bluuebunny/crossref_metadata_2025 huggingface dataset:
Solitary intraventricular tumors in dogs and cats treated with radiotherapy alone or combined with ventriculoperitoneal shunts: A retrospective descriptive case series
Authors: Beckmann Katrin , Kowalska Malwina , Meier Valeria
Published: November 2022

Background

Intraventricular tumors are rare, optimal treatment is not defined. Symptomatic patients often exhibit life‐threatening hydrocephalus. With several months time‐to‐effect after radiotherapy (RT), increased intracranial pressure is concerning. This increase in pressure can be overcome by ventriculoperitoneal shunting (VPS).

Objectives

Retrospective evaluation of outcome and complications in dogs and cats with intracranial tumors treated with either RT or VPS/RT.

Animals

Twelve client‐owned cats and dogs.

Methods

Dogs and cats with symptomatic intraventricular tumors treated with definitive‐intent RT or VPS/RT were included in a retrospective, descriptive case series. Complications, tumor volume evolution, time‐to‐progression, and survival time were determined.

Results

Twelve animals were included: 1 cat and 5 dogs treated with single‐modality RT and 4 cats and 2 dogs treated with VPS/RT. Neurological worsening seen in 4/6 animals during single‐modality RT and 2/6 died during RT (suspected brain herniation). All dogs with VPS normalized clinically by the end of RT or earlier. Complications occurred in 4/6 animals, all but 1 were successfully managed surgically. Imaging follow‐up in 8 animals surviving RT showed a marked decrease in tumor volume. Median survival time was 162 days (95% confidence interval [CI]: 16; infinity) for animals treated with RT and 1103 days (95%CI: 752; infinity) for animals treated with VPS/RT. Median time‐to‐progression was 71 days (95%CI: 7; infinity) and 895 days (95%CI: 704; infinity) for each group, respectively. Two dogs died because of intraventricular metastasis 427 and 461 days after single‐modality RT.

Conclusions and Clinical Importance

Ventriculoperitoneal shunting led to rapid normalization of neurological signs and RT had a measurable effect on tumor volume. Combination of VPS/RT seems to be beneficial.
Pharmacokinetics of a single dose of Aficamten (CK‐274) on cardiac contractility in a A31PMYBPC3 hypertrophic cardiomyopathy cat model
Authors: Sharpe Ashley N. , Oldach Maureen S. , Kaplan Joanna L. , Rivas Victor , Kovacs Samantha L. , Hwee Darren T. , Morgan Bradley P. , Malik Fady I. , Harris Samantha P. , Stern Joshua A.
Published: November 2022

Hypertrophic cardiomyopathy (HCM) is the most prevalent cardiac disease in cats and lacks efficacious preclinical pharmacologic intervention, prompting investigation of novel therapies. Genetic mutations encoding sarcomeric proteins are implicated in the development of HCM and small molecule myosin inhibitors are an emerging class of therapeutics designed to target the interaction of actin and myosin to alleviate the detrimental effects of inappropriate contractile protein interactions. The purpose of this study was to characterize the pharmacodynamic effects of a single oral dose of the novel cardiac myosin inhibitor aficamten (CK‐274) on cardiac function in purpose bred cats with naturally occurring A31P MYBPC3 mutation and a clinical diagnosis of HCM with left ventricular outflow tract obstruction (LVOTO). Five purpose bred cats were treated with aficamten (2 mg/kg) or vehicle and echocardiographic evaluations were performed at 0, 6, 24, and 48 h post‐dosing. High dose aficamten (2 mg/kg) reduced left ventricular fractional shortening (LVFS%) by increasing the LV systolic internal dimension (LVIDs) and reduced isovolumic relaxation time (IVRT) compared with baseline without significant adverse effects. The marked reduction in systolic function and reduced IVRT coupled with an increased heart rate in treated cats, suggest a lower dose may be optimal. Further studies to determine optimal dosing of aficamten are indicated.
Whole‐genome resequencing reveals complex effects of geographical–palaeoclimatic interactions on diversification of moustache toads in East Asia
Authors: Li Jun , Fu Chao , Ai Qingbo , Xie Siyu , Huang Chunhua , Zhao Mian , Fu Jinzhong , Wu Hua
Published: November 2022

Geographical features and palaeoclimatic fluctuations are two classical evolutionary forces that shape genetic diversification within species. Fine‐grained analysis of the mechanisms involved through population demographic processes, however, remains limited. Taking advantage of two recently published reference genomes, we resequenced the genomes and examined the evolutionary history of the moustache toads, a group endemic to East Asia where complex topography and fluctuating palaeoclimate are known to have had profound impacts on organisms. Moustache toads probably originated in southeast Yunnan, China, and diversified towards the northwestern of Yunnan, as well as central and eastern China. Further exploration based on three widespread species (Leptobrachium ailaonicum, L. boringii and L. liui) using demographic modelling and species distribution models revealed that mountains and river valleys in East Asia not only functioned as geographical barriers, but also provided dispersal corridors and facilitated continuous migration or post‐glacial secondary contact among moustache toad populations. Furthermore, periodic oscillation of effective population sizes accompanying fluctuations of historical temperature and population contraction at the Last Glacial Maximum support the widespread impact of climatic changes of the Pleistocene on species diversification in East Asia. This impact was moderate for populations of L. ailaonicum and L. boringii in the southwestern mountains but severe for populations of L. liui in the eastern lowland regions of continental East Asia, which is supported by different degrees of change of their effective population sizes. Our findings reveal mechanisms underlying genetic diversification among moustache toads, and highlight the power of genomic data and demographic modelling for examining complex historical population‐level processes and for understanding how geographical and palaeoclimatic factors interactively shape current intraspecific diversity.
What does it mean to trust blockchain technology?
Authors: Teng Yan
Published: November 2022

