bluuebunny/crossref_metadata_2025 huggingface dataset:
Daily and Peak Monitor Independent Movement Summary (MIMS) Values Associated With Metabolic Syndrome: NHANES 2011–12 and 2013–14
Authors: Aguiar Elroy J. , Turner Dusty T. , Pleuss James D. , Zheng Peixuan , Benitez Cristal J. , Ducharme Scott W.
Published: November 2024

The U.S. National Health and Nutrition Examination Survey (NHANES) physical activity monitor datasets for 2011–12 and 2013–14 were released in late 2020. To date, there has been limited interpretation of these nationally representative wrist‐worn accelerometer data (summarized and reported in Monitor Independent Movement Summary [MIMS] units) and their relationships with health‐related outcomes. This study examined the associations between free‐living DailyMIMS (volume), Peak 1MIMS and Peak 30MIMS (intensity), and risk of Metabolic Syndrome (MetS). Data from adults (N=3787; 18–80+ years) in the 2011–12 and 2013–14 NHANES cycles with health examination and accelerometry data were included. Accelerometer data were processed into Peak 1MIMS and Peak 30MIMS (MIMS/min), and DailyMIMS (MIMS/day). Design‐based generalized linear and logistic regressions, and a sample‐weighted decision tree, were used to examine associations between MIMS variables and MetS risk factors. Lower Peak 1MIMS, Peak 30MIMS, and DailyMIMS were observed for every one‐unit increase in the number of risk factors −3.9 [95% CI: −4.3, −3.4] and −2.3 [−2.6, −2.1] MIMS/min, (−672.1 [−772.7, −571.5] MIMS/day, respectively, all p< 0.001). The Decision Tree classified individuals ≥ 46.5 years with a DailyMIMS ≥ 12 245 MIMS/day and a Peak 30MIMS < 45.1 MIMS/min as having MetS (≥ 3 risk factors). Individuals < 46.5 years with a Peak 1MIMS ≥ 62.9 MIMS/min were classified with 0 risk factors. Higher DailyMIMS and PeakMIMS were associated with an absence of MetS risk factors, with a progressive decline as the number of risk factors increased. These findings may be considered as preliminary benchmarks for DailyMIMS and PeakMIMS associated with cardiometabolic risk.
Academic Air Travel: Debates, Tensions and the Need for a Sociology of Science Perspective
Authors: Rödder Simone , Braun Max
Published: November 2024

Traveling to conferences is a central feature of academic life. Yet recently, academic air travel has been called into question in the context of climate change, and the digital transformation has facilitated the spread of virtual alternatives to face‐to‐face meetings. The near‐total halt in air travel associated with the COVID‐19 pandemic has solidified the use of technologies of virtual meetings. However, the return to in‐person conferences after the pandemic suggests that these are not as easily replaced by virtual alternatives as is sometimes assumed. While this is increasingly coming into focus in the behavioral sciences, the sociology of science is largely absent in this debate. As a first step toward bringing this debate into conversation with the sociology of science, this review article examines the literature on academic air travel for key tensions, current debates and politicizations. It concludes that academic conference travel is an emerging, timely and relevant, yet under‐studied area of interest for sociologists of science, and suggests some avenues for future inquiry.
Why Fromm now? Envisioning a saner society
Authors: Chancer Lynn , McLaughlin Neil
Published: November 2024

Erich Fromm has long been a forgotten intellectual despite his status as a major critical theorist and sociologist in the 1930s, 1940s and 1950s but his work is being returned to with new enthusiasm because of events in the world, developments within sociology and new interest in psychosocial analysis dealing with emotions. This paper theorizes Fromm's return to scholarly influence now, offering analysis of both his reputational fate in light of the rise of global Trumpism and right wing‐populism and the value of the tools his unique merger of Freud, Marx and sociology offers in the contemporary moment.
Quantifying HLA Mismatches at Epitope Level in Haplo‐HSCT: Impact in the Outcome in Strategies Using PTCy
Authors: Gil‐Etayo Francisco Javier , Niño‐Ramírez Jairo Eduardo , Fonseca‐Santos Marta , Arroyo‐Sánchez Daniel , Navarro‐Bailón Almudena , Vicente Parra Ariadna , Jiménez Hernaz Isabel , Terradillos Sánchez Pilar , Boix Francisco , Alcoceba Miguel , Marín Luis , Pérez‐López Estefanía , Cabrero Mónica , Martín‐López Ana África , López Miriam , Baile Mónica , Avendaño Alejandro , Cabero Almudena , García‐Bacelar Ana , Vázquez Lourdes , Sánchez‐Guijo Fermín , García‐Sanz Ramón , López‐Corral Lucía , Tejeda Velarde Amalia
Published: November 2024

