bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

AbstractBackgroundEvidence 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.AimsThis 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.MethodsA 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.ResultsUsual 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 ActionTIME 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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.