This paper argues that the widespread belief that interactions between blockchains and their users are trust‐free is inaccurate and misleading, since this belief not only overlooks the vital role played by trust in the lack of knowledge and control but also conceals the moral and normative relevance of relying on blockchain applications. The paper reaches this argument by providing a close philosophical examination of the concept referred to as trust in blockchain technology, clarifying the trustor group, the structure, and the normatively loaded nature of this trust relation. The paper ends by critically reflecting on two of the most promising values (decentralization and transparency) that can invite users’ trust in blockchain technology, arguing that there is a tension between the pressing values that are intended to be achieved by developers and the predicament situations caused by current blockchain implementations.
Perturbations of immune landscape in COVID‐19 associated mucormycosis
Authors: Desai Nidhi , Pradhan Vandana , Chougule Durga , Tiwari Smrati , Mandke Charuta , Yadav Reetika Malik , Athvale Amita , Kawle Juhi , Pai Vinayak , Pawaskar Swapnal , Kharkar Harshada , Bhosale Snehal , Parab Ankita , Ansari Shazia , Kumar Kinnera Harish , Mhashal Shashikant , Redkar Neelam , Madkaikar Manisha
Published: November 2022

Background

A rise in secondary fungal infections during the COVID‐19 pandemic necessitates a deeper understanding of the associated immunological perturbations.

Objectives

To evaluate the clinical and immunological characteristics observed in patients with COVID‐19 associated mucormycosis (CAM) infection.

Patients/ Methods

Cases of mucormycosis with or post‐COVID‐19 infection were compared with cases of acute COVID‐19 and convalescent COVID‐19. Lymphocyte subsets, cytokines and other laboratory markers were compared between the groups.

Results

The frequency of proposed risk factors for CAM was diabetes mellitus (77%), recent history of steroid use (69%) and hypoxia during COVID‐19 infection (52%). Iron metabolism was dysregulated in CAM patients with low TIBC and total iron. Further, CAM was accompanied with lymphopenia with drastic reduction in B cell counts; however, plasmablasts were not altered. Further, CAM patients had low immunoglobulin levels and antibodies specific to mucor peptide did not increase in CAM suggesting dysfunction in B‐cell response. There was increase in activated effector cytotoxic CD8 T cells and NK cells in CAM compared with COVID‐19 infection and healthy controls. Among T helper cells, Tregs were reduced and Th‐1 frequency was increased in CAM compared with COVID‐19 infection. A distinct cytokine signature was evident in CAM with increase in IL‐1β, IFN‐γ, IL‐6, IL‐22, IL‐17A, IL‐10, IL‐2, IL‐8, IL‐7, IL‐21 and GM‐CSF.

Conclusion

This is the first study on immunophenotyping in CAM suggesting the need for long‐term monitoring of B‐cell function after SARS‐CoV‐2 in patients with dysregulated glycaemic control and the possible benefit of therapeutic supplementation with intravenous immunoglobulins in CAM.
The nurse apprentice and fundamental bedside care: An historical perspective
Authors: Tesseyman Sheri , Peterson Katelin , Beaumont Emma
Published: November 2022

This historical study aims to explain how the transition from student nurse service to fully qualified “graduate nurse” service in the United States in the 20th century affected assumptions about fundamental patient care in hospital wards and provide historical context for current apprenticeship programs. Through analysis of documents from 1920 when student nurse service, a nurse apprentice model, was the norm to 1960 when the nurse apprentice model was waning in favor of registered nurse service, this study found that the replacement of student nurses with registered nurses led to weakened standardization of fundamental bedside care and the introduction of large numbers of unlicensed nursing assistants. While student nurses could perform all the functions of fully qualified graduate nurses, nursing assistants could not, resulting in a separation of fundamental nursing care from the professional nurse role and changes in assumptions and attitudes toward fundamental care. These changes had a negative effect on fundamental nursing care. New apprenticeship programs provide opportunities for improvement.
When do societal stakeholders matter for agency‐CEOs? The role of stakeholder arrangements and social media attention
Authors: Brummel Lars
Published: November 2022