Haploidentical haematopoietic stem cell transplantation (haplo‐HSCT) is one of the most effective therapies for treating malignant haematological disorders. However, HLA disparities are significant barriers to the success of this process since they increase the risk of graft versus host disease (GvHD). HLA disparities quantification could help to anticipate the probability and degree of GvHD, but the best tool for such quantification remains a challenge. The aim of this study was to quantify the degree of HLA epitope incompatibilities using PIRCHE (Predicted Indirectly Recognisable HLA Epitopes) algorithm for immunogenicity prediction and their potential relationship with GvHD degree and clinical outcome. We studied 145 patients who underwent a haplo‐HSCT with post‐transplant cyclophosphamide (PTCy) from a related donor between 2013 and 2020 at our centre evaluating molecular HLA mismatches by PIRCHE‐algorithm. Patients with PIRCHE Score (PS) I + II > 38 in GvH direction, developed acute GvHD grade II–IV earlier than their counterparts (HR: 1.71, 95% CI: 1.02–2.87, p = 0.032). In addition, PS‐B > 1 in GvH direction was an independent risk factor for chronic GvHD, (HR: 2.51, 95% CI 1.17–5.36, p = 0.017). A higher incidence of relapse was found for patients with PS‐II > 28 HvG (HR: 9.16, 95% CI: 2.47–33.92, p < 0.001) which also favoured a worse GvHD/relapse‐free survival in patients with PS‐II > 27 in HvG direction (HR: 1.88, 95% CI: 1.11–3.18, p = 0.017). These findings indicate that the alloreactivity inferred by epitope HLA disparities is associated with post‐transplant outcomes. Thus, analysing PS could benefit the selection of the most suitable donors allowing patient stratification based on HLA mismatches in the context of haplo‐HSCT with PTCy.
Characterisation of the Novel HLA‐A*02:1189 Allele by Sequencing‐Based Typing
Authors: Blandin Lucie , Wojciechowski Elodie , Ralazamahaleo Mamy , Lemal Richard , Rouzaire Paul
Published: November 2024

HLA‐A2*02:1189 differs from HLA‐A*02:01:01:01 by one nucleotide substitution in codon 275 in exon 5.
The Novel HLA‐DQB1*04:01:01:03 Allele Identified Using HLA‐Targeted HiFi Sequencing
Authors: Li Yankun , Zhao Yuanli , Chen Yingjie , Wang Caili , Gao Peng
Published: November 2024

HLA‐DQB1*04:01:01:03 differs from HLA‐DQB1*04:01:01:01 by a 10‐nucleotide fragment insertion in intron 1.
The Novel HLA‐DPB1*05:01:22 Allele, Identified by Sanger Dideoxy Nucleotide Sequencing in a Chinese Individual
Authors: Xie Nan‐Nan , He Yan , Wu Danlei , Zhou Ying , Zheng Zhong‐Zheng
Published: November 2024

HLA‐DPB1*05:01:22 differs from HLA‐DPB1*05:01:01:01 by one nucleotide in exon 3.
The Novel HLA‐DRB1*04:388 Allele, Identified by Sanger Dideoxy Nucleotide Sequencing in a Chinese Individual
Authors: Liu Na , Wang Li Jun , Sun Tie Cheng , Jia Yan Jun
Published: November 2024

The HLA‐DRB1*04:388 allele differs from HLA‐DRB1*04:01:01:01 by one nucleotide substitution in codon 44 in exon 2.
The Novel HLA‐B*40:585 Allele, Identified by Sanger Dideoxy Nucleotide Sequencing in a Chinese Individual
Authors: Liu Na , Wang Dong Mei , Jia Yan Jun , Sun Tie Cheng
Published: November 2024

The HLA‐B*40:585 allele differs from HLA‐B*40:01:02:01 by one nucleotide substitution in codon 130 in exon 3.
The effects of deductive versus inductive pronunciation instruction in combination with captioned video on the acquisition of incongruent grapheme‐phoneme correspondences in L2 German
Authors: Jolitz Carolin , Mally Natacha Hélène Gilberte
Published: November 2024

Prior research indicates that first language (L1) orthography can adversely affect second language (L2) phonological acquisition, yet studies on orthographic interference and pedagogical strategies to mitigate these effects remain sparse. Addressing these research gaps, this study investigates the impact of orthographic interference on L2 German pronunciation among L1 English speakers, employing a technology‐based homework intervention with EdPuzzle, an educational video platform, in three beginner courses. Students in two experimental groups engaged with captioned video clips and were guided through questions using either an inductive or deductive approach, while a control group focused solely on meaning. Analysis of a pre‐intervention production task revealed German grapheme‐phoneme correspondences < v > ‐ /f/, < s > ‐ /z/, and < z > ‐ /ts/ were most frequently mispronounced. Comprehensibility ratings by native German speakers suggest that both experimental groups significantly improved in their word‐level productions, with the inductive group also significantly improving in their sentence‐level productions. This study also sheds light on student perceptions of these instructional activities, enhancing our understanding of engaging L2 German pronunciation pedagogy. This research underscores the potential of integrating technology and accessible content in homework assignments to enrich and supplement L2 German pronunciation instruction in the classroom.
Effects of burial in soil on seed longevity and germinability of the winter annual weed wild barley (Hordeum spontaneum)
Authors: Nozarpour Elham , Edalat Mohsen , Soltani Elias , Baskin Carol C. , Baskin Jerry M. , Kazemeini Seyed AbdolReza
Published: November 2024

