bluuebunny/crossref_metadata_2025 huggingface dataset:
Design of a Crawling Robot Based on Origami Structures
Authors: Yan Shuiqing , Wei Qi , Li Jiake , Ma Zhe , Zhou Xiang
Published: November 2022

A crawling robot with Kresling folding tubes as the motion joint is designed in this paper. The crawling robot is composed of a motion module and two load modules where the motion module is composed of four Kresling folding tubes and one corrugated tube. Based on the designed overall structure of the crawling robot, the corresponding linear and steering motion mechanisms are designed. The retraction and bending motions of motion module are analyzed and studied by the simulation in ABAQUS. Based on the designed overall structure of the crawling robot and the Kresling folding tube configuration, the mechanical structure of the crawling robot driven by the servo motor is designed. The robot prototype is made by 3D printing and metal machining. According to the test of the robot motion performance, the linear motion speed of the crawling robot can reach 33.7mm/s, and the steering speed can reach 5.2°/s. The designed crawling robot adopts modular design with a simple structure and driving method, and has fast movement speed. The design based on origami structure makes the robot smaller in size and flexible in deformation, and has potential applications ranging from pipeline detection to military reconnaissance, post-disaster rescue and ruin exploration.
Additive Manufacturability Analysis of Multiscale Aperiodic Structures: a Statistical Mechanics Approach
Authors: Trautschold Olivia , Dong Andy
Published: November 2022

Discrete, rule-based approaches to design for additive manufacturing can be inadequate or prohibitively complex for predicting the geometric fidelity of multiscale aperiodic structures that lack a representative volume element. In this paper, we introduce heuristics based upon statistical mechanics to assist in additive manufacturability analysis. The heuristics are derived from the following topological properties of complex network representations of the geometry of multiscale aperiodic structures: giant component size, node degree, average shortest path length, and robustness. We apply these heuristics to the design and additive manufacture of crumpled thin sheets, selected for their multiscale aperiodicity and reproducible statistical properties. Manufacturability is assessed by comparing these topo-logical network properties between a computationally designed crumpled structure and a microCT scan of an additively manufactured crumpled structure. Dimensionality reduction is performed on each network representation to balance spatial noise reduction and information loss. Consistent with expectations, the heuristics and topological properties show that crumpled structures at higher compaction levels are more readily manufactured. The results show the significance of statistical mechanics in providing insight into the additive manufacturability of multiscale aperiodic structures. We conclude by discussing the generality of this approach for alternative geometries and provide designers with a framework for interpreting manufacturability from a statistical mechanics perspective.
Multi-Input Shaping for a Multi-Mode Vibratory Structure Using Deadbeat Control Theory
Authors: Sinha Alok
Published: November 2022

A general approach of the multi-input shaping for an underdamped flexible structure is developed using the deadbeat control theory, which states that a discrete-time system can be made to reach the desired position in a minimum time by placing all the poles at the origin of z-plane. It is shown that this theory can also be used to minimize sensitivities to uncertainties in natural frequencies and modal damping ratios. Numerical results are presented for a spring mass model, and a finite element model of a tetrahedral truss structure. These examples deal with cases of number of inputs being equal to number of modes, and also non-integer ratio of numbers of states and inputs.
A Sequential Decision Making Approach to Learn Process Parameters by Conducting Experiments on Sacrificial Objects
Authors: Yoon Yeo Jung , Gupta Satyandra K.
Published: November 2022

Learning the right process parameters is essential to efficiently and safely execute tasks in automated manufacturing applications. When there is a risk of damaging the object, conducting experiments on sacrificial objects is a good option to explore the process parameters before performing the tasks on the object of interest. However, using too many sacrificial objects can incur unnecessary costs. We want to know the right amount of sacrificial objects to identify the right process parameters while keeping the expected task completion cost to a minimum. To do this, we use a sequential decision making approach to learn the process parameters. The sequential decision making approach we proposed is a combination of look ahead search, surrogate modeling, and a policy to select process parameters. In computation, the search tree simulates future decisions beyond the current decision and evaluates the costs by considering the effect of future experiments. The decision made in our approach only commits the user to the current stage and does not affect future decisions. When a decision is made to conduct experiments on the sacrificial object, the parameter policy to select process parameters should be simulated. In this policy, we consider the right trade-off between exploration and exploitation. In the beginning, the policy favors exploration since we do not have much data. In later stages, less exploration and more exploitation will be performed as we have sufficient number of experimental data. We validate our approach using the experimental setup of the robotic spray painting application.
Does Narrative Play a Role in Engineering Decision-Making and Design? A Preliminary Study
Authors: Ferguson Scott , Bryden Kenneth M.
Published: November 2022

