bluuebunny/crossref_metadata_2025_split huggingface dataset:
Particulate Deposition Effects on Internal Swirl Cooling of Turbine Blades
Authors: Yang Xing , Hao Zihan , Seibold Florian , Feng Zhenping , Ligrani Phillip , Weigand Bernhard 
Published: November 2022

Abstract
Particulate deposition effects on flow and heat transfer in an internal swirl tube subjected to fly ash ingestion were investigated by constructing an unsteady simulation framework, in which a particle–wall interaction model and a mesh morphing technique were implemented. Swirling flows in the swirl tube were induced by two tangential jet nozzles. Particles having a mean diameter of 6.5 μm were released from the nozzle inlets to model an exposure duration of 4500 h for engine operation in real fly ash environment using scale factors in the unsteady simulations. Particle deposition and its dynamic process were examined, and the effects of deposition on the swirling flow were quantified by comparing time-averaged velocity profiles, vorticity, pressure loss, and heat transfer with those from a clean tube without deposition. Results reveal that the most upstream section of the swirl tube captures the majority of the particles and the deposition distributions show a spiral pattern over the tube wall. The total mass of the deposits within the tube linearly increases, while local deposition thickness has a nonlinear relationship with the exposure time due to the interaction of the particles with the swirling flow. The deposition can generate a maximum of 15% reduction in cross-sectional area of the tube within the exposure duration, resulting in a reduced swirl number, because of the accelerated axial velocity and the decreased circumferential velocity, and further lower heat transfer in the downstream section of the tube relative to the clean tube case. However, as the heat transfer in the upstream deposition section is enhanced by the roughness due to the deposition, area-averaged heat transfer throughout the entire swirl tube is slightly improved by 4.0% but simultaneously a 179% higher pressure loss is observed, leading to an overall thermal performance value of 0.79 (relative to 1.0 for a clean tube), indicating substantial degradation of cooling performance in the fouled swirl tube.
Comparative Study of First-Order Moving Asymptotes Optimizers for the Moving Morphable Components Topology Optimization Framework
Authors: Rochefort-Beaudoin Thomas , Vadean Aurelian , Gamache Jean-François , Achiche Sofiane 
Published: November 2022

Abstract
Machine learning-accelerated topology optimization faces the challenge of generating large amounts of optimal topologies for supervised learning on a training dataset. This data generation step is challenging for the method of Moving Morphable Components (MMC) which presents an oscillatory behavior near local optimum that negatively affects its convergence speed and therefore present a high data generation cost. This paper presents a comparative study of the most-used first-order optimizers applied to minimum compliance problems under the MMC framework for topology optimization. The Method of Moving Asymptotes (MMA), its Globally Convergent version (GCMMA) and the hybrid MMA-GCMMA optimizer are compared using their final compliance and the total number of iterations until convergence as performance metrics. An extensive set of diversified boundary conditions for a rectangular beam minimum compliance problem is used as case study. The method of performance profiles is utilized to provide for each solver a probability distribution of outperforming its counterparts. Numerical results show that using a hybrid optimizer can accelerate the convergence speed of the MMC framework while still producing equally compliant topologies when compared to the MMA optimizer.
Resilience Modeling in Complex Engineered Systems With Human-Machine Interactions
Authors: Irshad Lukman , Hulse Daniel 
Published: November 2022

Abstract
In recent times, there has been a growing interest in resilience-based design. Resilience-based design operates on the concept that failures and unexpected events will happen, and when they occur, complex engineered systems should be able to operate within acceptable bounds and recover reasonably. Humans can contribute to the resilience of a system by quickly detecting unforeseen events and taking corrective measures. To this effect, researchers have proposed guidelines and design approaches that can help promote human-system resilience. However, there is no early design stage tool to validate if a system is indeed resilient after applying these guidelines and design methods. In this research, we integrate the Human Error and Functional Failure Reasoning (HEFFR) framework into the fmd-tools toolkit to enable designers to model the combined (machine, human, and joint) failures, including their propagation and dynamic effects, during early design stages. This integrated tool also allows designers to model the effects of performance shaping factors, team dynamics, and human-machine interactions in systems of systems. A demonstrative example of a remotely operated rover is explored to demonstrate how this approach can be applied to understand resilience in complex engineered systems with human interactions.
Quantifying Vision Obscuration of A-Pillar Concept Variants Using Digital Human Modeling
Authors: Srinivasan Sriram , Demirel H. Onan 
Published: November 2022

