bluuebunny/crossref_metadata_2025 huggingface dataset:
Natural Motion Grasping Design Using Control-Oriented Kinematic Models
Authors: Robson Nina , Audrey Vanessa , Hernandez Severino , Golden John , Ton Vincent
Published: November 2023

The paper explores the possibility of creating grasping devices based on a recently developed and experimentally validated control-oriented planar kinematic model for natural human hand configuration based on the geometry of a thumb-finger system, assumed to be softly enclosing the surface of a virtual cylindrical object. The model is able to calculate independently the values for the natural joint rotation angles, as well as joint velocities and accelerations of a two degrees of freedom (DOFs) thumb and a three DOFs long finger system by a single parameter, the radius of the virtual cylindrical object R. Based on the results obtained from the model, we explore the possibility of modifying the design of a four bar thumb and an eight bar index finger of the recently developed Closed Loop Articulated Wearable (CLAW) hand to enhance the wearability. The latter is done through assessing how closely the motion of the modified thumb-index finger pilot prototype resembles the thumb-index finger natural grasping motion. Future applications of the research include the synthesis and design of enhanced wearable hand devices and articulated robotic hands for natural motion.
Design Transformation Cards for Reconfigurable Robotics: Case Study of an Expandable Robot for Cleaning Tasks
Authors: Kalimuthu Manivannan , Hayat Abdullah Aamir , Pathmakumar Thejus , Veerajagadheswar Prabakaran , Elara Mohan Rajesh , Wood Kristin Lee
Published: November 2023

Reconfigurable robotic systems (RRS) have demonstrated their usefulness in various sectors, including surveillance, rescue missions, space exploration, maintenance, and cleaning. However, designing adaptable and flexible robotic systems is challenging, and researchers have developed innovative mechanical designs that can optimize the performance of these systems in different applications. This paper introduces a method for designing a reconfigurable robot using earlier proposed design transformation cards (DTC) that illustrate mechanical mechanisms based on the transformation design theory. These cards serve as a visual tool for ideation and design-by-analogy, enabling designers to explore the best possible combinations of mechanical mechanisms for their robot design. The case study application of the DTC is presented by designing the block of an expandable Tetris-inspired robot named e-Smorphi (expandable-Smorphi) which consists of four units of expandable blocks that can change their dimensions in length and width, allowing for adaptation to different environments in two dimensions. The proposed design has potential application to the cleaning and maintenance industry, where the objective is to be maximizing the area coverage. The paper also describes experiments with the selected prototype, highlighting the reconfiguration in two dimensions while accessing the environment for cleaning and maintenance (CnM) tasks. The limitations and advantages of using DTC are also discussed as insights into the development of new design tools and techniques for reconfigurable robotics, paving the way for further innovation and advancements in the field.
Modeling Distributed Situation Awareness in Resilience-Based Design of Complex Engineered Systems
Authors: Irshad Lukman , Hulse Daniel
Published: November 2023

Human operators play a major role in the resilience of complex systems — while human error is one of the biggest contributors to hazardous events, operators additionally play a critical role in mitigating hazardous events. A key factor underlying this operator resilience is situation awareness — the ability of operators to understand their environment and each other to achieve desired system functions. In contrast to situation awareness-related accident models in the literature, which are largely conceptual in nature, this work proposes the use of a dynamic simulation framework to concretely model both the effects of situation awareness-related human errors and situation awareness-related hazard-mitigating properties using the distributed situation awareness theory. This work then presents specialized model constructs to enable agents’ individual perceptions of the system state and transactions with other agents (and thus distributed situation awareness) to be represented in simulation. To demonstrate this framework, it is then adapted to an aircraft taxiway case study, where it is used to model aircraft conflicts due to lack of vision and poor communications from the air traffic controller. This demonstration shows the potential of using simulation models to rigorously understand situation awareness-related human errors and thus inform the design of resilience.
Fewer Triplets Than You Think: Novelty Error Converges Faster Than Triplet Violations in Ordinal Embeddings
Authors: Keeler Matthew , Fuge Mark
Published: November 2023

A practical and well-studied method for computing the novelty of a design is to construct an ordinal embedding via a collection of pairwise comparisons between items (called triplets), and use distances within that embedding to compute which designs are farthest from the center. Unfortunately, ordinal embedding methods can require a large number of triplets before their primary error measure — the triplet violation error — converges. But if our goal is accurate novelty estimation, is it really necessary to fully minimize all triplet violations? Can we extract useful information regarding the novelty of all or some items using fewer triplets than classical convergence rates might imply? This paper addresses this question by studying the relationship between triplet violation error and novelty score error when using ordinal embeddings. Specifically, we compare how errors in embeddings produced by Generalized Non-Metric Dimensional Scaling (GNMDS) converge under different sampling methods, for different numbers of embedded items, sizes of latent spaces, and for the top K most novel designs.

