bluuebunny/crossref_metadata_2025_split huggingface dataset:
Characterizing elementary students' authentic navigation through an engineering design task using a design space lens
Authors: Dalvi Tejaswini , Wendell Kristen 
Published: March 2024

AbstractInsights on students' own authentic design practices—nascent design practices without much adult guidance—are crucial to informing responsive facilitation of engineering design tasks. This study unpacks how elementary students interpret teacher given information about a design task, and interact with each other and given resources, to traverse a design trajectory with limited adult intervention. We present a qualitative case study of the trajectory of a 3rd grade student team working to design a solution in response to an engineering challenge, with minimum adult intervention. The design space lens, which emerges from studies of nonlinear, fluid nature of professionals' design trajectories, guides our analysis of elementary students' design trajectory. The use of design space reveals characteristics of student navigation as dictated by their interpretation of given information, and by their observation and analysis of how their solution behaves and performs. The findings show students' proactive and autonomous selection of design practices driven by the goal of solving the problem while navigating criteria and constraints. The study highlight a sophisticated trajectory that elementary students can traverse when given the autonomy. Awareness of these nascent strengths has implications for helping educators to responsively plan and facilitate design tasks.
Unsupervised Denoising and Super-Resolution of Vascular Flow Data by Physics-Informed Machine Learning
Authors: Sautory Théophile , Shadden Shawn C. 
Published: March 2024

Abstract
We present an unsupervised deep learning method to perform flow denoising and super-resolution without high-resolution labels. We demonstrate the ability of a single model to reconstruct three-dimensional stenosis and aneurysm flows, with varying geometries, orientations, and boundary conditions. Ground truth data was generated using computational fluid dynamics, and then corrupted with multiplicative Gaussian noise. Auto-encoders were used to compress the representations of the flow domain geometry and the (possibly noisy and low-resolution) flow field. These representations were used to condition a physics-informed neural network. A physics-based loss was implemented to train the model to recover lost information from the noisy input by transforming the flow to a solution of the Navier–Stokes equations. Our experiments achieved mean squared errors in the true flow reconstruction of O(1.0 × 10−4), and root mean squared residuals of O(1.0 × 10−2) for the momentum and continuity equations. Our method yielded correlation coefficients of 0.971 for the hidden pressure field and 0.82 for the derived wall shear stress field. By performing point-wise predictions of the flow, the model was able to robustly denoise and super-resolve the field to 20× the input resolution.
CarbonKG: Industrial Carbon Emission Knowledge Graph-Based Modeling and Application for Carbon Traceability of Complex Manufacturing Process
Authors: Wu Tao , Li Jie , Bao Jinsong , Liu Qiang , Jin Zhengkai , Gao Jun 
Published: March 2024

Abstract
In the context of the 2030 carbon dioxide emissions peak target, achieving carbon neutrality in manufacturing is essential. However, the complex and extensive supply chain, ranging from raw materials to the final product, presents significant challenges in assessing the carbon footprint throughout the lifecycle. This paper concentrates on the manufacturing process and introduces a modeling approach for quantifying and predicting carbon emissions, utilizing industrial intelligent technologies like process mining and knowledge graphs. First, it is crucial to establish a comprehensive carbon emission quantification model for each manufacturing stage, encompassing the energy, material, personnel, and carbon flow. Subsequently, this paper proposes an industrial carbon emission knowledge graph-based model (CarbonKG) to record and compute emissions at each production stage. Furthermore, process mining technology aids in analyzing the global distribution and movement of carbon emissions within the manufacturing process. Finally, this research presents a two-stage predictive approach for manufacturing process carbon emissions based on CarbonKG. The first stage involves initializing a local order carbon graph for prediction and developing a model to find similar cases. The second stage uses a graph-matching model to identify the Top-K similar order cases, using their carbon emission data for comprehensive prediction. Moreover, the feasibility of the proposed method was verified using actual production process data from industrial companies.
A Decentralized Multi-Agent Path Planning Approach Based on Imitation Learning and Selective Communication
Authors: Feng Bohan , Bi Youyi , Li Mian , Lin Liyong 
Published: March 2024