Abstract
Pillars in modern vehicles provide structural integrity by surrounding the greenhouse (windshield, rear, and side windows) and joining the roof with the frame. The forward-most roof support posts, A-pillars, straddle the windshield and have an essential design impact that affects the aerodynamic performance, roof crush, and frontal impact performance. Although they protect the driver and passengers, A-pillars also create obstruction zones by limiting drivers’ forward field of view (FOV). Therefore, the reduced forward vision caused by the obstruction zones leads to unforeseeable accidents or mishaps. We argue that providing a better pillar design that embodies a human-centered design approach could avoid such incidents. In this research, we propose an early design framework that integrates methods of generative design (GD) and digital human modeling (DHM) to demonstrate a proof-of-concept study that focuses on quantifying A-pillar obstruction in realistic traffic scenarios. This research also aims to illustrate how the framework functions through a case study, which compares concept pillar designs with cutout sections (see-through holes) to current pillar models (without see-through holes) in reducing the obstruction zones. The framework is used to assess whether the concept design approach improves the overall drivers’ visibility of traffic objects that would otherwise be obscured behind current pillars.
On the Influence of Mean Value on Random Fatigue Damage Computation
Authors: Sgamma Michele , Bucchi Francesco , Frendo Francesco 
Published: November 2022

Abstract
The frequency-domain approach to fatigue life estimation in random loading has been largely investigated due to its computational advantages, and several methods for the frequency translation of most common time-domain methods have been proposed. However, the largest part of frequency methods only focuses on the estimation of the marginal amplitude probability density function of cycles and do not consider the damage increment caused by the positive mean value of the counted cycles. This paper aims to deepen the effect of the random mean value of the cycles on fatigue damage estimation by simulating a large number of time histories with different power spectral densities and computing the resulting damage for a steel material using time-domain instruments like rainflow count, Goodman correction for mean value and Palmgren-Miner linear rule for damage cumulation. The influence of the presence of a non-zero global mean value of the process on the damage computation due to the random cycle mean value has been investigated. The study of the effect of signal PSD resulted in a non-monotonic influence on the ratio between the total damage and an approximated damage that does not consider random mean values. Finally, some further simulations have been done to study the effect of variations in the S-N curve of the material.
Upper Extremity Joint Torque Estimation Through an EMG-Driven Model
Authors: Tahmid Shadman , Font-Llagunes Josep Maria , Yang James 
Published: November 2022

Abstract
Cerebrovascular accidents like a stroke can affect lower limb as well as upper extremity joints (i.e., shoulder, elbow or wrist) and hinder the ability to produce necessary torque for activities of daily living. In such cases, muscles’ ability to generate force reduces, thus affecting the joint’s torque production. Understanding how muscles generate force is a key element to injury detection. Researchers developed several computational methods to obtain muscle forces and joint torques. Electromyography (EMG) driven modeling is one of the approaches to estimate muscle forces and obtain joint torques from muscle activity measurements. Musculoskeletal models and EMG-driven models require necessary muscle-specific parameters for the calculation. The focus of this research is to investigate the EMG-driven approach along with an upper extremity musculoskeletal model to determine muscle forces of two major muscle groups, biceps brachii and triceps brachii, consisting of seven muscle-tendon units. Estimated muscle forces were used to determine the elbow joint torque. Experimental EMG signals and motion capture data were collected for a healthy subject. The musculoskeletal model was scaled to match the geometric parameters of the subject. First, the approach calculated muscle forces and joint moment for simple elbow flexion-extension. Later, the same approach was applied to an exercise called triceps kickback, which trains the triceps muscle group. Individual muscle forces and net joint torques for both tasks were estimated.
A Morphological Evaluation of Shoulder Parameters: A Medical Support Tool for the Diagnosis
Authors: Ghidotti Anna , Locatelli Fabio , Belotti Nicolò , Regazzoni Daniele , Rizzi Caterina 
Published: November 2022