Hordeum spontaneum is a winter annual weed that reduces crop yields in Iran. The aim of this study was to quantitatively analyze the effects of burial on seed longevity and germinability and of water potential and temperature on germination. Seeds were placed in nylon‐mesh bags and buried in soil in a semi‐arid region on 1 July 2018 and exposed to natural temperature regimes. After 2 months of burial, seed viability started to decline with a slope of 0.0169%, and after 9 months all seeds were nonviable. Fresh seeds were dormant, but became non‐dormant during summer via dry after‐ripening. Thus, by late autumn (December) the seeds germinated to 100% in dark at 5 and 15°C. The base, optimum, and ceiling temperatures were 0.27, 17.5, and 25°C, respectively, at a water potential of 0 MPa. The hydrotime constant was 50.6–426.9 MPa h, base water potential −1.23 to −0.333 MPa and hydrothermal constant 1350.5 MPa °C h. These results can be used to predict timing and extent of weed emergence of H. spontaneum in crops and in planning for sustainable management strategies.
Direct conversion of dissolved air flotation sludge to mannosylerythritol lipids by Moesziomyces aphidis
Authors: Akkermans Veerle , D'aes Jolien , Dries Jan
Published: November 2024

Dissolved air flotation (DAF) sludge from wastewater treatment plants is often a lipid‐rich waste stream, which is now mostly being converted to biogas. Due to its high carbon content, DAF sludge has great potential in biotechnological applications. MEL production with floated grease from a sauce producer as substrate resulted in the accumulation of 1.1 ± 0.1 g/L MEL. With enhanced aeration through the use of baffled shaking flasks, MEL titres increased up to 9.7 ± 0.5 g/L. An adaptation period of 9 days was observed, after which the specific productivity reached 0.20 g/L.h. This study shows for the first time biosurfactant production from DAF sludge, with promising titres. Further research should focus on reducing the lag phase to increase productivity.
Site‐specific weed management on organic grain farms using variable rate seeding and data driven simulation
Authors: Loewen Sasha , Maxwell Bruce D.
Published: November 2024

Integrated weed management is integral to organic farming, with increased crop seeding rates as one effective weed suppression tactic. Precision agriculture, which uses guidance and sensor technologies to direct site‐specific management, could allow for targeted weed management using variable seeding rates. On farm precision experimentation (OFPE) can be used to predict site‐specific sub‐field weed biomass response to a range of varied crop and cover crop seed rates across whole fields to provide decision support directly to farmers. We used OFPE to compare five simulated precision seeding strategies to either maximize net return or minimize weed biomass. Five site‐years of OFPE data were collected from organic farms in Manitoba, Canada and Montana, USA. Seeding rate, weed biomass, crop yield, topographic variables and other remotely sensed data were collected on a 10‐m grid to model yield and weed response to seeding rates for each field. Simulated site‐specific variable seeding rate net returns improved upon farmer chosen uniform seeding rate net returns on average by $115 ha−1. When variable seeding rates were optimized to minimize weed biomass, simulated weeds were reduced on average by 10 kg ha−1 relative to the farmer chosen uniform seed rates. A combined variable rate approach which balanced net return and weed minimization improved both net return and weed suppression compared to farmer‐chosen seeding rates in every site‐year. The various seeding rate strategies represent different methods from which farmers can choose to implement OFPE to optimize sub‐field‐specific planting rates and to increase their field‐scale ecological knowledge.
Partitioning problems via random processes
Authors: Anastos Michael , Cooley Oliver , Kang Mihyun , Kwan Matthew
Published: November 2024

There are a number of well‐known problems and conjectures about partitioning graphs to satisfy local constraints. For example, the majority colouring conjecture of Kreutzer, Oum, Seymour, van der Zypen and Wood states that every directed graph has a 3‐colouring such that for every vertex , at most half of the out‐neighbours of have the same colour as . As another example, the internal partition conjecture, due to DeVos and to Ban and Linial, states that for every , all but finitely many ‐regular graphs have a partition into two non‐empty parts such that for every vertex , at least half of the neighbours of lie in the same part as . We prove several results in this spirit: in particular, two of our results are that the majority colouring conjecture holds for Erdős–Rényi random directed graphs (of any density), and that the internal partition conjecture holds if we permit a tiny number of ‘exceptional vertices’. Our proofs involve a variety of techniques, including several different methods to analyse random recolouring processes. One highlight is a personality‐changing scheme: we ‘forget’ certain information based on the state of a Markov chain, giving us more independence to work with.
Recalibrating R$\mathbb {R}$‐order trees and Homeo+(S1)$\mbox{Homeo}_+(S^1)$‐representations of link groups
Authors: Boyer Steven , Gordon Cameron McA. , Hu Ying
Published: November 2024