Abstract
Multi-agent path planning (MAPP) is crucial for large-scale mobile robot systems to work safely and properly in complex environments. Existing learning-based decentralized MAPP approaches allow each agent to gather information from nearby agents, leading to more efficient coordination among agents. However, these approaches often struggle with reasonably handling local information inputs for each agent, and their communication mechanisms between agents need to be further refined to treat those congested traffic scenarios effectively. To address these issues, we propose a decentralized MAPP approach based on imitation learning and selective communication. Our approach adopts an imitation learning architecture that enables agents to rapidly learn complex behaviors from expert planning experience. The information extraction layer is integrated with convolutional neural network (CNN) and gated recurrent unit (GRU) for capturing features from local field-of-view observations. A two-stage selective communication process based on graph attention neural network (GAT) is developed to reduce the required neighbor agents in inter-agent communication. In addition, an adaptive strategy switching mechanism utilizing local expert-planned paths is designed to support robots to escape from local traps. The effectiveness of our proposed approach is evaluated in simulated grid environments with varying map sizes, obstacle densities, and numbers of agents. Experimental results show that our approach outperforms other decentralized path planning methods in success rate while maintaining the lowest flowtime variation and communication frequency. Furthermore, our approach is computationally efficient and scalable, making it suitable for real-world applications.
Sensitivity Analysis and Optimization of Heat Transfer Performance of Ultra-Thin Vapor Chamber With Composite Wick
Authors: Huang Zhaohui , Li Rui , Gan Yunhua 
Published: March 2024

Abstract
An ultra-thin vapor chamber (VC) with the composite wick formed by four spiral woven meshes (SWMs) and a copper mesh was proposed to solve the heat dissipation problem in miniaturized electronic equipment because of its sufficient heat transfer capability under limited thickness. However, the influence factors on the thermal performance of the VC with composite wick are more than that of the VC with a single type of wick. In this study, in order to investigate the thermal performance of the VC with composite wick, a theoretical model was developed to calculate the maximum heat transfer capacity. Besides, a three-dimensional numerical model for the heat transfer characteristics was established, and the simulation results have a good match with the experimental results. The orthogonal test method was adopted to determine that both the width of the vapor channel (wv) and the thickness of the vapor channel (tv) have a significant effect on the maximum heat transfer capacity and thermal resistance, while the porosity of the mesh (εmesh) has a prominent effect on the maximum heat transfer capacity, but has little effect on the thermal resistance. Further optimization of the sensitive factors for VC heat transfer performance was achieved to enhance the maximum heat transfer capacity.
A Finite Element Analysis-Unascertained Measure Theory-Based Hybrid Approach to Safety Assessment for Pipelines Subject to Landslide Disasters
Authors: Siming Liu , Peng Zhang , Wei Liu , Hu Li , Qiao Tang , Qin Yang , Yunfei Huang 
Published: March 2024

Abstract
Pipeline safety faces a prevalent threat in mountainous areas due to landslides. The advent of landslides introduces the risk of pipeline leaks or ruptures, posing a significant threat to the environment, with the potential for casualties. Throughout the occurrence of landslides, uncertainties abound, yet few studies have addressed the incorporation of uncertainties in assessing pipeline safety. This work proposes a novel hybrid approach to the safety assessment for pipelines under landslides. The use of finite element analysis (FEA) models the pipeline under the action of landslides. The numerical outcomes, combined with unascertained measure theory (UMT), develop a multi-indicator unascertained measure (UM) matrix. Random forest (RF) algorithm is employed to determine the weight of indicators in the matrix. The hybrid application of set pair theory and the UM evaluation vector finally determine the pipeline safety degree and level. The proposed methodology has been well-validated through a case study on an in-service pipeline. The results indicate that the case pipeline safety degree is 2.777, 2.132, 3.132, 3.904, and 2.240, respectively. The corresponding safety level is III, II, III, IV, and II, respectively, which is consistent with the pipeline's actual condition. Different from the conventional safety assessment approach, the proposed methodology demonstrates the enhanced effectiveness, facilitating a more precise evaluation of the pipeline's safety condition.
Thermo-Hydrodynamic Performance Evaluation of a Mini-Channel Inserted With TPMS-Based Porous Lattice
Authors: Rathore Surendra Singh , Mehta Balkrishna , Kumar Pradeep , Asfer Mohammed 
Published: March 2024

Abstract
The coupled pore-scale and Darcy-scale numerical simulations are used in this work to investigate heat transport and hydrodynamic characteristics within a porous channel constructed by using a primitive lattice based on triply-periodic-minimal-surface. The pore-scale simulation is used in void subdomain, whereas the microporous-solid subdomain is simulated by Darcy-scale simulation for a range of mass transfer rates 4×10−7 to 2×10−3 kg/s (corresponding Reynolds numbers 0.1&lt;Re&lt;500). The liquid-water (Pr=7) is used as the working fluid. The Darcy number and inertial drag coefficient are calculated using the pressure drop in the channel along with the heat transfer coefficient (Nusselt number) on both internal and external walls. The quantifiable deviation from local thermal equilibrium (LTE) is also established. The results reveal two distinct, namely passive and active regimes, depending upon the permeability of the microporous-solid subdomain. It is found that the Nusselt number is almost constant for permeability values Kμ&lt;10−11m2(passive regime). However, significant variation is observed in the Nu for higher value of permeability Kμ≥10−11m2(active regime). The Darcy number and effective Nusselt number are found to be increasing, while the inertial drag coefficient and deviation from LTE are found to be decreasing with the permeability in the active regime only.
Quantification of Cartilage Poroelastic Material Properties Via Analysis of Loading-Induced Cell Death
Authors: Kotelsky Alexander , Carrier Joseph S. , Buckley Mark R. 
Published: March 2024