We find that estimating the novelty of a set of items via ordinal embedding can require significantly fewer human-provided triplets than is needed to converge the triplet error, and that this effect is modulated by the type of triplet sampling method (random versus uncertainty sampling). We also find that uncertainty sampling causes unique converge behavior in estimating most novel items compared to non-novel items. Our results imply that in certain situations one can use ordinal embedding techniques to estimate novelty error in fewer samples than is typically expected. Moreover, the convergence behavior of top K novel items motivates new potential triplet sampling methods that go beyond typical triplet reduction measures.
An Approach for Predicting Social, Environmental, and Economic Product Impacts and Navigating the Associated Impact Trade-Space in Engineering Design
Authors: Mabey Christopher S. , Dickerson Tevin J. , Salmon John L. , Mattson Christopher A.
Published: November 2023

There is a growing demand for sustainable products and systems. Sustainability encompasses environmental, social, and economic aspects, often referred to as the three pillars of sustainability. To make more sustainable design decisions, engineers need tools to predict the environmental, social, and economic impacts of products and navigate potential impact trade-offs. To capture societal adoption of technology, this article uses agent-based modeling (ABM) to predict impacts across all three pillars of sustainability and how to navigate the multidimensional impact trade space. The approach described in this article is based on three main components for the predictive modeling of product impacts and impact trade-space navigation, i) ABM of product adoption, ii) the assessment of product impacts, and iii) an approach for design decisions in the presence of impact trade-offs. The trade-space navigation uses a visual approach to find the non-dominated solutions in the product impact space. To illustrate and describe how to use the method, a case study is presented that predicts the impact of residential solar panels in a region of the United States under various scenarios. The findings of the case study can help policymakers understand suitable implementation strategies for residential solar panels while considering the impact trade-offs involved.
Predicting the Behavior of Large-Scale, Additively Manufacturable Lattice Structures via the Laws of Similitude
Authors: Rokade Sumant Dilip , Gutierrez Soto Mariantonieta , Meisel Nicholas
Published: November 2023

Additive Manufacturing (AM) use in construction has been on the rise in recent years. This means faster build times, less waste, and increased efficiency. Conventional structural building blocks are not fully leveraging the capabilities offered by AM. For this reason, the authors propose using AM-enabled self-supporting structures as a part of the construction process. Using these unconventional structural members requires testing; however, full-scale testing is not always economically feasible. From an experimental testing standpoint, it would be easier to test these in a controlled environment at a small scale; as the behavior of lattice structures in structural loading is yet unknown. To understand the behavior of these structures, it is imperative to understand whether the existing scaling laws can be applied to these non-homogeneous, discontinuous lattice structures. This paper explores the requirements for performing scaled-down testing on latticed beams with dissimilar materials in a virtual environment. The performance rating of the unit cells tested in order is Diamond, Gyroid, and Fluorite.
Acoustic Emission Damage Parameter for Cumulative Fatigue Damage Prediction in Gear Single Tooth Bending Fatigue
Authors: Anichowski Gabriel , Kahraman Ahmet , Hong Isaac
Published: November 2023

Power delivery in many industrial, automotive, and aerospace applications is subject to duty cycles based on the needs of the equipment over each mission. These blocks of usage at various power levels subjects the drivetrain to cyclic forces and stresses of variable mean and amplitude. Each block of applied cyclic stresses contributes some amount to the overall fatigue damage in the parts. It is critical that the design of the power transmission components such as gears accounts for the overall fatigue damage applied from the expected duty cycle loading. Contemporary cumulative fatigue damage models require experimental data of various proportions to regress required empirical parameters. At minimum, a constant amplitude stress life (SN) evaluation is necessary. Others require expansive multilevel load experimental programs to regress empirical material parameters. It is advantageous for all design engineers from a time and cost perspective to minimize the amount of fatigue testing required to regress empirical cumulative damage parameters. It is proposed here that measurement of Acoustic Emission (AE) in constant amplitude fatigue tests may serve as a suitable metric on which to base the regression of empirical cumulative damage parameters. AE hits, ringdown counts, and absolute energy is monitored during gear single tooth bending fatigue (STBF) tests. Material parameters as defined in the Manson and Halford Damage Curve Approach Model are then regressed using the AE metrics. A prediction of a set of multilevel STBF tests is made and compared to existing experimental results and predictions from other cumulative damage fatigue models.
Analysis of Mechanical Chemical and Thermal Properties of PLA Filaments After Mechanical Recycling
Authors: Shabani Mahsa , Turner Cameron J.
Published: November 2023