This paper presents data from a preliminary study designed to test the hypothesis that narrative is the primary decisionmaking tool employed in engineering design. We also look for evidence that analysis and optimization provide anchor points that drive the narrative forward, even in the presence of unresolvable uncertainties. The data in this paper comes from student engineers who are engaged in an 8-month, NASA-sponsored design competition. We code 46 statements from our interviews, spanning 10 different narrative elements as defined by Fenton-O’Creevy and Tuckett [1]. We find that engineers use multiple narrative elements to develop conviction when reaching action readiness, and that of the 46 coded statements, only 14 corresponded to quantitative analysis in the form of models and calculations. We find that in engineering design the decision-making journey involves a combination of quantitative analysis and qualitative engineering judgment that is evaluated to develop conviction and reach a state of action.
A Stress-Deflection Model for Fixed-Clamped Flexures Using a Pseudo Rigid Body Approach
Authors: Huxman Connor , Butler Jared
Published: November 2022

Fixed-clamped flexures are one common component of compliant mechanisms which remain difficult to design due to their unique force- and stress-deflection response profiles. In this work, a stress-deflection model for fixed-clamped flexures is proposed that utilizes a modified pseudo rigid body model. Special analysis is carried out on the characteristic radius factor, a parameter to which model accuracy is particularly sensitive. Effects of both deflection magnitude and flexure geometry on this parameter are explored and considerations for optimal parameter selection are included. Finite element analysis demonstrates that the model is able to predict the vertical applied force, horizontal reaction force, and maximum von Mises stress with a maximum percent error less than 3.5% at yield for a range of steel flexure topologies. Using the model to predict behavior of flexible polypropylene flexures undergoing larger deflections, the model has a maximum error between 4–14% using static model parameters, compared to 74–96% using the small deflection equations. The distinct combination of axial and bending stresses experienced in fixed-clamped flexures has made mechanisms which use these members challenging to design. This work provides a model that designers, engineers, and researchers can draw from to understand stress profiles present in these flexible members.
Toward a Comprehensive Framework for Preliminary Design Evaluation in Additive Manufacturing
Authors: Cayley Alexander , Mathur Jayant , Meisel Nicholas
Published: November 2022

Additive manufacturing (AM) is a rapidly growing technology within the industry and education sectors. Despite this, there lacks a comprehensive tool to guide AM-novices in evaluating the suitability of a given design for fabrication by the range of AM processes. Existing design for additive manufacturing (DfAM) evaluation tools tend to focus on only certain key process-dependent DfAM considerations. By contrast the purpose of this research is to propose a tool that guides a user to comprehensively evaluate their chosen design and educates the user on an appropriate DfAM strategy. The tool incorporates both opportunistic and restrictive elements, integrates the seven major AM processes and outputs an evaluative score and recommends processes and improvements for the input design. The paper presents a thorough framework for this evaluation tool and details the inclusion of features such as dual-DfAM consideration, process recommendations, and a weighting system for restrictive DfAM. The result is a detailed recommendation output that helps users to determine not only “can you print your design” but also “should you print your design” by combining several key research studies to build a comprehensive user design tool. This research demonstrates the potential of the framework through a series of case studies geometries. The preliminary framework presented in this paper establishes a foundation for future studies to refine the tool’s accuracy using more data and expert analysis.
Mean Squared Error May Lead You Astray When Optimizing Your Inverse Design Methods
Authors: Bernard Shai , Wang Jun , Fuge Mark
Published: November 2022

When performing time-intensive optimization tasks, such as those in Topology Optimization or Shape Optimization, researchers have turned to Machine Learned (ML) Inverse Design methods — i.e., algorithms that predict the optimized geometry from input conditions — to either replace or warm start traditional optimizers. Almost exclusively, such methods are trained and optimized to reduce the Mean Squared Error between a method’s output and a ground truth training dataset of optimized designs, this being the obvious choice for traditional supervised learning. While convenient, we show that this choice may be myopic. Specifically, we compare two methods of optimizing the hyper-parameters of both a random forest (RF) and k-nearest neighbors (KNN) model for predicting the optimal topology in a 2D heat sink example.

We show that under both direct Inverse Design as well as when warm starting further Topology Optimization (TO), using typical Mean Squared Error metrics produces less performant models than a proposed metric that directly evaluates the objective function, though both methods produce designs that are almost one order of magnitude better than a control condition that uses a uniform initialization common in TO. We also illustrate how warm starting TO with predicted solutions impacts both the convergence time, the type of solutions obtained during optimization, and the final designs. Sensitivity analyses on various model parameters demonstrate that the results are not dependent on other model hyperparameters. Overall, our initial results portend that researchers may need to revisit common choices for evaluating ID methods that subtly trade-off factors in how an ID method will actually be used. We hope our open-source dataset and evaluation environment will spur additional research in those directions.
Descriptor Aided Bayesian Optimization for Mixed Variable Materials Design With High Dimensional Qualitative Variables
Authors: Iyer Akshay , Yerramilli Suraj , Rondinelli James M. , Apley Daniel W. , Chen Wei
Published: November 2022

