bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pembelajaran Transformatif Dalam Kalangan Wanita Dewasa Yang Menunaikan Haji Di Perak
Authors: Azhar Nur Afiqah Amira , Abd Hamid Mohd Azhar , Mahmud Hussain , Zaini Fadilah , Markom Mohd. Nasir , Othman Muhammed Fauzi , Balwi Mohd Koharuddin 
Published: September 2021

Transformative learning is a process of engaging with an individual’s referral framework including perspectives of meaning, habit, mind and thinking. This study was conducted to study the transformational learning that takes place in adult women in performing the Hajj. This study is designed to identify triggers, factors and effects .The methodology of this study uses semi-structured interviews. A total of 5 informants consisting of adult women aged 45 to 65 were selected using the snowball method. The results show that different triggering events in each individual can change the perspective of informants to change. Factors help inform informants to support and encourage them to continue to move in a positive direction. Suggestions to future researchers may extend the scope of Malaysia including Sabah and Sarawak, study gender differences and use data triangulation.
Physiological complexity: influence of ageing, disease and neuromuscular fatigue on muscle force and torque fluctuations
Authors: Pethick Jamie , Winter Samantha L. , Burnley Mark 
Published: September 2021

New Findings

What is the topic of this review?
Physiological complexity in muscle force and torque fluctuations, specifically the quantification of complexity, how neuromuscular complexityis altered by perturbations and the potential mechanism underlying changes in neuromuscular complexity.

What advances does it highlight?
The necessity to calculate both magnitude‐ and complexity‐based measures for the thorough evaluation of force/torque fluctuations. Also the need for further research on neuromuscular complexity, particularly how it relates to the performance of functional activities (e.g. manual dexterity, balance, locomotion).

AbstractPhysiological time series produce inherently complex fluctuations. In the last 30 years, methods have been developed to characterise these fluctuations, and have revealed that they contain information about the function of the system producing them. Two broad classes of metrics are used: (1) those which quantify the regularity of the signal (e.g. entropy metrics); and (2) those which quantify the fractal properties of the signal (e.g. detrended fluctuation analysis). Using these techniques, it has been demonstrated that ageing results in a loss of complexity in the time series of a multitude of signals, including heart rate, respiration, gait and, crucially, muscle force or torque output. This suggests that as the body ages, physiological systems become less adaptable (i.e. the systems’ ability to respond rapidly to a changing external environment is diminished). More recently, it has been shown that neuromuscular fatigue causes a substantial loss of muscle torque complexity, a process that can be observed in a few minutes, rather than the decades it requires for the same system to degrade with ageing. The loss of torque complexity with neuromuscular fatigue appears to occur exclusively above the critical torque (at least for tasks lasting up to 30 min). The loss of torque complexity can be exacerbated with previous exercise of the same limb, and reduced by the administration of caffeine, suggesting both peripheral and central mechanisms contribute to this loss. The mechanisms underpinning the loss of complexity are not known but may be related to altered motor unit behaviour as the muscle fatigues.
Naked mole‐rat brain mitochondria tolerate in vitro ischaemia
Authors: Cheng Hang , Pamenter Matthew E. 
Published: September 2021

