bluuebunny/crossref_metadata_2025 huggingface dataset:
A Study on Hexapod Gait Adaptation by Enumerative Encoding and Particle Swarm Optimization
Authors: Parque Victor
Published: November 2022

Adapting to actuator failures is relevant robotic systems to continuously perform tasks such as exploration and mapping of unknown environments. This paper studies the adaptation ability to faulty hexapod legs by using an enumerative encoding scheme of the gaits locomotion strategy. The computational studies using the feasible set of leg failures through a physics-enabled simulation environment show (1) the feasibility of using the factoradic representation to explore feasible hexapod gait recoveries, and (2) the effectiveness of using an exploitative particle swarm-based heuristic to find feasible recovery strategies with minimal deviation to pre-defined locomotion commands under a small number of objective function evaluations. The results has the potential to further explore the enumerative encoding scheme and population-based optimization heuristics to render feasible hexapod gaits when actuators become malfunction on the field.
Framing Evolving Market Structures Using a Business Model Canvas
Authors: Starks Jacob , Rai Chandra , Devegowda Deepak , Allen Janet K. , Mistree Farrokh
Published: November 2022

As climate change makes it essential for the industry to move away from fossil fuels it is important to reconceptualize the entire system of energy production and distribution. In engineering design practice we focus on creating solutions and designs that may be highly effective and efficient without taking into account evolving market structures. We hypothesize that to realize evolving market structures, engineering design needs to be policy-driven and facilitate the monetization of investment by industry, and equitable social return on investment for the government and community.

There is interest in transitioning to a low carbon hydrogen economy. There is a considerable body of work on making green hydrogen production cost-effective while also creating useful forms of carbon. There is little work done, however, on modeling the storage and distribution services for at-scale hydrogen deployment and cross-disciplinary modeling of technologies and services to enable an effective transition. In essence, current translation research is limited by an underlying assumption that the only obstacle to hydrogen adoption is achieving cost competitiveness, which disregards the roles that stakeholders (government, industry, and community) may play in enabling or hindering a transition.

In this paper, we present an approach to identifying policies for transitioning to a low carbon hydrogen economy from a business (market structure) perspective. This involves framing a design problem for an evolving market from a policy-driven perspective. We use the business model canvas as the foundational framework to map the activities and services required to effectively supply resources and satisfy the demand to identify gaps in provided services and potential bottlenecks in transition or integration. We demonstrate this potential by using the business model canvas to identify a way forward in transitioning to a new market structure for a low carbon hydrogen economy.
Design and Development of a Sit-To-Stand Device Using A Variational Autoencoder-Based Deep Neural Network
Authors: Lyu Zhijie , Purwar Anurag
Published: November 2022

This work brings together rigid body kinematics with machine learning to present a mechanism synthesis pipeline for design and development of a Sit-to-Stand (STS) device. Practical device design problems require multiple constraints to be satisfied simultaneously. Most of the focus in the past has been on satisfying the key functional requirements presented as a path or motion generation problem and being content with a handful of solutions obtained. We present a new design pipeline, which begins with effective and compact data generation, to leveraging a deep neural network for representation of coupler curves and mechanism parameters, and finally ending with new metrics for quantitative evaluation of design constraints and rank ordering design concepts. This framework is capable of generating a large number of plausible solutions while meeting design constraints. As an example, we present many single-degree-of-freedom six-bar mechanisms that satisfy the given constraints and are ranked-ordered on the basis of the metric. While the focus of this paper is on the design of STS motion for integration in a multi-functional mobility assist device, this approach is broadly applicable to device design problems in other areas as well.
Incorporating Truck-Drone Bimodal Delivery System Into Mobile Additive Manufacturing Considering Preferred Delivery Time-Window and Optimal Printing Sequence
Authors: Ma Junfeng
Published: November 2022

Additive manufacturing technology is one of the leading technologies of the latest phase of the industrial revolution, Industry 4.0, and is attracting significant attention from both academia and industries. Truck-drone bimodal delivery system was studied and applied for multiple decades due to its high cost-savings and high customer satisfaction. However, to the best of our knowledge, there is no prior work combining additive manufacturing and truck-drone delivery system, although each of them has been widely implemented. This paper aims to fill this gap by integrating truck-drone delivery system with mobile additive manufacturing while also considering the customers’ preferred delivery time-window and the optimal printing sequence. A mixed integer linear programming model is proposed to establish this innovative mechanism. A computational case study was applied to validate the proposed model. The results confirm the applicability and effectiveness of the proposed model. The results also indicate that the maximum drone flight range and the printing job complexity have the significant impacts on the overall cost performance and satisfying the customers’ on-time delivery requirements. The proposed mechanism is applicable to multiple industrial sectors, including manufacturing, after-sales service, and warehouse inventory management.
Direct Torque Control of Electric Current Phase and Frequency in the Permanent-Magnet Synchronous Generator
Authors: Chumacero Erik , Akundi Aditya
Published: November 2022