Engineering design often involves qualitative and quantitative design variables, which requires systematic methods for the exploration of these mixed-variable design spaces. Expensive simulation techniques, such as those encountered in materials design, underline the need for efficient search strategies — Bayesian optimization being one of the most widely adopted. Although recent developments in mixed-variable Bayesian optimization have shown promise, the effects of dimensionality of qualitative variables have not been well studied. High dimensional qualitative variables, i.e., with many levels, impose a large design cost as they typically require a larger dataset to quantify the effect of each level on the optimization objective. We address this challenge by leveraging domain knowledge about underlying physical descriptors to infer the effect of unobserved levels that have not been sampled yet. We show that domain knowledge about physical descriptors can be intuitively embedded into the latent variable Gaussian process approach — a mixed-variable GP modeling technique — and used to selectively explore levels of qualitative variables in the Bayesian optimization framework. Our method is robust to certain types of incomplete domain knowledge and significantly reduces the design cost for problems with high-dimensional qualitative variables.
“On the Edge” Obstacle Surmounting Method Using Hybrid Locomotion
Authors: Yeldan Alper , Arora Abhimanyu , Soh Gim Song
Published: November 2022

This paper presents on the edge obstacle surmounting method for QuadRunner, a hybrid quadruped robot, to overcome obstacles using hybrid locomotion where both legged and wheel configurations are utilized. When obstacle heights exceed the workspace of its leg, QuadRunner becomes quasi-statically mismatched and fails to achieve the climbing task quasi-statically. By incorporating its body as contact support, the center of gravity (COG) of QuadRunner can be successfully shifted on top of the obstacle to perform surmounting task. The unique design of the QuadRunner leg allows it to behave as a four-bar or slider-crank mechanism depending on the leg’s configuration. Here we detail the sub-state strategy for its surmount task, where QuadRunner goes through the sub-states {L}EAN, {H}OOK, {F}OUR-BAR, {S}LIDE, {G}ET-UP to climb obstacles. In addition, limitations of the operation are analyzed and the requirements for climbing are identified. With our proposed method, QuadRunner can surmount obstacles of heights between 10 cm and 22 cm (higher than its kinematic max height of 16 cm) within 25 seconds. Lastly, a reliability test shows that the robot can climb the obstacle with a 70% success rate.
Multidisciplinary Optimization to Reduce Cost and Power Variation of a Wave Energy Converter
Authors: McCabe Rebecca , Murphy Olivia , Haji Maha
Published: November 2022

Wave energy converters (WECs) can advance the global energy transition by producing clean power for utility grids and offshore technologies. This paper provides a multidisciplinary, dual objective optimization of the Reference Model 3 (RM3), a two-body point absorber WEC design benchmark. The simulation model employs linear hydrodynamics with force saturation and probabilistic waves. The RM3 geometry and controller parameters are optimized using sequential quadratic programming to minimize the levelized cost of energy (LCOE) and the coefficient of variation of power. The minimum-LCOE design produces a power variation of 205% and an LCOE of $0.08/kWh, a seven-fold cost reduction and 23% lower variation from the RM3 baseline of $0.75/kWh and 255% variation. Parameter sensitivities show that LCOE depends more strongly on site and economic parameters than geometric or material parameters, while power variation is largely insensitive to all parameters. A Pareto trade-offbetween cost and power variation reveals different optimal designs depending on which objective is prioritized, suggesting application-specific design heuristics. Three representative optimal designs are investigated: a minimum-LCOE design for cost-sensitive operations like utility power, a minimum-variation design for cost-insensitive installations like small offshore systems, and a balanced design for intermediate applications. Power probability distributions are shown for each.
Exploring Partitioning in Subsystem Prototyping
Authors: Connor Zachariah , Camburn Bradley
Published: November 2022

The development of a novel complex system is often cost-prohibitive. One reason for this, is that system-level performance can typically only be evaluated once all subsystems have been designed; and therefore, it is difficult to iterate on full system prototypes. Subsystem isolation is a well-established prototyping method to enable efficient design at both the system and subsystem levels. In this process, a complex system is partitioned into manageable subsystems that are subsequently designed in detail. Subsystem isolation permits rapid, low-cost iteration which is known as a critical driver of prototype performance. This paper provides an exploration of the role of partitioning in subsystem isolation prototyping through a review of methods and an applied case study. The paper reviews two topics related to subsystem isolation prototyping: 1) partitioning approaches and 2) strategies for iteration at the system versus subsystem level. Methods from design theory, systems engineering, and multi-domain optimization are reported. The case study explored subsystem prototyping in the design and construction of an electric in space propulsion test facility utilized in DoD research. The case study is used to demonstrate the processes involved in system partitioning methods and subsystem level design. These approaches allow designers to efficiently explore options for architecture-level design, without expending unnecessary resources on iterations of the integrated system.
Development of a Novel Three-Dimensional Parallel-Plate Biochemical Sensor for Electrochemical Impedance Spectroscopy (EIS)
Authors: Rafiee Negar , Huffman Brian , Shavezipur Mohammad
Published: November 2022