In this paper, we study the left‐orderability of 3‐manifold groups using an enhancement, called recalibration, of Calegari and Dunfield's ‘flipping’ construction, used for modifying ‐representations of the fundamental groups of closed 3‐manifolds. The added flexibility accorded by recalibration allows us to produce ‐representations of hyperbolic link exteriors so that a chosen element in the peripheral subgroup is sent to any given rational rotation. We apply these representations to show that the branched covers of families of links associated to epimorphisms of the link group onto a finite cyclic group are left‐orderable. This applies, for instance, to fibred hyperbolic strongly quasi‐positive links. Our result on the orderability of branched covers implies that the degeneracy locus of any pseudo‐Anosov flow on an alternating knot complement must be meridional, which generalises the known result that the fractional Dehn twist coefficient of any hyperbolic fibred alternating knot is zero. Applications of these representations to order detection of slopes are also discussed in the paper.
Experimental and Simulation Investigation of J-Shaped and Kammtail Virtual Airfoils in Small-Scale Horizontal Axis Wind Turbines
Authors: Al Hamad Saif , Abousabae Mohamed , Shaker Omar , Amano Ryoichi S.
Published: November 2024

In this work, the performance of new wind blade designs for small-scale horizontal axis wind turbines (HAWTs) was studied and compared with the performance of a baseline design. Three J-shaped pressure-side truncation ratios (1/3, 1/2, and 2/3) and two Kammtail Virtual Foil (KVF) truncation ratios (1/8 and 1/4) were studied. The baseline design was experimentally investigated. Output power was measured using a digital rotary torque sensor at three different wind speeds. Tip speed ratio (TSR) was calculated after measuring each wind speed's free-rotating revolutions per minute (RPM). Three wind speeds and experimental TSRs were used in three-dimensional simulations to capture the performances of the proposed cases and compare them with the baseline. The simulation investigation was carried out for lab-scale and scaled cases. The three-dimensional study found that the J-shaped blades enhanced the performance of the HAWTs for both lab-scale and scaled cases. J-shaped blades with a 1/3 opening ratio yielded an average power coefficient enhancement of around 1.56% and 4.16% for lab-scale and scaled cases, respectively. J-shaped blades with a 1/2 opening ratio yielded an average power coefficient enhancement of around 1.15% and 4.23% for lab-scale and scaled cases, respectively. On the other hand, J-shaped blades with a 2/3 opening ratio yielded an average power coefficient enhancement of around −0.12% and 2.54% for lab-scale and scaled cases, respectively. Furthermore, it was found that the KVF blades diminished the performance for both lab-scale and scaled cases.
Physics-Constrained Convolutional Recurrent Neural Networks for Solving Spatial-Temporal PDEs With Arbitrary Boundary Conditions
Authors: Li Guangfa , Lu Yanglong , Liu Dehao
Published: November 2024

The inception of physics-constrained or physics-informed machine learning represents a paradigm shift, addressing the challenges associated with data scarcity and enhancing model interpretability. This innovative approach incorporates the fundamental laws of physics as constraints, guiding the training process of machine learning models. In this work, the physics-constrained convolutional recurrent neural network is further extended for solving spatial-temporal partial differential equations with arbitrary boundary conditions. Two notable advancements are introduced: the implementation of boundary conditions as soft constraints through finite difference-based differentiation, and the establishment of an adaptive weighting mechanism for the optimal allocation of weights to various losses. These enhancements significantly augment the network’s ability to manage intricate boundary conditions and expedite the training process. The efficacy of the proposed model is validated through its application to two-dimensional problems in heat transfer, phase transition, and fluid dynamics, which are pivotal in materials modeling. Compared to traditional physics-constrained neural networks, the physics-constrained convolutional recurrent neural network demonstrates a tenfold increase in prediction accuracy within a similar computational budget. Moreover, the model’s exceptional performance in extrapolating solutions for the Burgers’ equation underscores its utility. Consequently, this research establishes the physics-constrained recurrent neural network as a viable surrogate model for sophisticated spatiotemporal PDE systems, particularly beneficial in scenarios plagued by sparse and noisy datasets.
Improving Knowledge Capture, Access, and Reuse by a Model-Based and Data-Driven Management Using the Example of Aviation’s Retrofit
Authors: Laukotka Fabian N. , Krause Dieter
Published: November 2024

The retrofit of aircraft - the design and installation of new cabins into existing planes, comes with challenges, particularly regarding access to the necessary information. Although aircraft are generally similar, each single one becomes unique over time due to its individual usage and maintenance. While certification requires the changes made to be documented, this is typically done through numerous individual documents that are differentiated by domain, area within the aircraft, revision, and other factors that lead to a fragmentation of information. Engineers rely on implicit knowledge gained from years of experience and common knowledge as well as established structures like the ATA-Chapters, to tediously re-join these fragments and gain an overview of the current state of the aircraft and the relevant documents. This work presents an approach that utilizes ontologies to capture implicit knowledge and combines it with system models defining generic information as well as the definitions of standards that are identical for certain types of aircraft. A web application that functions as an access system is introduced to help engineers filter and identify required information and documents more easily when planning a new cabin. Finally, together with a partner from the industry, this approach is applied to a case study.
Automated Functional Basis Modeling Using Multi-Label Machine Learning on 3D Models
Authors: Connor Zachariah A. , Sable Shital , Campbell Matthew I. , Stone Robert B.
Published: November 2024

