bluuebunny/crossref_metadata_2025 huggingface dataset:
Familial Deaths and First Birth
Authors: Rackin Heather M. , Gibson‐Davis Christina M.
Published: November 2022

Motivated by the rise in premature mortality among working‐age adults, we examine the association between adult familial deaths and the transition to motherhood. Although many deaths can be disruptive, deaths that occur sooner than expected and to certain family members (e.g., mothers) may prompt changes in resources, time available for parenting, or psychological understandings in ways that change fertility behavior. Data come from the National Longitudinal Survey of Youth 1997 (N = 4,008 women aged 15–34). Using fixed effects to address selection, we compare the associations between recent off‐time deaths (mother, father, or sibling deaths) and on‐time deaths (grandparent deaths) on fertility. Women had higher odds of first birth when they recently experienced a sibling or maternal death compared to when they had not recently experienced that type of death. Effects of maternal and sibling deaths were statistically larger than the null effect found for grandparent death. The strong effect of sibling and maternal death on hastening first birth suggest that mortality may influence a psychological reevaluation of values vis‐à‐vis parenting and/or decrease psychological well‐being in ways that change sexual practices.
The endocast of Euparkeria sheds light on the ancestral archosaur nervous system
Authors: Fabbri Matteo , Bhullar Bhart‐Anjan S.
Published: November 2022

Understanding the evolution of the tetrapod brain is essential to trace the history of ecomorphological diversification of modern clades. While previous studies focused on the morphological transformation of the nervous system along the dinosaur–bird transition, little is known about the brain anatomy of archosauriformes and early archosaurs. Here, we describe the endocast of Euparkeria capensis, a small bodied, terrestrial archosauriform closely related to Archosauria, with the goal of resolving the current uncertainties surrounding the ancestral condition of the archosaurian nervous system. The endocast of Euparkeria is sigmoidal, with large olfactory bulbs, an expanded cerebral hemisphere and an elongated flocculus. We suggest that this pivotal taxon was an active predator with a remarkable olfactory acuity. Overall, the endocast of Euparkeria resembles the ones observed in phytosaurs, crocodilians and early dinosaurs, implying that modern crocodilians retain an archosaurian plesiomorphic brain morphology.
Combining ability of common bean (Phaseolus vulgaris) genotypes for root traits across diverse environments
Authors: Castiano Binaissa U. Levene , Kimurto Paul K. , Ojwang Pascal P. Okwiri
Published: November 2022

Root system architecture is important for common bean (Phaseolus vulgaris) adaptability to diverse environments. Beans employ complex adaptive root mechanisms for coping with multiple stresses in production environments. Understanding genetic control of root traits is central to improvement of common bean for adaptation to marginal environments. The objectives of this study were to (i) determine combining ability of root and agronomic traits and (ii) estimate the heritability and genetic correlation of root and agronomic traits in common bean. Four bean lines with superior root traits were crossed with four locally adapted varieties in a North Carolina II mating scheme to generate 16 crosses. The 16 F1s were selfed and advanced to F2 generation. Eight parents and their F2 progenies were evaluated in an alpha‐Lattice design with two replications. General and specific combing ability mean squares were significant (p ≤ .05) for all traits measured. General predictability ratios ranged from .47 to .68 across locations suggesting that both additive and non‐additive gene action modulate root traits and seed yield. Positive and significant (p ≤ .05) phenotypic and genetic correlations revealed significant association between root traits and yield. Moderate to high heritability estimates of between .43 and .67 were realized. Such estimates point to possible deployment of a successful selection programme. Genotype AFR398 displayed significant positive GCA effects among its crosses for both root and agronomic traits hence a potential candidate genotype for inclusion in a bean genetic improvement programme for marginal environments.
Burnout, mental health symptoms, and empathy in healthcare workers who care for children treated in a liver transplant center
Authors: Cansel Neslihan , Varol Fatma İlknur
Published: November 2022

Objective

The healthcare workers in pediatric liver transplantation units are exposed to various stresses; however, their burnout has not been clarified yet. This study aimed to determine burnout, psychological symptoms and empathy levels in healthcare workers, to examine the relationship between these variables and to investigate the predictors of burnout.

Method

This cross‐sectional study was conducted on healthcare workers in Turgut Özal Medical Center Pediatric Liver Transplant Institute. The participants filled out a questionnaire that investigating demographic and occupational characteristics, and including questions from Toronto Empathy Questionnaire, Maslach Burnout Inventory, and Brief Symptom Inventory.

Results

Fifty healthcare workers participated in the study. Based on moderate and higher severity scores, 48% of the participants were emotionally exhausted, 22% of them were depersonalized. All of them had a high perception of personal accomplishment. There was a correlation between psychological symptoms level and emotional exhaustion and personal accomplishment as well as empathy and personal accomplishment. Not choosing the profession and the department willingly, age, education level, marital status, duration of profession, unit, not receiving in‐service training, empathy, depressive symptoms and hostility scores predicted different aspects of burnout.