Miniaturized electrochemical impedance-based biochemical sensors have been extensively used for detection of chemical and biological agents at extremely low concentrations. The majority of the sensors presented in the literature have a two-dimensional (planar) geometry and use two interdigitated electrodes that are exposed to an aqueous solution for deteciotn. In this work, a novel three-dimensional sensor is presented that uses a parallel-plate geometry for sensor structure. The sensor has a digitated electrode patterned on the substrate and a planar and porous electrode suspended above the fixed electrode. The sensor is fabricated using PolyMUMPs and diethylhexyl phthalate (DEHP) is used to demonstrate the ability of the sensor for biochemical detection. The measurement results show that the sensor has distinct Nyquist responses for 0.02 ppm, 0.2 ppm, 2 ppm and 10 ppm concentrations of the DEHP. Different electrochemical phenomena such as double-layer capacitance formation and charge transport at high frequencies, and diffusion at low frequencies are detected using Nyquist plots. The experimental results show that, for 3D parallel-plate sensors, in contrast to interdigitated geometry, the electrostatic effect is noticeable, and the parallel-plate capacitance due to the dielectric effect of the solution notably affects the Nyquist response. This is an important effect, specially for biological detection.
Evaluating Designer Learning and Performance in Interactive Deep Generative Design
Authors: Chaudhari Ashish M. , Selva Daniel
Published: November 2022

Deep generative models have shown significant promise to improve performance in design space exploration (DSE), but they lack interpretability. A component of interpretability in DSE is helping designers learn how input design decisions influence multi-objective performance. This experimental study explores how human-machine collaboration influences both designer learning and design performance in deep learning-based DSE. A within-subject experiment is implemented with 42 subjects involving mechanical metamaterial design using a conditional variational auto-encoder. The independent variables in the experiment are two interactivity factors: (i) simulatability, e.g., manual design generation (high simulatability), manual feature-based design synthesis, and semi-automated feature-based synthesis (low simulatibility); and (ii) semanticity of features, e.g., meaningful versus abstract latent features. We perform assessment of designer learning using item response theory and design performance using metrics such as distance to utopia point and hypervolume improvement. The findings highlights a highly intertwined relationship between designer learning and design performance. Compared to manual design generation, the semi-automated synthesis generates designs closer to the utopia point. Still, it does not result in greater hyper-volume improvement. Further, the subjects learn the effects of semantic features better than abstract features, but only when the design performance is sensitive to those semantic features. Potential cognitive constructs, such as cognitive load and recognition heuristic, that may influence the interpretability of deep generative models are discussed.
Travel Links Prediction in Shared Mobility Networks Using Graph Neural Network Models
Authors: Xiao Yinshuang , Ahmed Faez , Sha Zhenghui
Published: November 2022

The emerging sharing mobility systems are gaining increasing popularity because of the significant economical and environmental benefits. To facilitate the operation of sharing mobility systems, many studies are conducted to analyze and predict users’ travel behaviors. However, most research focuses on investigating every station’s usage and demand; therefore, insight into the user behavior and travel demand between stations from origin to destination is little known. Aiming to better understand the factors that would influence origin-destination travel demand, we present a complex network-based approach to predicting the travel demand between stations (e.g., whether two stations have sufficient trips to form a strong connection in a month) in sharing mobility systems. Particularly, in this study, we are interested in knowing whether local network information (e.g., the neighboring station’s features of a station and its surrounding points of interest (POI), such as banks, schools, etc.) would influence the formation of a strong connection or not. If so, to what extent do such factors play a role in it. To answer this question, we adopt Graph Neural Network (GNN), in which the concept of network embedding can capture and quantify the effect of local network structures. The results are compared with the regular artificial neural network (ANN) model without network embedding. This study is demonstrated using the bike sharing system, Divvy Bike in Chicago, as an example. We observe that the GNN prediction gains up to 9% higher performance than that of the ANN model. This implies that the local network information contributes to the formation of sharing mobility network. Moreover, it is found that when predicting the following year’s network, the model that employs the node embedding obtained from the previous year’s network outperforms the model with the node embedding obtained from the ANN predicted networks.
An Information-Decision Framework to Support Cooperative Decision Making in the Top-Down Design of Cyber-Physical-Manufacturing Systems
Authors: Baby Mathew , Balu Nellippallil Anand
Published: November 2022

Decision-making in the design of cyber-physical manufacturing (CPM) systems is complex due to many decisional entities and their complex interactions that need to be appropriately modeled and analyzed.

One approach to designing these systems is the goal-oriented inverse design (GoID), using which satisficing design solutions are sought in a top-down manner. In this approach, entity decisions are directed towards meeting the goals propagated inversely from the subsequent entity in the manufacturing sequence. However, achieving the goals in a top-down manner may not be feasible for certain scenarios due to the defined constraints, available bounds, and targets for an entity. This leads to design conflicts between the entities and loss in entity and overall system-level performances.