Recent progress in neural network-based object detection algorithms allows engineers to automatically detect and track objects using camera data or LiDAR data. These algorithms have been applied to many engineering applications to automatically detect obstacles, defects, or objects of interest. However, these algorithms are only trained to detect the class label of an object representing a physical entity, not the object’s function, a concept that is more nebulous than a single entity. Functional Basis Modeling is a technique developed in mechanical engineering to abstract an object’s functionality from the form of the object. This paper describes a methodology for the automation of Functional Basis Modeling using the PointNet algorithm. A set of point clouds extracted from 3D models of common products were manually labeled with multiple functional basis labels. The resulting dataset was used to train and validate a modified version of the PointNet convolutional neural network algorithm. PointNet is traditionally a multi-class classification algorithm, but the authors modified it to perform multi-label classification. The authors were able to train PointNet to predict several mechanical functions of an input object. The method achieves a single-label maximum F1 Score of 89.6%. This work paves the way for an AI text input CAD generator and more useful object detection methodologies.
Designing for Grand Challenges: An Interactive Method for Tackling Coupled Device-System Designs
Authors: Jetton Cole , Hoyle Christopher , Campbell Matthew I.
Published: November 2024

When designing a system of devices to accomplish a large task, there is an inherent relationship between the capability of each device and the overall system effectiveness. If one is designing the devices from scratch, the engineering specifications depend on a budget and hence the number of devices in the fleet. Determining these engineering specifications depends on some unknown system strategy where the devices must cooperate to accomplish the overall goals. For grand challenges like ocean cleanup or forest fire fighting, the coupled problem of system effectiveness and device specifications can be impossible to determine. One may specify the capabilities of a device which may not be achievable by a design team or may be prohibitively expensive. To overcome this problem, we present an approach that guides an optimization process; one that can both simulate the fleets as well as incorporate designer feedback on design feasibility. This research proposes an interactive framework to address these issues by using Agent-Based Modeling to simulate fleets, utilizing Gaussian Process models to capture user feedback on feasibility and applying constrained Bayesian optimization to guide the design process. This method was tested on two case studies: a simplified ocean cleanup fleet and a higher-fidelity model of firefighting drones. This study provides insight into how performance parameters can be managed simultaneously at the device-level and the system-level.
Self-Supervised Multi-Label Melt Pool Anomaly Classification in Powder Bed Fusion Additive Manufacturing
Authors: Kim Jaehyuk , Yang Zhuo , Lu Yan , Hong Gisuk
Published: November 2024

Laser Powder Bed Fusion (LPBF) is a key technique in metal additive manufacturing (AM) that enables the fabrication of parts with complex geometries and enhanced mechanical properties. Despite its advantages, LPBF’s susceptibility to defects remains a challenge, directly linked to melt pool instability. Real-time Melt Pool Monitoring (MPM) systems offer immediate feedback on melt pool characteristics, which is crucial for identifying potential defects during the LPBF process. Recently, Machine Learning (ML) based approaches with these monitoring systems have been introduced for real-time process anomaly detection and classification. However, one major hurdle is the requirement for large volumes of labeled and balanced data for ML model development, which is necessary for accurate process outlier identification and categorization. To address the issues, this paper introduces a Self-supervised learning-based Visual Transformer (SiT) for multi-label classification of melt pool anomalies, such as abnormal sizes, irregular shapes, spatters, and plume. Experiments reveals that the self-supervised approach, with an average F1-score of 0.979, surpasses the performance of the supervised approach, which has an average F1-score of 0.836 across all classification cases, particularly in imbalanced datasets. The SiT model efficiently classifies multiple anomalies simultaneously, without extensive manual labeling, addressing data imbalance and enhancing MPM systems’ efficiency and reliability. This advancement marks a significant contribution to improving part quality in LPBF processes.
High-Dimensional Variance-Based Adaptive Sampling for Prediction of Aerodynamic Flows Using Non-Intrusive Reduced Order Models
Authors: Dikshit Abhijnan , Leifsson Leifur
Published: November 2024