Polylactic Acid (PLA) is a popular biopolymer used in additive manufacturing. In additive manufacturing, there are rafts, berms, and unfortunately, failed parts and support structures that will be discarded and therefore may end up in a landfill. While PLA is biodegradable, this is true only in certain circumstances. The degradation of PLA is naturally slow in the landfill, and it would need more methods (such as chemical hydrolysis) than the natural process to increase the degradability rate. In order to reduce the environmental impact of additive manufacturing, recycling of PLA is therefore desirable. In this paper, PLA was mechanically recycled into new filaments. Mechanical recycling is attractive as it can be readily performed even by individual small scale users. Suggested material preparation techniques found in the relevant literature were tested and a new material preparation process is presented that seems to resolve many issues occurring from using other techniques. Recycling was tested by three different extruders. The extruders were compared to find the most effective choice. Finally, virgin PLA, virgin PLA after additive manufacturing, recycled PLA, and recycled PLA after an additive manufacturing and recycling process were mechanically, thermally, and chemically tested to understand the changes experienced by the material during recycling process. The results demonstrate that the samples do not experience significant changes during recycling, and therefore, the recycled material has material properties comparable to virgin PLA without any impurities or significant property changes induced by the printing and recycling process.
State of the Art: A Review of AI Art Generation Methods for Rigorous Design
Authors: Bertelsen Lauren , Short Ada-Rhodes
Published: November 2023

Over the past several years, humans have developed various new AI art tools for artistic and conceptual design purposes — this paper aims to review the development of AI’s abilities to generate original, rigorous designs for technical applications. First, this paper examines three modern AI art methods: Generative Adversarial Networks, Convolutional Neural Networks, and stable diffusion. Then, we review the main concepts of each method and test a representative AI on its abilities for rigorous design. Various prompts are used, including simple, formulaic, AI-generated, and human-generated prompts from an architectural engineering class assignment.
On Selecting and Reconstructing Lyapunov Functions for Stability Analysis of Nonlinear Dynamical Systems
Authors: Wei Bin
Published: November 2023

Lyapunov theorems did not tell us how to choose the Lyapunov function to analyze the stability of equilibrium states for nonlinear systems, and currently there is no particular way or formula that can guide us on how to choose the right Lyapunov function when using Lyapunov theorems and LaSalle theorems. Typically, a Lyapunov candidate function is chosen based on the trial and error approach. In this paper, the author presents an approach that is able to guide us to choose the right Lyapunov function based on the initial random Lyapunov candidate function when using LaSalle theorems to study the equilibrium states’ stability for the nonlinear systems. Difference case studies are given in the paper in order to show the effectiveness of the approach.
Using Velocity Partitioning in the rA Formulation to Solve the Equations of Constrained Multibody Dynamics
Authors: Kissel Alexandra , Fang Luning , Negrut Dan
Published: November 2023

In mechanical systems, formulating the equations of motion in absolute coordinates results in a set of index-3 differential algebraic equations (DAEs). In this work, we present an approach to solving the DAE problem by partitioning the velocities in the system into dependent and independent coordinates, thereby reducing the problem to an ordinary differential equations (ODEs) problem. The independent coordinates are integrated directly, while the dependent coordinates are recovered through the kinematic constraint equations at the position and velocity levels. Notably, Lie group integration is employed to directly obtain the orientation matrix A at each time step of the simulation. The approach builds on a method presented in [1], which combines coordinate partitioning with an Euler parameter formulation. Herein, we outline the numerical method and demonstrate it in conjunction with three mechanisms: a single pendulum, a double pendulum, and a four-link mechanism. We report on the convergence order behavior of the proposed method. The open-source Python software developed to generate the reported results is available in a public repository for reproducibility studies [2].
Ship-D: Ship Hull Dataset for Design Optimization Using Machine Learning
Authors: Bagazinski Noah J. , Ahmed Faez
Published: November 2023

Machine learning has recently made significant strides in reducing design cycle time for complex products. Ship design, which currently involves years-long cycles and small batch production, could greatly benefit from these advancements. By developing a machine learning tool for ship design that learns from the design of many different types of ships, trade-offs in ship design could be identified and optimized. However, the lack of publicly available ship design datasets currently limits the potential for leveraging machine learning in generalized ship design. To address this gap, this paper presents a large dataset of 30,000 ship hulls, each with design and functional performance information, including parameterization, mesh, point-cloud, and image representations, as well as 32 hydrodynamic drag measures under different operating conditions. The dataset is structured to allow human input and is also designed for computational methods. Additionally, the paper introduces a set of 12 ship hulls from publicly available CAD repositories to showcase the proposed parameterization’s ability to accurately reconstruct existing hulls. A surrogate model was developed to predict the 32 wave drag coefficients, which was then implemented in a genetic algorithm case study to reduce the total drag of a hull by 60% while maintaining the shape of the hull’s cross section and the length of the parallel midbody. Our work provides a comprehensive dataset and application examples for other researchers to use in advancing data-driven ship design.
Optimization of Masking Parameters for Volumetric Material Manufacturing Using Inverse Methods
Authors: Graber Benjamin D. , Iliopoulos Athanasos P. , Michopoulos John G. , Steuben John C. , Birnbaum Andrew J. , Apetre Nicole A. , Gorzkowski Edward P. , Patterson Eric A. , Geltmacher Andrew B.
Published: November 2023