AbstractNaked mole‐rats (NMRs; Heterocephalus glaber) are among the most hypoxia‐tolerant mammals. There is evidence that the NMR brain tolerates in vitro hypoxia and NMR brain mitochondria exhibit functional plasticity following in vivo hypoxia; however, if and how these organelles tolerate ischaemia and how ischaemic stress impacts mitochondrial energetics and redox regulation is entirely unknown. We hypothesized that mitochondria fundamentally contribute to in vitro ischaemia resistance in the NMR brain. To test this, we treated NMR and CD‐1 mouse cortical brain sheets with an in vitro ischaemic mimic and evaluated mitochondrial respiration capacity and redox regulation following 15 or 30 min of ischaemia or ischaemia/reperfusion (I/R). We found that, relative to mice, the NMR brain largely retains mitochondrial function and redox balance post‐ischaemia and I/R. Specifically: (i) ischaemia reduced complex I and II‐linked respiration ∼50–70% in mice, vs. ∼20–40% in NMR brain, (ii) NMR but not mouse brain maintained relatively steady respiration control ratios and robust mitochondrial membrane integrity, (iii) electron leakage post‐ischaemia was lesser in NMR than mouse brain and NMR brain retained higher coupling efficiency, and (iv) free radical generation during and following ischaemia and I/R was lower from NMR brains than mice. Taken together, our results indicate that NMR brain mitochondria are more tolerant of ischaemia and I/R than mice and retain respiratory capacity while avoiding redox derangements. Overall, these findings support the hypothesis that hypoxia‐tolerant NMR brain is also ischaemia‐tolerant and suggest that NMRs may be a natural model of ischaemia tolerance in which to investigate evolutionarily derived solutions to ischaemic pathology.Key points
Ischaemia is highly deleterious to the mammalian brain and this damage is largely mediated by mitochondrial dysfunction.
Naked mole‐rats are among the most hypoxia‐tolerant mammals and their brain tolerates ischaemia ex vivo, but the impact of ischaemia on mitochondrial function is unknown.
Naked mole‐rat but not mouse brain mitochondria retain respiratory capacity and membrane integrity following ischaemia or ischaemia/reperfusion.
Differences in free radical management and respiratory pathway control between species may mediate this tolerance.
These results help us understand how natural models of hypoxia tolerance also tolerate ischaemia in the brain.
Magnetic Field-Assisted 3D Printing of Limpet Teeth Inspired Polymer Matrix Composite With Compression Reinforcement
Authors: Joralmon Dylan , Amonoo Evangeline , Zhu Yizhen , Li Xiangjia 
Published: September 2021

Abstract
Lightweight and cost-effective polymer matrix composites (PMCs) with extraordinary mechanical performance will be a key to the next generation of diverse industrial applications, such as aerospace, electric automobile, and biomedical devices. Limpet teeth made of mineral-polymer composites have been proved as nature’s strongest material due to the unique hierarchical architectures of mineral fiber alignment. Here, we present an approach to build limpet teeth inspired structural materials with precise control of geometric morphologies of microstructures by magnetic field-assisted 3D printing (MF-3DP). α-Iron (III) oxide-hydroxide nanoparticles (α-FeOOHs) are aligned by the magnetic field during 3D printing and aligned α-FeOOHs (aFeOOHs) bundles are further grown to aligned goethite-based bundles (aGBs) by rapid thermal treatment after printing. The mechanical reinforcement of aGBs in PMCs can be modulated by adjusting the geometric morphology and alignment of α-FeOOHs encapsulated inside the 3D printed PMCs. In order to identify the mechanical enhancement mechanism, physics-based modeling, simulation, and tests were conducted, and the results further guided the design of bioinspired goethite-based PMCs. The correlation of the geometric morphology of self-assembled α-FeOOHs, curing characteristics of α-FeOOHs/polymer composite, and process parameters were identified to establish the optimal design of goethite-based PMCs. The 3D printed PMCs with aGBs show promising mechanical reinforcement compared with PMCs without aGBs. This study opens intriguing perspectives for designing high strength 3D printed PMCs on the basis of bioinspired architectures with customized configurations.
Queries and Cues: Textual Stimuli for Reflective Thinking in Digital Mind-Mapping
Authors: Chen Ting-Ju , Mohanty Ronak R. , Krishnamurthy Vinayak R. 
Published: September 2021

