bluuebunny/crossref_metadata_2025 huggingface dataset:
Nature Versus Nurture: the Influence of Classroom Creative Climate on Risk-Taking Preferences of Engineering Students
Authors: Peng Aoran , Menold Jessica , Miller Scarlett R.
Published: November 2023

Engineering companies are increasingly placing more value on creativity and innovation as they are the key to solving wicked problems. However, engineers often avoid taking the risks that are necessary for innovation to occur due to the uncertainty associated with creative ideas. As design researchers and educators, it is crucial that we identify ways to foster creativity and innovation in engineering education and examine how our classroom practices may either restrict or encourage students’ willingness to take risks. This study makes a first attempt at answering these questions by investigating the role of creative climate in engineering education by exploring the relationship between individual preferences for creative risk-taking and perception of creative climate in the classroom, as well as the link between creative climate perception and individual creative-risk taking actions. Previous research has demonstrated a relationship between creative climate and creative risk-taking in other fields, but this study expands on those findings by examining this relationship in engineering classrooms. The results indicate a positive relationship between willingness to take risks and perception of creative climate, with creative climate influencing concept screening preferences. Class type and level were also shown to influence this relationship. These findings provide empirical evidence supporting the need for establishing a creative climate in engineering classrooms to support innovation.
2.5D Signage From Sheet Material With Orthogonal Cuts and Folds
Authors: Demaine Erik D. , Demaine Martin L. , Devadoss Satyan L. , Myers Perla , Parra Rubio Alfonso
Published: November 2023

Motivated by making signs with depth from bendable but thick sheet material such as metal or plastic, we analyze 2.5D structures manufacturable from a rectangular sheet by orthogonal cuts and folds (parallel to the sheet’s edges). We provide a practical universality algorithm for folding arbitrary pixelated 2.5D surfaces using many parallel cuts, which also supports subpixel features in one dimension. We develop two fonts that enable textual signs within this family of designs, and show real-world constructions from aluminum/polypropylene composite panels. We also study what is possible with just one cut, proving a necessary condition and developing a third font under this restriction.
An Enhanced Timed Elastic Band Method for Autonomous Navigation and its Collision Avoidance Reliability Analysis
Authors: Xi Zhimin
Published: November 2023

The timed elastic band (TEB) method is an optimization-based navigation algorithm which uses the idea of an elastic band to connect an agent’s current pose to its goal position. The method is composed of three main levels: i) a feasible global path, ii) deforming collision-free path in real time, and iii) a control method to move the robot along the path. Static and moving obstacles deform the elastic band but the agent will always be pulled toward the goal position while avoiding obstacles. The key issue is the required computation efficiency to calculate the collision-free path in real time under dynamically changing environment. In literature, various case studies have been implemented to demonstrate the performance considering various constraints. However, its collision avoidance reliability under safety critical navigation scenarios has not yet been studied. The major contributions of this paper are summarized as follows. Firstly, an enhanced version of the TEB (i.e., eTEB) is proposed to improve the computational efficiency by explicitly considering non-holonomic constraints of the TEB method. Secondly, collision avoidance reliability analysis of the eTEB method is systematically conducted considering non-reactive moving obstacles. Results indicate that the eTEB method is much more reliable than the DWA for head-on collision avoidance with a single non-reactive moving obstacle at a time.
Methods for Mapping Empirical Data to Authoritative Definitions for Additive Manufacturing Part Validation
Authors: Milaat Fahad Ali , Witherell Paul , Hardwick Martin , Yeung Ho
Published: November 2023

Traditionally, inspection and geometric dimensioning and tolerancing (GD&T) are deployed at the macroscale, where complete parts are tested to meet geometric and functional requirements. The additive manufacturing (AM) process is unique in that it is a digital process where the fabrication of the part at the macroscale is the result of a series of operations at micro and mesoscales. Subsequently any additively manufactured part is the aggregation of many points of localized fabrication, and these parts uniquely expose themselves to full volumetric inspection during part fabrication and postprocessing. While new methods have been developed for designers to communicate process definitions used for the fabrication of an AM part, methods to validate these specifications are lacking. This work will explore challenges in the validating against advanced part and process definitions. Leveraging the concepts of “authoritative product definition,” “digital twin,” and “time stepped commands,” novel methods, built on a “zero dimension” information model, will be proposed to validate AM parts at the macroscale using mesoscale and microscale measurements and observations. Specifically, new data representations in the Standard for the Exchange of Product model data Numerical Control (STEP-NC) are proposed for discretizing AM geometry and process definitions to achieve point-level controls. Through the facilitation of traceable information in authoritative data models, AM process qualification and part acceptance could be streamlined, and reliable and referential data for Digital Twin frameworks and real-time controls could be realized.
Open Quantum Models and Magnetoencephalography Derived Neural Correlates for Engineering Decision Dynamics
Authors: Mollan Calahan , Bowyer Susan , Pandey Vijitashwa
Published: November 2023

