Design Models Research Articles

Moving toward autonomous manufacturing by accelerating hydrodynamic fabrication of microstructures using deep neural networks

Manufacturing of microstructures using a microfluidic device is a largely empirical effort due to the multi-physical nature of the fabrication process. As such, in moving toward autonomous manufacturing, models are desired that will predict microstructure attributes (e.g., size, porosity, and stiffness) based on known inputs, such as sheath and core fluid flow rates. Potentially more useful is the prospect of inputting desired microfiber features into a design model to extract appropriate manufacturing parameters. In this study, we demonstrate that deep neural networks (DNNs) trained with sparse datasets augmented by synthetic data can produce accurate predictive and design models to accelerate materials development. For our predictive model with known sheath and core flow rates and bath solution percentage, calculated solid microfiber dimensions are shown to be greater than 95% accurate, with porosity and Young's modulus exhibiting greater than 90% accuracy for a majority of conditions. Likewise, the design model is able to recover sheath and core flow rates with 95% accuracy when provided values for microfiber dimensions, porosity, and Young's modulus. As a result, DNN-based modeling of the microfiber fabrication process demonstrates high potential for reducing time to manufacture of microstructures with desired characteristics.

Recent Trends and Technologies in rapid prototyping and its Inclination towards Industry 4.0

Prototyping technology is becoming vital in the business as a means of cutting costs and manufacturing time. At present, reverse engineering and rapid prototyping are important technologies that enhance prototype development. The traditional approaches require various intricate processes, such as selective heat sintering (SHS), digital-light-processing printer (DLP), remote distributed rapid prototyping model (RDRP), Stereo Lithography (STL) models, and reconstructing computer-aided design (CAD) models from scanned point data and these approaches has limitations in terms of time-consuming and expert knowledge required for automation. This study aims to explore the significance of Industry 4.0 and its impact on rapid prototyping. The study also addresses rapid prototyping in computer network architecture; digital-light-processing printers (DLP) in rapid prototyping, and software-defined network (SDN) networks in the context of rapid prototyping. Along with this powder bed fusion (PBF) method and electron beam melting (EBM) are included in the manuscript. Based on our exploration, the study suggested vital recommendations for the advancement in rapid prototyping using Industry 4.0

Electro-thermal co-design of a variable pole toroidal induction motor

The United States Department of Energy targets an 88 % reduction in motor and power electronics volume by the year 2025 while minimizing or eliminating magnet usage. Induction machines are a magnet-free alternative with advantages such as low cost, high reliability, and capability of withstanding high short-term overload. To meet these societal goals, we develop an induction machine that uses an electro-thermal co-designed converter-integrated thermal management approach. The machine utilizes toroidal windings in the stator, and an 18-phase power converter to achieve 40 kW/L power density. An optimized jacket design was developed to enable the effective thermal management of the stator and integrated power electronics. The toroidal winding configuration provides advantages for thermal performance over conventional distributed windings due to an increased winding copper surface area without reducing slot-fill factor, thereby allowing for a higher stator density. The improved thermal performance of the toroidal winding design was verified using three-dimensional conjugate computational fluid dynamic simulations. A reduced-order transient model was then developed to generate torque-speed-temperature plots at various load conditions. The transient thermal analysis was extended to verify safe operation of the motor during a typical drive cycle for the desired application. The thermal design models developed in this work are robust and scalable to other motor sizes providing a framework for co-designing and developing thermal management systems for electric motors.

Self-Provisioning Infrastructures for the Next Generation Serverless Computing

Serverless computing has ushered in a transformative paradigm, with a promise to alleviate developers from the intricacies of infrastructure management. However, current serverless platforms predominantly offer only serverless compute capabilities. As a consequence, the application developers are once again tasked to explicitly provision and manage the backend services (BaaS), such as object stores or API gateways, the infrastructure, and the configuration models. This violates the main promise of serverless computing and erases much of the practical benefits of the serverless paradigm. It also introduces the challenges of managing the application execution environment, which includes maintaining provisioning and deployment scripts, configuring and managing access permissions, and scaling the services during runtime. To address these challenges, in this paper we introduce a novel paradigm for the next generation of serverless computing, called self-provisioning infrastructure. The self-provisioning infrastructure is an infrastructure that is capable to automatically and autonomously (with zero-configuration and zero-touch) provision serverless functions, their infrastructure, and their supporting BaaS services. To achieve this vision, we introduce novel design principles, models, and mechanisms that are formalized via novel programming, function, and system models. With this novel paradigm, we intend to fortify the core design principles of serverless computing but also extend them to the entire application execution environment. By doing so, we aim to enable the next-generation serverless computing in the Edge-Cloud continuum.