Conclusion

Considering that burnout and psychological stress lead to reduced professional abilities and the development of serious complications, the results obtained can guide managers on the measures to be taken.
Transparency and corruption: Measuring real transparency by a new index
Authors: Mungiu‐Pippidi Alina
Published: November 2022

Despite the salience of transparency in policy and democracy debates a global measurement of transparency has always been missing. In its absence, measuring the impact of transparency on accountability and corruption for a large number of countries has been difficult, with scholars using more or less adequate proxies. This paper introduces a new measurement of real transparency—the T‐index—using 14de factocomponents, based on direct observations of official websites in 129 countries and fivede jurecomponents, based on the transparency laws and conventions adopted. The resulting index is a measurement with very good internal and external validity and moderate precision. The paper argues thatde factotransparency must be considered alongsidede jure(legal) transparency if we are to judge the impact (or lack of) transparency against accountability and corruption, as a large implementation gap exists, in particular in poor countries, between legal commitments and real transparency. The T‐index has significant impact on both perception and objective indicators of corruption, including perceived change in corruption over time as measured by the Global Corruption Barometer. An analysis of outliers shows that high transparency alone is not sufficient to achieve control of corruption, especially in countries with low human development and poor rule of law, although transparency is a robust predictor of corruption with GDP controls. The data with all sources is available for download as T‐index 2022 dataset: DOI10.5281/zenodo.7225627and an interactive webpage developed for updates is available atwww.corruptionrisk.org/transparency.
An analysis of Fundeb's contribution to the quality of public education in Brazilian municipalities
Authors: Becker Kalinca Léia
Published: November 2022

This study analyzed the effect of the Fund for the Maintenance and Development of Basic Education and for the Appreciation of Education Professionals (Fundeb) on the National System of Basic Education Assessment (Saeb) results of Brazilian municipalities by estimating a spatial panel model with information from 2007 and 2017, which include the 10 years of the fund's implementation. The results indicated that the 10% increase in the Fundeb amount per student received by the municipality raised the Saeb score by approximately one point. Furthermore, the increase in the fund's value in neighboring municipalities contributed to increasing the municipality's Saeb score by approximately three points. These results are evidence in favor of the hypothesis that Fundeb has encouraged the management and adoption of good practices in municipal education, which influence the decisions and behavior of the neighboring municipalities’ managers to also improve their practices and results.
Molecular and serological analysis of the D variant in the Chinese population and identification of seven novel RHD alleles
Authors: Wen Jizhi , Jia Shuangshuang , Wang Zhen , Chen Jingwang , Liang Qianni , Wei Ling , Richard Gaëlle , Fichou Yann , Luo Guangping , Ji Yanli
Published: November 2022

Background

The molecular basis of the D variant phenotype in the Chinese differs greatly from that of the Caucasian. Adapting a specific D typing strategy to the spectrum of prevalent RHD variant alleles is necessary.

Study Design and Methods

Blood samples with ambiguous D phenotypes were collected in the Southern Chinese population. A special three‐step typing strategy was applied. First, the common DVI type 3 was identified from epitope profiles of D antigen. Then, another common weak D type 15 (RHD*845A) was identified by epitope profiles of D antigen and Sanger sequencing of RHD exon 6. Finally, the remaining D variants were genotyped mainly by Sanger sequencing. For the novel RHD alleles in the coding region and exon–intron junction, in vitro transfection and minigene splicing assays were performed, respectively. The anti‐D investigation was performed.

Results

DVI type 3 (65/253, 25.7%) and weak D type 15 (62/253, 24.5%) were common Chinese D variants, and RHD*960A, DFR, RHD*weak D type 25, 72, and 136 were frequent variant RHD alleles. Besides, twenty‐two sporadic and seven novel RHD alleles (RHD*188A; RHD*688C; RHD*782 T; RHD*1181C; RHD*165 T, 993A; RHD*148 + 3G > T and RHD*1227 + 5G > C) were identified. The deleterious effect of the novel RHD alleles on D antigen or mRNA expression was confirmed. Anti‐D was detected in two DVI type 3 pregnant women.