Human decision making affects all aspects of engineering practice and has been extensively studied in the literature. However, it has been observed that our decision-making behavior is not properly modeled using classical probability techniques. Consequently, alternative models have been explored with varying degrees of success. One of these approaches is based on using the mathematics of quantum mechanics to model human decisions, which has successfully explained paradoxes such as Ellsberg paradox, question order effect and violation of the law of total probability. In this paper, we explore the use of Open Quantum Systems to model decision making and propose their use in engineering decision making modeling and prediction. It is also proposed that such models can be better parameterized by analyzing associated brain activity using Magnetoencephalography (MEG). We select MEG as the imaging method of choice as it provides multiple advantages over widely used techniques of EEG and fMRI. The paper provides a framework for possible experiments that can be conducted to investigate the dynamics. We provide preliminary data on how this can be accomplished in practice and provide avenues for future research in this area.
Fifty-Five Prompt Questions for Identifying Social Impacts of Engineered Products
Authors: Mattson Christopher A. , Geilman Thomas B. , Cook-Wright Joshua F. , Mabey Christopher S. , Dahlin Eric , Salmon John L.
Published: November 2023

This paper introduces 55 prompt questions that can be used by design teams to consider the social impacts of the engineered products they develop. These 55 questions were developed by a team of engineers and social scientists to help design teams consider the wide range of social impacts that can result from their design decisions. After their development, these 55 questions were tested in a controlled experiment involving 12 design teams. Given a one-hour period of time, 6 control teams were asked to identify many social impacts within each of the 11 social impact categories identified by Rainock et al. [1], while 6 treatment groups were asked to do the same while using the 55 questions as prompts to the ideation session. Considering all 1271 social impacts identified by the teams combined and using 99% confidence intervals, the analysis of the data shows that the 55 questions cause teams to more evenly identify high quality, high variety, high novelty, impacts across all 11 social impact categories during an ideation session, as opposed to focusing too heavily on a subset of impact categories. The questions (treatment) do this without reducing the quantity, quality, or novelty of impacts identified, compared to the control group.
Characterizing Designs via Isometric Embeddings: Applications to Airfoil Inverse Design
Authors: Chen Qiuyi , Fuge Mark D.
Published: November 2023

Many data analysis and design problems involve reasoning about points in high-dimensional space. A common strategy is to embed points from this high-dimensional space into a low-dimensional one. As we will show in this paper, a critical property of good embeddings is that they preserve isometry — i.e., preserving the geodesic distance between points on the original data manifold within their embedded locations in the latent space. However, enforcing isometry is non-trivial for common Neural embedding models, such as autoencoders and generative models. Moreover, while theoretically appealing, it is not clear to what extent enforcing isometry is really necessary for a given design or analysis task. This paper answers these questions by constructing an isometric embedding via an isometric autoencoder, which we employ to analyze an inverse airfoil design problem. Specifically, the paper describes how to train an isometric autoencoder and demonstrates its usefulness compared to non-isometric autoencoders on both simple pedagogical examples and for airfoil embeddings using the UIUC airfoil dataset.

Our ablation study illustrates that enforcing isometry is necessary to accurately discover latent space clusters — a common analysis method researchers typically perform on low-dimensional embeddings. We also show how isometric autoencoders can uncover pathologies in typical gradient-based Shape Optimization solvers through an analysis on the SU2-optimized airfoil dataset, wherein we find an over-reliance of the gradient solver on angle of attack. Overall, this paper motivates the use of isometry constraints in Neural embedding models, particularly in cases where researchers or designer intend to use distance-based analysis measures (such as clustering, k-Nearest Neighbors methods, etc.) to analyze designs within the latent space. While this work focuses on airfoil design as an illustrative example, it applies to any domain where analyzing isometric design or data embeddings would be useful.
Tailored Imaging and Sensing Technologies Enabled by 3D Rapid Micro- to Nano-Fabrication
Authors: Salerni Anthony , Ruiz-Cadalso Daniel , Furlong Cosme
Published: November 2023

Micro- to nano-scale additive manufacturing has enabled the realization of 3D microstructures at sub-micron resolution for complex miniaturized optical and mechanical systems. There is a growing demand for integrable technologies and miniaturization in applications from biomedical diagnostics and environmental sensing to high-precision industrial tasks. The ability to rapidly prototype facilitates the development of custom-tailored components for such specialized applications. This paper presents three case studies highlighting developments utilizing two-photon direct laser writing (TP-DLW). The aim of this work is to provide a comprehensive overview of the current state-of-the-art fabrication tool for the purpose of miniaturizing imaging and sensing technologies.
Preliminary Design of a Deployable Optical Space Array Based on a Thickened Origami Flasher Pattern
Authors: Hunter Jared , Roubicek Clark , Stephen Mark , Magleby Spencer , Howell Larry
Published: November 2023

Performance of space-based optics could be greatly enhanced by using deployable origami-based arrays, which can offer a large aperture size relative to their stowed volume when compared to traditional technology, thus improving imaging quality.

