicators that promote personalization. Additionally, it will be presented how these indications could be applied for a product family. Based on an analysis of the aim of personalization as well as research on variety indicators in the literature and a comprehensive analysis of product families where personalization is already applied, personalization indicators are derived. Personalization is particularly relevant where engineer-to-order solutions are used before, for components already having a high variety and where fit and appearance play a role. With the help of an adapted Module Interface Graph (MIG), the analysis results can be documented and visualized in order to derive personalization potentials. Thus, the approach presented in this article represents a significant element of personalization-oriented product architecture design.
An Exploration of Teachers’ Competencies in Interdisciplinary Engineering Education
Authors: Feng Xiaoqi , Hölttä-Otto Katja
Published: November 2022

Engineering education requires competent teachers to facilitate students’ interdisciplinary learning in solving complex problems and engage students in interdisciplinary teamwork. While most studies in the literature have focused on identifying or measuring students’ competencies, few studies have focused on teachers. What teacher competencies are essential for effective interdisciplinary teaching in engineering education? To answer this question and contribute to teacher development and improve student learning, this study analyzes teachers’ competencies in interdisciplinary engineering education. We conducted qualitative interviews with 14 teachers in engineering education with diverse disciplinary backgrounds in engineering, design, technology, science and business. Thematic analysis is used to highlight teachers’ social, affective, and pedagogical competencies. The results show that all three areas of competencies are considered to be essential for teachers. Teachers appear to have a good level of social competency, as they recognize the importance of effective communication with students and co-teachers across disciplines, as well as with industry partners. Teachers also exhibit a decent level of affective competency through their motivation for learning. In addition, we found that the lack of interdisciplinary-related knowledge added more difficulties for teachers to gain pedagogical competency in interdisciplinary engineering education. The results of this study can be used for designing teachers’ professional development programs related to interdisciplinarity.
A Deep Long Short-Term Memory Network for Bearing Fault Diagnosis Under Time-Varying Conditions
Authors: Zhou Kai
Published: November 2022

The fault diagnosis of bearing in machinery system plays a vital role in ensuring the normal operating performance of system. Machine learning-based fault diagnosis using vibration measurement recently has become a prevailing approach, which aims at identifying the fault through exploring the correlation between the measurement and respective fault. Nevertheless, such correlation will become very complex for the practical scenario where the system is operated under time-varying conditions. To fulfill the reliable bearing fault diagnosis under time-varying condition, this study presents a tailored deep learning model, so called deep long short-term memory (LSTM) network. By fully exploiting the strength of this model in characterizing the temporal dependence of time-series vibration measurement, the negative consequence of time-varying conditions can be minimized, thereby improving the diagnosis performance. The published bearing dataset with various time-varying operating speeds is utilized in case illustrations to validate the effectiveness of proposed methodology.
Automated and Customized CAD Drawings by Utilizing Machine Learning Algorithms: A Case Study
Authors: Villena Toro Javier , Tarkian Mehdi
Published: November 2022

This paper describes a methodology for automation of measurements in Computer-Aided Design (CAD) software by enabling the use of supervised learning algorithms. The paper presents a proof of concept of how dimensions are placed automatically in the drawing at predicted positions. The framework consists of two trained neural networks and a rule-based system. Four steps compound the methodology. 1. Create a data set of labeled images for training a pre-built convolutional neural network (YOLOv5) using CAD automatic procedures. 2. Train the model to make predictions on 2D drawing imagery, identifying their relevant features. 3. Reuse the information extracted from YOLOv5 in a new neural network to produce measurement data. The output of this model is a matrix containing measurement location and size data. 4. Convert the final data output into actual measurements of an unseen geometry using a rule-based system for automatic dimension generation. Although the rule-based system is highly dependent on the problem and the CAD software, both supervised learning models exhibit high performance and reusability. Future work aims to make the framework suitable for more complex products. The methodology presented is promising and shows potential for minimizing human resources in repetitive CAD work, particularly in the task of creating engineering drawings.
Mechanical Design and Experimental Validation of a Novel Five-Bar Mechanism With Variable Topology
Authors: Vaculik Tanner , Slaboch Brian , Rodriguez Luis A.
Published: November 2022