Discussion

The three‐step typing strategy provides an effective approach for Chinese D variant typing. It can be anticipated that commercially available RHD genotyping kits have limitations for testing Chinese D variants, as some of the frequent variants are not interrogated.
War in Ukraine: The options for Europe's energy supply
Authors: Aitken Christopher , Ersoy Erkal
Published: November 2022

Europe is likely to remain reliant on Russian gas in the short term. In the absence of this gas supply, the continent is likely to experience shortages and associated inflation. Immediate solutions include diversification of Europe's natural gas supply and the use of alternative fossil fuels, such as coal, but the latter would come with significant climate costs. Over a longer period, there is more scope for reshaping Europe's energy policy and diversifying its energy supply, but these would come at a cost. Expanding renewable energy capacity within Europe is critical. Fine‐tuning policy to balance climate objectives against the security of the energy supply will be difficult. To ensure energy security and emerge from this conflict greener, the answer may be renewable energy produced in Europe. Investment in renewables in developing countries, particularly in the continent's trading partners, also holds promise, as it would allow more natural gas imports into Europe while contributing to Europe's climate commitments.
A Chronology of Seeing
Authors: Abazeri Mariam
Published: November 2022

Participatory video (PV) is a collective videomaking practice used to identify and address shared issues among its participants. In this article, I discuss the promises and challenges of this process by examining a long‐term PV project carried out among young rural women in Kerman, Iran. The project, which took place over six years, offers a unique window into the multiple ways participants coupled PV with other social interventions to challenge and reassert gender norms and narratives in their community, inspire personal transformation, build communal networks, and engage in incremental social change.
Teachers’ language attitudes and production patterns in St. Kitts
Authors: Schmalz Mirjam
Published: November 2022

This study looks at language attitudes and production patterns of teachers in St. Kitts in the Eastern Caribbean, based on metalinguistic interviews conducted with nine teachers and one principal, as well as field notes from classroom observations. The analysis of these data shows generally positive attitudes towards Kittitian Creole (KC) amongst the teachers, who value KC for its cultural significance. However, KC was rather seen as ancillary in the education setting, with English taking centre stage. The classroom observations exhibit a majority use of standardised English by the teachers, with instances of KC in certain situations, such as for direct questions, scolding, or motivating, indicating ‘functional mixing’ (Youssef, 2014, p. 201) by teachers. This article provides first insights into de facto language policies in Kittitian classrooms, adding data from a lesser studied variety to the academic discourse on the role of English and Creole in Caribbean education.
Effectiveness of game‐based exercise interventions on modifiable cardiovascular risk factors of individuals with type two diabetes mellitus: A systematic review and meta‐analysis
Authors: Lim Ye Sheng , Ho Ben , Goh Yong‐Shian
Published: November 2022

Background

Sedentary lifestyles have been identified as a major risk factor for cardiovascular complications for individuals with type 2 diabetes mellitus (T2DM). Using video‐gaming components, game‐based exercise interventions can encourage these individuals to engage in regular physical activity (PA), given their favorable interactive interfaces, feedback, and on‐demand flow experiences.

Aim

To evaluate the effectiveness of game‐based exercise interventions, on modifiable cardiovascular risk factors, quality of life, and PA levels among individuals with T2DM.

Methods

Published and unpublished studies were retrieved from eight electronic databases and reference lists of the included studies. Articles included in this study were from the inception of the databases to January 2022. Two reviewers performed screening, quality appraisal, and data extraction independently. Meta‐analyses were conducted for the primary and secondary outcomes through RevMan. The protocol was registered on PROSPERO (CRD42022298894).

Results

Of the 11 selected studies involving 1045 individuals of both genders with T2DM, 9 were included in the meta‐analyses for glycosylated hemoglobin, blood pressure (BP), low‐density lipoprotein cholesterol, quality of life, and physical activity levels. The meta‐analyses reported very small to large effect sizes favoring game‐based exercise interventions. Large effect sizes were reported for systolic BP, diastolic BP, and quality of life measured through SF‐12 Mental Component Summary scores.

Linking Evidence to Action

Game‐based exercise interventions may improve modifiable cardiovascular risk factors, quality of life, and PA levels among individuals with T2DM. Supervision, PA levels, or motivation can affect the effectiveness of game‐based exercise interventions.
Developing Access to Digital Archaeology Data Resources in Ireland
Authors: Corns Anthony , O'Keeffe John , Swan Ronan
Published: November 2022

This article aims to summarise the current situation regarding digital archaeological archives in Ireland.
Coherent Flow Structures Near Tongue Region in a Centrifugal Fan With Forward-Curved Blades
Authors: Shaofei Wang , Haowen Xu , Zhongyang Qi , Chong Pan
Published: November 2022