In this work, we select, develop, and adapt the origami flasher pattern to serve as the foundation for a deployable array that shows promise for meeting stringent optical requirements. We apply a novel thickness accommodation technique, outline an approach for implementing the technique, improve stability by adjusting geometric characteristics of the pattern, and create an array of frames for housing optical elements in a co-planar configuration. Problems of non-rigid foldability in the flasher pattern are addressed. A prototype is created and tested. We find that the deployable flasher shows promise as an optical array. By following the guidelines in this work, more efficient and powerful optical arrays can be developed.
Experimental Modal Analysis of Half-Scale Business Jet Aft Fuselage Section
Authors: Sekijoba John M. , Mechefske Chris K.
Published: November 2023

Structure borne noise caused by the engine is a significant source of discomfort for business aircraft passengers. Aircraft with fuselage mounted engines have increased structure borne noise levels in the cabin due to the shortened vibration transmission path. The research objective is to reduce structure vibrations in the aircraft cabin, yielding reduced noise output and increased passenger comfort. Experimental modal testing can identify the dynamic properties of a system (i.e. natural frequencies, mode shapes) which can be used to validate finite element models. This information can be used to guide structural dynamic modifications, reducing vibration levels in the structure. This research involved using modal testing with an electromechanical shaker to excite a half-scale model aft fuselage section at the engine support yolks and measuring the response at the bulkhead. A half scale model was used to reduce the complexity and time cost of experimentation. The results from excitation at each driving point were compared. This paper details the fuselage construction, methodology of excitation, validation techniques, and results. Experimental modes within the engine operating range (50 Hz to 400 Hz) were identified. The RESF-RESF and FESF-FESF measurements yielded an average of 63% more correlated mode pairs than the RESF-FESF measurements. This was expected due to structural symmetry about the longitudinal mid-plane. The results provide a basis for verifying and updating a finite element model in future work.
Generating Large Datasets of Simplified Automotive Body-in-White Structures to Predict Springback Using Machine Learning
Authors: Bolar Abhishek Lokesh , Alawadhi Ibraheem , Ramnath Satchit , Kumar Prakash , Korkolis Yannis , Davidson Joseph K. , Shah Jami J.
Published: November 2023

Automotive structures are primarily made of flexible sheet metal assemblies. Flexible assemblies are prone to manufacturing variations like springback which may be caused due to non-isotropic material properties from cold rolling, springback in the forming process, and distortion from residual stresses when components are clamped, and spot welded. This paper describes the curation of a large data set for machine learning. The domain is that of flexible assembly manufacturing in multi stages: component stamping, configuring components into sub-assemblies, clamping and joining. The dataset is generated by nonlinear FEA. Due to the size of the data set, the simulation workflow has been automated and designed to produce variety and balance of key parameters. Simulation results are available not just as raw FE deformed (sprung back) geometries and residual stresses at different manufacturing stages, but also in the form of variation zones and fits. The NUMISHEET 1993 U-draw/bending was used a reference for tooling geometry and verification of the forming process. Additional variation in the dataset is obtained by using multiple materials and geometrical dimensions. In summary, the proposed simulation method provides a means of generating a design space of flexible multi-part assemblies for applications such as dataset generation, design optimization, and machine learning.
Multi-Fidelity Modeling for Dynamic Power Control and Optimization of Nuclear-Renewable Hybrid Energy Systems
Authors: Chung In-Bum , Wang Pingfeng
Published: November 2023

The attempt to utilize renewable energy that are not as stable as carbon-based power has led to hybrid energy systems (HES), where multiple generation sources are combined to supply the target sector. Nuclear-renewable hybrid energy system (N-R HES) is a promising technology that couples nuclear power plant to the renewable energy sources to send the generated power to the grid. Due to the fluctuations in the generation as well as the demand, an industrial process is typically connected to the system to utilize the produced surplus or byproducts. However, it is important to build a control strategy that can manage the power distribution between the grid and the industrial process. This study focuses on creating a multi-fidelity model to predict the appropriate control state for satisfying each demand on the given timescale of the nuclear renewable HES. A high-fidelity model was constructed using Simulink and a forward calculation was used as a low-fidelity model, where the data generated from the high-fidelity model are a nested set of the low-fidelity model’s input domain. The multi-fidelity surrogate model was trained using co-kriging method and the surrogate was tested on a synthesized example problem to validate its practicality in controlling a dynamic energy system.
A Multi-Objective Bayesian Optimized Human Assessed Multi-Target Generated Spectral Recommender System for Rapid Pareto Discoveries of Material Properties
Authors: Biswas Arpan , Liu Yongtao , Ziatdinov Maxim , Liu Yu-Chen , Jesse Stephen , Yang Jan-Chi , Kalinin Sergei , Vasudevan Rama
Published: November 2023