The control design, formal stability analysis, and simulation results of a direct-torque controller applied to the permanent magnet synchronous generator (PMSG) is presented here. The controlled variables are the PMSG’s phase, amplitude, and frequency of the alternating current (AC) signal. For control design purposes and computer simulation, the PMSG is modeled in the dq frame of reference. On the other hand, the control algorithm is designed via backstepping technique and Lyapunov asymptotic stability is demonstrated for tracking error of the mechanical velocity and the electrical current. Uniform asymptotic stability is proved in a twofold procedure. First, it is shown that both mechanical and electrical subsystems of the PMSG are input to state stable with respect to conveniently selected inputs. Second, uniform asymptotic stability of the interconnected mechanical and electrical subsystems is shown via Lyapunov’s stability direct method. Finally, the performance of the closed-loop system, composed of the PMSG model and the control algorithm, is tested via numerical simulations, which show that the three-phase electric currents in the PMSG and the rotor’s mechanical velocity track the imposed reference signals.
Relationships Among Band Tension, Sensor Pressure, Patient Comfort, and Puslatile Signal Quality for Wrist Worn Health Monitoring Devices
Authors: Naylor Thomas , Black Roger , Hernandez Cameron , Jensen Brian D.
Published: November 2022

This paper presents the design and testing of a wrist worn health sensing band used to gather relationship data among band tension, sensor pressure, patient comfort, and measured pulsatile signal quality. It uses micromachined carbon-infiltrated carbon nanotube electrodes to detect a patient’s pulse using a bioimpedance approach. The micromachined electrodes experience no corrosion, and they are both strongly conductive and hypoallergenic. A simple mathematical model was developed to show the relationship between pressure on the wrist and tension in the wristband. The wristband was tested by being worn by 10 different research subjects over 15 tests. During each test, the tension in the band was varied, and the tension, pressure, and bioimpedance signal were measured and recorded. The test subject also reported a comfort and pain level score for each level of band tension. The results show that the model correctly predicts that tension varies linearly with pressure, and that the pressure vs. tension slope increases with increasing wrist width. There also exists a linear relationship between tension and patient pain/comfort, but pressure does not show an effect on the patient discomfort or pain experienced. Signal quality when measured in the range of 0–4 N of tension and 0–20 kPa of sensor pressure does not have a direct correlation to either tension or pressure.
Drive the Cobots Aright: Guidelines for Industrial Application of Cobots
Authors: Malik Ali Ahmad , Pandey Vijitashwa
Published: November 2022

Collaborative robots (cobots) are a new class of industrial robots with a safe, lightweight structure and easy to program interface. They have received attention from manufacturers seeking to automate some simple and repetitive tasks, while still retaining humans for the remaining ones, thereby taking advantage of their complementary abilities. From an implementation perspective, cobots need to accommodate and potentially benefit from direct human-robot interaction. They have demonstrated a high potential in manual operations where a set of interdependent tasks need to be performed, however, due to the complexity of some of the tasks, a subset of them are kept manual — for example, assembly related tasks. Since they share the same physical space, humans and cobots operate in close proximity, and this directly impacts the cobots’ design and the speed at which they can operate. Therefore, it can be argued that possible advantages of cobots tend to be less dependent on the design of the cobot manipulator itself, and more on its application design. In this work, we investigate the impact of these limitations along with the benefits, and contrast them with conventional high-speed industrial robots. This study explores a manufacturing company that has invested in cobot automation for assembly tasks. Three use-cases of cobots are investigated for their productivity and flexibility against conventional industrial robots. The conclusion is made as a set of guidelines for practitioners for the implementation of cobots both from a product perspective, as well as the perspective of the specific tasks and implementations.
Towards the Understanding of Nudging Strategies in Cyber-Physical-Social System In Manufacturing Environments
Authors: Yang Xiaoou , Lim Ahreum , Nicolaides Aliki , Morkos Beshoy
Published: November 2022

The involvement of artificial intelligence in manufacturing settings has revolutionized the relationship between humans, computers, and workforce environments. The prior cyber-physical systems are developing rapidly due to advances in technology. However, human consideration has not advanced at the same rate. Only highlighting technology advancement is not sufficient, given the high interconnectivity between human-technology in Industry 4.0. Integrating the human factor in design and implementation of cyber-physical technology leads to the holistic development of cyber-physical-social systems (CPSS). Nonetheless, little is known regarding workers’ behavior and ethics that mainly pertain to the human factors of CPSSs. Also, little effort is given to seek a methodologically rigorous way to investigate human factors in CPSS. To fill the gap, this paper proposes a research framework that aims to explore the interaction between humans and AI agents in manufacturing setting. Incorporating human considerations properly can enhanced the development of an integrated cyber-physical-social system. The goal of the research methodology presented here is to fundamentally understand a common dimension in CPSS. The objective of the proposed study is to determine how nudging impacts user response on a manufacturing line, both in terms of manufacturing performance and human response. To do so, we introduced the concept of nudging, which can come in the form of audio, visual, and haptic, refers to signals sent by a cyber-physical device to humans to illicit a performance related response. The design of an experiment is discussed in this paper. Quantitative data (assembly time, quality, and assembly errors) and qualitative data (recorded video, pre-experiment questionnaire, and post-experiment interviews) will be used for analysis to determine if and how the types of nudging are observed in cyber-physical-social environments that we see today can impact human response. The results of this research methodology will help inform engineers how cyber physical systems should be implemented in manufacturing environments while considering the impact it has on the human.
On the Combination of Geometrically Nonlinear Models and Substructuring for Multibody Simulation of Wind Turbine Blades
Authors: Jensen Christian Sigurd L. , Pedersen Rasmus B. E. , Blanco Blas , Escalona José L. , Balling Ole
Published: November 2022