Abstract
Articular cartilage (AC) is a load-bearing tissue that covers long bones in synovial joints. The biphasic/poroelastic mechanical properties of AC help it to protect joints by distributing loads, absorbing impact forces, and reducing friction. Unfortunately, alterations in these mechanical properties adversely impact cartilage function and precede joint degeneration in the form of osteoarthritis (OA). Thus, understanding what factors regulate the poroelastic mechanical properties of cartilage is of great scientific and clinical interest. Transgenic mouse models provide a valuable platform to delineate how specific genes contribute to cartilage mechanical properties. However, the poroelastic mechanical properties of murine articular cartilage are challenging to measure due to its small size (thickness ∼ 50 microns). In the current study, our objective was to test whether the poroelastic mechanical properties of murine articular cartilage can be determined based solely on time-dependent cell death measurements under constant loading conditions. We hypothesized that in murine articular cartilage subjected to constant, sub-impact loading from an incongruent surface, cell death area and tissue strain are closely correlated. We further hypothesized that the relationship between cell death area and tissue strain can be used—in combination with inverse finite element modeling—to compute poroelastic mechanical properties. To test these hypotheses, murine cartilage-on-bone explants from different anatomical locations were subjected to constant loading conditions by an incongruent surface in a custom device. Cell death area increased over time and scaled linearly with strain, which rose in magnitude over time due to poroelastic creep. Thus, we were able to infer tissue strain from cell death area measurements. Moreover, using tissue strain values inferred from cell death area measurements, we applied an inverse finite element modeling procedure to compute poroelastic material properties and acquired data consistent with previous studies. Collectively, our findings demonstrate in the key role poroelastic creep plays in mediating cell survival in mechanically loaded cartilage and verify that cell death area can be used as a surrogate measure of tissue strain that enables determination of murine cartilage mechanical properties.
Fatigue-Life Prediction of the Optimized Savonius Vertical-Axis Wind Turbine Composite Blades
Authors: Ghoneam Sobhy M. , Hamada Ahmed A. , Sherif Taha S. 
Published: March 2024

Abstract
The sustainability of modern human civilization is significantly concerned with enhancing sustainable energy resources. Vertical-Axis Wind Turbines (VAWTs) gained a promising impact on the global energy mix due to their belief in sustainable energy solutions. This paper presents an integrated study of the dynamic performance and fatigue-life characteristics of the optimized Savonius-rotor VAWT composite blades. Furthermore, this paper introduces an experimental validation, numerical modeling, and visualization/verification study of the optimized VAWT composite blades and assesses its impact on the whole performance of the optimized Savonius VAWTs. Four combination levels of the optimal settings of the S-VAWT composite blade configurations are designed, modeled, simulated, and fabricated of Carbon/Glass—Polyester. The modal parameters such as natural frequency, mode shape, and damping factor are investigated using fast Fourier transformer (FFT) analyzer. The numerical finite element models (FEM) are generated to model, visualize, simulate, and predict the dynamic nature and fatigue-life characteristics of the optimal S-VAWT composite blade configurations. A correlation between the experimental and numerical results is established. The study findings highlight the significant impact of VAWT composite blades in enhancing the dynamic performance and fatigue-life characteristics of Savonius VAWTs. Furthermore, a small damage percentage of 2.8% predicted by the finite element analysis (FEA) for the optimal S-VAWT composite blade configuration validated the sustainability of the VAWT composite blade configurations in dynamic loading conditions.
The Influence of High-Intensity Work on the Record Power Profile of Under-23, Pro Team, and World Tour Cyclists
Authors: Leo Peter , Mateo-March Manuel , Giorgi Andrea , Muriel Xabier , Javaloyes Alejandro , Barranco-Gil David , Pallarés Jesús G. , Lucia Alejandro , Mujika Iñigo , Valenzuela Pedro L. 
Published: March 2024