Public agencies have to deal with accountability demands from various societal stakeholders. Stakeholders may address their accountability demands via formal stakeholder arrangements but can also use social media for account‐holding purposes. This contribution analyzes to what extent these different venues of social accountability affect the perceived relevance of societal stakeholders to agency‐CEOs—their so‐called “stakeholder orientation.” By combining quantitative and qualitative data, this study shows that agency‐CEOs' stakeholder orientation is strongly associated with actual social media attention. In contrast, the relationship between formal stakeholder arrangements and stakeholder orientation is weak. Qualitative insights from focus group discussions suggest that the associations between social media attention and stakeholder orientation can be explained by mediatization processes within agencies and the reputational risks of social media storms. Overall, the findings support that social media attention is the most important catalyst for stakeholder orientation, rather than stakeholder arrangements.
The coexpression of two desaturases provides an optimized reduction of saturates in camelina oil
Authors: Bengtsson Jesse D. , Wallis James G. , Bai Shuangyi , Browse John
Published: November 2022

Reducing the saturate content of vegetable oils is key to increasing their utility and adoption as a feedstock for the production of biofuels. Expression of either the FAT5 16 : 0‐CoA desaturase from Caenorhabditis elegans, or an engineered cyanobacterial 16 : 0/18 : 0‐glycerolipid desaturase, DES9*, in seeds of Arabidopsis (Arabidopsis thaliana) substantially lowered oil saturates. However, because pathway fluxes and regulation of oil synthesis are known to differ across species, translating this transgene technology from the model plant to crop species requires additional investigation. In the work reported here, we found that high expression of FAT5 in seeds of camelina (Camelina sativa) provided only a moderate decrease in saturates, from 12.9% of total oil fatty acids in untransformed controls to 8.6%. Expression of DES9* reduced saturates to 4.6%, but compromised seed physiology and oil content. However, the coexpression of the two desaturases together cooperatively reduced saturates to only 4.0%, less than one‐third of the level in the parental line, without compromising oil yield or seedling germination and establishment. Our successful lowering of oil saturates in camelina identifies strategies that can now be integrated with genetic engineering approaches that reduce polyunsaturates to provide optimized oil composition for biofuels in camelina and other oil seed crops.
Genetic basis of resistance to southern corn leaf blight in the maize multi‐parent population and diversity panel
Authors: Chen Gengshen , Xiao Yingjie , Dai Sha , Dai Zhikang , Wang Xiaoming , Li Bailin , Jaqueth Jennifer S. , Li Wenqiang , Lai Zhibing , Ding Junqiang , Yan Jianbing
Published: November 2022

Southern corn leaf blight (SLB), caused by the necrotrophic pathogen Cochliobolus heterostrophus, is one of the maize foliar diseases and poses a great threat to corn production around the world. Identification of genetic variations underlying resistance to SLB is of paramount importance to maize yield and quality. Here, we used a random‐open‐parent association mapping population containing eight recombinant inbred line populations and one association mapping panel consisting of 513 diversity maize inbred lines with high‐density genetic markers to dissect the genetic basis of SLB resistance. Overall, 109 quantitative trait loci (QTLs) with predominantly small or moderate additive effects, and little epistatic effects were identified. We found 35 (32.1%) novel loci in comparison with the reported QTLs. We revealed that resistant alleles were significantly enriched in tropical accessions and the frequency of about half of resistant alleles decreased during the adaptation process owing to the selection of agronomic traits. A large number of annotated genes located in the SLB‐resistant QTLs were shown to be involved in plant defence pathways. Integrating genome‐wide association study, transcriptomic profiling, resequencing and gene editing, we identified ZmFUT1 and MYBR92 as the putative genes responsible for the major QTLs for resistance to C. heterostrophus. Our results present a comprehensive insight into the genetic basis of SLB resistance and provide resistant loci or genes as direct targets for crop genetic improvement.
Database for queries of melanoma cohorts with molecular and clinical data
Authors: Philip Manuel , Puppel Sven Holger , Stiller Mathias , Paschen Annette , Roesch Alexander , Zaremba Anne , Schadendorf Dirk , Horn Susanne
Published: November 2022

Large genome‐scale studies are deposited in various public sequence repositories. However, their access and analysis can be non‐trivial to infrequent users. Here, we present a new database connecting whole transcriptomes with clinical data for straight‐forward access and analysis of patient‐specific samples. Users can perform association tests of survival and gene expression across different cohorts, identify cell‐type expressions, or correlate the presence of immune cells. In summary, we present a new data hub for bench scientists to perform replication and discovery studies.
The co‐occurrence of segmental vitiligo and linear morphea in a pediatric patient and a review of the literature
Authors: Kahle Jordan , Rohr Bethany , Shah Sonal D.
Published: November 2022

Linear morphea and segmental vitiligo are both autoimmune diseases that are observed in the pediatric population, with rare reports of their co‐existence. We describe a case of linear morphea and segmental vitiligo with an overlapping distribution in a pediatric patient and review the literature. Including our own case, we summarize 10 cases of co‐occurring segmental vitiligo and morphea in pediatric patients; most of these lesions follow a blaschkolinear distribution, and none of the patients had autoimmune thyroid disease. Although uncommon, the coexistence of segmental vitiligo and linear morphea within lines of Blaschko can occur, and this case suggests that linear morphea and segmental vitiligo may be disorders related to genetic mosaicism.
Presentation of cryopyrin‐associated periodic fever syndrome as chronic, afebrile urticaria in a 12‐month‐old female
Authors: Fitzgerald Lauren A. , Drissi Madeeha , Sepulveda Kirstin , Hovland Christopher , Hendrick Sophia J. , Dubrocq Gueorgui
Published: November 2022