Optimization for different tasks like material characterization, synthesis, and functional properties for desired applications over multi-dimensional control parameter and function spaces need a rapid strategic search through active learning. However, in all cases prior to optimization, the target material properties are assumed known and fixed, which mostly deviates from real-world scenarios in material synthesis. This can be critical for running expensive experiments on new materials, when the experimental results are fuzzy for any scientific outcomes due to improper target setting, ultimately wasting time and cost. The failure rate and cost are even higher over exploring on multi-target space, where we want to learn the pareto among multiple properties, to jointly optimize during material synthesis for desired applications. To address the challenge, here we introduce the human-operator attempt flexibility in the active learning based automated experiment framework, with generating multiple human assessed targets through a voting-based recommender system during real-time microscope measurements over the large material image space, sequentially learn/update multiple desired targets through a weighting system, and adaptively search in multiple material properties functional space for non-dominated pareto discoveries to maximize the custom structural similarity based acquisition function. We term this a multi-objective Bayesian optimized human assessed multi-target generated spectral recommender systems (MOBO-HAM-SRS). The approach has been demonstrated to peizoresponse force spectroscopy of a ferroelectric thin film, exploring with different kernels and acquisition functions. This work shows an advancement towards human-AI collaborated automated experiments, steering optimization trajectories through human overpowering AI at the early stage when uncertainty is high and AI overpowering human at the later stage with rapid exploration towards optimal goal, following human-assessed multiple targets properties.
Enhancing Design Guidelines for Metal Powder Bed Fusion: Analyzing Geometric Features to Improve Part Quality
Authors: Bushra Jannatul , Budinoff Hannah D. , Luna Falcon Pablo , Latypov Marat
Published: November 2023

Additive manufacturing (AM) part quality relies on many factors, including part geometry that impacts both the manufacturability and resulting dimensional accuracy of the part. To improve the dimensional accuracy of AM parts, data-driven approaches can be utilized to explore the effect of different process parameters on both simple and complex geometries. However, to provide general design guidelines, it is necessary to develop models and tools that accurately predict geometry-driven distortion across a broad range of geometries, while also being user-interpretable. Identifying and analyzing common part features that contribute to geometrical deviations and using them to design better parts could improve AM part quality. In this paper, a Gaussian process regression surrogate model was trained using 21 geometric features (selected from a set of 92 shape descriptors) from 324 different axisymmetric parts to predict maximum part distortion and identify the features that impact part distortion the most. Validated high-fidelity finite element analysis simulations were used to determine the maximum distortion corresponding to each part. Our results show the surrogate model approach can accurately predict part distortion, with a predictive error of approximately 0.07 mm for the testing set. The findings of this study can have implications for the exploration of new part designs by adjusting these identified features or incorporating them as design rules in AM product designs.
Design of a Biologically Inspired Active Visual Communication Strategy for Robotic Applications
Authors: Cochran Bryan , Bras Bert
Published: November 2023

This research considers the development of a biologically inspired active visual communication strategy for use within Multiple Robot Systems. Robotic systems currently communicate using radio frequency (RF) broadcasts, but they cannot transition from one communication modality to another unlike biological organisms. Given the extent in which robotic platforms employ machine vision systems, robotic systems could benefit from implementation of a visual communication modality. Humans commonly employ visual and auditory communication modalities and actively use a variety of different methods of visual communication, but none have been applied to robotic communication. Within biology however, there are numerous active visual communication strategies that may be better suited for robotic applications as compared to current human systems. Biological organisms employ a variety of different communication strategies to allow communication to occur in different environments, and some organisms are capable of transitioning from one communication strategy to another if a situation necessitates it. in a variety of different environments. The work detailed in this article investigates biological visual communication methods to be used for the development of an active robotic visual communication system suitable for use within a Multiple Robot System. This research in this article will highlight an investigation and the subsequent functional decomposition of these communication systems. The decomposition will be used to develop the foundation of the robotic visual communication system in later work.
A Framework to Support Multilevel Robust Co-Design of Manufacturing Supply Networks
Authors: Baby Mathew , Guptan Akshay , Broussard Jacob , Allen Janet K. , Mistree Farrokh , Nellippallil Anand Balu
Published: November 2023

Decision-making in the design of Manufacturing Supply Networks (MSNs) is complex owing to the need to account for decisions made by groups at multiple levels and the interactions that include potential conflicts and uncertainties. Decisions are made based on information available from simulations that employ computational models. These models are abstractions of reality and are therefore sources of uncertainty. This necessitates that the focus be placed on design space exploration to identify a robust satisficing solution set. Hence, the need to support the efficient exploration of multilevel design/solution spaces, simultaneously.