In this paper, we propose an information-decision framework that allows designers to model entity decision-making in a goal-directed manner, detect potential conflicts between entities, and regulate entity-level decisions to achieve improved entity and system-level performances. The regulation of entity decisions is accomplished by modifying active design variable bounds (considering the sensitivity of the goals to design variables), active constraint limits, or both jointly. The efficacy of the proposed framework is tested using a hot rod rolling problem involving sequential decisions. Using the problem, we showcase the use of the framework in detecting and systematically managing conflicts while designing the material, product, and manufacturing processes involved. The framework is generic, facilitates the top-down sequential design of interacting entities, and promotes cooperative design decision-making to manage design conflicts.
Let’s Role Play! the Impact of Video Frequency and Role Playing on the Utility of a Psychological Safety Team Intervention
Authors: Drum Ava , Cole Courtney , Ritter Sarah , Mohammed Susan , Jablokow Kathryn , Miller Scarlett
Published: November 2022

There is growing evidence on the importance of psychological safety, or how comfortable participants feel in sharing their opinions and ideas in a team, in engineering team performance. However, how to support it in engineering student teams has yet to be explored. The goal of this study was to investigate whether a video intervention with assigned roles could foster psychological safety in student engineering teams. In addition, we sought to explore the impact of the frequency of the videos and the utility of the roles on the self-efficacy of students and the perceived psychological safety of the team. Specifically, this study introduces video interventions and the four lenses of psychological safety (Turn-Taking Equalizer, Point of View Shifter, Affirmation Advocate, and Creativity Promoter), and seeks to determine their effectiveness at increasing psychological safety self-efficacy and individual levels of psychological safety. A pilot study was completed with 54 participants (36 males, 17 females, 1 non-binary/third gender) enrolled in a cornerstone engineering design course. Over 10 weeks, data was collected at 5 time points. The results present four key findings. Most notably, 1) a video educating all students about psychological safety in general was effective in improving psychological safety self-efficacy and students retained this information to the end of the project;2) intervention groups taught to use the four lenses did not have a statistically significant higher level of psychological safety than nonintervention groups; and 3) intervention groups perceived the use of the lenses to increase psychological safety. These results provide a baseline understanding that is needed to support psychological safety including: when to intervene, how to intervene, and how frequently to intervene.
Stability Analysis of a One Degree-of-Freedom Robot Model With Sampled Digital Acceleration Feedback Controller in Turning
Authors: Bártfai András , Barrios Asier , Dombovari Zoltan
Published: November 2022

This study investigates the stability of robots in machining. The goal is to improve the dynamic performance of robots using an additional acceleration signal fed back through the conventional built-in proportional-derivative controller provided by the manufacturer. The structure of the robot is modelled with a simple one degree-of-freedom lumped model. The control signals are fed back via a linear spring and damping. The time delays of feedback controllers are considered as zero-order holds, which results in sawtooth-like time-periodic time delays. The resulting equation of motion is an advanced delay differential equation. The semidiscretization method is shown for such systems with multiple sampled digital delays. First, we establish the stable regions in the plane of the sampling delay and the gain of the acceleration signal without machining. Then we show the possibility to improve stability in the simplest possible cutting case using the additional acceleration feedback controller compared to the cases without any controller or using only the proportional-derivative controller.
Period-1 Motions to Homoclinic Orbits in the Rössler System
Authors: Xing Siyuan , Luo Albert C. J.
Published: November 2022

In this study, period-1 motions in the Rössler system are predicted through a mapping structure of an implicit mapping. The implicit mapping is obtained from the discretization of the differential equations. The stability and bifurcation of the period-1 motions are discussed through the eigenvalue analysis. The period-1 motions varying with a system parameter is presented. The period-1 motion at its two limit ends connects to two homoclinic orbits through system parameter variation, and the corresponding approximate homoclinic orbit is presented semi-analytically. Numerical simulations of stable periodic motions are carried out for comparison to semi-analytical predictions.
Evaluating the Effect of Project Frame on Communicative Patterns in Capstone Design Pitches
Authors: Link Samantha , Krishnakumar Sandeep , Menold Jessica
Published: November 2022

Effective communication is an integral part of engineering design and leads to successful design outcomes. While there have been extensive calls to equip novice designers with effective communication skills, there is only a limited body of work that has attempted to characterize the communication patterns of novice designers, particularly when engaging with external audiences. This work seeks to characterize how the project type, or the nature of design problem, shapes the communicative patterns of novice designers when communicating design outcomes to external audiences. Presentations of design solutions from 46 teams were collected at the end of a semester-long capstone design program. These presentations were then characterized as industry- and human-centered projects. Analysis was conducted using topic modeling and Linguistic Inquiry and Word Count analysis to identify differences in linguistic patterns of novice designers between the two project types. Contrary to prior findings, no significant differences were found, implying that the communication patterns of novice designers are not affected by the type of project (or design problem being solved).
Dynamic Contact Analysis of Grosh Wheel Using Reduced Order System Approach
Authors: G. Akilesh , Pandey Manoj
Published: November 2022