This study develops a geometrically nonlinear model of a wind turbine blade utilizing finite strain theory for the calculation of elastic forces. The model is based on the floating frame of reference (FFR) formulation, which is a common choice in the modeling of long and flexible wind turbine blades. To model the nonlinear deformation of blades, the FFR formulation divides the structure into several substructures, which involves a significant increase of the system degrees of freedom. In the presented model, a nonlinear description of the elastic forces is introduced to achieve the convergence of the dynamic blade model at a lower number of substructures. The nonlinear elastic forces are formulated according to the Euler-Bernoulli beam theory, and they account for third-order terms of the potential elastic energy, the so-called geometric stiffness. The developed blade model is formulated in two dimensions and tested in a blade of 44.8 m length, which corresponds to a 2.75 MW wind turbine. Firstly, the results show that linear models do not accurately represent tip blade transverse displacement, and the substructuring technique becomes necessary to account for geometric nonlinearity. Secondly, using nonlinear elastic models significantly reduces the number of substructures needed to achieve convergence of the solution.
Deriving Recommendations for the Use of Agent-Based Models in Engineering Design
Authors: Agyemang Malena , Radwan Noriana , Hicks Sierra , Azhar Fariha , Valencia-Romero Ambrosio , McComb Christopher
Published: November 2022

Agent-based modeling (ABM) is a widely used simulation methodology. The use of ABM in engineering design continues to grow due to increasing interest in modeling and simulating human systems. However, there are few resources specific to engineering design that guide how to develop an ABM, execute an ABM experiment, and assess the subsequent results. This inhibits the effective use of ABM by design researchers who are new to the methodology. The goal of this paper is to provide a resource for both novice and experienced design researchers that details a systematic approach to ABM. First, this paper reviews current ABM research in engineering design, with a specific focus on models used to simulate human systems. Specifically, we review this work with respect to a step-by-step approach to modeling developed in the systems simulation literature. Second, each of the steps is contextualized with respect to the literature review, identifying heuristics and best practices. Through this contextualization, gaps in current practice are also identified.
Mechanical Face Seal Dynamics Subjected to Machine Vibration and Noise
Authors: Green Itzhak
Published: November 2022

Mechanical face seals are wide-spread in many applications, such as centrifugal compressors, submersible pumps, drill-bits for oil and water, hydrocarbon processing equipment, and turbomachinery. Often vibration and noise are unavoidable because of changing environments which can be persistent and forceful. In critical applications when seals fail, they may have significant or even catastrophic consequences. To ensure the safety of such machinery and its associated mechanical components, the vibration and noise must be diagnosed and kept within certain limits. This work focuses on the dynamics of a flexibly mounted stator mechanical face seal that is subjected to combinations of axial broad-band noisy vibrations of the shaft and the housing. The current analysis builds upon the dynamic models that have been developed over the last four decades. In all that body of work, the positions of the housing and the shaft have been considered fixed. The current work relaxes that condition, augmenting the equations of motion to incorporate the said noisy vibrations. While the exact root-causes and sources of machinery vibration are very difficult to ascertain, the current analysis uses some general common causes of noisy seal operation, and provides markers to be observed for diagnosis. While the forcing noises are assumed to originate along the main axis of the seal, because of the strong coupling between the axial and angular modes, the seal is affected (even strongly) in all modes (axial and angular). A numerical simulation ensues, and the results are subject to spectral analyses. Results show that under some design conditions, the seal is largely insensitive to machine vibrations. However, under other design parameters, the seal response exhibits a rich spectral content that stems from various transient phenomena that include (among others) half frequency whirl, synchronous steady-state response, and the natural (eigen) response. Under aggravated noisy conditions the investigated seal exhibits large oscillations and leakage, along with face contact and rubbing. Prolonged rubbing causes faces to wear and leads ultimately to seal failure.
Data Augmentation of Engineering Drawings for Data-Driven Component Segmentation
Authors: Zhang Wentai , Chen Quan , Koz Can , Xie Liuyue , Regmi Amit , Yamakawa Soji , Furuhata Tomotake , Shimada Kenji , Kara Levent Burak
Published: November 2022