Volumetric material manufacturing involves forming a solid shape within a precursor powder volume as a whole instead of the traditional layer by layer technique used for most additive manufacturing methodologies. This work presents an inverse technique for designing masking parameters in order to generate a predetermined shape within the precursor powder volume. The model utilizes a microwave cavity for inducing heat in BaTiO3 precursor powder via Joule heating. Material sintering and microwave propagation is modeled by coupling electromagnetics and heat conduction using finite element discretization. This interaction is complicated by the nonlinear thermal dependence of dielectric properties of BaTiO3 (i.e. density, specific heat, dielectric constant and loss tangent), which are considered here. Heat can be localized within the powder volume using one or multiple conductive masks that modulate incident energy. Utilizing both 3D and 2D models, it is demonstrated that an arbitrary nonuniform mask can create nonuniform heating in the powder volume. An experimental framework is then established to conduct a forward experiment parameterized with five masking and cavity design variables causing different powder heating patterns. Then an inverse problem is solved by specifying a desired shape and outputting a mask and cavity design capable of generating said shape. It is demonstrated that mask parameters can be optimized in order to produce a desired shape by this inverse method.
Multi-Input Multi-Output Vibration Model for Motorbike Excited by Ride Test
Authors: Cavacece Massimo , Figliolini Giorgio , Lanni Chiara
Published: November 2023

This research proposes a multi-input multi-output model (MIMO) to identify the modal properties of a motorbike during the riding test. The MIMO process verifies that a linear combination of all inputs causes each output, and there are no causal relationships between the inputs. If there are causal relationships between one of the outputs, the MIMO process redefines the input signals. The process verifies the connection between input-output and the presence of noise extraneous to the inputs and outputs. The MIMO process represents a multiple-input system with a single output. The peculiar aspect is the decoupling of individual information. In the presence of coupling between the separate inputs, the authors propose the principal component analysis (PCA) to decouple the inputs.
Drag Coefficient Characterization of the Origami Magic Ball
Authors: Chen Guanyu , Chen Dongsheng , Weakly Jessica , Sung Cynthia
Published: November 2023

The drag coefficient plays a vital role in the design and optimization of robots that move through fluids. From aircraft to underwater vehicles, their geometries are specially engineered so that the drag coefficients are as low as possible to achieve energy-efficient performances. Origami magic balls are 3-dimensional reconfigurable geometries composed of repeated simple water-bomb units. Their volumes can change as their geometries vary and we have used this concept in a recent underwater robot design. This paper characterizes the drag coefficient of an origami magic ball in a wind tunnel. Through dimensional analysis, the scenario where the robot swims underwater is equivalently transferred to the situation when it is in the wind tunnel. With experiments, we have collected and analyzed the drag force data. It is concluded that the drag coefficient of the magic ball increases from around 0.64 to 1.26 as it transforms from a slim ellipsoidal shape to an oblate spherical shape. Additionally, three different magic balls produce increases in the drag coefficient of between 57% and 86% on average compared to the smooth geometries of the same size and aspect ratio. The results will be useful in future designs of robots using waterbomb origami in fluidic environments.
Data Fusion Cognitive Computing for Characterization of Mechanical Property in Friction Stir Welding Process
Authors: Hoang Danny , Chen Ruimin , Mishra Debasish , K. Pal Surjya , Imani Farhad
Published: November 2023

High intricacy of the friction stir welding (FSW) process and severe plastic deformation typically result in serious part quality concerns, including thinning, groove, and kissing bond. These defects, in turn, impact mechanical properties (e.g., Young’s modulus and yield strength), thereby deteriorating the performance of a joint part. Machine learning methods such as feature-based support vector machines and end-to-end deep neural networks are recently coupled with in-situ sensing to link process parameters with the final properties, yet the low accuracy and precision, poor sample efficiency, and lack of interpretable learning limit their capabilities for reliable characterization. This research introduces hyperdimensional cognitive computing (HCC) that mimics human brain functionalities to fuse power, torque, and force data to provide robust, sample-efficient, and explainable learning for process-property characterization in FSW. Data augmentation is integrated with HCC to tackle the imbalanced data problem. The experimental results show that HCC seamlessly fuses in-situ data to predict the ultimate tensile strength with an Fl-score of 0.899 ± 0.048, which is 21.579%, 47.779%, 1.933%, and 68.139% better than support vector machine, logistic regression, Naive-Bayes and multilayer perceptron, respectively. Besides the superior prediction capability, HCC symbolic representation reveals that measured force is the critical factor in the characterization of ultimate tensile strength. The proposed HCC is shown to be effective for data fusion and single-pass learning and eliminates the necessity of costly and long retraining in various manufacturing processes such as 3D printing and bio-fabrication.
On the Construction of Confidence Regions for Uncertain Planar Displacements
Authors: Yu Zihan , Ge Qiaode Jeffrey , Langer Mark P. , Arbab Mona
Published: November 2023