Abstract
Mind-mapping is useful for externalizing ideas and their relationships surrounding a central problem. However, balancing between the exploration of different aspects (breadth) of the problem with respect to the detailed exploration of each of its aspects (depth) can be challenging, especially for novices. The goal of this paper is to investigate the notion of “reflection-in-design” through a novel interactive digital mind-mapping workflow that we call “QCue.” The idea behind this workflow is to incorporate the notion of reflective thinking through two mechanisms: (1) offering suggestions to promote depth exploration through user’s queries (Q) and (2) asking questions (Cue) to promote reflection for breadth exploration. This paper is an extension of our prior work (Chen et al., 2020, “Qcue: Queries and Cues for Computer-Facilitated Mind-Mapping,” Proceedings of Graphics Interface 2020, GI 2020, Canadian Human-Computer Communications Society/Société canadienne du Dialogue Human-Machine, pp. 125–136) where our focus was mainly on the algorithmic development and implementation of a cognitive support mechanism behind QCue enabled by ConceptNet (a graph-based rich ontology with “commonsense” knowledge). In this extended work, we first present a detailed summary of how QCue facilitated the breadth-depth balance in a mind-mapping task. Second, we present a comparison between QCue and conventional digital mind-mapping, i.e., without our algorithm through a between-subjects user study. Third, we present new detailed analysis on the usage of different cognitive mechanisms provided by QCue. We further consolidate our prior quantitative analysis and build a connection with our observational analysis. Finally, we discuss in detail the different cognitive mechanisms provided by QCue to stimulate reflection in design.
Toward Reusable Surrogate Models: Graph-Based Transfer Learning on Trusses
Authors: Whalen Eamon , Mueller Caitlin 
Published: September 2021

AbstractSurrogate models have several uses in engineering design, including speeding up design optimization, noise reduction, test measurement interpolation, gradient estimation, portability, and protection of intellectual property. Traditionally, surrogate models require that all training data conform to the same parametrization (e.g., design variables), limiting design freedom and prohibiting the reuse of historical data. In response, this article proposes graph-based surrogate models (GSMs) for trusses. The GSM can accurately predict displacement fields from static loads given the structure’s geometry as input, enabling training across multiple parametrizations. GSMs build upon recent advancements in geometric deep learning, which have led to the ability to learn on undirected graphs: a natural representation for trusses. To further promote flexible surrogate models, this article explores transfer learning within the context of engineering design and demonstrates positive knowledge transfer across data sets of different topologies, complexities, loads, and applications, resulting in more flexible and data-efficient surrogate models for trusses.
Analyzing Real Options and Flexibility in Engineering Systems Design Using Decision Rules and Deep Reinforcement Learning
Authors: Caputo Cesare , Cardin Michel-Alexandre 
Published: September 2021

AbstractEngineering systems provide essential services to society, e.g., power generation, transportation. Their performance, however, is directly affected by their ability to cope with uncertainty, especially given the realities of climate change and pandemics. Standard design methods often fail to recognize uncertainty in early conceptual activities, leading to rigid systems that are vulnerable to change. Real options and flexibility in design are important paradigms to improve a system’s ability to adapt and respond to unforeseen conditions. Existing approaches to analyze flexibility, however, do not leverage sufficiently recent developments in machine learning enabling deeper exploration of the computational design space. There is untapped potential for new solutions that are not readily accessible using existing methods. Here, a novel approach to analyze flexibility is proposed based on deep reinforcement learning (DRL). It explores available datasets systematically and considers a wider range of adaptability strategies. The methodology is evaluated on an example waste-to-energy (WTE) system. Low and high flexibility DRL models are compared against stochastically optimal inflexible and flexible solutions using decision rules. The results show highly dynamic solutions, with action space parametrized via artificial neural network (ANN). They show improved expected economic value up to 69% compared with previous solutions. Combining information from action space probability distributions along expert insights and risk tolerance helps make better decisions in real-world design and system operations. Out of sample testing shows that the policies are generalizable, but subject to tradeoffs between flexibility and inherent limitations of the learning process.
Inverse Design Framework With Invertible Neural Networks for Passive Vibration Suppression in Phononic Structures
Authors: Oddiraju Manaswin , Behjat Amir , Nouh Mostafa , Chowdhury Souma 
Published: September 2021