We present a new data generation method to facilitate an automatic machine interpretation of 2D engineering part drawings. While such drawings are a common medium for clients to encode design and manufacturing requirements, a lack of computer support to automatically interpret these drawings necessitates part manufacturers to resort to laborious manual approaches for interpretation which, in turn, severely limits processing capacity. Although recent advances in trainable computer vision methods may enable automatic machine interpretation, it remains challenging to apply such methods to engineering drawings due to a lack of labeled training data. As one step toward this challenge, we propose a constrained data synthesis method to generate an arbitrarily large set of synthetic training drawings using only a handful of labeled examples. Our method is based on the randomization of the dimension sets subject to two major constraints to ensure the validity of the synthetic drawings. The effectiveness of our method is demonstrated in the context of a binary component segmentation task with a proposed list of descriptors. An evaluation of several image segmentation methods trained on our synthetic dataset shows that our approach to new data generation can boost the segmentation accuracy and the generalizability of the machine learning models to unseen drawings.
Multi-Stop Band Wave Propagation in a Honeycomb Metamaterial With Embedded Resonators
Authors: Murer Mauro , Lacarbonara Walter , Formica Giovanni
Published: November 2022

This work discusses the stop-band propagation properties of a honeycomb metamaterial hosting a periodic arrangement of highly tunable, infinite-dimensional resonators. The cellular material system architecture takes inspiration from lightweight honeycombs — due to their inherent periodicity, high flexural/shear stiffness — which host resonators with lumped masses exhibiting infinitely many frequencies and modes. These resonators are designed to possess high dynamic resilience and frequency/damping tunability.

In the present work, two resonator architectures are investigated and compared, namely, (i) a cantilever with a tip mass, (ii) a spider-web-like structure with a central mass. Contrary to traditional approaches making use of discrete spring-mass-damper resonators, here the infinite-dimensional resonators are intentionally tailored for their potential of generating, in principle, infinitely many band gaps. The nondimensional dispersion curves are obtained via the Plane Wave Expansion method. By retaining the lowest modes of the two investigated resonators designs, the outcomes show the appearance of multiple band gaps whose bandwidth and central frequency depend on the mass ratio and the nondimensional stiffness of the resonators. Preliminary experimental results based on laser scanning vibrometry, and conducted on 3D printed honeycomb samples, corroborate the theoretical model and predictions.
Learning the Part Shape and Part Quality Generation Capabilities of Machining and Finishing Processes Using a Neural Network Model
Authors: Zhao Changxuan , Melkote Shreyes N.
Published: November 2022

Automatically acquiring knowledge of manufacturing process capabilities from existing data is essential for automated process selection in digital and cyber manufacturing. In this work, we present a neural network model to automatically learn the capabilities of discrete manufacturing processes such as machining and finishing from design and manufacturing data. Concatenating a 3D Convolutional Neural Network (3D CNN) with an artificial neural network, the combined model can learn the part shape and part quality generation capabilities of the manufacturing processes. Specifically, the proposed method takes the voxelized part geometry and part quality information as inputs and utilizes a mixed neural network model (3D CNN + artificial neural network) to predict the manufacturing process label as output. The manufacturing process capability knowledge embedded in the neural network model is scalable and updatable as new manufacturing data becomes available. We present an example implementation of the proposed method with a synthesized manufacturing dataset to illustrate how the method enables automatic manufacturing process selection. The high prediction accuracy shows its predictive strength for use in Computer Aided Process Planning (CAPP).
Bifurcation Analysis of a PD Controlled Motion Stage With a Nonlinear Friction Isolator
Authors: Basta Ehab E. , Gupta Sunit K. , Barry Oumar R.
Published: November 2022

The utilization of mechanical-bearing-based precision motion stages (MBMS) is prevalent in the advanced manufacturing industries. However, the productivity of the MBMS is plagued by friction-induced vibrations, which can be controlled to a certain extent using a friction isolator. Earlier works investigating the dynamics of MBMS with a friction isolator considered a linear friction isolator, and the source of nonlinearity in the system was realized through the friction model only. In this work, we present the nonlinear analysis of the MBMS with a nonlinear friction isolator for the first time. We consider a two-degree-of-freedom spring-mass-damper system to model the servo-controlled motion stage with a nonlinear friction isolator. The characteristic of the dynamical friction in the system is captured using the Lu-Gre friction model. The system’s stability and nonlinear analysis are carried out using analytical methods. More specifically, the method of multiple scales is used to determine the nature of Hopf bifurcation on the stability lobe. The analytical results indicate the existence of subcritical and supercritical Hopf bifurcations in the system, which are later validated through numerical bifurcation. This observation implies that the nonlinearity in the system can be stabilizing or destabilizing in nature, depending on the choice of operating parameters.
A Variable Stiffness Continuum Parallel Manipulator With 3D Printed Pneumatic Artificial Muscles
Authors: Jiang Zhujin , Liu Chen , Zhang Ketao
Published: November 2022