A healthy 12‐month‐old female presented with relapsing and remitting urticaria since birth that was resistant to treatment with antihistamines. A thorough history revealed extensive rheumatic disease on the father's side of the family, and subsequent genetic testing was positive for a missense variant of NLRP3, indicating cryopyrin‐associated periodic fever syndrome (CAPS). CAPS encompasses a spectrum of diseases, all related to a defect in the same gene; manifestations vary in severity and presentation, but most are associated with recurrent rash and fever. Because the patient's only presenting symptom was rash, this case highlights the importance of having a high index of suspicion for cryopyrin‐associated periodic fever syndrome in infants with persistent, early urticaria.
Disseminated idiopathic lipid keratopathy in a normolipemic cat
Authors: Botello‐Bárcenas Andrés , García‐Santisteban Rodrigo , Rodriguez‐Reyes Abelardo , Brooks Dennis E. , García‐Sánchez Gustavo Adolfo
Published: November 2022

Objective

To report a case of idiopathic lipid keratopathy in a normolipemic cat.

Animal Studied

A 10‐year‐old neutered female European domestic cat.

Results

A cat was evaluated for bilateral white corneal deposits. Slit‐lamp examination revealed multiple, well‐defined, round, stromal, cream‐colored deposits of different sizes associated with generalized superficial corneal vascularization. Blood lipids were normal, and no history of travel to tropical locations or ocular trauma was present. Topical betamethasone/gentamicin 0.1% suspension q 12 hours did not result in any improvement of clinical appearance after one week. Tomography following the initial therapy revealed dense, hyperreflective deposits with posterior shadowing in the anterior and mid stroma of both corneas. A four‐week course of itraconazole 0.01% ophthalmic cream was prescribed q 12 hours with no improvement. Four months after the initial examination, a diagnostic superficial keratectomy and amniotic membrane implantation were performed. Histopathological analysis showed membrane bound vacuoles with infiltration of foamy macrophages suggesting a diagnosis of primary lipidosis. The post‐surgical period was unremarkable, and ten days later, the patient was re‐examined. Opacification from a corneal leukoma was observed in the excision site with mild fibrotic tissue. Two months post‐keratectomy, no further changes were detected in the cornea, and the patient was managed only with topical lubricant.

Conclusions

To our knowledge, this is the first report of idiopathic corneal lipidosis in a cat and may be considered as a differential diagnosis of corneal disease in felines.
Retrospective investigation of perioperative risk factors for immediate postoperative corneal erosions in dogs undergoing phacoemulsification
Authors: Chen Tiffany Mei , Lasarev Michael R. , Eaton Joshua Seth
Published: November 2022

Objective

To characterize the incidence, contributing risk factors, and healing characteristics of immediate postoperative corneal erosions (IPCE) in dogs undergoing routine phacoemulsification.

Animals Studied

Medical records of 313 canine eyes (159 dogs) undergoing routine phacoemulsification surgery.

Procedures

Medical records of dogs undergoing planned cataract surgery at UW Veterinary Care were retrospectively reviewed. Patient‐related variables including age, skull conformation, diabetes status, and cataract stage at the time of surgery were recorded. Intraoperative variables per eye were also recorded including surgical technique, surgeon expertise level, average phacoemulsification power, and phacoemulsification time. Diagnosis of IPCE ≤ 24 h after completion of surgery and time to IPCE healing were recorded where follow‐up data were available.

Results

Immediate postoperative corneal erosions were observed in 48/313 (15.3%) operated eyes. The presence of diabetes mellitus or brachycephalic skull conformation, preoperative Schirmer tear test (STT) value, surgical technique and surgeon experience level, phacoemulsification time, and absolute phacoemulsification time were not statistically significant risk factors for IPCE. Average phacoemulsification power was associated with IPCE (RR1.52,p = .001). Time to IPCE healing was similar in diabetic and non‐diabetic dogs (median [IQR] 8 [6–11] days and 8 [6–15] days, respectively). Diabetes mellitus, brachycephaly, and phacoemulsification parameters were not associated with IPCE healing at 7 or 14 days postoperatively.