The unsteady characteristics of the velocity field around the tongue of the volute in a centrifugal fan with forward-curved blades were investigated by time-resolved particle image velocimetry and mode decomposition analysis, i.e., proper orthogonal decomposition (POD) and spectral proper orthogonal decomposition (SPOD). Both POD and SPOD analysis reveal the existence of two types of unsteadiness in the flow field in the volute, one is the large-scale fluctuations at rotation frequency and its high-order harmonics in the main outlet flow, the other is due to the jet–wake structures at blade passing frequency in the cutoff clearance region. Additionally, SPOD analysis reveals a third type of disturbance, which is characterized as strip-like velocity structures at the intermediate frequency. The geometric and dynamic features of these coherent flow structures are inspected by the eigenfunction and their reduced-order reconstruction. In comparison with the POD analysis, the SPOD analysis enables the examination of the spatial pattern of each frequency component due to its dual-orthogonality nature. These structures direct impact the tongue, and thus might be responsible for the generation of strong wall-pressure fluctuation on the nose of the tongue. Due to the frequency mixing limitation of POD analysis, they cannot be easily identified from the leading-order POD modes. Furthermore, the frequency range of these three groups of disturbances, as well as their spatial extension, are determined via SPOD analysis and reduced-order reconstruction.
A Focused Regions Identification Method for Nonlinear Least Squares Curve Fitting Problems
Authors: Zhang Guanglu , Allaire Douglas , Cagan Jonathan
Published: November 2022

Important for many science and engineering fields, meaningful nonlinear models result from fitting such models to data by estimating the value of each parameter in the model. Since parameters in nonlinear models often characterize a substance or a system (e.g., mass diffusivity), it is critical to find the optimal parameter estimators that minimize or maximize a chosen objective function. In practice, iterative local methods (e.g., Levenberg-Marquardt method) and heuristic methods (e.g., genetic algorithms) are commonly employed for least squares parameter estimation in nonlinear models. However, practitioners are not able to know whether the parameter estimators derived through these methods are the optimal parameter estimators that correspond to the global minimum of the squared error of the fit. In this paper, a focused regions identification method is introduced for least squares parameter estimation in nonlinear models. Using expected fitting accuracy and derivatives of the squared error of the fit, this method rules out the regions in parameter space where the optimal parameter estimators cannot exist. Practitioners are guaranteed to find the optimal parameter estimators through an exhaustive search in the remaining regions (i.e., focused regions). The focused regions identification method is validated through a case study in which the Michaelis-Menten model is fitted to an experimental data set. The case study shows that the focused regions identification method can find the optimal parameter estimators and the corresponding global minimum effectively and efficiently.
SMART Standards: Modularization Approach for Engineering Standards
Authors: Luttmer Janosch , Ehring Dominik , Pluhnau Robin , Kocks Christine , Nagarajah Arun
Published: November 2022

Standards are a valuable source of knowledge in product development and support engineering activities throughout the entire product development process. However, against the industry-wide trend of digitization, the provision and usage of standards has not changed significantly over the last decade. To satisfy customer requirements, standards development organizations are increasingly interested in publishing their content outside of traditional formats such as print or PDF. One example is the content-based modularization rather than the provision of whole documents (Content-as-a-Service). This paper examines existing modularization approaches as well as their transferability towards the domain of standardization. Based on these approaches, a concept for the modularization of formulas and especially the development of a formula module is designed and elaborated. Therefore, descriptive elements of formulas are identified and structured. The module then serves as template for the future documentation of formulas in XML standards. Afterwards, the identified modules are integrated into an existing SMART standards expert system in order to demonstrate possible applications of content-based standard provision. Future work will investigate methods for the automatic extraction of the identified descriptive formula elements as well as their semantic modelling in knowledge graphs. Moreover, the described concept serves as starting point for future research in modularization of engineering standards.
Topology Optimization Realization of a Spatially Parallel Compliant Mechanism With Constant Motion Transmission Characteristics
Authors: Liang Kaixian , Zhu Dachang , Liu Jie
Published: November 2022

This paper presents a new topology optimization method of spatial compliant parallel mechanism. The constant motion transmission characteristic matrix of a special parallel mechanism is analyzed. Combining the matrix with topology optimization, a new multi-objective topology optimization formula of multiple input and output compliant mechanism is proposed. The strategy is capable of optimizing the compliant mechanism free of considering the replacement of rigid hinges by flexible ones, so as to obtain a compliant mechanism with higher motion accuracy and there is a linear mapping relationship between input and output. Through several numerical examples, it is verified that the compliant mechanism obtained by this method is isomorphic with the original parallel mechanism in kinematics.
Assessments and Evaluation Methods for Upper Limb Exoskeleton - a Literature Survey
Authors: Lee Seunghun , Xiang Yujiang , Xia Ting , Yang James
Published: November 2022

Exoskeleton technology has gained great interests in several fields including robotics, medicine, rehabilitation, ergonomics, and military. Especially, upper-limb exoskeletons are developed aiming to increase worker’s physical ability such as stability, force and power production and reduce biomechanical loads, working fatigue, which relieves overexertion risk. Extensive research has been conducted to assess existing and newly proposed exoskeletons, but they still have trade-off and user convenience issues to resolve. Therefore, the primary purpose of this paper is to review classification of the upper-limb exoskeletons and functional assessment, particularly regarding the complex interactions between human and exoskeleton. Secondly the paper is to provide insight in issues associated with the upper-limb exoskeletons. Finally, discussion on future directions for upper limb exoskeleton development and assessment is presented.
Optimal Position of Cameras Design in a 4D Foot Scanner
Authors: Tajdari Farzam , Eijck Christiaan , Kwa Felix , Versteegh Christiaan , Huysmans Toon , Song Yu
Published: November 2022