Non-intrusive reduced order modeling methods (ROMs) have been widely used as a computationally cheap method of modeling aerodynamic flows. A problem with the construction of these models is that a large number of samples are required to achieve global accuracy leading to a high cost of obtaining samples to create the ROMs. In this work, a novel adaptive sampling strategy is proposed that uses the uncertainty estimate of the high-dimensional prediction of the ROM as a sampling objective. The high-dimensional uncertainty estimate is obtained by performing Monte Carlo simulations to propagate the uncertainty of the low-dimensional space obtained using a multi-task Gaussian process model. The proposed algorithms are demonstrated on a two- and fourteen-dimensional aerodynamics modeling test case and compared to the performance of Halton sequences. The results of the two-dimensional case illustrate that the adaptive sampling strategies can achieve up to a 15% improvement over Halton sequences in terms of the mean relative error for the same number of samples. In the fourteen-dimensional test case, the adaptive sampling strategies achieve up to a 7.6% improvement over the Halton sequences at the same number of samples. All sampling strategies have comparable performance once the computational budget is exhausted, achieving a mean relative error of approximately 0.050 and 0.085 in the two- and fourteen-dimensional test cases, respectively. The adaptive sampling strategies can also better predict important features of the pressure distribution, such as shock waves and suction peaks.
Enhancing VR Shopping With Dynamic Space Re-Arrangements: Towards Contextualized Information Displays and Improved Locomotion
Authors: Chen Liuqing , Cai Yu , Wang Ruyue , Hansen Preben , Zhou Tingting , Zhen Yankun , Sun Lingyun
Published: November 2024

Traditional VR shopping applications often rely on static designs for shopping spaces, necessitating frequent teleportation. However, an exploratory case study involving experienced online shoppers revealed several issues with this approach, including problems with interactivity, efficiency, motion sickness, and product presentation. To address these challenges, a workshop was organized with professional designers to gather design strategies, proposing the concept of the dynamic rearrangement of shopping space. This solution enables users to dynamically rearrange the shopping space and the products within it, utilizing space forms such as the department store, the shelf, and the room to suit various shopping intentions: exploration, product comparison, and product matching. In this way, users can obtain access to desired products by rearranging the shopping space rather than through excessive navigation in the static shopping space, creating a contextualized and efficient shopping experience. The solution was evaluated in a within-subjects comparison experiment, which demonstrated our solution’s significant improvements in involvement, motion sickness, efficiency in task performance, product perception, and overall user experience. Our findings suggest that treating the VR shopping space as a flexible, manipulable object through dynamic rearrangements and enriching it with contextualized design elements can provide users with an immersive and efficient VR shopping experience. Space rearrangements may also become a design reference for other VR application scenarios that require frequent locomotion events.
Melt Pool Depth Prediction in Directed Energy Deposition Single-Track Prints Using Point Cloud Analysis
Authors: Mahmoud Youmna , Lyu Jiaqi , Vallabh Chaitanya Krishna Prasad , Manoochehri Souran
Published: November 2024

Traditional assessment methods of melt pool size in metal additive manufacturing (AM) are known for their time-consuming and expensive nature, involving tasks such as cutting, polishing, and detailed microscopy. In this paper, we present a novel approach to predict the melt pool depth in directed energy deposition (DED) AM process, by exclusively relying on point cloud data acquired through a laser scanner coupled with machine learning (ML) tools. Our method streamlines the process of identifying the size of a melt pool by automating the entire procedure, negating the need for any manual intervention starting from the initial data collection stage. This improved analysis opens up possibilities for point-cloud-based real-time predictions and control, making it a highly efficient and adaptable solution.

By training multiple ML models using track width and height data extracted from the point cloud, we surpass the limitations of relying solely on process parameters to accurately predict melt pool depth — a parameter not visible during print inspection, especially using a laser scanner. Among the various regression models utilized, the Gaussian Process Regression model demonstrates superior performance, yielding a Mean Absolute Error (MAE) of 18.89 μm and a Root Mean Squared Error (RMSE) of 25.5 μm. The results indicate a promising potential for the developed methods to eventually subside the requirement for external post-processing of print tracks in order to characterize the melt pool.
Transfer Learning in Multi-Objective Generative Design of Metamaterials
Authors: Apaza Gabriel , Selva Daniel
Published: November 2024

This paper investigates leveraging transfer learning to improve the performance of reinforcement learning based generative agents for multi-objective design optimization. First, a transformer-based generative agent is developed and its training procedure is outlined. Then, the generative agent is pre-trained on a metamaterial design problem in two different multitask settings. One setting is defined by different parameterizations of the metamaterial design problem. The objective of this setting is to determine the agent’s ability to transfer to problems with new parameterizations and new physics models. The second setting is defined by the same parameterizations, but each problem in the training set has unique constraints. The objective of this setting is to determine the agent’s ability to transfer to unseen constraints. For each multitask setting, the pre-trained agent is fine-tuned on different validation metamaterial design problems to assess its ability to transfer to new problem parameterizations, new stiffness models, and new stiffness ratio constraints. Finally, the agent’s performance on the validation problems is baselined against a genetic algorithm (GA) designed to handle constrained problems. Results show that pre-training greatly improves the performance of the generative agent on the validation problems. Furthermore, the pre-trained agent is able to achieve the same or better hypervolume of the GA while only requiring a fraction (10–20%) the number of function evaluations.
Evaluating Design Rationale
Authors: Mirabito Yakira , Liu Xiaowen , Goucher-Lambert Kosa
Published: November 2024