Conclusions

Higher average phacoemulsification power may be associated with the development of IPCE in canine eyes. The presence of diabetes mellitus or brachycephaly are not risk factors for the development of IPCE, nor are they factors that influence IPCE healing.
Variation in the imperfective in Bahamian English
Authors: Laube Alexander
Published: November 2022

The current study investigates variation in the marking of two aspectual subcategories of the imperfective in Bahamian English. First, it looks into variable auxiliarybeuse in progressive and future constructions, that is, the variation between full, contracted and zerobein non‐past V‐ingenvironments and related contexts. Second, the paper examines variable application of preverbaldoes/is/’sin non‐past habitual environments. The two variables were selected to represent the ‘informal’ and ‘anti‐formal’ group respectively, that is, one feature that classifies as a reduction of English structure and one direct transfer from the creole (Allsopp, 1996, pp. lvi–lvii). Thus, in addition to examining the linguistic constraints, the study will take a close look at the stylistic factors conditioning the variation, placing a special focus on the distribution of the non‐standardized variants over various registers as well as how speakers employ them to create linguistic styles.
Community Resilience to Muddy Flood Disasters in the Dijle Catchment Region, Belgium: Study Cases of Bertem and Beauvechain Municipalities
Authors: Lara Nicolás Esteban , Quiñónez Natalia Beatriz , Chia Ntumwel Bonito , Ghozlani Radhouane , Salinas Juan Pablo
Published: November 2022

In a context prone to flood-related disasters, com munities living in vulnerable areas within important catchments like the Dijle in central Belgium have been developing strategies to mitigate, prepare for, respond to and recover from the consequences of these extreme events. The present
study analyses the structure of the local governance model and community resilience systems concerning muddy floods in the municipalities of Beauvechain (Wallonia) and Bertem (Flanders). Data was collected through semi-structured and open-ended interviews with governmental and non-governmental
stakeholders. The analysis reveals that both communities have developed resources, actions, competencies and infrastructure to prevent and act when muddy flood disasters occur. In this context, municipalities are at the centre of the flood-disaster risk reduction man agement strategy; however,
stronger collaborations with local communities and other stakeholders could be developed to build resilience against future events of high magnitude. This paper recommends harmonising existing policies to foster participation, community empowerment and knowledge exchange to provide an enabling
environment and conditions to collectively build resilience against upcoming climate change-induced disasters like muddy floods.
Learning for the Future: A Case Study of Transdisciplinary Collaboration to Improve Pandemic Preparedness
Authors: Nguyen T. , Ronse M. , Kiekens A. , Thyssen P. , Nova Blanco J.R. , Van den Cruyce N. , Craps M. , Vandamme A.‐M.
Published: November 2022

Since the World Health Organization (WHO) announced the COVID-19 pandemic, attention has turned to the impact of societal initiatives and what can be learned from them for the future beyond COVID-19. Little attention has been paid, however, to how 'learning for the future,' as an organizational
process, is concretely accomplished. This paper offers a collaborative autoethnography of our team's project to 'learn for the future' through transdisciplinary collaboration during the first year of the COVID-19 pandemic, where our broader goal was to help improve future pandemic preparedness
for Belgium and beyond. We engage practice theory, with its processual, relational ontology, to understand the empirical phenomenon of 'learning for the future' as a practice or set of relational activities and artifacts that constituted our experience and collective sense that we were 'learning
for the future' in a transdisciplinary way. Our interpretive analysis uncovered three relational activities:inclusively broad sharing , participatory concretizing, and collective suspending of sense. The analysis further revealed that, at the same time, these activities
were the means through which the tension our team repeatedly experienced between the present and future (i.e.making an impact on the present pandemic versus taking a step back from the present to 'learn for the future') was being reproduced. This explains why our team's repeated attempts to
clarify priorities and reestablish the focus on the future did not simply resolve the tension. From a processual, relational perspective, 'learning for the future' emerged through ongoing efforts that relate to making a differ ence in the present. We discuss what our theoretical perspective
and findings may mean for organizing for a more resilient society and future directions for research.
The State of the Art of Digital Archiving in Romania
Authors: Streinu Marius
Published: November 2022

This article aims to summarise the current situation regarding digital archaeological archives in Romania.
Digital Archiving Best Practice and Future Challenges
Authors: Seaton K-L. , Lauzikas Rimvydas
Published: November 2022

This article will set out best practice and future challenges of digital archiving.
A Real-Time Artificial Intelligence Process Manager for Engineering Design
Authors: Gyory Joshua T. , Soria Zurita Nicolás F. , Martin Jay D. , Balon Corey M. , McComb Christopher , Kotovsky Kenneth , Cagan Jonathan
Published: November 2022

Managing the design process of teams has been shown to considerably improve problem-solving behaviors and resulting final outcomes. Automating this activity presents significant opportunities in delivering interventions that dynamically adapt to the state of a team to reap the most impact. In this work, an Artificial Intelligence (AI) agent is created to manage the design process of engineering teams in real time, tracking features of teams’ actions and communications during a complex design and path-planning task with multidisciplinary team members. Teams are also placed under the guidance of human process managers for comparison. Regarding outcomes, teams perform equally as well under both types of management, with trends towards even superior performance from the AI-managed teams. The managers’ intervention strategies and team perceptions of those strategies are also explored, illuminating some intriguing similarities. Both the AI and human process managers focus largely on communication-based interventions, though differences start to emerge in the distribution of interventions across team roles. Furthermore, team members perceive the interventions from the both the AI and human manager as equally relevant and helpful and believe the AI agent to be just as sensitive to the needs of the team. Thus, the overall results show that the AI manager agent introduced in this work matches the capabilities of humans, showing potential in automating the management of a complex design process.
Application of Transfer Learning in Metalworking Fluid Distinction
Authors: Wei Xiao , Jochmann Fabian , Demmerling Anna Lena , Söffker Dirk
Published: November 2022

