bluuebunny/crossref_metadata_2025_split huggingface dataset:
Gene expression profiling reveals transcription factor networks and subgenome bias during Brassica napus seed development
Authors: Khan Deirdre , Ziegler Dylan J. , Kalichuk Jenna L. , Hoi Vanessa , Huynh Nina , Hajihassani Abolfazl , Parkin Isobel A. P. , Robinson Stephen J. , Belmonte Mark F. 
Published: November 2021

SUMMARYWe profiled the global gene expression landscape across the reproductive lifecycle of Brassica napus. Comparative analysis of this nascent amphidiploid revealed the contribution of each subgenome to plant reproduction. Whole‐genome transcription factor networks identified BZIP11 as a transcriptional regulator of early B. napus seed development. Knockdown of BZIP11 using RNA interference resulted in a similar reduction in gene activity of predicted gene targets, and a reproductive‐lethal phenotype. Global mRNA profiling revealed lower accumulation of Cn subgenome transcripts relative to the An subgenome. Subgenome‐specific transcription factor networks identified distinct transcription factor families enriched in each of the An and Cn subgenomes early in seed development. Analysis of laser‐microdissected seed subregions further reveal subgenome expression dynamics in the embryo, endosperm and seed coat of early stage seeds. Transcription factors predicted to be regulators encoded by the An subgenome are expressed primarily in the seed coat, whereas regulators encoded by the Cn subgenome were expressed primarily in the embryo. Data suggest subgenome bias are characteristic features of the B. napus seed throughout development, and that such bias might not be universal across the embryo, endosperm and seed coat of the developing seed. Transcriptional networks spanning both the An and Cn genomes of the whole B. napus seed can identify valuable targets for seed development research and that ‐omics level approaches to studying gene regulation in B. napus can benefit from both broad and high‐resolution analyses.
High-Temperature Dynamic Mechanical Properties Characterization of Polymer Coatings via Nanoindentation
Authors: Matsuda Yusuke , Samadi-Dooki Aref , Cen Yinjie , Vasquez Gisela , Bu Luke 
Published: November 2021

Abstract
Polymer coatings are widely used in industrial applications. The mechanical properties of these polymer coatings are known to vary with temperature and deformation rate. The characterization of the dynamic mechanical properties of these coatings at high temperatures via traditional uniaxial testing is challenging often due to their brittleness and small size. In this paper, the mechanical properties of polymer coatings are reported with emphasis on their dynamic mechanical properties at temperatures up to 280 °C characterized by a dynamic nanoindentation technique with a sharp indenter tip. Nanoindentation was used to characterize the mechanical response with emphasis on dynamic mechanical properties of polymer coatings enclosed in a high-temperature stage. To verify the method, the viscoelastic properties of a reference polyethylene terephthalate polyester film were also characterized by uniaxial cyclic tensile testing which exhibited an excellent agreement with the proposed technique. The proposed nanoindentation method can be applied to other polymer coatings and thin films that are used in applications at high temperatures.
Experimental Study of the Sealing Performance of Metal on Polymer Conical Seals
Authors: Goltsberg R. , Gilad M. 
Published: November 2021

Abstract
This study describes an experimental investigation of the sealing performance of a metallic conical poppet and a polymeric annular seat with a round sealing edge. The experiments were carried out with nitrogen. The influence of the radius of the sealing edge and its surface roughness, on the sealing performance, is evaluated. Generally, it was found that for a given load, as the radius of the edge decreases, the maximum contact pressure increases, and the sealing performance improves. In addition, a lower surface roughness yielded better sealing for a given load. Surprisingly, a range of radii was identified where a smaller radius would have worse sealing compared to a larger radius under the same maximum contact pressure. It is suggested that this is due to a secondary effect of the sealing width on the sealing performance. It was shown that the contact width is influential only for relatively low loads, while for higher loads, the main contributors are the maximum contact pressure and surface roughness. This can imply that for a limited range of compressive loads, an optimal radius of the sealing edge exists, for best sealing performance.
Enhanced Thermal Conductivity of Composite Materials by Filling Sheet and Fiber Under Effect of Filler Contact
Authors: Wang Xiaojian , Wang Liangbi 
Published: November 2021