Optical motion capturing explains the three-Dimensional (3D) position estimation of points through triangulation employing several depth cameras. Prosperous performance relies on level of visibility of points from different cameras and the overlap of captured meshes in-between. Generally, the accuracy of the estimation is practically based on the camera parameters e.g., location and orientations. Accordingly, the camera network configurations play a key role in the quality of the estimated mesh. This paper proposes an optimal approach for camera placement based on characteristics of a depth camera D435i - Intel RealSense. The optimal problem includes a cost function that contains several minimisation and maximisation terms. The minimisation terms are distance of the cameras to the center of the scanning object, resolution error, and sparsity. And the maximisation terms are distance between each two pair of cameras, percent of captured point from an object, and the level of overlap between cameras. The object is designed based on practical experiments of human walking and is a bounding box around one step of dynamic foot work-space from heel strike posture to toe-off posture. The accuracy and robustness of the algorithms are assessed via experiment measurement, and sensitivity to the number of cameras is investigated. Accordingly, the experiment results determined that the scanning accuracy can be as high as 2.5 % based on a reference scan with a high-end scanner (Artec Eva).
Manufacturing Process Classification Based on Distance Rotationally Invariant Convolutions
Authors: Wang Zhichao , Rosen David
Published: November 2022

Given a design part, the task of manufacturing process classification identifies an appropriate manufacturing process to fabricate it. Our previous research proposed a large dataset for manufacturing process classification and achieved accurate results based on a combination of a convolutional neural network (CNN) and the heat kernel signature (HKS) for triangle mesh. In this paper, we constructed a classification method based on rotation-invariant shape descriptors and a neural network for point clouds, and it achieved better accuracy than all previous methods. This method uses a point cloud part representation, in contrast to the triangle mesh representation used in our previous work. The first step extracted rotation-invariant features consisting of a set of distances between points in the point cloud. Then, the extracted shape descriptors were fed into a CNN for the classification of manufacturing processes. In addition, we provided two visualization methods for interpreting the intermediate layers of the neural network. Last, the performance of the method was tested on some ambiguous examples and their performance was consistent with expectations. In this paper, we have considered only shape information, while non-shape information like materials and tolerances were ignored. Additionally, only parts that required one manufacturing process were considered in this research. Our work demonstrates that part shape attributes alone are adequate for discriminating between different manufacturing processes considered.
Digital Twin Approach to Build Predictive Maintenance Model and Its Case Study
Authors: Yang Wenqiang , Bao Xiangyu , Zheng Yu
Published: November 2022

Predictive maintenance is considered to be an effective strategy to optimize system operation. In the execution of increasingly complex tasks, efficient and intelligent management becomes crucial. As the basis of Digital twin (DT), predictive capabilities contribute to the value of systems and help describe their complex behavior. But the challenge in Digital twin model building is still exist, it cannot accurately reproduce the physical resources, and the introduction of error will lead to the differential extension of virtual system from physical space. The challenge is how to build Digital twin capabilities and reduce accumulative error at the same time. Based on the current research progress, this paper analyzed the existing challenges in realizing predictive maintenance capability driven by Digital twin, and then, it described the predictive control process with flow path and layer framework, In addition, the way of inserting the optimization algorithm for Digital twin was explored. Finally, a practical trajectory prediction problem was taken as a case study to effectively utilize the cyclic interaction mechanism and data fusion method of Digital twin, which can consider the offset cumulative signal, and correct the prediction state in real time. This research may provide the reference for Digital twin configuration and further study.
GraMMaCAD: Interactively Defining Spatially Varying FGMs on BRep CAD Models
Authors: Luu Thu Huong , Altenhofen Christian , Stork André , Fellner Dieter
Published: November 2022

Additive manufacturing (AM) has enabled control over heterogeneous materials and structures in ways that were not previously possible, including functionally graded materials (FGM). However, typical computer aided design (CAD) systems — mainly based on boundary representations (B-Rep) — are not built for modeling and representing spatially varying material information, which are the key features for designing FGM parts. While volumetric representations (V-Rep) such as tetrahedral or hexahedral meshes, trivariate splines or subdivision solids can hold the necessary FGM information, they are by default not compatible with existing CAD models. Thus, the capabilities of AM motivate enhancing CAD systems to support volumetric material allocation and distribution. We present an interactive, intuitive and efficient interaction concept based on grading features and transition functions for augmenting CAD models with smooth volumetric material gradients for multi-material fabrication using our system called GraMMaCAD (Graded Multi-Material CAD). This concept has the advantage of being extremely versatile, since the material property functions are left to the arbitrariness of the user, who is free to modify their definition as needed. By providing interactive preview modes, the system allows for efficient exploration of the volumetric gradation of the designed FGM part. We fabricated two functional examples that demonstrate the usability of the system and show the ability to quickly augment models with functionally graded material transitions.
Semantic Search With Sentence-BERT for Design Information Retrieval
Authors: Walsh Hannah S. , Andrade Sequoia R.
Published: November 2022