A nonconforming surface mesh generation method by binary tree

Computer-aided engineering (CAE) has emerged as an indispensable tool for facilitating engineering practices and driving industrial innovation. However, the insufficient quality and efficiency of discretizing complex computer-aided design (CAD) models significantly impede the advancement of CAE calculation accuracy and automation. The presence of “dirty” geometry leads to the fact that it is almost impossible to generate a traditional conforming mesh without geometry repair. In most instances, automating geometry repair proves to be even more challenging than meshing. To cope with intricate CAD model with “dirty” geometry, a novel binary tree surface subdivision method (BSSM) is proposed for automatically generated conforming and nonconforming meshes directly on CAD models. In contrast to the conventional conforming mesh, the nonconforming mesh allows for hanging nodes, thereby eliminating the limitations of the mesh conformance. This facilitates rapid transitions in mesh size for automatic mesh generation when dealing with “dirty” geometry. A series of numerical models employing BSSM are presented in this study. Results reveal that BSSM can automatically, efficiently and reliably generate high level quality mesh for arbitrary structures.

Emerging Innovations in Preoperative Planning and Motion Analysis in Orthopedic Surgery.

In recent years, preoperative planning has undergone significant advancements, with a dual focus: improving the accuracy of implant placement and enhancing the prediction of functional outcomes. These breakthroughs have been made possible through the development of advanced processing methods for 3D preoperative images. These methods not only offer novel visualization techniques but can also be seamlessly integrated into computer-aided design models. Additionally, the refinement of motion capture systems has played a pivotal role in this progress. These "markerless" systems are more straightforward to implement and facilitate easier data analysis. Simultaneously, the emergence of machine learning algorithms, utilizing artificial intelligence, has enabled the amalgamation of anatomical and functional data, leading to highly personalized preoperative plans for patients. The shift in preoperative planning from 2D towards 3D, from static to dynamic, is closely linked to technological advances, which will be described in this instructional review. Finally, the concept of 4D planning, encompassing periarticular soft tissues, will be introduced as a forward-looking development in the field of orthopedic surgery.

Rationally seeded computational protein design of ɑ-helical barrels

Computational protein design is advancing rapidly. Here we describe efficient routes starting from validated parallel and antiparallel peptide assemblies to design two families of α-helical barrel proteins with central channels that bind small molecules. Computational designs are seeded by the sequences and structures of defined de novo oligomeric barrel-forming peptides, and adjacent helices are connected by loop building. For targets with antiparallel helices, short loops are sufficient. However, targets with parallel helices require longer connectors; namely, an outer layer of helix–turn–helix–turn–helix motifs that are packed onto the barrels. Throughout these computational pipelines, residues that define open states of the barrels are maintained. This minimizes sequence sampling, accelerating the design process. For each of six targets, just two to six synthetic genes are made for expression in Escherichia coli. On average, 70% of these genes express to give soluble monomeric proteins that are fully characterized, including high-resolution structures for most targets that match the design models with high accuracy.

Sensitivity analysis of non-uniform rational B-splines–based finite element/boundary element coupling in structural-acoustic design

For the purpose of modeling the acoustic fluid-structure interaction using direct differentiation method and conducting a structural-acoustic sensitivity analysis, a coupling approach based on the finite element method and the fast multipole boundary element method is suggested. Non-uniform rational B-splines isogeometric analysis bypasses the difficult volume parameterization procedure in the isogeometric finite element method and the time-consuming meshing process in classical finite element/boundary element method, allowing numerical analysis on computer-aided design models to be completed directly. The finite element/fast multipole boundary element method based on non-uniform rational B-splines isogeometric analysis enables the numerical prediction of the effects of arbitrarily formed vibrating structures on the sound field. Several numerical examples are shown to demonstrate the usefulness and efficiency of the proposed method.