This paper studies the statistical concept of confidence region for a set of uncertain planar displacements with a certain level of confidence or probabilities. Three different representations of planar displacements are compared in this context and it is shown that the most commonly used representation based on the coordinates of the moving frame is the least effective. The other two methods, namely the exponential coordinates and planar quaternions, are equally effective in capturing the group structure of SE(2). However, the former relies on the exponential map to parameterize an element of SE(2), while the latter uses a quadratic map, which is often more advantageous computationally. This paper focus on the use of planar quaternions to develop a method for computing the confidence region for a given set of uncertain planar displacements. Principal component analysis (PCA) is another tool used in our study to capture the dominant direction of movements. To demonstrate the effectiveness of our approach, we compare it to an existing method called rotational and translational confidence limit (RTCL). Our examples show that the planar quaternion formulation leads to a swept volume that is more compact and more effective than the RTCL method, especially in cases when off-axis rotation is present.
Functional Reasoning of System Architecture in the System Modeling Language (SysML) With XML Representation
Authors: Ganeriwala Parth , Chambers Candice , Sen Chiradeep , Bhattacharyya Siddhartha
Published: November 2023

Formal and structured modeling of system or software behavior enables automated reasoning early in the design process. This permits checking the correctness of the flow of information, consistency among the interacting components, and dependencies among components, to mention a few potential automated reasoning. Presently, research efforts have emphasized the integration of formal analytical capabilities with systems modeling methods. But, the approaches lack formal representations for function-based modeling and reasoning. Prior research in function-based design has produced formal languages, graphical representations, and reasoning algorithms that support model consistency checks, physics-based verification of model correctness, natural-language and qualitative physics-based grammars that grow and decompose given function models, and formal representation of system states that support causal analysis within the function modeling context. We posit that by integrating these capabilities into the system modeling languages, we could significantly augment the languages’ capability to support formal function-based reasoning, which was not possible so far. In this paper, we address this gap with the creation of domain-specific language for function-based modeling. This is illustrated with a coffeemaker example designed in a systems engineering framework along with XML-based reasoning.
Data-Driven Design of High Electron Mobility Transistor Devices Using Physics-Informed Gaussian Process Modeling
Authors: Renteria Anabel , Xu Yanwen , Hamdan Bayan , Li Zhou , Cordero Sergio , Senesky Debbie G. , Wang Pingfeng
Published: November 2023

High electron-mobility transistors (HEMTs) have emerged as an attractive alternative for high-efficiency power systems, due to their good material properties to perform at high voltages, temperatures, and frequencies. For that reason, design optimization of HEMTs becomes imperative to ensure the quality and capability of the device when in service. There have been models derived from experimentation to guide the design of HEMTs. Nonetheless, due to its expensive manufacturing process, the relationship of the temperature channel with respect to the design parameters has not been investigated thoroughly. This paper presents a multiphysics finite element (FE) simulation to predict the HEMT’s device maximum channel temperature when varying different design parameters. Furthermore, Gaussian Process (GP) based surrogate model was developed using the simulation results as the training database with adaptive sampling techniques for the optimization process. The proposed high-fidelity surrogate model effectively predicts the channel temperature of the HEMT device and enables an optimum search over the design space.
Design of a Person-Carrying Robot for Contact Compliant Navigation
Authors: Gonzalez Carlos , Lee Samantha , Montano Francisco , Ortega Steven , Kang Dong Ho , Jaiswal Mehar , Jiao Junfeng , Sentis Luis
Published: November 2023

Navigating human-centered environments is a complex task for robots. It requires having hardware capable of sensing the environment as humans do and requires software that efficiently reasons about this information in real time. Current robotic platforms tend to avoid contacts, while human robot interaction highly depends on this. We believe having platforms and algorithms that combine visual and tactile information is crucial in developing navigation algorithms for human-centered environments. In this work, we present the mechatronic design of our new mobile robot which is capable of carrying a person and that can sense its environment through contact and vision. We detail our design procedure, parting from design specifications to material selection and show its preliminary appearance along with some experimental navigation while carrying a person. We provide detailed analyses on our mechanical design through Finite Element Analysis simulations, present our material seleection for all of the hardware components, and give details on our low-level code controlling our robot. We have set up our robot to work with the ROS framework and have shared our code, which includes the low-level code to communicate with the EtherCAT servo drives. We believe this information will ease development of new robots by the robotics research community.
Modeling a Concentrated Solar Collector (CSC) - Convection-Enhanced Evaporation (CEE) System for Small-Scale Brine Management
Authors: Guillen Rodriguez Nallely , Kaddoura Mustafa F. , Wright Natasha C.
Published: November 2023

Desalination is the process of removing salts from a saline water source to obtain fresh water. All desalination processes produce a salt-rich brine. If not disposed of properly, brine can lead to negative environmental consequences. Solar evaporation ponds facilitate its adequate disposal; however, this method requires a large footprint. As an alternative for small-scale desalination plants, a convection-enhanced evaporation (CEE) system has been proposed.