Abstract
The most common nongranular fillers are sheet and fiber. When they are distributed along the heat flux direction, the thermal conductivity of the composite increases greatly. Meanwhile, the filler contact also has a large effect on the thermal conductivity. However, the effect of filler contacts on the thermal conductivity of the composite with directional fillers has not been investigated. In this paper, combined effects of the filler contact, content, and orientation are investigated. The results show that the effect of filler orientations on the thermal conductivity is greater than filler contacts in the low filler content, and the exact opposite in the high filler content. The effect of filler contacts on fibrous and sheet fillers is far greater than cube and sphere fillers. This rule is affected by the filler contact. The filler content of 8% is the ideal percolation threshold of composites with fibrous and sheet fillers. It is lower than cube filler and previous reports. The space for the thermal conductivity growth of composites with directional fillers is still very large. The effect of the interfacial thermal resistance should be considered in predicting the thermal conductivity of the composite under high interfacial thermal resistances (&gt;10−4).
Boosting the Model Discovery of Hybrid Dynamical Systems in an Informed Sparse Regression Approach
Authors: Novelli Nico , Lenci Stefano , Belardinelli Pierpaolo 
Published: November 2021

Abstract
We present an efficient data-driven sparse identification of dynamical systems. The work aims at reconstructing the different sets of governing equations and identify discontinuity surfaces in hybrid systems when the number of discontinuities is known a priori. In our approach, we first focus to identify the switches between the separate vector fields. Then, the dynamics among the manifolds are regressed by making use of the model discovery algorithm of Brunton et al. [1]. The reconstruction of the discontinuity surfaces comes as the outcome of a statistical analysis implemented via symbolic regression with small clusters (micro-clusters) and a rigid library of models. This allows to identify all the many possible switch points that are clustered to determine the actual discontinuity surfaces. The performances of the method are tested on two numerical examples, namely, a canonical spring–mass hopper and a free/impact electromagnetic energy harvester. These applications are characterized by the presence of a single and double discontinuity, respectively. The analyses demonstrate that in the supervised approach, i.e. where the number of discontinuities is preassigned, we are capable to determine accurately both discontinuities and set of governing equations. It is found a great improvement in time of computation reaching the maximum achievable reliability that outperform existing data-driven identification approaches for hybrid systems.
Generative Optimization for Automatic Creation of 3D CAD Platforms
Authors: Quintero-Restrepo William F. , Smith Brian K. , Ma Junfeng 
Published: November 2021

Abstract
The efficient creation of 3D CAD platforms can be achieved by the optimization of their design process. The research presented in this article showcases a method for allowing such efficiency improvement. The method is based on the DMADV six sigma approach. During the Define step, the definition of the scope and design space is established. In the Measure step, the initial evaluation of the platforms to be improved is done with the help of a Metrics framework for 3D CAD platforms. The Analyze Step includes the identification and optimization of the systems’ model of the process based on the architecture and the multiple objectives required for the improvement. The optimization method used that is based on evolutionary algorithms allows for the identification of the best improvement alternatives for the next step. During Design step of the method, the improvement alternatives are planned and executed. In the final Verification step, the evaluation of the improved process is tested against the previous status with the help of the Metrics Framework for 3D CAD platforms. The method is explained with an example case of a 3D CAD platform for creating metallic boxes for electric machinery.
Fractional Order Filter Discretization With Marine Predators Algorithm
Authors: Ates Abdullah , Chen YangQuan 
Published: November 2021