Many of the frameworks that facilitate multilevel design employ optimization-based iterative approaches that are computationally expensive and ill-suited for design exploration. Frameworks that support robust design exploration are limited by their capability to support the efficient exploration of multilevel design/solution spaces, simultaneously. Hence, the requirement to facilitate the ‘multilevel robust co-design and solution space exploration’ of MSNs.

In this paper, we present a framework that allows designers to: i) model robust decision-making and their interactions across multiple levels, and ii) visualize and systematically carry out co-design exploration of multilevel design/solution spaces. In the framework, we combine a Preemptive formulation of coupled Decision Support Problem with robust design constructs and interpretable-Self-Organizing Maps (iSOM) based visualization to facilitate multilevel robust co-design. We use a steel MSN problem with decisions being made at two levels to test the framework. Using the problem, we demonstrate the utility of the framework in supporting designers in modeling multilevel decision-making under uncertainty and their interactions, and efficient co-design exploration of the robust design/solution spaces in MSNs. The proposed framework is based on information and decision flows, making it generic and capable of supporting designers in the robust co-design of multilevel systems.
Back-EMF Sensing of Linear Resonant Actuators for Detection of Tap-Induced Vibration
Authors: Schmidt Ryan , Guzman Melissa Hernandez , Schlagenhauf Franziska , Singhose William
Published: November 2023

This paper examines the dynamics, detection, and interpretation of back electromotive force (EMF) in linear resonant actuators (LRAs). LRAs are small motors that are commonly used to generate haptic signals in a wide variety of devices including phones, smartwatches, and video game controllers. LRAs use a coil attached to the motor chassis to generate a magnetic field. This magnetic field causes displacement of a magnetic mass that is attached to the LRA chassis using a spring. The spring acts to return the mass to its equilibrium position, causing it to vibrate. The physical design of an LRA produces dynamics that are similar to a mass-spring-damper system. Haptic signals are generated by moving the internal mass at frequencies that can be felt by the user. A possible application of sensing back-EMF in LRAs is detecting user taps on consumer electronic devices such as smartphones and smart watches. Active sensing, which requires driving the LRA with an alternating current, can be used for pressure and environment sensing but requires careful consideration of the frequency of the driving signal and sampling rate. Passive sensing such as tap, swipe, and motion sensing can be realized with limited current draw, but are difficult to utilize when the LRA is actively being used for generating haptic effects. This paper applies passive back-EMF sensing to detect taps and explores the classification of different tap intensities.
The Impact of COVID-19 on Mass Personalization Supply Chain Networks – A Qualitative Inquiry
Authors: Schuebert Bryce , Shah Devanshi , Mullis Jesse , Mozaffar Fatemeh , Morkos Beshoy
Published: November 2023

Mass personalization (MPP) is a novel production method that integrates the consumer into the design process, efficiently producing distinct, scalable products that satisfy latent consumer needs. Previous MPP research has focused on the Industry 4.0 manufacturing processes and technologies required to make personalization a feasible method. Inherently, MPP necessitates a configurable supply chain that accommodates fluctuating product requirements. However, there has been little effort to examine the resilience and robustness of MPP supply chain networks in instances of external disruptions such as the ongoing COVID-19 supply chain crisis. This research takes a formal qualitative approach to investigate three MPP focal firms and the pandemic’s impact on their supply chain network through interviews with industry leaders. In doing so, we address research questions centered on MPP robust and resilience against external disruptions. The study finds that faced COVID-19 disruptions, MPP firms with centralized, specialty suppliers were vulnerable, while firms with increased supplier redundancy, network transitivity, and amplified visibility were robust and resilient. Evidence indicates that MPP capabilities provided robustness advantages over other production methods. Further, This work discovered that MPP FFs were particularly vulnerable to COVID-19 disruptions when using non-redundant, centralized suppliers. However, FFs taking advantage of MPP technologies to increase visibility and supplier redundancy were robust to disruptions and quick to recover from interruptions.
Inverse Determination of Detailed Internal Flow and Temperature Fields From Wall Measurements
Authors: Aryal Kapil , Nair Vivek , Dennis Brian H.
Published: November 2023