This paper introduces a novel one degree of freedom five bar Type II mechanism with variable topology (MVT). A Type II MVT is a mechanism that can change its topology due to a change in its kinematic pair or representative orientation. The advantages of Type II MVTs are that they are lightweight, have low power consumption, and reduce the mechanism’s required number of actuators. This new mechanism also has the advantage of combining the characteristics of both a four-bar and a crank-slider mechanism into a one DOF mechanism that can achieve an end-effector trajectory that is not possible by using either a four-bar or crank-slider. The mechanism is intended as a general-purpose mechanism that could be used across many industries; however, a pick and place application will be presented as an illustrative example to show the mechanism’s utility for a practical application. The mechanism’s topology was analyzed using kinematic diagrams. A prototype mechanism was built, and experimental data shows that the mechanism achieves excellent position tracking, making this mechanism one of the first ever fully automated MVTs.
A Study of Machine Learning Framework for Enabling Early Defect Detection in Wire Arc Additive Manufacturing Processes
Authors: Surovi Nowrin Akter , Hussain Shaista , Soh Gim Song
Published: November 2022

This paper presents the study on the performance of a variety of proposed time-domain acoustic features-based frameworks for the detection of geometrically defective print segments during the Wire Arc Additive Manufacturing (WAAM) process. Specifically, we investigate into a variety of acoustic features, namely the Root Mean Square of Pressure (RMSP), Energy, Mean Amplitude, Kurtosis, Zero Crossing Rate (ZCR), Skewness, Crest Factor and Peak-to-peak, and print process parameters, namely Torch Speed (TS) and Wire Feed Rate (WFR) combined with Machine Learning (ML) frameworks for detecting geometrically defective print segments. Experiments carried out on Inconel 718 show that among the studied frameworks, using acoustic features and process parameters with Random Forest (RF) performs best in terms of F1 score at 89%, while using acoustic features and process parameters with Support Vector Machine (SVM) performs best in picking out defective segments based on the Confusion Matrix. These findings serve as our first step in developing an intelligent sensing system for the early identification of defective beads in the WAAM printing process, so that appropriate intervention can be implemented to save printing resources and material costs. In addition, the proposed approach has the advantage of detecting defects within a more localized region for more targeted intervention.
The Foot in Walking - Towards Developing a Constrained Model of Stance Phase Dynamics
Authors: Renjewski Daniel , Lipfert Susanne , Günther Michael
Published: November 2022

Bipedal walking is the most prevalent form of human locomotion — versatile, robust, and efficient. However, it is a form of motion few other animals share, and plantigrade bipedalism is fairly unique to our species. Plantigrade feet play an important role in shaping the fundamental gait dynamics, and understanding their role unlocks a huge potential for biomechanical insights for locomotion research, gait rehabilitation, and humanoid robotics. Still, a comprehensive functional model of the foot, which ties the fundamental dynamics of walking to the interaction of foot and ground, does not yet exist due to the inherent mechanical complexity.

In this paper, we present a set of assumptions in detail to simplify the development of a mathematical description of the foot’s motion during stance phase in human walking. These assumptions are validated using experimental data. We can show that the complex motion of the foot can be ultimately reduced to a simple rotation, which allows the formulation of a single degree of freedom equation of motion that ties proximal dynamics and constraints into a planar foot model of dynamic interactions.
A Quantitative Approach and an Open-Source Tool for Social Impacts Assessment
Authors: Walters Justin , Mirkouei Amin , Makrakis Georgios Michail
Published: November 2022

Social impact analysis urgently needs attention in various sectors due to its critical roles in enhancing sustainability benefits. Properly assessing social impacts requires a consistent set of guidelines and requirements to reduce the practitioners’ bias. Life cycle assessment (LCA) is a widely recognized method that can be utilized to quantitatively assess three dimensions of sustainable development in an integrated manner. This study proposes an open-source tool for social impact analysis, that particularly evaluates work environment health impacts, following the LCA-defined principles, framework, requirements, and guidelines for sustainability assessment. The proposed tool in this study can quantify the social impacts and determine the categories that are being affected either negatively or positively. In addition, this study provides an overview of the existing social impact assessment approaches, identifies the critical challenges of the current techniques, and highlights opportunities for continued research to achieve more effective solutions. As of yet, a reliable and open-source tool has not been achieved to help decision-makers in both academia and industry. Thus, we developed a web-based tool for assessing social impacts in eight domains by allowing the use of multiple metrics and comparing two processes.
Mixed Plastic Waste Conversion to Value-Added Products: Sustainability Assessment and a Case Study in Idaho
Authors: Albor Galo , Mirkouei Amin , Struhs Ethan
Published: November 2022