Abstract
Automated inverse design methods are critical to the development of metamaterial systems that exhibit special user-demanded properties. While machine learning approaches represent an emerging paradigm in the design of metamaterial structures, the ability to retrieve inverse designs on-demand remains lacking. Such an ability can be useful in accelerating optimization-based inverse design processes. This paper develops an inverse design framework that provides this capability through the novel usage of invertible neural networks (INNs). We exploit an INN architecture that can be trained to perform forward prediction over a set of high-fidelity samples and automatically learns the reverse mapping with guaranteed invertibility. We apply this INN for modeling the frequency response of periodic and aperiodic phononic structures, with the performance demonstrated on vibration suppression of drill pipes. Training and testing samples are generated by employing a transfer matrix method. The INN models provide competitive forward and inverse prediction performance compared to typical deep neural networks (DNNs). These INN models are used to retrieve approximate inverse designs for a queried non-resonant frequency range; the inverse designs are then used to initialize a constrained gradient-based optimization process to find a more accurate inverse design that also minimizes mass. The INN-initialized optimizations are found to be generally superior in terms of the queried property and mass compared to randomly initialized and inverse DNN-initialized optimizations. Particle swarm optimization with INN-derived initial points is then found to provide even better solutions, especially for the higher-dimensional aperiodic structures.
Inverse Aerodynamic Design of Gas Turbine Blades using Probabilistic Machine Learning
Authors: Ghosh Sayan , Anantha Padmanabha Govinda , Peng Cheng , Andreoli Valeria , Atkinson Steven , Pandita Piyush , Vandeputte Thomas , Zabaras Nicholas , Wang Liping 
Published: September 2021

Abstract
One of the critical components in Industrial Gas Turbines (IGT) is the turbine blade. Design of turbine blades needs to consider multiple aspects like aerodynamic efficiency, durability, safety and manufacturing, which make the design process sequential and iterative. The sequential nature of these iterations forces a long design cycle time, ranging from several months to years. Due to the reactionary nature of these iterations, little effort has been made to accumulate data in a manner that allows for deep exploration and understanding of the total design space. This is exemplified in the process of designing the individual components of the IGT resulting in a potential unrealized efficiency. To overcome the aforementioned challenges, we demonstrate a probabilistic inverse design machine learning framework, namely PMI (PMI), to carry out an explicit inverse design. PMI calculates the design explicitly without costly iteration and overcomes the challenges associated with ill-posed inverse problems. In this work the framework will be demonstrated on inverse aerodynamic design of three-dimensional turbine blades.
Feature Extraction and Data Analysis of Basketball Motion Postures: Acquisition With an Inertial Sensor
Authors: Li Zhuqing 
Published: September 2021

Abstract
This paper mainly analyzed the application of inertial sensors in basketball posture analysis. The data of 20 basketball players in different postures were collected by Micro-electromechanical systems inertial sensors. The mean, variance, and skewness were taken as features to compare the performance of C4.5, random forest (RF), k-nearest neighbor, and support vector machine (SVM) algorithms in analyzing posture data. It was found that the classification accuracy of the k-nearest neighbor algorithm was around 90%, and the classification accuracy of C4.5, RF, and SVM algorithms was all above 90%. The classification accuracy of the RF algorithm was the highest (98.72%), which was significantly higher than C4.5 and SVM algorithms. The results verified the advantages of the RF algorithm in basketball posture analysis. The research results confirm the reliability of the inertial sensor in the field of motion posture analysis and make some contributions to its application in sport training. This paper provides support for the analysis of motion posture.
Design and In Silico Evaluation of a Closed-Loop Hemorrhage Resuscitation Algorithm With Blood Pressure as Controlled Variable
Authors: Alsalti Mohammad , Tivay Ali , Jin Xin , Kramer George C. , Hahn Jin-Oh 
Published: September 2021