Assessment of design models for aluminium alloy CHS beam-columns with stocky cross-sections

There is little information on the prediction accuracy of the international design standards for aluminium alloy circular hollow section (CHS) beam-columns. To bridge this gap, this paper presents a comprehensive numerical investigation on the performance of aluminium alloy CHS beam-columns with stocky cross-sections. A non-linear finite element (FE) model was developed and validated against experimental results. The validated FE model was employed to conduct an extensive parametric study of aluminium CHS members subjected to combined axial compression and bending over a range of lengths, cross-sections and applied eccentricities. The effect of these parameters was investigated for two material grades, 6061-T6 and 6082-T6, resulting in a total of 168 FE models. The numerically obtained capacities were used to assess the accuracy of the strength predictions of Eurocode 9 (EC9), the Aluminium Design Manual (AA) and the Direct Strength Method (DSM). On the basis of the results, EC9, with the exponent of the interaction formula, ψc, calculated as a function of the buckling reduction factor χ, was found to provide the most accurate strength predictions with a mean value equal to 1.01 for both examined material grades. The most conservative predictions were given by the DSM, whilst the most scattered predictions by the AA, with a Coefficient of Variation (COV) equal to 0.17. Finally, based on the DSM buckling strength and the plastic cross-sectional moment resistance, a new interaction curve is proposed for CHS beam-columns with stocky cross-sections.

Interior Scene Coloring Design Model Combining Improved K-means and SAA

Due to technological progress and changes in people’s aesthetic standards, traditional design models need to be constantly broken through, seeking more efficient and accurate design methods. Seeking effective design models to improve design efficiency and prediction accuracy is an important task. Therefore, this study proposes an indoor scene coloring design model that combines improved K-means clustering and simulated annealing algorithm for this important task. Based on the analysis of indoor scene coloring, particle swarm optimization algorithm is used to optimize K-means clustering to achieve color classification. Combined with simulated annealing algorithm, adaptive adjustment of lighting conditions is achieved to enhance the naturalness and realism of coloring. These results confirmed that the proposed method had the highest average F-value, with an average F-value of 92.524 and 143.601 on both datasets, respectively. The average ARI values were 0.361 and 0.897, respectively. The designed algorithm performed the best and converged faster than other three. Therefore, the proposed method can effectively ensure the consistency between the distribution of data objects after clustering and the actual situation. For indoor scene coloring design, it has important practical significance and provides new possible paths for improving design efficiency and prediction accuracy.

Development of a Flipped Learning Instructional Design Model for Clinical Reasoning Development in Preclinical Students Based on Illness Scripts

Objectives Recent educational demands have arisen in medical schools to systematically and directly teach clinical reasoning skills to preclinical medical students. This has highlighted the need for instructional design models to guide clinical professors in designing such educational programs. Instructional design models can reduce the challenges faced by clinical professors during course design by providing comprehensive guidance on the process. However, despite this practical necessity, research in this area is lacking. Thus, this study aimed to develop and validate an instructional design model and accompanying detailed design guidelines for developing a flipped learning course aimed at enhancing clinical reasoning skills in preclinical medical students based on illness scripts. The purposes of this study were to specify variables related to convergence competency, and to analyze the structural relationships among them. Methods To achieve this goal, developmental research, including model development and validation, was employed. The instructional design model and detailed design guidelines were developed based on literature reviews and a case study, and model validation was conducted through two rounds of expert validation and a usability test. Results The developed instructional design model was structured as a procedural model explaining the steps of instructional design, and designed at two levels (course and individual class). Furthermore, the model comprises six stages and 22 major design activities, while the detailed design guidelines include comprehensive explanations of design activities carried out in each stage of instructional design. Conclusions The developed instructional design model and detailed design guidelines can provide clinical professors with practical assistance in systematically designing flipped learning to enhance clinical reasoning skills in preclinical medical students based on illness scripts.

Reducing embodied carbon with material optimization in structural engineering practice: Perceived barriers and opportunities

Structural engineers play an essential role in reducing embodied carbon emissions in buildings, which contribute significantly to global carbon emissions. In this work, 39 structural engineers primarily from the Northeastern US are surveyed on how embodied carbon is reduced in practice, particularly through computational tools that facilitate material efficiency (e.g. parametric design models and structural optimization tools). Leading barriers to embodied carbon reductions are identified as: (i) lack of awareness of how to quantify embodied carbon or reduce it, (ii) perception of increased costs, and (iii) lack of power in relation to other project stakeholders. The use of computational tools is found to be primarily inhibited by increased design time and costs. These results highlight opportunities to overcome these barriers, such as increasing awareness of strategies to reduce embodied carbon and targeting time and cost (or perceived time and cost) in computational design workflows for structural engineers.