This contribution introduces a Transfer Learning (TL) approach for the diagnostic task to distinguish the ingredients of a typical production machine element: metalworking fluid (MWF). Metalworking fluids are oil or water-based fluids used during machining and shaping of metals to provide lubrication and cooling. Additives in MWF affect their performance in different metalworking processes. Performance evaluation of MWF is of relevance for product development as well as for condition monitoring. In this contribution, for the first time, Transfer Learning is adapted for MWF distinction. Firstly, two experiments are designed to get Acoustic Emission (AE) signals from thread forming processes using variant MWF. In the first experiment, eleven kinds of water-based MWF are applied and AE signals are saved into dataset A, while in the second experiment, other five MWF are used in the process of thread forming and AE signals are stored in dataset B. A convolutional neural network (CNN)-based data mining approach including data segmentation, Short-Time Fourier Transform (STFT) and data normalization algorithms is developed from dataset A. Performance of the proposed approach in dataset A is good. Afterwards, parameters in data processing and hyperparameters in CNN of the approach are transferred into dataset B. Results of dataset B show that Transfer Learning allows suitable MWF distinction.
Iterative Uncertainty Calibration for Modeling Metal Additive Manufacturing Processes Using Statistical Moment-Based Metric
Authors: Dehaghani M. Rahmani , Tang Yifan , Wang G. Gary
Published: November 2022

Metal additive manufacturing (AM) has recently attracted attention due to its potential for batch/mass production of metal parts. This process, however, currently suffers from problems including low productivity, inconsistency in the properties of the printed parts, and defects such as lack of fusion, keyholing, and un-melted powders. Finite Element (FE) modeling cannot accurately model the metal AM process and has a high computational cost. Empirical models based on experiments are time-consuming and expensive. This paper improves a previously developed framework that takes advantages of both empirical and FE models. The validity and accuracy of the metamodel developed in the previous framework depend on the initial assumption of parameter uncertainties. This causes a problem when the assumed uncertainties are far from the actual values. The proposed framework introduces an iterative calibration process to overcome this limitation. In addition, the u_pooling metric used as the calibration metric in the previous framework is found not as good as the second-order statistical moment-based metric (SMM), after comparing several calibration metrics. The proposed framework is then applied to a four-variable porosity modeling problem. The obtained model is more accurate than using other approaches with only 10 available experimental data points for calibration and validation.
SWAGE: A 3D Arbitrary-Order Element Mesh Library to Support Diverse Numerical Methods
Authors: Morgan Nathaniel R. , Moore Jacob , Kiviaho Jan , Diaz Adrian
Published: November 2022

This paper presents the details on a new open-source swift arbitrary Lagrangian Eulerian (SWAGE) software library that supports diverse numerical methods on unstructured, arbitrarily moving curvilinear meshes — from stationary (Eulerian) to moving at the fluid velocity (Lagrangian), and anywhere in between (arbitrary Eulerian Lagrangian). Each element of the unstructured mesh in the physical coordinate system is represented by a spatial map from a single reference element. Foundational numerical methods are provided in the reference element that are key to solving diverse types of physical equations and calculating geometric quantities. A large set of index spaces in the physical coordinate space and the reference coordinate space are provided in SWAGE and the companion reference element type library respectively. Mesh connectivity data structures are provided to access various index spaces or a neighboring index on the physical mesh or within the reference element. The SWAGE mesh library supports continuous finite element, finite volume, and discontinuous Galerkin (DG) methods using arbitrary-order 3D curvilinear elements. The SWAGE library fills an existing technology gap by supporting arbitrary-order 3D Lagrangian DG hydrodynamic methods, along with many other numerical methods and solvers. The Lagrangian DG hydrodynamic method requires unique index spaces and very intricate mesh connectivity structures not currently available in other open-source libraries. The details of the SWAGE library are presented in this paper along with examples and results.
Self Learning Design Agent (SLDA): Enabling Deep Learning and Tree Search in Complex Action Spaces
Authors: Raina Ayush , Cagan Jonathan , McComb Christopher
Published: November 2022

Building an AI agent that can design on its own has been a goal since the 1980s. Recently, deep learning has shown the ability to learn from large-scale data, enabling significant advances in data-driven design. However, learning over prior data limits us only to solve problems that have been solved before and biases us towards existing solutions. The ultimate goal for a design agent is the ability to learn generalizable design behavior in a problem space without having seen it before. We introduce a self-learning agent framework in this work that achieves this goal. This framework integrates a deep policy network with a novel tree search algorithm, where the tree search explores the problem space, and the deep policy network leverages self-generated experience to guide the search further. This framework first demonstrates an ability to discover high-performing generative strategies without any prior data, and second, it illustrates a zero-shot generalization of generative strategies across various unseen boundary conditions. This work evaluates the effectiveness and versatility of the framework by solving multiple versions of the truss design problem without retraining. Overall, this paper presents a methodology to self-learn high-performing and generalizable problem-solving behavior in an arbitrary problem space, circumventing the needs for expert data, existing solutions, and problem-specific learning.
Ship Deck Object Placement Optimization Using a Many-Objective Bilevel Approach
Authors: Bagazinski Noah J. , Ahmed Faez
Published: November 2022