Abstract
In this study, discrete time models of continuous time fractional order filters are obtained by using the Marine Predators Algorithm (MPA).
Marine Predators optimization algorithm is a population-based heuristic method. This method is inspired by the hunting behavior of marine predators. The algorithm works on three basic phases. These phases occur according to the difference or equality of the velocity of the prey and the predator. As it is known, uniform distribution is generally used in stochastic based optimization algorithms. However, in the MPA method, Brownian and Levy distributions are also used as well as uniform distribution.
First, continuous time frequency responses of fractional order filters are generated. Then, fourth order discrete time filters are designed that can give similar responses with generated continues time filter frequency responses. Ten parameters were optimized for the design of fourth order discrete time filters numerator and denominator. The Marine Predators method’s results are compared with the results of the Fractional order Darwinian Particle Swarm Optimization (FODPSO) algorithm, from which discrete time filters are obtained for two fractional order continuous time filter models. In this way, it has been shown comparatively that the Marine Predators Algorithm can be used in real engineering problems and can do filter discretization better.
Printability and Fidelity of Protein-Enriched 3D Printed Foods: A Case Study Using Cricket and Pea Protein Powder
Authors: Chirico Scheele Stefania , Hoque Mohammed Naimul , Christopher Gordon , Egan Paul F. 
Published: November 2021

Abstract
3D food printing has received high attention in personalized meal production and customized food designs in recent years due to its potential advantages over traditional food manufacturing methods. A current challenge in 3D food printing is the design of extrudable food materials that enable customized shape fabrication and retention. Additives such as starches and gums have been employed to improve food printability, however, these often detrimentally affect taste, texture, and nutrients. Our study explores the printability and shape fidelity of mashed potatoes when adding protein-rich cricket and pea protein powders. Different percentages of these additives (5%, 15%, and 30%) with varied water to protein ratios (0, 1, 2, and 3) were added to 100g of mashed potatoes. Mashed potatoes with the addition of cricket powder and pea powder provided the highest fidelity prints for water to additive ratios of 2 and 3, respectively. Rheological testing demonstrated these high-fidelity prints had complex modulus values ranging from 15Pa to 25Pa. Trade-offs were explored between print fidelity, complex modulus, and protein content for mashed potatoes with cricket protein that highlighted the relative trade-offs in 3D food printing recipes. These findings demonstrate that a design space including shape fidelity, printability, and nutritional profile provides rich trade-offs for promoting user satisfaction and health, thereby providing designers new opportunities to leverage 3D food printing to provide value for consumer needs and health.
Learning From Insects to Increase Multi-Agent System Resilience: Functional Decomposition and Transfer to Support Biologically Inspired Design
Authors: Hernandez Isabella V. , Watson Bryan C. , Weissburg Marc , Bras Bert 
Published: November 2021

Abstract
Resilience is an emergent property of complex systems that describes the ability to detect, respond, and recover from adversity. Much of the modern world consists of multiple, interacting, and independent agents (i.e. Multi-Agent Systems). However, the process of improving Multi-Agent System resilience is not well understood. We seek to address this gap by applying Biologically Inspired Design to increase complex system resilience. Eusocial insect colonies are an ideal case study for system resilience. Although individual insects have low computing power, the colonies collectively perform complex tasks and demonstrate resilience. Therefore, analyzing key elements of eusocial insect colonies may offer insight on how to increase Multi-Agent System resilience. Before the strategies used in eusocial insects can be adapted for Multi-Agent Systems, however, the existing research must be identified and transferred from the biological sciences to the engineering field. These transfers often hinder or limit biologically inspired design. This paper translates the biological investigation of individual insects and colony network behavior into strategies that can be tested to increase Multi-Agent System resilience. These strategies are formulated to be applied to Agent-Based Modeling. Agent-Based Modeling has been applied to many Multi-Agent Systems including epidemiology, traffic management, and marketing. This provides a key step in the design-by-analogy process: Identifying and decoding analogies from their original context. The design principles proposed in this work provide a foundation for future testing and eventual implementation into Multi-Agent Systems.
On MBS Constraints and Projections
Authors: Pfeiffer Friedrich 
Published: November 2021