This paper presents a variable stiffness continuum parallel manipulator (VSCPM) with 3D printed pneumatic artificial muscles, which is inspired by parallel mechanisms and soft robotics. The proposed VSCPM is designed by connecting a fixed base and a moving platform with three pneumatic bellows muscles in parallel and circular distribution. A set of optimized 3D printing parameters for fabricating air tight pneumatic muscles by flexible Thermoplastic Polyurethane (TPU) filament is introduced, aiming at achieving rapid and low-cost manufacturing in comparison with other approach such as injecting silicon rubber into prefabricated molds. A prototype of the VSCPM is fabricated using the optimized 3D printing settings. The stiffness of a 3D printed bellows muscle under both positive and negative pressures is tested. The position and stiffness of the VSCPM controlled by both positive and negative pressures are also evaluated. Experimental results show that the VSCPM is capable of achieving a range of steering angle from 0° to 360° and a range of inclination angle from 0° to 45°. The stiffness of the VSCPM can also be adjusted by changing the air pressure supplied to the pneumatic artificial muscles. The novel VSCPM is compact, lightweight and soft to ensure the safe interaction with humans, which has a broad range of application such as wrist rehabilitation.
Multi-Fidelity Physics-Constrained Neural Networks With Minimax Architecture for Materials Modeling
Authors: Liu Dehao , Pusarla Pranav , Wang Yan
Published: November 2022

Data sparsity is still the main challenge to apply machine learning models to solve complex scientific and engineering problems. The root cause is the “curse of dimensionality” in training these models. Training algorithms need to explore and exploit in a very high dimensional parameter space to search the optimal parameters for complex models. In this work, a new scheme of multi-fidelity physics-constrained neural networks with minimax architecture is proposed to improve the data efficiency of training neural networks by incorporating physical knowledge as constraints and sampling data with various fidelities. In this new framework, fully-connected neural networks with two levels of fidelities are combined to improve the prediction accuracy. The low-fidelity neural network is used to approximate the low-fidelity data, whereas the high-fidelity neural network is adopted to approximate the correlation function between the low-fidelity and high-fidelity data. To systematically search the optimal weights of various losses for reducing the training time, the Dual-Dimer algorithm is adopted to search high-order saddle points of the minimax optimization problem. The proposed framework is demonstrated with two-dimensional heat transfer, phase transition, and dendritic growth problems, which are fundamental in materials modeling. With the same set of training data, the prediction error of the multi-fidelity physics-constrained neural network with minimax architecture can be two orders of magnitude lower than that of the multi-fidelity neural network with minimax architecture.
Investigating Priming Effects of Sketch Evaluation Instructions on Idea Generation Productivity
Authors: Weaver Morgan B. , Buck Jacob , Merzdorf Hillary , Dorozhkin Denis , Douglas Kerrie , Linsey Julie
Published: November 2022

Engineering design involves intensive visual-spatial reasoning, and engineers depend upon external representation to develop concepts during idea generation. Previous research has not explored how our visual representation skills influence our idea generation effectiveness. A designer’s deficit in sketching skills could create a need for increased focus on the task of visual representation reducing cognitive resources available for the task at hand — generating concept. Further, this effect could be compounded if designers believed that their sketching skill would be evaluated or judged by their peers. This evaluation apprehension could cause additional mental workload distracting from the production of idea generation.

The goal of this study is to investigate and better understand the relationship between designers’ sketching skills and idea generation abilities. In this paper, we present preliminary results of the relationship between independent measures of sketching skill and idea generation ability from an entry-level engineering design and graphics course. During data collection, task instructions were given in two ways to independent groups: one group was instructed upfront that sketching would be evaluated, while the second group was kept blind to the sketch evaluation. In this paper, we also examine the potential priming effects of sketch quality evaluation apprehension on idea generation productivity. The results show that sketching quality and idea quantity are largely independent, and that the priming effects of sketch evaluation instructions are small to negligible on idea generation productivity.
Ellipse Synthesis of a Five-Bar Linkage
Authors: Plecnik Mark
Published: November 2022

This paper formulates synthesis equations for a two degree-of-freedom planar five-bar linkage to reproduce specified velocity ellipses at specified points in its workspace. The synthesis procedure finds a mechanism that exactly reproduces two ellipses, and affords the designer the free choice of one ground pivot. This paper shows how four solutions to these synthesis equations can be obtained in closed form. Each solution describes a five-bar linkage. The solution procedure reveals shared structure between these five-bars. The new synthesis procedure is applied to a few examples.
Multi-Objective Bayesian Optimization Supported by Gaussian Process Classifiers and Conditional Probabilities
Authors: Valladares Homero , Tovar Andres
Published: November 2022