Managing and referencing design knowledge is a critical activity in the design process. However, reliably retrieving useful knowledge can be a frustrating experience for users of knowledge management systems due to inherent limitations of standard keyword-based searches. In this research, we consider the task of retrieving relevant lessons learned from the NASA Lessons Learned Information System (LLIS). To this end, we apply a state-of-the-art natural language processing (NLP) technique for information retrieval (IR): semantic search with sentence-BERT, which is a modification of a Bidirectional Encoder Representations from Transformers (BERT) model that uses siamese and triplet network architectures to obtain semantically meaningful sentence embeddings. While the pre-trained sBERT model performs well out-of-the-box, we further fine-tune the model on data from the LLIS so that it learns on design engineering-relevant vocabulary. We quantify the improvement in query results using both standard sBERT and fine-tuned sBERT over a keyword search. Our use case throughout the paper is to use queries related to specific requirements from a NASA project. Fine tuning the sBERT model on LLIS data yields a mean average precision (MAP) of 0.807 on queries based on information needs from a real NASA project. Results indicate that applying state-of-the-art natural language processing techniques, especially when fine-tuned using engineering data, to design information retrieval tasks shows significant promise in modernizing design knowledge management systems.
Holistic Optimal Design of Face-Milled Hypoid Gearsets
Authors: Grabovic Eugeniu , Artoni Alessio , Gabiccini Marco
Published: November 2022

The aim of this paper is to showcase a holistic approach to design optimized spiral bevel and hypoid gearsets. The first step is the definition of the gear and pinion blanks starting from the basic transmission data. The second step is to synthesize the basic machine-tool settings required to cut the two toothed members. The gear machine-tool settings are obtained first, whereas the basic pinion settings are identified by minimizing the deviations of the pinion tooth surface from the gear conjugate member accounting for the presence of misalignments. The proposed novel identification strategy can handle all the higher-order motions while offering a remarkable speedup with respect to existing techniques. The result of the macro-geometry design phase is a conjugate spiral bevel or hypoid gearset.

As a last step, the design of the pinion micro-geometry is formulated as a multi-objective optimization problem where the obtained optimal ease-off is guaranteed to be manufacturable. To this end, an original strategy is devised where the search for the optimal tooth surface is driven by the coefficients of a free-form polynomial representation of its micro-topography. However, the tooth geometry evaluated by the loaded tooth contact solver is actually its closest manufacturable analogue. A fully worked out numerical test case substantiates the whole method.
Aspect-Sentiment-Guided Opinion Summarization for User Need Elicitation From Online Reviews
Authors: Han Yi , Moghaddam Mohsen , Suthar Meet Tusharbhai , Nanda Gaurav
Published: November 2022

Extracting and analyzing informative user opinion from large-scale online reviews is a key success factor in product design processes. However, user reviews are naturally unstructured, noisy, and verbose. Recent advances in abstractive text summrization provide an unprecedented opportunity to systematically generate summaries of user opinions to facilitate need finding for designers. Yet, two main gaps in the state-of-the-art opinion summarization methods limit their applicability to the product design domain. First is the lack of capabilities to guide the generative process with respect to various product aspects and user sentiments (e.g., polarity, subjectivity), and the second gap is the lack of annotated training datasets for supervised learning. This paper tackles these gaps by (1) devising an efficient and scalable methodology for abstractive opinion summarization from online reviews guided by aspects terms and sentiment polarities, and (2) automatically generating a reusable synthetic training dataset that captures various degrees of granularity and polarity. The methodology contributes a multi-instance pooling model with aspect and sentiment information integrated (MAS), a synthetic data assembled using the results of the MAS model, and a fine-tuned pretrained sequence-to-sequence model “T5” for summary generation. Numerical experiments are conducted on a large dataset scraped from a major e-commerce retail store for sneakers to demonstrate the performance, feasibility, and potentials of the developed methodology. Several directions are provided for future exploration in the area of automated opinion summarization for user-centered product design.
Two-Dimensional Lineup Design of Intermediate Functional Products With Enumerative Optimization of Combinatorial Mini-Max Optimality
Authors: Fujita Kikuo , Ono Naoki , Nomaguchi Yutaka
Published: November 2022