A CEE unit consists of several packed surfaces, distributed by uniform spacing. Brine is injected along the width of the surfaces, forming a liquid thin film. As ambient temperature and relative humidity vary on an hourly and daily basis, a controller is necessary to select the optimal brine injection rate, brine temperature, and air speed that maintains a constant evaporation rate. The optimized operating conditions results in a thermal energy load required to preheat the brine before it enters the evaporation system. This thermal load oscillates in magnitude, as optimal brine inlet temperature varies from less than 1° C up to 90° C, depending on time of the day and year.

In this paper, a model is developed to evaluate the use of a concentrated solar collector (CSC) system for the supply of the CEE system’s thermal energy requirements. The model predicts the performance of the coupled CSC-CEE system in terms of specific energy consumption, solar fraction, and evaporation fraction (relative to the target).

Findings establish that due to the high thermal energy demand of the system, the solar fraction can be relatively low, with values of less than 20% for small collector-tank configurations. However, it was also observed that low solar fractions do not necessarily translate to low evaporation fractions, which can be attributed to the high variability in the magnitude of the hourly heat load, and the fact that a high percentage of the evaporating hours depend exclusively on forced air convection through the CEE system without preheating the brine. Further work is needed to understand the tradeoff in footprint required for the CSC system, CEE system, and any non-evaporated brine storage.
Drilling Holes in CFRP for Aircraft Using CBN Electroplated Ball End Mill Using Helical Interpolation Motion
Authors: Hamamoto Sora , Hirogaki Toshiki , Aoyama Eiichi , Fujiwara Kazuna , Taketani Masashi
Published: November 2023

The demand for carbon-fiber-reinforced plastics (CFRP) has recently increased in various fields. However, few reports consider the wear of machining tools, quality of the machined surface, decrease in the mechanical properties of CFRP by machining temperature, and price of machining tools. Therefore, in this study, we focused on cubic boron nitride (cBN) and developed a novel cBN-electroplated end mill, which can be used to cut and ground without the need for changing tools.

In this study, we used a cBN-electroplated ball-end mill to drill holes in CFRP using a ball-end mill and helical interpolation motion. The laminated carbon fibers are delaminated at the entrance and exit of the hole, with the delamination at the exit side investigated using strain gauges. As a result, we found that surface delamination was reduced by decreasing the axial and tangential feed rates and increasing the cutting speed. The timing of the delamination of the machined hole can be examined by comparing and considering the strain gauge and FEM results.
Platform Hydrodynamic and Structural Control Co-Optimization for the Floating Offshore Wind Turbines
Authors: Liang Jinbin , Du Xianping , Yi Jianbo , Qian Guowei , Xie Peng , Xu Hongyi
Published: November 2023

The design of floating offshore wind turbines requires a rigorous method for handling the complex multi-systems and multi-physics interactions involved. The traditional method optimizes subsystems of different disciplines sequentially and independently and the control is always implemented at the last step. This neglects the coupling between sub-systems and may miss the global optimum. Control co-design could formulate the plant and control in a single formula for reaching the global optimum. The floating offshore wind turbine is a complex system with the interaction of aerodynamics, structural, hydrodynamics, and control, which causes the difficulty of co-design problem formulation. In this study, a framework for the co-design of platform hydrodynamics and structural control is established using the coupled time-domain simulation. A case study is conducted with the DTU 10 MW reference wind turbine and the NAUTILUS-10 semi-submersible platform for demonstration. The platform shapes and tuned mass dampers are co-optimized by establishing and solving a formula with eight design variables under the constraints of power generation and platform stability. An efficient Kriging model-enhanced genetic algorithm solves problems for minimizing platform mass. Compared with the baseline model, the platform mass is reduced with the lowered platform motion, which increased the annual energy production by 2.96%. The time history analysis on rated condition suggests lowered blade tip deflection and rotor thrust. This demonstrates the improvement made by the co-design method under limited numbers of constraints and loading conditions.
Teaching rotational physics with bivectors
Authors: Jensen Steuard , Poling Jack
Published: November 2023

Angular momentum is traditionally taught as a (pseudo)vector quantity, tied closely to the cross product. This approach is familiar to experts but challenging for students, and full of subtleties. Here, we present an alternative pedagogical approach: angular momentum is described using bivectors which can be visualized as “tiles” with area and orientation, and whose components form an antisymmetric matrix. Although bivectors have historically been studied in specialized contexts like spacetime classification or geometric algebra, they are no more complicated to understand than cross products. The bivector language provides a more fundamental definition for rotational physics and opens the door to understanding rotations in relativity and in extra dimensions.
Pandemic modeling with the game of life
Authors: Faux David , Thavaraja Thesha , Croucher Alana
Published: November 2023