In the last years, there has been an increasing effort to develop Bayesian methodologies to solve multi-objective optimization problems. Most of these methods can be classified in two groups: infilling criterion-based methods and aggregation-based methods. The first group employs an index that quantifies the gain that a new design can produce in the current Pareto front while the last group uses a (possibly non-linear) aggregation function and a weighting vector to identify a Pareto design. Most infilling-based methods have been developed to solve two-objective optimization problems. Aggregation-based methods enable the solution of many-objective optimization problems but their performance depends on the set of weighting vectors, which are often selected randomly. This study proposes a novel multi-objective Bayesian framework that exploits the rich probabilistic information that can be extracted from Gaussian process (GP) classifiers and the ability of conditional probabilities to capture design preferences. In the proposed framework, a GP classifier is trained to identify design zones that potentially contain Pareto designs. The training process involves the inference of a latent GP that encodes input-space interactions that describe a Pareto design. This latent GP enables the solution of many-objective optimization problems with any standard acquisition function and without the prescription of a weighting vector. Conditional probabilities are utilized to define design goals that promote a uniform expansion of the Pareto front. The proposed approach is demonstrated with two benchmark analytical problems and the design optimization of sandwich composite armors for blast mitigation, which involves expensive finite element simulations.
Is the Concept, Method, or Measurement to Blame for Testing Error? An Illustration Using the Force-Velocity-Power Profile
Authors: Samozino Pierre , Rivière Jean Romain , Jimenez-Reyes Pedro , Cross Matt R. , Morin Jean-Benoît
Published: November 2022

When poor reliability of “output” variables is reported, it can be difficult to discern whether blame lies with the measurement (ie, the inputs) or the overarching concept. This commentary addresses this issue, using the force-velocity-power (FvP) profile in jumping to illustrate the interplay between concept, method, and measurement reliability. While FvP testing has risen in popularity and accessibility, some studies have challenged the reliability and subsequent utility of the concept itself without clearly considering the potential for imprecise procedures to impact reliability measures. To this end, simulations based on virtual athletes confirmed that push-off distance and jump-height variability should be <4% to 5% to guarantee well-fitted force–velocity relationships and acceptable typical error (<10%) in FvP outputs, which was in line with previous experimental findings. Thus, while arguably acceptable in isolation, the 5% to 10% variability in push-off distance or jump height reported in the critiquing studies suggests that their methods were not reliable enough (lack of familiarization, inaccurate procedures, or submaximal efforts) to infer underpinning force-production capacities. Instead of challenging only the concept of FvP relationship testing, an alternative conclusion should have considered the context in which the results were observed: If procedures’ and/or tasks’ execution is too variable, FvP outputs will be unreliable. As for some other neuromuscular or physiological testing, the FvP relationship, which magnifies measurement errors, is unreliable when the input measurements or testing procedures are inaccurate independently from the method or concept used. Field “simple” methods require the same methodological rigor as “lab” methods to obtain reliable output data.
Intersectionality in the Sport Psychology Classroom: Reflections From a Neophyte Instructor
Authors: Anderson Shelby N.
Published: November 2022

Sport psychology scholars have long called for the field to take intersectional approaches to research and applied practice. Missing from this call is the study of intersectionality in the classroom. Therefore, the purpose of this practice paper is to provide a resource for sport psychology practitioners to take an intersectional approach in their teaching. First, the author provides a brief overview of intersectional theory and approaches to using anti-oppressive practices in the classroom. The author then reflects on their experience utilizing an intersectional lens as a neophyte instructor. Finally, the author discusses lessons learned from this teaching experience. This practice paper serves as a resource for sport psychology scholars and practitioners to integrate the study of intersectionality in their roles. While this paper is written for the higher education classroom, all readers will gain knowledge on intersectional theory and how it can be integrated in their scholarship or applied practice.
Spontaneous beat synchronization in rats: Neural dynamics and motor entrainment
Authors: Ito Yoshiki , Shiramatsu Tomoyo Isoguchi , Ishida Naoki , Oshima Karin , Magami Kaho , Takahashi Hirokazu
Published: November 2022

Beat perception and synchronization within 120 to 140 beats/min (BPM) are common in humans and frequently used in music composition. Why beat synchronization is uncommon in some species and the mechanism determining the optimal tempo are unclear. Here, we examined physical movements and neural activities in rats to determine their beat sensitivity. Close inspection of head movements and neural recordings revealed that rats displayed prominent beat synchronization and activities in the auditory cortex within 120 to 140 BPM. Mathematical modeling suggests that short-term adaptation underlies this beat tuning. Our results support the hypothesis that the optimal tempo for beat synchronization is determined by the time constant of neural dynamics conserved across species, rather than the species-specific time constant of physical movements. Thus, latent neural propensity for auditory motor entrainment may provide a basis for human entrainment that is much more widespread than currently thought. Further studies comparing humans and animals will offer insights into the origins of music and dancing.
Group B
Streptococcus
adaptation promotes survival in a hyperinflammatory diabetic wound environment
Authors: Keogh Rebecca A. , Haeberle Amanda L. , Langouët-Astrié Christophe J. , Kavanaugh Jeffrey S. , Schmidt Eric P. , Moore Garrett D. , Horswill Alexander R. , Doran Kelly S.
Published: November 2022