Abstract
Shoulder disorders are very common in the middle-aged population, due to several causes. The traditional diagnosis relies on the knowledge and the experience of the physician but a clinical misinterpretation in this early phase can have serious consequences for the patient’s health. The aim of this study is to investigate morphological shoulder parameters, as indicators of healthy or pathological conditions. In this way, it is possible to generate a quantitative report, based on measurements. It can be exploited as a medical support tool for physicians to either confirm the diagnosis, or to raise reasonable doubts, as far as the results differed. However, not all the shoulder disorders can be identified through this approach. Magnetic Resonance and Computed Tomography images of pathological shoulders have been employed for the study. The predefined morphological parameters have been measured on 2D medical images as well as from 3D reconstructed virtual model. Critical Shoulder Angle has been identified as the most significant parameter. It is well known that it is affected by Glenoid Inclination, Lateral Acromial Extension and Acromial Height. However, the contribution of each factor is not clear. Hence, a statistical analysis has been performed to understand how its sub-parameters influence it.
Knowledge Extraction Method to Support Domain Integrated Design Methodology
Authors: Sun Siyuan , Velivela Pavan Tejaswi , Zeng Yong , Zhao Yaoyao Fiona 
Published: November 2022

Abstract
Nowadays, bio-inspiration has enhanced the creation of sustainable and innovative solutions to modern engineering problems. Nature could inspire mechanical engineers to develop innovative ideas as a great source for multifunctional and optimized designs. However, it is very difficult to extract desired design knowledge from primarily text-based databases and mainly focus on describing the biological system. The main objective of this study is to build a multi-label classification system to classify bio-inspired designs to support the Domain Integrated Design methodology. The proposed system integrates NLP and text mining with several machine learning models to learn and predict the functionalities of bio-inspired design. Various design functionalities were summarized based on the available resources from the AskNature database, then the main information extracted from the database, and they were labelled with corresponding multi-functionalities. Due to the high complexity of the multi-label classification system, multi-label classifiers were built based on different combinations of baseline classifiers and trained to classify selected AskNature pages. One case study was conducted to verify the impact of the proposed system. The results showed that the proposed system is feasible and would be a solution for classifying the bio-inspired design and functional basis knowledge extraction method to support DID methodology.
Are You Feeling Happy? the Effect of Emotions on People’s Interaction Experience Toward Empathetic Chatbots
Authors: Liao Ting , Yan Bei 
Published: November 2022

Abstract
People may experience emotions before interacting with automated agents to seek information and support. However, existing literature has not well examined how human emotional states affect their interaction experience with agents or how automated agents should react to emotions. This study proposes to test how participants perceive an empathetic agent (chatbot) vs. a non-empathetic one under various emotional states (i.e., positive, neutral, negative) when the chatbot mediates the initial screening process for student advising. Participants are prompted to recall a previous emotional experience and have text-based conversations with the chatbot. The study confirms the importance of presenting empathetic cues in the design of automated agents to support human-agent collaboration. Participants who recall a positive experience are more sensitive to the chatbot’s empathetic behavior. The empathetic behavior of the chatbot improves participants’ satisfaction and makes those who recall a neutral experience feel more positive during the interaction. The results reveal that participants’ emotional states are likely to influence their tendency to self-disclose, interaction experience, and perception of the chatbot’s empathetic behavior. The study also highlights the increasing need for emotional acknowledgment of people who experience positive emotions so that design efforts need to be designated according to people’s dynamic emotional states.
Classification of Surface Defects on Galvanized Cold-Rolled Steel Sheets Using Data-Driven Fault Model With Attention Mechanism
Authors: Chen Hao , Nie Zhenguo , Xu Qingfeng , Fei Jianghua , Yang Kang , Li Yaguan , Fan Wenhui , Liu Xin-Jun 
Published: November 2022