A data driven reduced order model (ROM) formulation is presented that is both quick and computationally efficient in estimating field variables for multi-physics inverse estimation applications. The approach is based on training data obtained by performing high fidelity simulations for a design of experiments generated in parameter space. Proper Orthogonal Decomposition (POD) in the parameter space is used to extract the most efficient modes and its coefficients. The trajectories of the modal coefficients are first analyzed and based on this information, a feed-forward neural network is trained with parameters as input and the modal coefficients as output. The resulting ROM is applied to the prediction of steady full temperature and velocity field inside a cylindrical channel with a spherical heat source at the center of the channel. The input parameters are the temperature measurements at three points on the wall surface, or at locations very close to the heated sphere. This approach is valuable for applications where non-invasive measurements are available yet internal detailed information is desired. As a proof-of-principle, the method is applied to a steady flow problem in a 2-D axisymmetric geometry at various flow rates and heat loads. The black-box approach makes use of full order solutions obtained by the finite element package COMSOL. The predicted nodal temperatures and the predicted nodal velocities show very accurate agreements with FOM solutions. The POD-ANN ROM framework clearly captures the coupling relationship between temperature and velocity because the velocity field was predicted very accurately based only on temperature data from the sensors. The high recall speed enabled by the ANN allows for real-time prediction of FOM fields as a function of the input parameters. This framework proved to be extremely reliable even in the presence of high Gaussian noise in the measured data.
Machine Learning Based Deflection Prediction and Inverse Design for Discrete Variable Stiffness Units
Authors: Fu Jiaming , Guo Qianyu , Gan Dongming
Published: November 2023

Large deflection modeling is a crucial field of study in the analysis and design of compliant mechanisms (CM). This paper proposes a machine learning (ML) approach for predicting the deflection of discrete variable stiffness units (DSUs) that cover a range from small to large deflections. The primary structure of a DSU consists of a parallel guide beam with a hollow cavity that can change stiffness discretely by inserting or extracting a solid block. The principle is based on changing the cross-sectional area properties of the hollow section. Prior to model training, a large volume of data was collected using finite element analysis (FEA) under different loads and various dimensional parameters. Additionally, we present three widely used machine learning-based models for predicting beam deflection, taking into account prediction accuracy and speed. Several experiments are conducted to evaluate the performance of the ML models that were compared with the FEA and analytical model results. The optimal ML model, multilayer perceptron (MLP), can achieve a 7.9% maximum error compared to FEA. Furthermore, the model was employed in a practical application for inverse design, with various cases presented depending on the number of solved variables. This method provides a innovative perspective for studying the modeling of compliant mechanisms and may be extended to other mechanical mechanisms.
Three-Dimensional Symmetric Tossing Motion Prediction – A Pilot Study
Authors: Lee Seunghun , Yang James
Published: November 2023

Weight tossing is a complex motion that combines multiple manual material handling tasks. The combined motion reduces labor time but comes at the cost of increased physical strain. After the pandemic, workplace injuries have been emerging as a hot potato. The industry spent an astronomical budget on improving workplace safety. It is critical to investigate different strategies for tossing motion to prevent injury risks. Tossing motion prediction provides insights into the status of the human biodynamic system during tossing. This paper reported an optimization-based 3D symmetric tossing motion prediction with a forty-one degree of freedom digital human model. The general equation of motion is generated using forward recursive kinematics and backward recursive dynamics. The dynamic effort is the objective function to be minimized. Design variables are the control points of the B-spline for the joint angles. Both time-dependent and independent non-linear constraints were applied to the problem. Lab experimental data collection for tossing a sandbag to a target point was collected and used to evaluate the predicted motion. The validation result showed that the predicted motion had a good agreement with experimental data in major joint angles, but the ground reaction forces showed some discrepancies. Future work was discussed to improve this prediction technique.
Efficient and Accurate Real-Time Simulations of Coupled Mechanical Systems Using the Universal Hydraulic Surrogate
Authors: Khadim Qasim , Kurvinen Emil , Mikkola Aki , Orzechowski Grzegorz
Published: November 2023

The real-time simulation of coupled multibody and hydraulics systems can be challenging due to the complexity, accuracy, and non-linear behavior of the modeling methods and the computational requirements. To this end, this study introduces the universal hydraulic surrogate approach instead of the conventional lumped fluid theory to solve the dynamics of hydraulically driven systems. The universal hydraulic surrogate is prepared with an approximated force model and trained with the behaviors of lumped fluid theory using the Covariance Matrix Adaption Evolutionary Strategy (CMA-ES). The universal hydraulic surrogate is used to solve the dynamics of the hydraulically actuated four-bar mechanism. The performance of the universal hydraulic surrogate is compared with the lumped fluid theory in terms of numerical accuracy and computational efficiency. The maximum percent normalized root mean square error (PN-RMSE) in the states of the four-bar mechanism using a simple force model compared to the lumped fluid theory is 2.06 %. The simulation with the universal hydraulic surrogate approach is up to 45 times faster than the lumped fluid theory in the studied case. The proposed study will enhance the computational efficiency of the coupled multibody systems and enhances their use in artificial intelligence, neural networks, and machine learning applications for complicated industrial systems.
An Investigation Into an Approach for Automated Supply Chain Onboarding
Authors: Jelisic Elena , Ivezic Nenad , Kulvatunyou Boonserm , Charoenwut Perawit , Nikolov Ana
Published: November 2023