The placement of objects on a ship is critical to many facets of the performance of a ship. Most notably, the mass distribution properties of objects in a ship affect the ship’s stability, trim, and structural loading. Information gathered from object placement optimization can allow naval architects to further optimize the design of the whole ship by potentially reducing the structural weight of the vessel, and adjusting the shape of the hull or the general arrangements based on available space in the ship. This paper presents a novel, many-objective bin packing problem for object placement across multiple decks on a ship. This problem is also highly constrained to avoid object intersection and protrusion. The problem was optimized with the NSGA-II algorithm, utilizing a heuristic population initialization and by separating the objectives into a bilevel optimization scheme. The bilevel scheme decouples certain objectives and design variables from the rest of the problem and sequences the evaluation for the objectives in a two-stage process. The hypervolume of the final population measured the performance of the optimization test. The results indicate that sequencing the objectives with a bilevel scheme produces an 80.3% larger hypervolume than an all-in-one optimization for the same problem. The findings from this study provide a systematic way by combining concepts from many-objective optimization, bin packing heuristics, and bilevel optimization to sequence the optimization of many-objective, object placement problems.
Modeling and Optimizing Multi-Stage Design With Gaussian Process Based on Surrogate Model Chain
Authors: Yang Siyu , Jia Liangyue , Hao Jia , Alizadeh Reza
Published: November 2022

Continued progress in the surrogate-model-based evaluation for the single-stage has been explored, but multistage has higher dimension and uncertainty. High dimension and low overall data of multi-stage leads to low accuracy of prediction, and cannot characterize the uncertainty of the final prediction performance. We propose a Gaussian Process-based surrogate model chain (GP-SMC) to evaluate the performance of multi-stage. Also, we combine the GP-SMC with the quasi-Newton method (L-BFGS-B), make full use of the gradient information of the GP-SMC to get an optimization solution rapidly. The MAE (Mean Absolute Error) and MRE (Mean Relative Error) and STD (standard deviation) of GP-SMC’s predicted value are 10% of the prediction of a single surrogate model, which achieves a significant improvement in prediction accuracy and a significant reduction in uncertainty. Compared with the original optimization results, the average performance is improved by 21.05%. Based on the optimal solution and GP-SMC, the confidence interval of the final performance under the optimal solution is obtained, and the confidence level is 99%. The truth probability of GP-SMC is 91.25% in the test dataset, which is higher than single GP’s 85% truth probability. The technology is used in the case of Hot Rod Rolling, and can also be applied to complex product design with multi-stage.
Exploring the Usefulness of Agent-Based Product Social Impact Modeling Through a Systematic Literature Review
Authors: Mabey Christopher S. , Mattson Christopher A. , Salmon John L.
Published: November 2022

A key part of an engineer’s purpose is to create products and services that benefit society, or, in other words, create products with a positive social impact. While engineers have many predictive models to aid in making design decisions about the performance or safety of a product, very few models exist for estimating or planning for the wide range of social impacts an engineered product can have. To model social impact, a model must contain representations of the product and society. Agent-based modeling is a tool that can model society and incorporate social impact factors. In this paper, we investigate factors that have historically limited the usefulness of product adoption agent-based models, and predictive social impact models through a systematic literature review. Common themes of limiting factors are identified and steps are presented to improve the usefulness of agent-based product adoption models and predictive social impact models. The goal of a predictive social impact model is to help an engineer/designer make better decisions. Predictive social impact models can help identify areas in the design space for improving the social impact of products. When coupled with existing design methods, agent-based predictive social impact models can help increase the probability that a product achieves positive social impact.
A Comparative Study of Surrogate Modeling of Nonlinear Dynamic Systems
Authors: Zhao Ying , Jiang Chen , Vega Manuel A. , Todd Michael D. , Hu Zhen
Published: November 2022

Surrogate models play a vital role in overcoming the computational challenge in designing and analyzing nonlinear dynamic systems, especially in the presence of uncertainty. This paper presents a comparative study of different surrogate modeling techniques for nonlinear dynamic systems. Four surrogate modeling methods, namely Gaussian process (GP) regression, a long short-term memory (LSTM) network, a convolutional neural network (CNN) with LSTM (CNN-LSTM), and a CNN with bidirectional LSTM (CNN-BLSTM), are studied and compared. All these model types can predict future behavior of dynamic systems over long periods based on training data from relatively short periods. The multidimensional inputs of surrogate models are organized in a nonlinear autoregressive exogenous model (NARX) scheme to enable recursive prediction over long periods, where current predictions replace inputs from the previous time window. The Bouc-Wen nonlinear dynamic model, which can flexibly capture the behavior of many inelastic material models, is used to compare the performance of the four surrogate modeling techniques. The results show that the GP-NARX surrogate model tends to have more stable performance than the other three deep learning-based methods for this particular example. The tuning effort of GP-NARX is also much lower than its deep learning-based counterparts.
STEP-NC Compliant Data Representations for Powder Bed Fusion in Additive Manufacturing
Authors: Milaat Fahad , Witherell Paul , Hardwick Martin , Yeung Ho , Ferrero Vincenzo , Monnier Laetitia , Brown Matthew
Published: November 2022