Design problems using high fidelity numerical methods such as Finite Element Analysis (FEA) can be computationally intensive, especially if they require multiple runs for different loading conditions or varying parameters. Hence reduced-order models (ROM) which can reproduce the simulation results with high accuracy, while working at a very low computational budget are desirable. Subspace projection-based ROMs are widely used for the analysis of linear systems, using linear eigenmodes as projection basis and can be extended to nonlinear systems using empirical eigenmodes such as Proper Orthogonal modes (POM). However, problems involving moving contact are difficult to handle for such procedure due to moving boundary conditions of the underlying PDE. Here we use the approach proposed by [1] towards reduced-order dynamic analysis of Hyperelastic wheel rolling, incorporating the geometric and material nonlinearities in addition to contact with a view to extend it to tire rolling analysis. The simulations are performed using commercial Finite Element software Abaqus and the ROM based results are verified using Matlab and show a very good match for displacement and contact forces with a model that is orders of magnitude smaller than the full order system.
Digital Engineering Platform for Synergistic Decision-Making In Manufacturing Plant Operations: Research Questions
Authors: Gautham B. P. , Swaminathan Natarajan , Shukla Rishabh , Gaduparthi Trinath , Malhotra Chetan
Published: November 2022

Decision makers in the manufacturing industry ranging from plant operators to senior management make decisions based on a combination of defined procedures and rules, expert inputs and analysis, and their own knowledge and understanding of the problem context. Decision spaces are getting more complex, with the business paradigms shifting towards autonomous plants and servitization of products. With the advent of Internet of Things (IoT) and technologies such as machine learning and digital twins, the resources and capabilities available to decision-makers are expanding vastly. The gamut of concerns to be addressed is also expanding, with new challenges such as sustainability and its concomitant regulations and the pressure to make businesses more socially aware. Further, it would be ideal if decision-makers could easily draw upon the relevant knowledge, intuition, and experience of human experts, as well as knowledge currently buried in documents and data, and synthesize all the diverse inputs towards informed decision-making by integrating cyber, physical, and social systems. This motivates the question, “How do we create platforms that synergize these diverse knowledge sources and capabilities to facilitate better decision-making?” In this paper, we try to delve into identification of few key research questions and discuss opportunities and requirements around the same, that can aid in creating a digital platform to synergize all these diverse inputs and support decision-making. While this paper uses decision-making in manufacturing plant operations to explore the challenges and discuss one possible approach, the problem of enabling seamless synergy between the knowledge and capabilities of diverse human, IT and physical elements applies to all Cyber Physical Social Systems (CPSS).
A Variable Stiffness End-of-Arm Mechanism for Safe Operation in Industrial Environments
Authors: Jujjavarapu Sri Sadhan , Esfahani Ehsan Tarkesh
Published: November 2022

In robotic manipulation, uncertainties in the operating environment can result in the exchange of unintended forces that can harm the operator, robot, tool, or the operating environment. In this study, we present a variable stiffness end-of-arm mechanism (VSEAM) to improve the collision safety of industrial robots. In VSEAM, the endpoint stiffness of the robotic manipulator is controlled using the displacement of permanent magnets. Additionally, a calibrated force model is developed for these magnets to detect external collisions. In the event of an unintended collision, the nonlinear compliance of the VSEAM provides passive displacement of the tool and provides enough time for the robot to react to the collision. Such passive displacement helps in absorbing the partial impact energy and thus protects the tool and the environment from damage. The proposed mechanism is validated during unintended collisions with two different operational speeds (end-effector speeds of 0.3 m/s, and 0.5 m/s) and two different loads at the end-effector (0.4 Kg and 1 Kg). The transmitted forces in low and medium stiffness settings of the VSEAM is compared to the high stiffness setting of the robot (in the absence of the VSEAM) demonstrating the capability of the proposed system to reduce the transmitted forced by approximately 50%.
Higher education for all
Authors: McNutt Marcia
Published: November 2022

Universities are one of the oldest human institutions, enduring with essentially the same blueprint for a thousand years. Before the current COVID-19 pandemic, there was much talk about the promise of massive open online courses, distance learning, and other innovations to scale and expand the reach of universities, but with only limited success. Given the experience gained from educating during the pandemic, it is time for educators to ask which innovations can be introduced and, importantly, sustained, to expand the accessibility of higher education to meet the needs of the 21st century.
Invasive mosquito adds to Africa’s malaria toll
Authors: Vogel Gretchen
Published: November 2022

Anopheles stephensi
may dramatically increase the number of people at risk
Clinical Use of a 16S Ribosomal RNA Gene-Based Sanger and/or Next Generation Sequencing Assay to Test Preoperative Synovial Fluid for Periprosthetic Joint Infection Diagnosis
Authors: Flurin Laure , Hemenway Joseph J. , Fisher Cody R. , Vaillant James J. , Azad Marisa , Wolf Matthew J. , Greenwood-Quaintance Kerryl E. , Abdel Matthew P. , Patel Robin
Published: November 2022