Design rationale captures the justification behind a design decision. Often, rationales vary in the content and depth of information, making the study and comparison of rationales challenging. This project aims to characterize design rationale and develop a computational approach to evaluate design rationale quality at scale. In total, 2250 rationales were machine-generated using GPT across two different representations, and a portion of the rationales (n = 512) were evaluated by two raters across five dimensions of quality. Rationales were then characterized using natural language processing techniques, resulting in 108 linguistic features for each rationale. The evaluations and linguistic features were used to build eight predictive models for each quality dimension. The main results show that structured rationales were rated higher than unstructured rationales across the five dimensions. Thus, the tested feature, specification, and evidence (FSE) framework was shown to be a worthwhile approach to represent the justification behind a design decision. Moreover, key linguistic features that correlate with higher quality ratings were identified. Future work will explore how design rationale quality and characteristics impact design decisions, particularly in a human-AI teaming context where generative design recommendations could benefit from including generative design rationales.
Parametric Analysis of Relationships Among Part Geometry, Build Time, and Energy Consumption for Laser Powder Bed Fusion
Authors: Gee Kaitlyn , Hart A. John
Published: November 2024

Sustainable manufacturing systems must have sufficient throughput to meet demand, be cost-effective, and have an acceptably low environmental impact. For additive manufacturing by laser powder bed fusion (LPBF), part designers and process engineers must understand the underlying scalings among energy utilization, process parameters, and part geometry to attain efficient, low-carbon LPBF production.

To this end, we present a family of parameterized models that digitally analyze part geometry, estimate laser scan paths, and calculate energy consumption associated with single-geometry LPBF production. Using a custom-built slicer and computational pipeline, geometry-specific build time is numerically calculated. A unit process life cycle inventory (UPLCI) is applied to articulate energy consumption across printer warm up, cool down, idling, and scanning. For parts (n = 15) each with a mass of 100g in AlSi10Mg built on a simulated EOS m290 machine, per-part build time varies from 29.2 to 44.3 minutes, a difference of up to 51.7%. Specific energy consumption varies between 20.3 to 30.5 MJ/kg, a difference of up to 50.3%. This work articulates how throughput and energy scale with design decisions including part design and selection, and translate these findings into guidance for manufacturing and design engineers.
Long Term Performance Analysis of an Experimental Sliding Vane Energy Recovery Device for Village Scale Reverse Osmosis
Authors: Welsh Emily , Costello Jeffrey , Winter Amos G.
Published: November 2024

Many communities globally experience a combination of brackish groundwater, low coverage of piped water, and poor access to an electricity grid. Community-scale solar powered reverse osmosis systems can improve water access, but community adoption is hindered because the high specific energy costs of these systems require large expensive solar systems. Prior art identified a novel architecture for an energy recovery device using off-the-shelf sliding vane pumps that could recover up to 62% of the energy lost in the system thereby reducing the specific energy costs. This study assesses the feasibility of the energy recovery concept for resource constrained regions by measuring the power consumption and maintenance requirements of the system over time. Two long-term tests were completed using a prototype reverse osmosis system. One test measured power consumption over time during continuous water desalination. The second test evaluated system reliability and maintenance requirements in a run-to-fail test. This work demonstrated that a coupled sliding vane pump could reduce power consumption during desalination, however the results of the testing revealed a potential catastrophic failure mode that may be caused by pressure pulsing of the vane pumps as they interact with the hydraulic system. Future work should focus on improving system reliability for this configuration.
Gaussian Processes for Non-Differentiable Functions With Jump Discontinuities
Authors: van Beek Anton
Published: November 2024

Gaussian processes (GPs) are used in many disciplines, but the approximation of functions with jump discontinuities and non-differentiabilities remains a challenge. This is a substantial limitation as many contemporary scientific and engineering challenges involve functions with these characteristics (e.g., contact problems in mechanics, and material properties because of phase changes). Available GP models are inappropriate as jump discontinuities and non-differentiabilities are local phenomena that cannot be modeled through covariance structures that describe a function’s global behavior. To overcome this limitation, we introduce the discontinuous Gaussian process (DCGP) model that involves learning a parametric, yet flexible function, on the training data to ensure that the residuals of the random process are continuous and differentiable. Through validation on a set of six test problems and one engineering problem, we show that the DCGP model has superior performance over available models and that there exist physical systems for which the correlation between a function’s outputs can be modeled across discontinuities and non-differentiabilities (i.e., globally). We believe the latter to be a profound insight as it suggests that statistical models, such as DCGP, could be used to expedite scientific discovery even for discontinuous and non-differentiable functions.
Bond-DQN: Deep Q-Learning of Lumped-Element Systems Design via Bond Graphs
Authors: Nguyen Daniel , Tucker Conrad
Published: November 2024