Diabetic wounds have poor healing outcomes due to the presence of numerous pathogens and a dysregulated immune response. Group B
Streptococcus
(GBS) is commonly isolated from diabetic wound infections, but the mechanisms of GBS virulence during these infections have not been investigated. Here, we develop a murine model of GBS diabetic wound infection and, using dual RNA sequencing, demonstrate that GBS infection triggers an inflammatory response. GBS adapts to this hyperinflammatory environment by up-regulating virulence factors including those known to be regulated by the two-component system
covRS
, such as the surface protein
pbsP
, and the
cyl
operon, which is responsible for hemolysin/pigmentation production. We recover hyperpigmented/hemolytic GBS colonies from the murine diabetic wound, which we determined encode mutations in
covR
. We further demonstrate that GBS mutants in
cylE
and
pbsP
are attenuated in the diabetic wound. This foundational study provides insight into the pathogenesis of GBS diabetic wound infections.
Paracentral acute middle maculopathy with occlusive retinal vasculitis in presumed intraocular tuberculosis and hyperhomocysteinemia
Authors: Samanta Ramanuj , Dogra Mohit , Puthalath Athul Suresh , Sood Gitanjli
Published: November 2022

A male patient presented with a sudden visual decline in the right eye (OD). Fundus revealed bilateral vasculitis; OD also showed an occluded inferior retinal vein and a wedge-shaped retinal opacification of the inferior macula and nasal retina. Fluorescein angiography revealed occlusive retinal vasculitis, while optical coherence tomography showed paracentral acute middle maculopathy (PAMM) in the OD. A thorough systemic evaluation revealed hyperhomocysteinemia and a positive Mantoux test. A diagnosis of PAMM with occlusive retinal vasculitis in presumed intraocular tuberculosis and hyperhomocysteinemia was made. Retinal vasculitis improved with oral corticosteroid, intravitreal anti-vascular endothelial growth factor and laser photocoagulation. However, the patient declined antitubercular therapy despite recommendations. This unique report indicates that PAMM may complicate tubercular retinal vasculitis, especially in the presence of systemic hypercoagulable states.
Rare MYC-N11S germline mutation indicative of inherited breast cancer in a multigeneration family
Authors: Budurlean Laura , Baker Maria , Broach James
Published: November 2022

We present a case of unexplained familial breast cancer (BC) from six family members, including four affected and two unaffected women, for whom clinical genetic testing panels were inconclusive. Exome sequencing data revealed heterozygous and rare germline variants to be inherited in an autosomal dominant manner in the family, in addition to several unclassified mutations in DNA repair and cell cycle-regulating genes that were not included in the family’s clinical genetic testing. A rare MYC-N11S germline mutation with conflicting interpretations of pathogenicity in the literature, and predicted to be deleterious, was present in all affected individuals. Whole exome sequencing provided a more comprehensive picture of inherited BC in this family that was missed by cancer gene panels alone.
Development of a core outcome set on Traditional Chinese Medicine andWestern Medicinefor rheumatic heart disease: a study protocol
Authors: Sheng Xiaodi , Chen Chao , Ji Zhaochen , Hu Haiyin , Zhang Mingyan , Wang Hui , Pang Bo , Zhai Jingbo , Zhang Dong , Zhang Junhua , Guo Liping
Published: November 2022

Introduction

Globally, rheumatic heart disease (RHD) is an important cause of acquired heart disease in children and adolescents. Clinical trials on RHD treatment with Traditional Chinese Medicine (TCM) or integrated medicine are gradually increasing in China. However, because the outcomes of clinical trials are subject to heterogeneity and selective reporting, similar studies cannot be merged and compared, complicating assessing the effectiveness and safety of TCM, and diminishing the value of clinical trials. Therefore, there is an urgent need to design a TCM or integrated medicine core outcome set (COS) for RHD.

Methods and analysis

The development of this study will take place in four stages under the direction of a multidisciplinary advisory board. (1) Establishing a comprehensive outcomes checklist through a systematic review of previously published research, retrieval of clinical trial registration centres, patient’s semistructured interviews, and clinician’s questionnaire surveys; (2) Screen stakeholder groups from various fields to participate in the Delphi survey; (3) Two e-Delphi surveys will be conducted to determine the outcomes of various concerned stakeholder groups; (4) Hold a face-to-face consensus meeting to develop the COS-TCM-RHD.

Ethics and dissemination

The ethical approval for this study has been obtained from the Tianjin University of Traditional Chinese Medicine Ethics Committee (TJUTCM-EC20210008). The findings will be disseminated in peer-reviewed journals and meetings.

Trial registration number

This study has been registered at the Core Outcome Measures in Effectiveness Trials (COMET) database (Registration #1743).
Assessment of sex-related differences and outcome in patients who underwent cryoballoon pulmonary vein isolation for atrial fibrillation: an observational cohort study
Authors: Ekrami Neda Khalilian , Magni Federico T , Dayalani Vijay , van Gelder Isabelle C , Groenveld Hessel F , Tieleman Robert G , Wiesfeld Ans CP , Tan Eng S , Rienstra Michiel , Blaauw Yuri , Mulder Bart A
Published: November 2022

Objectives

Pulmonary vein isolation (PVI) is widely accepted as an effective and safe treatment for symptomatic atrial fibrillation (AF). However, data on sex-related differences and associations with clinical outcome and safety of PVI with cryoballoon ablation are limited. We sought to compare sexrelated efficacy and safety of cryoballoon ablation and identify sex-related associations with clinical outcomes.