Periprosthetic joint infection (PJI) is a dreadful complication of joint replacement. Noninvasive identification of infectious pathogens has been traditionnally limited to culture-based testing of synovial fluid which has poor sensitivity.
Distinct Cytosolic Complexes Containing the Type III Secretion System ATPase Resolved by Three-Dimensional Single-Molecule Tracking in Live Yersinia enterocolitica
Authors: Prindle Joshua R. , Wang Yibo , Rocha Julian M. , Diepold Andreas , Gahlmann Andreas
Published: November 2022

Injectisomes are membrane-embedded, multiprotein assemblies used by bacterial pathogens to inject virulent effector proteins into eukaryotic host cells. Protein secretion is regulated by cytosolic proteins that dynamically bind and unbind at injectisomes.
Comparative Genomics of the Genus Pseudomonas Reveals Host- and Environment-Specific Evolution
Authors: Saati-Santamaría Zaki , Baroncelli Riccardo , Rivas Raúl , García-Fraile Paula
Published: November 2022

Microbes play important roles in the health of living beings and in the environment. The knowledge of these functions may be useful for the development of new clinical and biotechnological applications and the restoration and preservation of natural ecosystems.
Cancer research across Africa: a comparative bibliometric analysis
Authors: Mutebi Miriam , Lewison Grant , Aggarwal Ajay , Alatise Olusegun Isaac , Booth Christopher , Cira Miska , Grover Surbhi , Ginsburg Ophira , Gralow Julie , Gueye Serine , Kithaka Benda , Kingham T Peter , Kochbati Lofti , Moodley Jennifer , Mohammed Sulma Ibrahim , Mutombo Alex , Ndlovu Ntokozo , Ntizimira Christian , Parham Groesbeck Preer , Walter Fiona , Parkes Jeannette , Shamely Delva , Hammad Nazik , Seeley Janet , Torode Julie , Sullivan Richard , Vanderpuye Verna
Published: November 2022

Introduction

Research is a critical pillar in national cancer control planning. However, there is a dearth of evidence for countries to implement affordable strategies. The WHO and various Commissions have recommended developing stakeholder-based needs assessments based on objective data to generate evidence to inform national and regional prioritisation of cancer research needs and goals.

Methodology

Bibliometric algorithms (macros) were developed and validated to assess cancer research outputs of all 54 African countries over a 12-year period (2009–2020). Subanalysis included collaboration patterns, site and domain-specific focus of research and understanding authorship dynamics by both position and sex. Detailed subanalysis was performed to understand multiple impact metrics and context relative outputs in comparison with the disease burden as well as the application of a funding thesaurus to determine funding resources.

Results

African countries in total published 23 679 cancer research papers over the 12-year period (2009–2020) with the fractional African contribution totalling 16 201 papers and the remaining 7478 from authors from out with the continent. The total number of papers increased rapidly with time, with an annual growth rate of 15%. The 49 sub-Saharan African (SSA) countries together published just 5281 papers, of which South Africa’s contribution was 2206 (42% of the SSA total, 14% of all Africa) and Nigeria’s contribution was 997 (19% of the SSA total, 4% of all Africa). Cancer research accounted for 7.9% of all African biomedical research outputs (African research in infectious diseases was 5.1 times than that of cancer research). Research outputs that are proportionally low relative to their burden across Africa are paediatric, cervical, oesophageal and prostate cancer. African research mirrored that of Western countries in terms of its focus on discovery science and pharmaceutical research. The percentages of female researchers in Africa were comparable with those elsewhere, but only in North African and some Anglophone countries.

Conclusions

There is an imbalance in relevant local research generation on the continent and cancer control efforts. The recommendations articulated in our five-point plan arising from these data are broadly focused on structural changes, for example, overt inclusion of research into national cancer control planning and financial, for example, for countries to spend 10% of a notional 1% gross domestic expenditure on research and development on cancer.
Physician consultation rates and characteristics among workers with chronic pain or headache who participated in a behavioural change program: a retrospective database analysis using real-world healthcare data
Authors: Nozawa Kazutaka , Matsuyama Satoshi , Higa Shingo , Yamamoto Yuji , Asami Yuko
Published: November 2022

Objective

To assess the potential benefit of a behavioural change programme in working individuals with chronic pain or headache, in the form of increased physician consultation.

Design

Retrospective observational database study.

Setting

Members of employment-based healthcare insurance in Japan.

Participants

Individual-level data of working individuals aged <75 years from November 2019 through March 2020 were extracted from a database managed by MinaCare Co., Ltd. Included individuals had records of programme participation and chronic pain or headache (self-reported), and did not consult physicians for ≥3 months before programme participation.