Lumped-element schematics (e.g. free-body diagrams, electrical circuit schematics) have been utilized for design synthesis in a wide variety of domains. They are invaluable for generalizing design synthesis in a process which consists of taking a set of available components and exploring the space of possible connectivity. The connectivity of a given lumped-element model (e.g. the netlist of an electrical schematic) represents a parameterized model which may be optimized using generalizable state space techniques and has the added benefit of being highly interpretable. Some recent machine learning models, particularly in the analog electrical circuit synthesis community, have attempted to leverage such lumped-element models in design synthesis or forward modeling tasks. They either learn faster neural network proxies for the forward dynamics of human-crafted lumped-element simulators or use hand crafted lumped-element representations in the networks themselves. An issue with these approaches is the models are difficult to generalize to different domains due to their reliance on a domain-specific dataset or simulator for design performance evaluation. In this work, we propose Bond-DQN, a Deep Q-Learning method to sequentially generate lumped-parameter designs for state-space based objectives. Specifically, our method uses a grammar based on bond graphs, which are an energy-based graph representation which have been historically used to simulate lumped-parameter models in a variety of domains. The combination of bond graphs with DQL allows us to accurately simulate and sample the large design space of multi-domain systems without retraining on external simulators or new datasets. We demonstrate Bond-DQN’s capability for generalizeable lumped-parameter design by benchmarking against a suspension optimization problem in which the previous SOTA utilizes the Harris Hawks Optimization algorithm, demonstrating a reduction in the peak vehicle translational and pitch acceleration of 20.5% and 4.1% respectively. Additionally, we showcase its ability for multi-domain design by applying to an electrical circuit voltage regulation problem.
A New Combined Finite Element - Analytical Model for Strip Profile Control in Cold Rolling Mills
Authors: Stabile Pietro , Ballo Federico , Sghirlanzoni Davide , Frascino Leonardo , Previati Giorgio
Published: November 2024

The strip profile control problem in cold rolling mills is of paramount importance to ensure product quality. The paper presents a new combined finite element - analytical model of a 20-high cold rolling mill suitable for strip profile simulation and control. The model features rolls, nonlinear contacts between rolls, backup bearings, and mill housing. Rolls are modelled with Timoshenko beam elements and consider roll crown and taper. Contacts between rolls are modelled by Winkler foundation elements with nonlinear stiffness constant derived by the Hertz contact model. The mill housing is introduced by the matrix condensation method and the backup rolling bearings are defined by nonlinear Winkler foundation elements. A full 3D finite element model of the 20-high rolling mill is built by using a commercial software and used to validate the developed finite element - analytical model. A comparison with data extracted from measurements on a real case study is also provided. Finally, the developed combined finite element - analytical model is exploited to solve a strip profile control problem. A Singular Value Decomposition (SVD) optimization of the taper and backup actuators positions is performed, aiming to achieve a desired strip profile. Actuators saturation is considered. The actuators’ influence maps are also obtained.
TRIZ-GPT: An LLM-Augmented Method For Problem-Solving
Authors: Chen Liuqing , Song Yaxuan , Ding Shixian , Sun Lingyun , Childs Peter , Zuo Haoyu
Published: November 2024

TRIZ, the Theory of Inventive Problem Solving, is derived from a comprehensive analysis of patents across various domains, offering a framework and practical tools for problem-solving. Despite its potential to foster innovative solutions, the complexity and abstractness of TRIZ methodology often make its application challenging. This can require users to have a deep understanding of the theory, as well as substantial practical experience and knowledge across various disciplines. The advent of Large Language Models (LLMs) presents an opportunity to address these challenges by leveraging their extensive knowledge bases and reasoning capabilities for innovative solution generation within TRIZ-based problem-solving process. This study explores and evaluates the application of LLMs within the TRIZ-based problem-solving process. The construction of TRIZ case collections establishes a solid empirical foundation for our experiments and offers valuable resources to the TRIZ community. A specifically designed workflow, utilizing step-by-step reasoning and evaluation-validated prompt strategies, effectively transforms concrete problems into TRIZ problems and finally generates inventive solutions. We present a case study in the mechanical engineering field that highlights the practical application of this LLM-augmented method. This showcases GPT-4’s ability to generate solutions that closely resonate with original solutions and suggests more implementation mechanisms.
Integrated Design for Wave Energy Converter Farms: Assessing Plant, Control, Layout, and Site Selection Coupling in the Presence of Irregular Waves
Authors: Azad Saeed , Khanal Suraj R. , Herber Daniel R. , Jia Gaofeng
Published: November 2024

A promising direction towards reducing the levelized cost of energy for wave energy converter (WEC) farms is to improve their performance. WEC design studies generally focus on a single design domain (e.g., geometry, control, or layout) to improve the farm’s performance under simplifying assumptions, such as regular waves. This strategy, however, has resulted in design recommendations that are impractical or limited in scope because WEC farms are complex systems that exhibit strong coupling among geometry, control, and layout domains. In addition, the location of the candidate site, which has a large impact on the performance of the farm, is often overlooked. Motivated by some of the limitations observed in WEC literature, this study uses an integrated design framework, based on simultaneous control co-design (CCD) principles, to discuss the impact of site selection and wave type on WEC farm design. Interactions among plant, control, and layout are also investigated and discussed using a wide range of simulations and optimization studies. All of the studies were conducted using frequency-domain heaving cylinder WEC devices within a farm with a linear reactive controller in the presence of irregular probabilistic waves. The results provide high-level guidelines to help the WEC design community move toward an integrated design perspective.