Abstract
Constraints in multibody systems are usually treated by a Lagrange I - method resulting in equations of motion together with the constraint forces. Going from non-minimal coordinates to minimal ones opens the possibility to project the original equations directly to the minimal ones, thus eliminating the constraint forces. The necessary procedure is described, a general example of combined machine-process dynamics discussed and a specific example given. For a n-link robot tracking a path the equations of motion are projected onto this path resulting in quadratic form linear differential equations. They define the space of allowed motion, which is generated by a polygon-system.
A Novel Dual-Mass Accelerometer Exploiting Mode Localization in Electrostatically Coupled Resonators
Authors: Lyu Ming , Zhao Jian , Kacem Najib , Liu Pengbo 
Published: November 2021

Abstract
A novel dual-mass accelerometer is proposed while exploiting the phenomenon of mode localization in two electrostatically coupled resonators with an adjustable coupling strength. The external inertial forces are transmitted differentially to the resonators in term of axial load change through the two levering mechanisms, breaking the balanced state and resulting in a drastic change in the amplitudes of the two resonators. Based on the Euler Bernoulli theory, the governing equations of the coupled system are derived and numerically solved. The sensitivity in term of relative shift of amplitude ratio can be improved by 4 orders of magnitude compared to frequency shift. Finally, the effect of the quality factor on the sensor dynamics has also been investigated, and the results show that it only affects the vibration magnitude of the resonators while operating below the critical amplitude.
Differential Utility: Accounting for Correlation in Performance Among Design Alternatives
Authors: Jolini Sahar , Hazelrigg George A. 
Published: November 2021

Abstract
Recognizing expected utility as a valid design criterion, there are cases where uncertainty is such that this criterion fails to distinguish clearly between design alternatives. These cases may be characterized by broad and significantly overlapping utility probability distributions. Not uncommonly in such cases, the utility distributions of the alternatives may be highly correlated as the result of some uncertain variables being shared by the alternatives, because modeling assumptions may be the same across alternatives, or because difference information may be obtained by means of an independent source. Because expected utility is evaluated for alternatives independently, maximization of expected utility typically fails to take these correlations into account, thus failing to make use of all available design information. Correlation in expected utility across design alternatives can be taken into account only by computing the expected utility difference, namely the “differential expected utility,” between pairs of design alternatives. However, performing this calculation can present significant difficulties of which excessive computing times may be key. This paper outlines the mathematics of differential utility and presents an example case, showing how a few simplifying assumptions enabled the computations to be completed with approximately 24 hours of desktop computing time. The use of differential utility in design decision making can, in some cases, provide significant additional clarity, assuring better design choices.
Fast Cutter Location Surface Computation Using Ray Tracing Cores
Authors: Ishii Daiki , Inui Masatomo , Umezu Nobuyuki 
Published: November 2021

Abstract
By using the cutter location (CL) surface, fast and stable computation of the cutter path for machining complicated molds and dies can be realized. State-of-the-art graphics processing units (GPUs) are equipped with special hardware named ray tracing (RT) cores dedicated to image processing (called ray tracing) for 3D computer graphics. Using RT cores, it is possible to quickly compute the intersection points between a set of straight lines and polygons. In this paper, we propose a novel CL surface computation method using the RT core. The RT core was originally designed to accelerate 3D computer graphics processing. For the development of software using RT cores, it is necessary to use the OptiX application programming interface (API) library for computer graphics. We demonstrate how to use the OptiX API in the development of software for CL surface computations. Computational experiments were carried out, and it was confirmed that it is possible to obtain the CL surface based on a very high-resolution Z-map several times faster than the depth buffer-based method, which has been considered to be the fastest to date.
SuperMeshing: A Deep Learning Method for Boosting Mesh Density in Numerical Computation Within 2D Domain
Authors: Xu Handing , Nie Zhenguo , Xu Qingfeng , Liu Xinjun 
Published: November 2021