Outcome measures

Physician consultation rates after participating in the programme were examined from December 2019 through March 2020, separately for chronic pain and headache. Baseline characteristics included age, pain numeric rating scale (NRS) score (for chronic pain), suspected migraine (for headache), labour productivity including absenteeism and presenteeism, and 4-month indirect costs in Japanese yen (JPY).

Results

The baseline mean age (±SD) of 506 individuals with chronic pain was 46.8±10.1 years; that of 352 individuals with headache was 43.6±9.9 years. Of those with chronic pain, 71.4% had an NRS score≥4, and 49.7% of those with headache had suspected migraine. Overall, 11.3% and 5.4% of those with chronic pain or headache consulted physicians, respectively. The mean baseline absenteeism and presenteeism were 1.5% and 19.1% in those with chronic pain, and 1.5% and 23.0% in those with headache. The baseline indirect costs were 586 941.6 JPY and 1 060 281.6 JPY among those with chronic pain or headache, respectively.

Conclusion

Given that the individuals did not regularly consult physicians before the programme despite reporting substantial symptoms, our results suggest the potential benefit of educational programmes encouraging physician consultation. Further studies are required to evaluate how to effectively implement such educational programmes via healthcare insurers to reduce the burden of pain symptoms and overall medical costs.
Barriers and facilitators to healthy active living in South Asian families in Canada: a thematic analysis
Authors: Mirza Sabina , Kandasamy Sujane , de Souza Russell J , Wahi Gita , Desai Dipika , Anand Sonia S , Ritvo Paul
Published: November 2022

Objectives

The study objective was to understand the barriers and facilitators to healthy active living in South Asian families living in Canada.

Design

Semi-structured interviews of 30–60-minute duration with South Asian women with young families, and analysed using a thematic analytical approach.

Setting

Community-dwelling South Asian women interviewed in the home environment or by phone.

Participants

Fifteen married South Asian women (mean age=34.2 years) living in the Peel region of Ontario, Canada, with at least 1 child under the age of 5 years. The majority of women had immigrated to Canada (13/15), during a 5–10-year interval preceding interviews.

Results

57 different codes were derived from 18 interview hours, and further evaluated through member checking. The top three barriers to healthy eating were: (1) not having enough time for healthy food preparation, (2) lack of knowledge about what is healthy eating and (3) viewing healthy eating as a matter of engaging in time limited dieting. These barriers were addressed with: (1) knowledge and awareness of healthy eating, (2) clear goal setting, (3) access to fresh vegetables and fruits and (4) better arrangements and more time for food preparation. The top five barriers to physical activity were: (1) not enough time and energy, (2) competing priorities, (3) lack of childcare, (4) lack of family-engaging exercise and (5) limited access to interesting exercise programming. These barriers were addressed by: (1) experiencing exercise as enjoyable and stress releasing, (2) commitments to walking exercise, (3) use of an electronic exercise-tracking device, (4) offspring exercise supported by spouse and family and (5) success stories about exercise from others.

Conclusions

Barriers to healthy active living in South Asian women with young families can be addressed with facilitators that stimulate clear goal setting and healthy food preparation skills, and exercise formats that engage mothers and offspring, with or without exercise tracking.
Seasonality and predictors of childhood stunting and wasting in drought-prone areas in Ethiopia: a cohort study
Authors: Kabalo Bereket Yohannes , Lindtjørn Bernt
Published: November 2022

Background and objectives

Over centuries, Ethiopia has experienced severe famines and periods of serious drought, and malnutrition remains a major public health problem. The aims of this study were to estimate seasonal variations in child stunting and wasting, and identify factors associated with both forms of child malnutrition in drought-prone areas.

Methods

This cohort study was conducted among a random sample of 909 children in rural southern Ethiopia. The same children were followed for 1 year (2017–2018) with quarterly repeated measurements of their outcomes: height-for-age and weight-for-height indices (Z-scores). Linear regression models were used to analyse the association between both outcomes and baseline factors (eg, household participation in a social safety net programme and water access) and some time-varying factors (eg, household food insecurity).

Results

Child wasting rates varied with seasonal household food insecurity (ᵪ2trend= 15.9, p=0.001), but stunting rates did not. Household participation in a social safety net programme was associated with decreased stunting (p=0.001) and wasting (p=0.002). In addition to its association with decreased wasting (p=0.001), protected drinking water access enhanced the association between household participation in a social safety net programme and decreased stunting (p=0.009). Absence of a household latrine (p=0.011), lower maternal education level (p=0.001), larger family size (p=0.004) and lack of non-farming income (p=0.002) were associated with increased child stunting.

Conclusions

Seasonal household food insecurity was associated with child undernutrition in rural Ethiopia. Strengthening community-based food security programmes, such as the Ethiopian social safety net programme, could help to reduce child undernutrition in drought-prone areas. Improving clean water access and sanitation could also decrease child undernutrition.

Key terms:Z-scores; Social safety net program; Water access