As mechanical products have become further complicated and diversified, integrative design of their components, such as motors, fans, and pumps, through commonalization has become essential for shorter lead time and more cost-saving behind the mainstream. This paper names those products as intermediate functional products and proposes their lineup design method. Scale and coefficient identify their performance. Thus, they are arranged over a two-dimensional space of design requirements to minimize the lead time and production cost with maintaining performance optimality. The former cannot be measured quantitatively before actual production. Thus, minimizing the process complexity is demanded. The latter criterion translates into optimizing the worst-case performance across the requirement space. Those understanding leads to a design method by enumerating possible commonalization patterns, exclusively arranging the lineup for each pattern optimally, and investigating the best lineup through trade-off analysis. While the lineup arrangement under a pattern takes a mini-max type formulation, its optimization is performed based on monotonicity analysis efficiently. This paper demonstrates the formulation and procedures through an application to universal motors.
Human-Centric Facility Layout and Production Planning in Mixed Reality
Authors: Baroroh Dawi Karomati , Chu Chih-Hsing
Published: November 2022

A production system with manual operations tends to have variations in system performance induced by individual differences of human workers. Adjusting the production parameters to meet a specific target under such uncertainty can be time-consuming without the support of advanced simulation tools. Therefore, this research proposes a novel idea of facility layout and system simulation in mixed reality (MR) that facilitates production planning involving human operators. A production planning tool is implemented as an MR application to verify the feasibility of this idea using a test case of logistic facility design. In this case, a manager adjusts the current facility and production parameters designed for one product in order to operate the logistics of two different products that must achieve a given production target. An operator is responsible for manually sorting, transporting, and placing the products for storage. Both are participating in the system simulation as different roles via HoloLens. The MR planning tool provides decision-making support for the manager to quickly complete the adjustment for the target. The planning results show that the tool outperforms traditional simulation software in terms of planning quality and flexibility. Thus, this work verifies the feasibility of MR as a new approach to realizing human-centric production system design.
Towards a Porosity Aware Stochastic Framework for Computing Apparent Mechanical Properties of Additively Manufactured Parts
Authors: Iliopoulos Athanasios , Stewart Colin A. , Birnbaum Andrew J. , Steuben John C. , Rowenhorst David J. , Michopoulos John G.
Published: November 2022

The presence of pores in parts generated via metal Additive Manufacturing (AM) may substantially impact their mechanical performance. To understand the resulting performance, it is essential to identify the quantitative relationship between the size, shape, and location of the pores and the mechanical properties of the manufactured part. To obtain insight into this relationship, we have initiated the development of a stochastic framework that takes as input digital microscope images of AM part sections and provides as output the distribution of mechanical properties of interest such as the apparent (in the global sense) yield stress and the stress-strain response. The distribution of these pores has a semi-stochastic nature, which depends on the process type, process parameters, material type, and AM path. Firstly, we calculate various pore metrics using digital image processing techniques. The metrics are related to geometric characteristics, such as the distance of the pore from the specimen surface. Subsequently, we generate a two-dimensional distribution based on non-parametric principles. We use this distribution to sample exemplified geometries and develop multiple Finite Element Models (FEM). Then we perform virtual experiments to calculate the non-linear stress-strain response for each FEM. The results are then distributed to bins in order to generate distributions and histograms of mechanical properties of interest. We demonstrate the framework by applying it on an AM-produced conformal pressure vessel to show its capacity in computing the distribution of relevant quantities.
Spatial-Temporal Modeling Using Deep Learning for Real-Time Monitoring of Additive Manufacturing
Authors: Ko Hyunwoong , Kim Jaehyuk , Lu Yan , Shin Dongmin , Yang Zhuo , Oh Yosep
Published: November 2022

Real-time monitoring for Additive Manufacturing (AM) processes can greatly benefit from spatial-temporal modeling using deep learning. However, existing, deep-learning approaches in AM are case-dependent, and therefore not robust to changes of control inputs and data types. As AM is dynamic and complex, this limitation leads to a lack of systematic, DL approaches for real-time monitoring of AM, which involves a large number of varying control parameters and monitoring data. To address the challenge, this paper introduces a novel approach for developing spatial-temporal models to monitor Laser Powder Bed Fusion (LPBF) processes using deep learning on real-time, monitoring data. First, we present a novel model for representing in-situ-monitoring and control data of LPBF at multiple scales. Second, from the model, we extract spatial-temporal relationships for in-situ monitoring of LPBF processes. Third, we present a spatial-temporal, modeling approach using the architecture of convolutional long short-term memory (LSTM) to monitor the spatial-temporal relationships and detect anomalies. A case study used convolutional LSTM Autoencoder on optical, melt-pool-monitoring data, one of the most widely adopted data types in in-situ monitoring of LPBF. The data was generated from an LPBF testbed called the Additive Manufacturing Metrology Testbed. The novel, learning approach enables spatial-temporal modeling of AM dynamics directly from real-time data for the monitoring of varying AM environments. This methodical approach provides the potential to fuse realtime data at multiple, spatial-temporal scales.
Work Process Transfer Reinforcement Learning: Feature Extraction and Finetuning in Ship Collision Avoidance
Authors: Wang Xinrui , Jin Yan
Published: November 2022