Abstract
In the production of the galvanized cold-rolled steel sheets used for stamping car body parts, in-situ and real-time defective detecting is crucial for quality control, in which various types of defects will inevitably occur. It is challenging to improve the accuracy of defect image classification by appropriate means to assist the manual screening process better. Defects under actual production conditions are often not prominent enough in defect characteristics, and there may be a significant similarity between different defect categories. To eliminate this weakness, we propose a data-driven faulty detection model named Steel Faulty Detection Attention Net (SFDANet) that uses images of the galvanized steel surface as input to identify whether the product is qualified and automatic classification of defect types instantaneously. This method can shorten product inspection time and improve production line efficiency automatically. In addition, the attention mechanism is utilized, enhancing the performance of SFDANet. Compared with the baseline that applied the ResNet method, SFDANet achieves a noticeable improvement in the classification accuracy of the test data. The well-trained model can successfully show an improved performance than the baseline models on the multiple types of faulty. Enhanced by SFDANet with high classification accuracy, the defect rate of products is significantly reduced, and the production speed of the production line is significantly improved.
Fuzzy-Based Adaptive Model Predictive Control for Deteriorating Model Uncertainty of Hydraulic Servo Systems
Authors: Sun Hao , Tao Jianfeng , Yu Honggan , Liu Chengliang 
Published: November 2022

Abstract
The deterioration of model uncertainties is an important factor restricting the control performance for hydraulic servo systems. This paper proposes a fuzzy-based adaptive model predictive control (MPC) method with a linear extended state observer (LESO) for such systems. The LESO is employed to estimate the total disturbances and other unmeasured states to predict system behavior. Based on the constructed objective function, the optimal output sequence is obtained via the receding horizon and repeating optimization. As for the output feedback MPC, the influence of key parameters on observation errors is analyzed and revealed in the face of deteriorating model uncertainties. Furthermore, its impact on the tracking performance of the system is considered according to the closed-loop state equation on the premise of asymptotic stability. Based on this, a fuzzy-based weight coefficient self-tuning strategy is developed to achieve the bounded observation errors. That is, during the repeating optimization, the defined and estimated disturbances are dynamically adjusted, simultaneously. The closed-loop tracking errors are thus improved compared with the case of the fixed weight coefficient. The simulation results indicate that the proposed fuzzy-based adaptive MPC controller shows an excellent tracking performance in the case of the deterioration of the model parameter uncertainty and the unmodeled uncertainty.
Association of Recess Provision With Elementary School-Aged Children’s Physical Activity, Adiposity, and Cardiorespiratory and Muscular Fitness
Authors: Clevenger Kimberly A. , McNarry Melitta A. , Mackintosh Kelly A. , Berrigan David 
Published: November 2022

Purpose: To identify associations between amount of school recess provision and children’s physical activity (PA), weight status, adiposity, cardiorespiratory endurance, muscular strength, and muscular endurance. Method: Data from 6- to 11-year-old participants (n = 499) in the 2012 National Youth Fitness Survey were analyzed. Parents/guardians reported children’s PA levels and recess provision, categorized as no/minimal (9.0%), low (26.1%), medium (46.0%), or high (18.9%). Children wore a wrist-worn accelerometer for 7 days and completed anthropometric measurements. Fitness was assessed using grip strength and treadmill, pull-up, and plank tests. Cross-sectional linear and logistic regression compared outcomes across levels of recess provision adjusting for the survey’s complex sampling design. Results: Children with high provision of recess were 2.31 times more likely to meet PA guidelines according to parent report than those with no/minimal recess. Accelerometer-measured PA followed a more U-shaped pattern, wherein PA was higher in children with high, compared to low, recess provision but comparable to those with no/minimal recess provision. There were no associations with weight status, adiposity, or fitness. Conclusion: Current recess recommendations (20 min·d−1) may be insufficient as 30 minutes per day of recess was associated with a 2-fold greater likelihood of achieving recommended PA levels. Additional research on recess quantity and quality is needed.
Insights into complex I assembly: Function of NDUFAF1 and a link with cardiolipin remodeling
Authors: Schiller Jonathan , Laube Eike , Wittig Ilka , Kühlbrandt Werner , Vonck Janet , Zickermann Volker 
Published: November 2022