Powder bed fusion (PBF) is an additive manufacturing (AM) technology that uses powerful beams to fuse powder material into layers of scanned patterns, thus producing parts with great geometric complexity. For PBF, process parameters, environmental control, and machining functions play critical roles in maintaining fabrication consistency and reducing potential part defects such as pores and grain growth. However, a major contributor to such defects can be attributed to poor data representations in the form of tessellated geometry and incoherent process plans. To address this issue, the Standard for the Exchange of Product model data Numerical Control (STEP-NC) recently added standardized data elements, entities, and attributes specifically for AM applications. Yet, the current STEP-NC data representations for AM lack definitions of process parameters and scan strategies that are commonly used in PBF processes. Therefore, characterization of the relationship between joint features, especially for PBF in AM, is missing. To bridge this gap, in this paper, an amended STEP-NC compliant data representation for PBF in AM is proposed. Specifically, the characteristics of the interlayer relationships in PBF, along with beam technology and AM strategy controls, are defined. Simulation results demonstrate the feasibility of granular process planning control, and the potential for producing high-quality parts with exact geometry and tolerance.
Information Fusion-Based Meta-Learning for Few-Shot Fault Diagnosis Under Different Working Conditions
Authors: Xie Tingli , Huang Xufeng , Choi Seung-Kyum
Published: November 2022

With the development of deep learning and information technologies, intelligent fault diagnosis has been further developed, which achieves satisfactory identification of mechanical faults. However, the lack of labeled samples and complex working conditions can hinder the improvement of diagnostics models. In this article, a novel method called Information Fusion-based Meta-Learning (IFML) is explored for fault diagnosis with few-shot problems under different working conditions. Firstly, an information fusion and embedding module is applied to perform both data- and feature-level fusion of multi-source. The embedding module only contains one input layer and multiple convolutions, residual and batch normalization (BN) layers, which has the advantage of low computational cost and high generalization. Then the prototypical module is proposed to reduce the influence of domain-shift caused by different working conditions using the fusion representation, which can improve the performance of fault diagnosis. The approach is verified on artificial and real faults under 4 different working conditions from the KAt-DataCenter at Paderborn University. For the 3-way 1-shot classification on Task T1, the average testing accuracy of the proposed method is 97.14%. For the K-shot classification on different tasks, the proposed method achieves the highest average testing accuracy of 94.21%. The results show the proposed method outperforms other typical meta-learning methods in terms of testing accuracy and generalization capability.
Effects of Augmented Information System on Design Communication: A Human-Subject Study Using Aircraft Design Studio
Authors: Thekinen Joseph , Grogan Paul T.
Published: November 2022

This paper performs a human subject study to investigate the effect of an augmented design interface on the communication behavior and performance of multidisciplinary design teams. We frame an aircraft design task as an iterative multidisciplinary design process with 12 design parameters shared across four design actors, each in charge of one discipline, implemented in a browser-based virtual design studio. We conduct a baseline experiment to collect observational data on design communication and performance and recommend two augmentations to improve team performance: a flexible design timer to control design iterations and a history catalog that quantifies the quality of previous design states. Results of an augmented experiment show the new features increase design iteration rate and decrease chat message rate, enabling the most successful teams to complete the task faster. We discuss the implications of our results in the broader context of digitalization and transformations due to information systems.
Concurrent Shape and Topology Optimization of Metamaterials Based on Periodic Surface Modeling
Authors: Lu Yanglong , Wang Yan
Published: November 2022

In the most recent decades, structural optimization (SO) methods including shape and topology optimization have been employed in designing metamaterials. However, shape optimization and topology optimization are usually performed separately. Conventional topology optimization techniques are limited by high computational cost because of the high-dimensional search space. Maintaining the structural continuity and smooth boundaries of metamaterials is also challenging. In this paper, a new SO method based on periodic surface (PS) modeling is proposed to optimize the shape and topology of metamaterials concurrently. The PS model can represent a wide variety of topology with only a small number of design parameters, including periodic moments, basis vectors, and scale parameters. By limiting the number of available basis vectors to choose from, the search efficiency of topology optimization is significantly improved. To solve the mix-integer optimization problem, a mixed-integer Bayesian optimization method is also developed with a new Gaussian process kernel, which is customized for the design parameters in the PS model. The new SO approach is applied to design mechanical metamaterials with high strength-weight ratio and negative Poisson’s ratio. The comparison with other topology optimization methods shows the high efficiency of the proposed approach.