Background: Durability (ie, the ability to attenuate the decline in performance after accumulated work) has been identified as a performance determinant in elite cyclists. The aim of the present study was to compare durability in elite cyclists of various performance levels, particularly after high-intensity work, referred to as “high-intensity durability.” Methods: Forty-nine (N = 49) male road cyclists were categorized as either under 23 years of age (U23) (N = 11), Pro Team (N = 13), or World Tour (N = 24). The participants’ critical power (CP) was assessed during the preseason. Thereafter, the participants’ maximum mean power (MMP) values were determined for efforts of different durations (from 5 s to 30 min) after different levels of accumulated work above CP (from 0 to 7.5 kJ·kg−1). Results: U23 cyclists showed a significant reduction of all relative MMP values for durations ≥1 minute after ≥5 kJ·kg−1 above CP compared with the “fresh” state (0 kJ·kg−1), whereas in Pro Team and World Tour cyclists, a significant reduction was not observed until 7.5 kJ·kg−1 above CP. In the “fresh” state, both Pro Team and particularly World Tour cyclists attained higher MMP values for efforts ≥10 minutes than U23 riders. However, more differences emerged with greater previous work levels, and indeed after 7.5 kJ·kg−1 above CP World Tour cyclists attained higher MMP values than both U23 and Pro Team cyclists for most efforts (≥30 s). Conclusion: Pro Team and particularly World Tour cyclists tolerate greater levels of accumulated work at high intensity, which might support the importance of high-intensity durability for performance.
Chagas disease in immunocompromised patients
Authors: Clark Eva H. , Messenger Louisa A. , Whitman Jeffrey D. , Bern Caryn 
Published: March 2024

SUMMARY

As Chagas disease remains prevalent in the Americas, it is important that healthcare professionals and researchers are aware of the screening, diagnosis, monitoring, and treatment recommendations for the populations of patients they care for and study. Management of
Trypanosoma cruzi
infection in immunocompromised hosts is challenging, particularly because, regardless of antitrypanosomal treatment status, immunocompromised patients with Chagas disease are at risk for
T. cruzi
reactivation, which can be lethal. Evidence-based practices to prevent and manage
T. cruzi
reactivation vary depending on the type of immunocompromise. Here, we review available data describing Chagas disease epidemiology, testing, and management practices for various populations of immunocompromised individuals, including people with HIV and patients undergoing solid organ and hematopoietic stem cell transplantation.
HIV-1 capsid stability and reverse transcription are finely balanced to minimize sensing of reverse transcription products
via
the cGAS-STING pathway
Authors: Eschbach Jenna E. , Puray-Chavez Maritza , Mohammed Shawn , Wang Qiankun , Xia Ming , Huang Lin-Chen , Shan Liang , Kutluay Sebla B. 
Published: March 2024

ABSTRACT

A critical determinant for early post-entry events, the HIV-1 capsid (CA) protein forms the conical core when it rearranges around the dimeric RNA genome and associated viral proteins. Although mutations in CA have been reported to alter innate immune sensing of HIV-1, a direct link between core stability and sensing of HIV-1 nucleic acids has not been established. Herein, we assessed how manipulating the stability of the CA lattice through chemical and genetic approaches affects innate immune recognition of HIV-1. We found that destabilization of the CA lattice resulted in potent sensing of reverse transcription products when destabilization
per se
does not completely block reverse transcription. Surprisingly, due to the combined effects of enhanced reverse transcription and defects in nuclear entry, two separate CA mutants that form hyperstable cores induced innate immune sensing more potently than destabilizing CA mutations. At low concentrations that allowed the accumulation of reverse transcription products, CA-targeting compounds GS-CA1 and lenacapavir measurably impacted CA lattice stability in cells and modestly enhanced innate immune sensing of HIV. Interestingly, innate immune activation observed with viruses containing unstable cores was abolished by low doses of lenacapavir. Innate immune activation observed with both hyperstable and unstable CA mutants was dependent on the cGAS-STING DNA-sensing pathway and reverse transcription. Overall, our findings demonstrate that CA lattice stability and reverse transcription are finely balanced to support reverse transcription and minimize cGAS-STING-mediated sensing of the resulting viral DNA.

IMPORTANCE
In HIV-1 particles, the dimeric RNA genome and associated viral proteins and enzymes are encased in a proteinaceous lattice composed of the viral capsid protein. Herein, we assessed how altering the stability of this capsid lattice through orthogonal genetic and chemical approaches impacts the induction of innate immune responses. Specifically, we found that decreasing capsid lattice stability results in more potent sensing of viral reverse transcription products, but not the genomic RNA, in a cGAS-STING-dependent manner. The recently developed capsid inhibitors lenacapavir and GS-CA1 enhanced the innate immune sensing of HIV-1. Unexpectedly, due to increased levels of reverse transcription and cytosolic accumulation of the resulting viral cDNA, capsid mutants with hyperstable cores also resulted in the potent induction of type I interferon-mediated innate immunity. Our findings suggest that HIV-1 capsid lattice stability and reverse transcription are finely balanced to minimize exposure of reverse transcription products in the cytosol of host cells.