Abstract
This paper concerns the design and rigorous in silico evaluation of a closed-loop hemorrhage resuscitation algorithm with blood pressure (BP) as controlled variable. A lumped-parameter control design model relating volume resuscitation input to blood volume (BV) and BP responses was developed and experimentally validated. Then, three alternative adaptive control algorithms were developed using the control design model: (i) model reference adaptive control (MRAC) with BP feedback, (ii) composite adaptive control (CAC) with BP feedback, and (iii) CAC with BV and BP feedback. To the best of our knowledge, this is the first work to demonstrate model-based control design for hemorrhage resuscitation with readily available BP as feedback. The efficacy of these closed-loop control algorithms was comparatively evaluated as well as compared with an empiric expert knowledge-based algorithm based on 100 realistic virtual patients created using a well-established physiological model of cardiovascular (CV) hemodynamics. The in silico evaluation results suggested that the adaptive control algorithms outperformed the knowledge-based algorithm in terms of both accuracy and robustness in BP set point tracking: the average median performance error (MDPE) and median absolute performance error (MDAPE) were significantly smaller by &gt;99% and &gt;91%, and as well, their interindividual variability was significantly smaller by &gt;88% and &gt;94%. Pending in vivo evaluation, model-based control design may advance the medical autonomy in closed-loop hemorrhage resuscitation.
Deployable Euler Spiral Connectors
Authors: Ynchausti Collin , Brown Nathan , Magleby Spencer P. , Bowden Anton E. , Howell Larry L. 
Published: September 2021

Abstract
Deployable Euler spiral connectors (DESCs) are introduced as compliant deployable flexures that can span gaps between segments in a mechanism and then lay flat when under strain in a stowed position. This paper presents models of Euler spiral beams combined in series and parallel that can be used to design compact compliant mechanisms. Constraints on the flexure parameters of DESCs are also presented. Analytic models developed for the force-deflection behavior and stress were compared to finite element analysis and experimental data. A spinal implant and a linear ratcheting system are presented as illustrative applications of DESCs.
Quadrupedal Human-Assistive Robotic Platform (Q-HARP): Design, Control, and Preliminary Testing
Authors: Shen Tao , Afsar Md Rayhan , Haque Md Rejwanul , McClain Eric , Meek Sanford , Shen Xiangrong 
Published: September 2021

Abstract
With the rapid expansion of older adult populations around the world, mobility impairment is becoming an increasingly challenging issue. For the assistance of individuals with mobility impairments, there are two major types of tools in the current practice, including the passive (unpowered) walking aids (canes, walkers, rollators, etc.) and wheelchairs (powered and unpowered). Despite their extensive use, there are significant weaknesses that affect their effectiveness in daily use, especially when challenging uneven terrains are encountered. To address these issues, the authors developed a novel robotic platform intended for the assistance of mobility-challenged individuals. Unlike the existing assistive robots serving similar purposes, the proposed robot, namely, quadrupedal human-assistive robotic platform (Q-HARP), utilizes legged locomotion to provide an unprecedented potential to adapt to a wide variety of challenging terrains, many of which are common in people’s daily life (e.g., roadside curbs and the few steps leading to a front door). In this paper, the design of the robot is presented, including the overall structure of the robot and the design details of the actuated robotic leg joints. For the motion control of the robot, a joint trajectory generator is formulated, with the purpose of generating a stable walking gait to provide reliable support to its human user in the robot’s future application. The Q-HARP robot and its control system were experimentally tested, and the results demonstrated that the robot was able to provide a smooth gait during walking.
Performance Simulation and Energetic Analysis of TBot High-Speed Cable-Driven Parallel Robot
Authors: Duan Jinhao , Shao Zhufeng , Zhang Zhaokun , Peng Fazhong 
Published: September 2021

Abstract
Compared with serial robots, parallel robots have the advantages of high stiffness and good dynamics. By replacing the rigid limbs with cables, the cable-driven parallel robot (CDPR) is greatly simplified in structure and lightweight. We designed a high-speed CDPR tensioned by the passive rod and spring, named TBot. The robot can realize the SCARA movement as the classical Delta parallel robot. Comparison analysis of TBot and Delta is carried out to reveal the natures of the CDPRs and rigid parallel robots, identify the key issues, and promote industrial applications. Performance of both robots is analyzed with simulation under a typical Adept Motion trajectory. Results illustrate that TBot has advantages of low cost, low inertia, low energy consumption, and adjustable workspace and has great application potential. Energy consumption of TBot is discussed, and the trajectory planning is studied with the genetic algorithm to further reduce the energy consumption, considering the influence of the passive spring. Finally, on the basis of 30% less energy consumption for the Adept Motion than Delta, extra 14.3% energy is saved through the trajectory planning of TBot.