Abstract
Due to the limit of mesh density, the improvement of the spatial resolution of numerical computation always leads to a decrease in computing efficiency. Aiming at this inability of numerical computation, we propose a novel method for boosting the mesh density in finite element method (FEM) within 2D domain. Based on the von Mises stress fields of 2D plane-strain problems computed by the FEM, this method utilizes a deep neural network named SuperMeshingNet to learn a non-linear mapping from low mesh-density to high mesh-density in stress fields, and realizes the improvement of numerical computation accuracy and efficiency simultaneously. We adopt residual dense blocks into our mesh-density boost model – SuperMeshingNet to extract abundant local features and enhance the prediction capacity. The results indicate that SuperMeshingNet is able to effectively increase the spatial resolution of the von Mises stress fields under the multiple scaling factors: 2X,4X,and8X. Compared with the targets, the relative error of SuperMeshingNet is 2.44%, which shows better performance than the interpolation methods. Besides, SuperMeshingNet reveals an astonishing strength in predicting the maximum stress value. We publicly share our work with full detail of implementation at https://github.com/zhenguonie/2021_SuperMeshing_2D_Plane_Strain.
Systems Thinking Assessment: A Method Through Computer Simulation
Authors: Arnold Ross D. , Wade Jon P. , Bayrak Alparslan Emrah 
Published: November 2021

Abstract
This paper presents a novel assessment method for Systems Thinking and its supporting competencies. Systems Thinking is a key component in engineering education, providing students with the means to explore, understand, and design engineered systems both holistically and in terms of the relationships between their components, which can be both technical and non-technical. The assessment method described in this paper is implemented as a software simulation of a domain-agnostic system to support integration of Systems Thinking into engineering education. The simulation was tested with a group of beta-testers and then fully deployed online as a freely available tool for a 6-month experimental period. The pool of volunteer participants included students and mixed professionals from a diverse set of geographical, educational, and career backgrounds. Results of the assessment show success at both evaluating Systems Thinking Maturity as a whole, and at assessing complex facets of Systems Thinking that have eluded assessment in prior methods. The tool shows promise at evaluating competencies within all four Systems Thinking domains — Mindset, Content, Structure, and Behavior. These domains contain key systemic skills such as the ability to recognize interconnections and feedback loops, see non-linear causal relationships, and understand dynamic behavior. When examined holistically through multiple regression analysis, participants’ scores in the 11 assessed competencies show a moderate to high ability to predict their levels of overall Systems Thinking performance in the simulation. The results also reveal previously unknown dependencies and strengths of relationships between Systems Thinking competencies.
Vibration Analysis of Beams Using Alternative Admissible Functions With Penalties
Authors: Kateel Srividyadhare , Baddour Natalie 
Published: November 2021

Abstract
Assumed mode methods are often used in vibrations analysis, where the choice of assumed mode affects the stability and useability of the method. System eigenfunctions are often used for these expansions, however a change in the boundary conditions usually results in a change in eigenfunction. This paper investigates the use of Alternative Admissible Functions (AAF) with penalties for the vibration analysis of an Euler-Bernoulli beam for different boundary conditions. A key advantage of the proposed approach is that the choice of AAF does not depend on the boundary conditions since the boundary conditions are modelled via penalty functions. The mathematical formulation of the system matrices, and the effect of beam geometry changes on the computed natural frequencies and modeshapes are presented. The computed natural frequencies and mode shapes show an excellent agreement when compared with closed-form Euler-Bernoulli beam values. The study reveals that with an increase in the stiffness of the beam, the values of the penalties need to be increased. The results of this study suggest that boundary conditions, as well as beam geometrical parameters should be considered when selecting appropriate values of the penalties.