The generalized semi-classical game of life (gSCGOL) is a cellular automaton in which qubits evolve through repeated applications of birth, death, and survival operators. gSCGOL is used to model human–virus interactions during the COVID-19 pandemic by using cell “liveness” to be equivalent to the human population that is vulnerable to the disease. A match to the United Kingdom (UK) daily 7-day average COVID-19 deaths was achieved by daily changes to a parameter V representing the vulnerability of the population to the virus. The model predicts that the first COVID-19 virus entered the UK on December 22, 2019 with an uncertainty of ±1 days. The changes in the vulnerability V elucidate the human response to the unfolding pandemic during 2020, quantifying the impact of the lockdown, the relaxation of restrictions, and the opening and closing of schools. The model predicts that starting the first UK national lockdown one week earlier would have led to 40% fewer COVID-19-related deaths, equivalent to 17 000 lives.
Contribution of acoustic cues to prominence ratings for four Mandarin vowels
Authors: Zhang Wei , Clayards Meghan
Published: November 2023

The acoustic cues for prosodic prominence have been explored extensively, but one open question is to what extent they differ by context. This study investigates the extent to which vowel type affects how acoustic cues are related to prominence ratings provided in a corpus of spoken Mandarin. In the corpus, each syllable was rated as either prominent or non-prominent. We predicted prominence ratings using Bayesian mixed-effect regression models for each of four Mandarin vowels (/a, i, ɤ, u/), using fundamental frequency (F0), intensity, duration, the first and second formants, and tone type as predictors. We compared the role of each cue within and across the four models. We found that overall duration was the best predictor of prominence ratings and that formants were the weakest, but the role of each cue differed by vowel. We did not find credible evidence that F0 was relevant for /a/, or that intensity was relevant for /i/. We also found evidence that duration was more important for /ɤ/ than for /i/. The results suggest that vowel type credibly affects prominence ratings, which may reflect differences in the coordination of acoustic cues in prominence marking.
Diagnostic accuracy of the 5.07 monofilament test for diabetes polyneuropathy: influence of age, sex, neuropathic pain and neuropathy severity
Authors: Dunker Øystein , Uglem Martin , Bu Kvaløy Marie , Løseth Sissel , Hjelland Ina Elen , Allen Sara Maria , Dehli Vigeland Maria , Kleggetveit Inge Petter , Sand Trond , Nilsen Kristian Bernhard
Published: November 2023

Introduction

There is a need for simple and cheap diagnostic tools for diabetic polyneuropathy (DPN). We aimed to assess the diagnostic accuracy of the 5.07/10 g monofilament test in patients referred to polyneuropathy assessments, as well as to examine how disease severity, age, sex and neuropathic pain (NP) impact diagnostic accuracy.

Research design and methods

Five Norwegian university hospitals recruited patients with diabetes aged 18–70 referred to neurological outpatient clinics for polyneuropathy assessments. The 5.07/10 g Semmes-Weinstein monofilament examination (SWME) was validated against the Toronto consensus for diagnosing diabetic neuropathies; the results were stratified by age, sex and NP. Disease severity was graded by a combined nerve conduction study (NCS) Z-score, and logistic regression was applied to assess whether disease severity was a predictor of diagnostic accuracy.

Results

In total, 506 patients were included in the study. Global sensitivity was 0.60 (95% CI 0.55, 0.66), specificity 0.82 (95% CI 0.75, 0.87), positive and negative predictive values were 0.86 (95% CI 0.81, 0.90) and 0.52 (95% CI 0.46, 0.58), respectively, positive and negative likelihood ratios were 3.28 (95% CI 2.37, 4.53) and 0.49 (95% CI 0.42, 0.57), respectively. The SWME was less sensitive in females (0.43), had lower specificity in patients with NP (0.56), and performed worse in patients ≥50 years. NCS-based disease severity did not affect diagnostic accuracy (OR 1.15, 95% CI 0.95, 1.40).

Conclusions

This multicenter study demonstrates poor diagnostic performance for the 5.07/10 g SWME in patients with diabetes referred to polyneuropathy assessments; it is particularly unsuited for female patients and those with NP. The diagnostic accuracy of the SWME was not influenced by NCS-based disease severity, demonstrating that it does not perform better in patients with later stages of DPN. We do not recommend the use of the 5.07/10 g monofilament in the evaluation of patients with diabetes referred to polyneuropathy assessments.
How do pilot and feasibility studies inform randomised placebo-controlled trials in surgery? A systematic review
Authors: Cousins Sian , Gormley Alexander , Chalmers Katy , Campbell Marion K , Beard David J , Blencowe Natalie S , Blazeby Jane M
Published: November 2023

Introduction

Randomised controlled trials (RCTs) with a placebo comparator are considered the gold standard study design when evaluating healthcare interventions. These are challenging to design and deliver in surgery. Guidance recommends pilot and feasibility work to optimise main trial design and conduct; however, the extent to which this occurs in surgery is unknown.