The COVID-19 pandemic has brought the cold chain for biopharmaceutical products to the spotlight. To preserve their structure and function, biopharmaceuticals such as cells, proteins and enzymes, RNA molecules, and RNA-based drugs (e.g., mRNA-based COVID-19 vaccines) require low temperatures for proper handling, storage, and transport. Due to the sensitivity of these products transported through cold chains and the lack of traceability, risks associated with the stability, efficacy, safety, tardiness, quality, and waste are significant. For this reason, the ability to quickly find, connect, and do business with a new supply chain partner, such as in cold chain partnering is essential. Many researchers suggested various approaches to improve the effectiveness of cold chains. However, standardization and interoperability remain open challenges. Motivated by the biopharma risks and the need for interoperability, this paper presents a preliminary investigation into a novel approach for automating the onboarding of new supply chain partners. Based on the analysis of previous efforts in the cold chain domain, the notion of external context is recognized. In addition, the paper introduces the notion of business context which is proposed as a basis for a novel approach to achieve agile supply chain via automatic supply chain partner onboarding. Cold chain networking is used as a specific case study. The investigation results show that, with identified improvements, the approach has a potential to increase the automation in supply chain integration.
Effect of Tool Profile Modification on the Loaded Contact Performance for Face-Hobbed Hypoid Gear
Authors: Wei Xinqi , Wang Yawen , Lim Teik
Published: November 2023

Tool profile modification of face-hobbed hypoid gears is widely used to reduce the meshing shock associated with edge contact during heavy load meshing of gears. It is necessary to study the effect of different modification amounts and modification types on gear meshing performance to determine the optimal modification settings. This paper presents a mathematical model for tool profile modification of face-hobbed hypoid gears. Then, a set of modification parameters is designed, and the tooth surface deviation distribution is analyzed based on the proposed model. Finally, the effect of different modification parameters and types on the meshing performance is investigated based on a semi-finite element contact analysis model. The results show that the appropriate tool profile modification types and amounts can eliminate the edge contact of gear under heavy load. The curved blade has a drum correction effect on the tooth surface, and the edge contact of the tooth surface can be effectively removed with the increase of the radius curvature, but the transmission error also increases. The Toprem can change the position of the contact pattern in the tooth height direction, but it cannot eliminate the stress concentration phenomenon, and the transmission error increases sharply with the increase of the length of the Toprem.
Beige is the color of keeping weight off
Authors: Wong Wei
Published: November 2023

A monocyte population induced by weight loss promotes white fat beiging to limit weight regain.
Boosting student performance with inclusive writing-to-learn assignments through graphic organizers in large enrollment undergraduate biology courses
Authors: Wallace Alison , Barosh Theresa , Brisch Ellen , Laybourn Paul , Balgopal Meena M.
Published: November 2023

Logistical challenges in large enrollment classes are often mentioned as obstacles to active learning. Writing is an integral part of being a scientist and is often one of the first tools considered by STEM instructors to increase student engagement, but iterative writing assignments in large classes require creativity on the part of the instructor. We found an association between writing-to-learn assignments designed to be consistent with inclusive learning pedagogies and student performance measures in a large enrollment undergraduate biology course. They provide ample opportunity for deliberate practice and inclusive engagement, components of the “heads and hearts” hypothesis posed to explain the variation in active learning impacts on the performance of minoritized students.
Supplemental Material: Geochemical mapping of lithospheric architecture disproves Archean terrane accretion in the Yilgarn craton
Authors: Smithies Robert , al. et
Published: November 2023

Tables S1–S3 and Figures S1–S3. 
Three-Component Synthesis of New C3-Substituted 5,6-Dihydropyrrolo[2,1-a]isoquinolines
Authors: Miftyakhova A. R. , Sidakov M. B. , Borisova T. N. , Fakhrutdinov A. N. , Titov A. A. , Sorokina E. A. , Varlamov A. V.
Published: November 2023

New C3-substituted 5,6-dihydropyrrolo[2,1-a]isoquinolines have been synthesized via three-com­ponent domino reaction of 1-aroyl-3,4-dihydroisoquinolines, dimethyl acetylenedicarboxylate, and CH acids in anhydrous acetonitrile under microwave irradiation at 130°C.
Role of pharmacists in the context of rare diseases: a scoping review protocol
Authors: Cunico Cássia , Leite Silvana Nair
Published: November 2023

Introduction

Rare diseases are chronic conditions, generally incurable, progressive and disabling, which may result in early death. Access to therapeutic products, both medicines and appropriate medical devices, is essential to prevent the progression of the disease and maintain the patients’ quality of life. Pharmacists can be part of health teams, in charge of guiding patients’ journey, monitoring pharmacotherapy and identifying risks. This scoping review aims to identify and summarise evidence on the role of pharmacists and its impact in the field of rare diseases.