Value-added products from petroleum-based wastes (e.g., mixed plastics) have shown great potential to address sustainability challenges, such as global waste management and environmental pollution from petroleum-based products. However, recycling mixed plastic wastes (MPW) has been debated for producing renewable materials and value-added products, such as C10-C50 liquid hydrocarbon compounds. This study investigates the economic feasibility and sustainability benefits of producing MPW pyrolysis oil (p-oil), using a transportable refinery unit. The presented approach was evaluated by conducting a case study in southeast Idaho, USA. A multi-criteria decision-making model consisting of techno-economic analysis and life cycle assessment is utilized to evaluate the proposed pathway. The techno-economic analysis estimates the total cost of MPW to value added products supply chains. The life cycle assessment (LCA) evaluates the negative environmental impacts of MPW-to-products life cycle. LCA assesses the global warming potential for a 100-year time horizon. The p-oil production cost per metric ton is $228, while the total emission is 2,262 kg CO2 per 100 metric tons of MPW. The results indicate that on-site operation can reduce MPW management and carbon footprint. Based on the results, MPW conversion to liquid hydrocarbon products using the mobile pyrolysis conversion process can address the supply chain sustainability challenges and lead to sustainable production.
Experimental Verification of Pulse Shaping in Elastic Metamaterials Under Impact Excitation
Authors: Dorgant Greg , DeLima Washington , Leamy Michael J.
Published: November 2022

In this paper, we develop a procedure to design, fabricate, and verify pulse shapers using elastic metamaterials under impact excitation as applied to the Split Hopkinson Pressure Bar (SHPB) test. The SHPB test, a fundamental dynamic test for over 70 years, has incorporated pulse shaping as a key feature which we re-examine through the use of elastic metamaterials. Elastic metamaterials, which exhibit properties atypical of their constitutive elements, hold promise for expanding on conventional pulse shaping abilities and improve the capabilities of the SHPB test. We first design the pulse shaper by numerically optimizing its geometry using finite element analysis. The pulse shaper consists of repeated unit cells which are based on a combination of a phononic crystal and a local resonator. Then, we fabricate and test pulse shaper candidates to validate the procedure efficacy. We incorporate an iterative element to this procedure such that we learn from inaccuracies in input force and material properties to converge on an appropriate pulse shaper. We carry-out this procedure by designing pulse shapers fabricated from 3D-printed PLA to achieve an extended dwell acceleration pulse shape. In experimental impact tests, we verify that the procedure results in ramp-up, acceleration dwell, and ramp-down comparable to that predicted, effectively confirming the efficacy of the presented procedure.
Does Empathising With Users Contribute to Better Need Finding?
Authors: Li Jie , Hölttä-Otto Katja
Published: November 2022

In user-centered product design, empathizing with users is an effective way to step into their shoes. Ideally, it helps designers understand user needs better and in more depth. However, the degree to which accurate empathic understanding contributes to needfinding and what exactly constitutes a ‘good’ need remain unclear. In this study, 13 designers watched a standard stimulus contextual user interview, performed an empathic accuracy task, and identified user needs based on the video. The needs identified from all participants were affinitized and summarized into a set of 23 user needs. They were scored for overall need quality, consisting of importance and satisfaction with current solutions as well as for need depth and latency. Designers’ empathic understanding was measured as the accuracy with which they could infer users’ mental contents and emotional tone. The designers’ emotional tone accuracy correlated with their performance in identifying important unfulfilled needs and needs where the users were most satisfied with existing solutions. However, no correlations were found for the empathic understanding of mental contents. In addition, need depth or latency was not found to correlate with any empathic understanding measure. These results are among the first to link empathy and designers’ performance in needfinding, but further studies are necessary to replicate and validate the results.
Sensitivity Study With Sobol’ Indices in Planar Multistable Mechanisms
Authors: Dold Edward J. , Voglewede Philip A.
Published: November 2022

This paper introduces the application of Sobol’ sensitivity indices to the design of toggle mechanisms. Sobol’ indices measure how the variance of the inputs (mechanism parameters) affect the variance of the output (potential energy). The kinematics of the mechanism are calculated to determine the mechanism parameters and potential energy. The indices reveal which parameter has the greatest effect on the variance of the potential energy, pointing designers to the area of the mechanism that should be the focus of improvement. Calculation and interpretation of Sobol’ indices are applied to three examples to show that the results are in line with engineering intuition. The applicability of the indices to any mechanism and their influence on design decisions are also shown. The benefits of the index being normalized are discussed along with methods of reducing computational cost.