Method

A systematic review identified randomised placebo-controlled surgical trials. Articles published from database inception to 31 December 2020 were retrieved from Ovid-MEDLINE, Ovid-EMBASE and CENTRAL electronic databases, hand-searching and expert knowledge. Pilot/feasibility work conducted prior to the RCTs was then identified from examining citations and reference lists. Where studies explicitly stated their intent to inform the design and/or conduct of the future main placebo-controlled surgical trial, they were included. Publication type, clinical area, treatment intervention, number of centres, sample size, comparators, aims and text about the invasive placebo intervention were extracted.

Results

From 131 placebo surgical RCTs included in the systematic review, 47 potentially eligible pilot/feasibility studies were identified. Of these, four were included as true pilot/feasibility work. Three were original articles, one a conference abstract; three were conducted in orthopaedic surgery and one in oral and maxillofacial surgery. All four included pilot RCTs, with an invasive surgical placebo intervention, randomising 9–49 participants in 1 or 2 centres. They explored the acceptability of recruitment and the invasive placebo intervention to patients and trial personnel, and whether blinding was possible. One study examined the characteristics of the proposed invasive placebo intervention using in-depth interviews.

Conclusion

Published studies reporting feasibility/pilot work undertaken to inform main placebo surgical trials are scarce. In view of the difficulties of undertaking placebo surgical trials, it is recommended that pilot/feasibility studies are conducted, and more are reported to share key findings and optimise the design of main RCTs.

PROSPERO registration number

CRD42021287371.
Association of body fat distribution with all-cause and cardiovascular mortality in US adults: a secondary analysis using NHANES
Authors: Li Fuhai , Wang Zhi , Li Shaohua , Liu Wenheng , Li Yan , Jiang Chen , Tian Yulong , Cui Weining , Zhang Xuejuan , Zhang Jidong , Xu Yan
Published: November 2023

Objective

To investigate the association of fat and lean mass in speciﬁc regions with all-cause and cardiovascular-related mortality.

Design

Population based cohort study.

Setting

US National Health and Nutrition Examination Survey (2003–2006 and 2011–2018).

Participants

22 652 US adults aged 20 years or older.

Exposures

Fat and lean mass in speciﬁc regions obtained from the whole-body dual-energy X-ray absorptiometry.

Main outcome measures

All-cause and cardiovascular-related mortality.

Results

During a median of 83 months of follow-up, 1432 deaths were identified. Associations between body composition metrics and mortality risks were evident above specific thresholds. For all-cause mortality, Android fat mass showed elevated HRs above 2.46 kg (HR: 1.17, 95% CI 1.02 to 1.34), while Android lean mass (ALM) had similar trends above 2.75 kg (HR: 1.17, 95% CI 1.03 to 1.33), and Android total mass above 5.75 kg (HR: 1.08, 95% CI 1.01 to 1.16). Conversely, lower HRs were observed below certain thresholds: Gynoid fat mass (GFM) below 3.71 kg (HR: 0.72, 95% CI 0.56 to 0.93), Gynoid lean mass below 6.44 kg (HR: 0.77, 95% CI 0.64 to 0.92), and Gynoid total mass below 11.78 kg (HR: 0.76, 95% CI 0.70 to 0.84). Notably, below 0.722 kg, the HR of visceral adipose tissue mass (VATM) was 1.25 (95% CI 1.04 to 1.48) for all-cause mortality, and above 3.18 kg, the HR of total abdominal fat mass was 2.41 (95% CI 1.15 to 5.05). Cardiovascular-related mortality exhibited associations as well, particularly for Android fat mass (AFM) above 1.78 kg (HR: 1.22, 95% CI 1.01 to 1.47) and below 7.16 kg (HR: 0.50, 95% CI 0.36 to 0.69). HRs varied for Gynoid total mass below and above 10.98 kg (HRs: 0.70, 95% CI 0.54 to 0.93, and 1.12, 95% CI 1.02 to 1.23). Android per cent fat, subcutaneous fat mass (SFM), AFM/GFM, and VATM/SFM were not statistically associated with all-cause mortality. Android per cent fat, Gynoid per cent fat, AFM/GFM, and VATM/SFM were not statistically associated with cardiovascular-related mortality. Conicity index showed that the ALM/GLM had the highest performance for all-cause and cardiovascular-related mortality with AUCs of 0.785, and 0.746, respectively.

Conclusions

The relationship between fat or lean mass and all-cause mortality varies by region. Fat mass was positively correlated with cardiovascular mortality, regardless of the region in which they located. ALM/GLM might be a better predictor of all-cause and cardiovascular-related mortality than other body components or body mass index.