Methods and analysis

The searches will be conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guideline for protocols. Three electronic databases will be consulted. Studies reporting on qualitative and/or quantitative data from any world region will be considered. There will be no language or initial time limit for studies inclusion, until December 2022. To be eligible for inclusion, studies must focus on the role pharmacists in clinical services aimed at promote the access to medicines, prevention and resolution of problems related to pharmacotherapy. No assessments of items’ quality will be made, as the purpose of this scoping review is to synthesise and describe the coverage of the evidence. Clinical, humanistic or economic outcomes from studies that meet the inclusion criteria will be included in the review. The analysis will synthesise the available evidence and may be able to push pharmaceutical practice forward, aiding professionals, educators and managers in the implementation of new approaches to better meet the needs of rare diseases and providing opportunities for future research.

Ethics and dissemination

Primary data will not be collected in this study and formal ethical approval is not required. The findings of this study will be disseminated through peer-reviewed publications and conference presentations.
Communication between consultants providing advice and referring physicians: a scoping review
Authors: Muftah Amira , Stempien James , Goodridge Donna , Watson Erin , Oyedokun Taofiq
Published: November 2023

Objective

Communication during consultations between referring and consultant physicians is often cited as a source of adverse events, medical error and professional incivility. While existing literature focuses on the role of referring physicians, few studies acknowledge the role of consultant physicians in enhancing communication during consultations. This scoping review aims to identify and synthesise available recommendations to enhance the communication practices of consultants during real-time consultations.

Design

A scoping review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews.

Data sources

Medline, EMBASE and PsycINFO databases were searched from inception to August 2022.

Eligibility criteria for selecting studies

English-language publications which describe recommendations, strategies or frameworks to improve the communication practices of consultant physicians during real-time consultations with referring physicians.

Data extraction and synthesis

The search strategy included the following concepts: consultation, physician, communication, interprofessional relations and best practice. Two authors independently performed each phase of title and abstract screening, full-text review and data extraction. Discrepancies were resolved by a third author. Extracted data were iteratively analysed and summarised thematically.

Results

Sixteen publications met the inclusion criteria. Synthesis of available recommendations identified organisation, expertise and interpersonal skills as three overarching and interconnected dimensions of communication demonstrated by consultants during effective consultations. Twelve studies identified interpersonal skills as being critical in alleviating the widespread professional incivility that is reported during consultations. Existing recommendations to improve the communication practices of consultants are limited as they lack standardised interventions and fail to comprehensively address all three elements identified in this review.

Conclusion

This scoping review synthesises available recommendations to improve the communication practices of consultant physicians during real-time consultations. An opportunity exists to develop communication tools or educational interventions based on the findings of this review to enhance interphysician consultation encounters.
Maternal serum levels of prokineticin-1 related to pregnancy complications and metformin use in women with polycystic ovary syndrome: a post hoc analysis of two prospective, randomised, placebo-controlled trials
Authors: Ujvari Dorina , Trouva Anastasia , Hirschberg Angelica Lindén , Vanky Eszter
Published: November 2023

Objective

Serum prokineticin-1 (s-PROK1) in the second and third trimester of pregnancy is positively correlated to preeclampsia, intrauterine growth restriction (IUGR) and preterm delivery. Women with polycystic ovary syndrome (PCOS) are prone to these adverse pregnancy outcomes. However, the contribution of PROK1 to the development of pregnancy complications and the effect of metformin and hyperandrogenism on s-PROK1 in PCOS have not been studied previously.

Design

This work is a post hoc analysis of two prospective, randomised, placebo-controlled trials.

Setting

Pregnant women with PCOS were included from 11 study centres in Norway.

Participants

From 313 women, 264 participated in the present study after exclusions due to dropouts or insufficient serum samples.

Intervention

Women with PCOS were randomly administered with metformin or placebo, from first trimester to delivery.

Primary and secondary outcome measures

s-PROK1 was analysed using ELISA at gestational week 19 and related to pregnancy complications, fasting insulin levels, homoeostatic model assessment for insulin resistance (HOMA-IR), testosterone, or androstenedione levels, metformin use, PCOS phenotype and hyperandrogenism.

Results

Maternal s-PROK1 in the second trimester did not predict pregnancy-induced hypertension, pre-eclampsia or late miscarriage/preterm delivery in women with PCOS. However, s-PROK1 was lower in women who used metformin before inclusion, both in those randomised to metformin and to placebo, compared with those who did not. s-PROK1 was also lower in those who used metformin both at conception and during pregnancy compared with those who used metformin from inclusion or did not use metformin at all. s-PROK1 was lower in hyperandrogenic compared with normo-androgenic women with PCOS.

Conclusions

Maternal s-PROK1 in the second trimester did not predict pregnancy complications in PCOS. Those who used metformin at conception and/or during pregnancy had lower s-PROK1. PCOS women with hyperandrogenism exhibited lower s-PROK1 compared with normo-adrogenic phenotypes.

Trial registration number

NCT03259919andNCT00159536.