The advancement of artificial intelligence and machine learning technologies has led to significant changes in work processes. The computer agents are applied to perform not only routine and repetitive jobs but also highly complex tasks such as driving a car and steering a ship. Given the sensory information of the environment, a reinforcement learning method has been applied for agents to learn how to perform complex tasks by trial and error through interactions with the environment. To overcome the issues such as limited and sparse training data, researchers are attempting to reuse the previously learned knowledge in new task situations. In this paper, we investigate how feature extraction and finetuning methods can be combined to allow computer agents to perform transfer reinforcement learning more effectively and efficiently in the context of ship collision avoidance. Taking a computer simulation-based empirical approach, we first develop a ship collision avoidance gameplay environment by introducing the own ship, target ships, and the base case and target cases. A deep neural network including four convolutional layers and three fully connected layers is devised for work process feature capturing through deep reinforcement learning. The case study results have shown that features do exist in work processes, and they can be captured and reused. The similarity between the source case and the target case is a key factor that determines how the feature extract and finetuning methods should be combined for effective task results and efficient learning processes.
Requirements Mapping of a High-Powered Rocket System to Explain Solution Similarities Across Generations
Authors: Jacobson Lindsey , Ferguson Scott
Published: November 2022

Designers have historically used existing solutions as a baseline for modifying and improving the next generation solution. However, it is also possible that certain design solutions are pursued by designers irrelevant of previous design history, suggesting that there is some dominant system architecture for a given set of requirements. While the reason for system architecture similarities can never be determined with certainty without consulting designers, it is possible to speculate about why design decisions might have been made, especially when certain system architectures and design elements have dominated a space for so long. The NASA SL Challenge serves as a compelling area for study, given that NASA SL vehicle and recovery performance requirements are similar each year, while payload integration and payload function requirements differ each year. Student design teams are tasked with designing a new launch vehicle and payload each year to meet all of the requirements. Despite yearly changes in payload requirements, some teams’ vehicle architectures remain largely unchanged, prompting a question of whether these design solutions are inherently used because of the nature of the design problem or if it is a reuse of previous design solutions. To investigate this question, we relate system requirements to vehicle elements using a DSM-style requirements mapping process. These relationships are then translated into tree structures that are centered around a single element/requirement. Finally, the tree structures are analyzed to determine why certain design solutions may have been selected based on entanglement between requirements. These mappings are used to identify areas of tension between requirements and independent variables within system architectures for a NASA SL team. Our analysis of the mappings allows us to suggest that certain elements of system architectures or mission profile may be more dominant, that is more favorable, based solely on the design problem definition. In NASA SL for example, teams could target a lower apogee because it aids in meeting entangled recovery requirements. In future work we will explore how requirements mapping can be used to identify dominant architectures during the conceptual phase of design. Finally, based on observations made in this case study, there is evidence that requirements mappings could help guide the strategic placement of excess in a system. Future work is needed to determine how requirements mapping could support the placement of excess and to explore how to conduct requirements mapping in a streamlined and efficient way.
Gerotor Pump Simulation Modules for Enhancing Fluid Power Education
Authors: Azzam Israa , Dhar Sujan , Patil Veeranagouda , Maiti Dipak , Asunda Paul , Garcia Bravo Jose , Breidi Farid
Published: November 2022

Fluid power is one of the courses required for most engineering technology programs and thus enrolls more than 200 students annually. Studies show that fluid power modules related to hydraulic and pneumatic systems result in a relatively high W/D/F rate (Withdrawals, D and F grades), where many students struggle to connect the theory with practice. From a pedagogical perspective, this rate is due to the traditional laboratory syllabus that is insufficient to address the complexity of hydraulic and pneumatic systems. This work aims to enhance fluid power education by conducting a research study for testing and examining the efficacy of integrating state-of-the-art simulation courseware into the laboratory work of fluid power courses. For conducting the study, interfaceable Computational Fluid Dynamics (CFD) modules of a hydraulic Gerotor pump are developed and incorporated into the laboratory syllabus of the MET:23000 Fluid Power course at Purdue University. The CFD simulation modules were designed and implemented in the labs in collaboration with Simerics. A survey was designed and conducted with 93 students to measure their perceived learning of fluid power concepts and examine their attitudes toward the CFD simulation used for instruction. The survey outcomes revealed that the executed CFD simulation assisted the students in understanding the overall operation of the Gerotor pump. The results show that the simulation courseware supported students in acquiring the fundamentals of fluid power, which could assist them in conducting future engineering decisions. Besides the conceptual understanding, students were engaged in the lab modules. They enjoyed visualizing the variation in pressure contours, velocity vectors, and cavitation bubbles.