Methods and results

We included 650 consecutive patients with AF undergoing PVI with cryoballoon ablation at our institution between 2013 and 2017. The efficacy outcome was the first documented recurrence (>30 s) of AF, atrial flutter or atrial tachycardia (AF/AT) or repeat ablation during follow-up, after a 90-day blanking period. The safety outcome was the incidence of periprocedural complications. Mean age of the population was 58±10, and 210 (32.3%) patients were women. Women were older, had a higher body mass index, had more renal dysfunction and less coronary artery disease as compared with men. The rate of AF/AT recurrence was similar between women and men at 12-month follow-up (27.6% vs 24.8%, p=0.445). The incidence of periprocedural complications was higher in women (12.9% vs 4.6%; p<0.001), specifically groin haematomas and phrenic nerve palsy. On multivariate analysis, left atrial volume index (adjusted OR 1.05, 95% CI 1.00 to 1.10; p=0.032) was associated with the incidence of procedural complications in women. For men, no relation with complications could be found.

Conclusion

The efficacy of cryoballoon ablation was similar between women and men; however, women had a higher risk of procedural complications.
Gauge invariant massive vector Kaluza–Klein modes of U(1)gauge field in braneworld
Authors: Fu Chun-E
Published: November 2022

It was found that in a RS-like brane model the effective action for the massive vector KK modes of a U(1) gauge field was gauge invariant Fu et al., JHEP 2019(1) (2019). https://doi.org/10.1007/jhep01(2019)021. It is interesting to investigate the factors this gauge invariance maybe depend on, such as the geometry of the space-time, the number of the extra dimensions, and the dimension of the brane. We demonstrate that the three factors do not affect the gauge invariant formulation of the effective action, but influence the localization of the gauge invariant massive vector KK modes.
Prediction of a superhard high-pressure phase for CN: First-principles
Authors: Ying Chun , Zhao Erjun , Lin Lin , Tao Hongshuai , Meng Xiaokai
Published: November 2022

The energetically favorable crystal structures for stoichiometric ratio as 1:1 (CN) are studied systematically based on the density functional theory combined with the particle-swarm optimization (PSO) method. The predicted favorite phase Pnnm-C4N4 (Pnnm, No. 58) is in excellent agreement with the results of experimental and theoretical studies. The predicted Pnnm-C4N4 phase exhibits the highest bulk modulus and shear modulus of 351 and 335 GPa, respectively. Those are comparable to that of 376 and 390 GPa for c-BN. The calculated phonon dispersions indicate that the Pnnm-C4N4 phase is dynamically stable at ambient conditions, while others require high-pressure condition. The calculated DOS illustrates that the Pnnm-C4N4 phase is an insulator with indirect band gap of 3.746 eV. Moreover, the high hardness of 54 GPa is estimated to the most stable Pnnm-C4N4 phase, indicating it is a potentially superhard material.
A NUMERICAL STUDY ON THE DYNAMICS OF DENGUE DISEASE MODEL WITH FRACTIONAL PIECEWISE DERIVATIVE
Authors: KHAN JAVED , UR RAHMAN MATI , RIAZ MUHAMMAD BILAL , AWREJCEWICZ JAN
Published: November 2022

The aim of this paper is to study the dynamics of Dengue disease model using a novel piecewise derivative approach in the sense of singular and non-singular kernels. The singular kernel operator is in the sense of Caputo, whereas the non-singular kernel operator is the Atangana–Baleanu Caputo operator. The existence and uniqueness of a solution with piecewise derivative are examined for the aforementioned problem. The suggested problem’s approximate solution was found using the piecewise numerical iterative Newton polynomial approach. In terms of singular and non-singular kernels, a numerical scheme for piecewise derivatives has been established. The numerical simulation for the piecewise derivable problem under consideration is drawn using data for various fractional orders. This work makes the idea of piecewise derivatives and the dynamics of the crossover problem clearer.
A Flexible Two-Dimensional Warranty in Optimizing the Dealer and Customer Satisfaction
Authors: Salmasnia Ali , Doroudi Ali , Kohan Fatemeh
Published: November 2022

Nowadays, in competitive market, in order to increase confidence in the quality of products, dealers set a warranty for selling them. This issue seems to have a tremendous effect on attracting more customers, especially when it comes to purchasing second-hand products. In this regard, this paper tends to present a two-dimensional warranty policy for second-hand products containing age and usage in two cases, L-shaped and Rectangular. Since maximizing customer repurchase probability is directly linked with dealer’s profit as well as minimizing costs, dealers ought to consider customer satisfaction. In fact, customer satisfaction depends on warranty length and the expected numbers of failures in the post-warranty period. To demonstrate the characteristics of the model, two comparative studies are conducted to present the effect of bi-objective function on warranty models. Finally, in order to extend insights into the matter, a sensitivity analysis is carried out on the varying parameters.