Respiratory complex I is a ~1-MDa proton pump in mitochondria. Its structure has been revealed in great detail, but the structural basis of its assembly, in humans involving at least 15 assembly factors, is essentially unknown. We determined cryo–electron microscopy structures of assembly intermediates associated with assembly factor NDUFAF1 in a yeast model system. Subunits ND2 and NDUFC2 together with assembly factors NDUFAF1 and CIA84 form the nucleation point of the NDUFAF1-dependent assembly pathway. Unexpectedly, the cardiolipin remodeling enzyme tafazzin is an integral component of this core complex. In a later intermediate, all 12 subunits of the proximal proton pump module have assembled. NDUFAF1 locks the central ND3 subunit in an assembly-competent conformation, and major rearrangements of central subunits are required for complex I maturation.
Diet-derived metabolites and mucus link the gut microbiome to fever after cytotoxic cancer treatment
Authors: Schwabkey Zaker I. , Wiesnoski Diana H. , Chang Chia-Chi , Tsai Wen-Bin , Pham Dung , Ahmed Saira S. , Hayase Tomo , Ortega Turrubiates Miriam R. , El-Himri Rawan K. , Sanchez Christopher A. , Hayase Eiko , Frenk Oquendo Annette C. , Miyama Takahiko , Halsey Taylor M. , Heckel Brooke E. , Brown Alexandria N. , Jin Yimei , Raybaud Mathilde , Prasad Rishika , Flores Ivonne , McDaniel Lauren , Chapa Valerie , Lorenzi Philip L. , Warmoes Marc O. , Tan Lin , Swennes Alton G. , Fowler Stephanie , Conner Margaret , McHugh Kevin , Graf Tyler , Jensen Vanessa B. , Peterson Christine B. , Do Kim-Anh , Zhang Liangliang , Shi Yushu , Wang Yinghong , Galloway-Pena Jessica R. , Okhuysen Pablo C. , Daniel-MacDougall Carrie R. , Shono Yusuke , Burgos da Silva Marina , Peled Jonathan U. , van den Brink Marcel R.M. , Ajami Nadim , Wargo Jennifer A. , Reddy Pavan , Valdivia Raphael H. , Davey Lauren , Rondon Gabriela , Srour Samer A. , Mehta Rohtesh S. , Alousi Amin M. , Shpall Elizabeth J. , Champlin Richard E. , Shelburne Samuel A. , Molldrem Jeffrey J. , Jamal Mohamed A. , Karmouch Jennifer L. , Jenq Robert R. 
Published: November 2022

Not all patients with cancer and severe neutropenia develop fever, and the fecal microbiome may play a role. In a single-center study of patients undergoing hematopoietic cell transplant (
n
= 119), the fecal microbiome was characterized at onset of severe neutropenia. A total of 63 patients (53%) developed a subsequent fever, and their fecal microbiome displayed increased relative abundances of
Akkermansia muciniphila
, a species of mucin-degrading bacteria (
P
= 0.006, corrected for multiple comparisons). Two therapies that induce neutropenia, irradiation and melphalan, similarly expanded
A. muciniphila
and additionally thinned the colonic mucus layer in mice. Caloric restriction of unirradiated mice also expanded
A. muciniphila
and thinned the colonic mucus layer. Antibiotic treatment to eradicate
A. muciniphila
before caloric restriction preserved colonic mucus, whereas
A. muciniphila
reintroduction restored mucus thinning. Caloric restriction of unirradiated mice raised colonic luminal pH and reduced acetate, propionate, and butyrate. Culturing
A. muciniphila
in vitro with propionate reduced utilization of mucin as well as of fucose. Treating irradiated mice with an antibiotic targeting
A. muciniphila
or propionate preserved the mucus layer, suppressed translocation of flagellin, reduced inflammatory cytokines in the colon, and improved thermoregulation. These results suggest that diet, metabolites, and colonic mucus link the microbiome to neutropenic fever and may guide future microbiome-based preventive strategies.