Review on Current Issues related to Work Related Musculoskeletal Disorders

Work-related musculoskeletal disorders (WMSDs) are one of the most common occupational ailments in recent decades, significantly limiting people's daily lives. Globally, WMSDs are the major cause of employee pain, impairment, absenteeism, reduced productivity, and large financial costs. WRMSDs are illnesses that develop over time because of long-term occupational exposure to varying-intensity loads. WRMSDs are produced by discomfort or injury to the muscles and bones of the upper limbs because of activity. Low-extremity illnesses, like upper-limb diseases, may be just as dangerous, There is a high prevalence of neck and upper extremity problems among computer users. Upper extremities diseases were more common in older personnel. The physiological outcome of prolonged, repetitive, or repeating muscle contractions with insufficient recovery is localised muscular fatigue (WMSDs). Muscle tiredness has a substantial impact on occupational task performance, hence preventing it is crucial. The key challenge in ergonomics is to create work that avoids WMSDs while retaining high levels of output quality and productivity. Uncomfortable postures are commonly recognised as a major contributor of MSD among construction workers. Because of their linearity, ergonomic metrics can be incorporated into assembly line design models. They can also be utilised to take use of efficient solution methods established for optimal line design which are beneficial in the assembly line industry. Real-time risk assessment for work-related musculoskeletal disorders (MSDs) has proven to be challenging to research. Working at a fast speed and performing repetitive activities, as well as maintaining non-neutral body postures, are all physical risk factors for WMSDs. This review discuses common issue related to WMSDs which includes upper extremity, upper limbs, carpal tunnel syndrome, awkward postures, muscle activity, muscle fatigue, assembly line and risk assessment. The aim is to provide an overview of the problems related to the subject matter.

Aggregate interlock and effective strain of FRP-strengthened flanged RC members subjected to torsion: Experimental evaluation and analytical modeling

The effective strain of externally bonded FRP strengthening systems and the loss of concrete aggregate interlock are critical factors for predicting strength and developing design models for structural strengthening. Current research on FRP strengthening of reinforced concrete (RC) members primarily focuses on flexural, shear, and axial loading, neglecting torsional systems. This study introduces an innovative analytical-experimental model/formulation predicting FRP effective strain and concrete strain, based on Digital Image Correlation (DIC) analysis outcomes of an experimental study on RC beams strengthened with FRP sheets under torsion. Eight flanged RC beams, strengthened with various FRP ratios, configurations, and installation methods were also systematically tested. The findings emphasize the need for reevaluation of strain limits in existing guidelines. It points out that the shear integrity of concrete is influenced by concrete compressive strength and strengthening scheme. The DIC results suggest that the strain limit for concrete surfaces between FRP strips should be in the range of 0.0055–0.008, as opposed to the limit of 0.004 set by ACI 440.2R-17. The use of the grooving method in certain systems also requires an increase factor of 1.65 and 1.45 for the effective strain of transverse and longitudinal FRP sheets.

Confinement-based direct design of square normal and high strength steel tube confined concrete (STCC) short columns

In this paper, a confinement-based direct design model is developed to estimate the axial compression resistance of square steel tube confined concrete (STCC) short columns, which differ from standard concrete-filled steel tube (CFST) columns in load application. While the load in CFST columns is applied to the entire section, it is applied to concrete only in case of STCC columns. Accordingly, STCC columns show superior confinement effect to the concrete core, which will be used directly in the design model. First, the experimental test results of axially-loaded STCC short columns were collected from the literature. The test resistances of the columns were then compared with the predictions obtained by EC4 (2004), American specifications (ANSI/AISC (2010) and ACI 318 R (2014)), AIJ (2001), British standard BS5400 (2005) and CISC (2007), as well as the available design models in the literature. This comparative study showed that all design codes as well as other design models are highly conservative and all do not have the same accuracy over the full range of steel tube slenderness. Then, the resistances of test specimens were used to propose a formula that evaluates the lateral confinement pressure (frp) provided by the steel tube on the concrete core. Finally, the confinement-based direct design model was proposed to estimate the strengths of square STCC short columns. The results of the proposed design model far outperform the available design predictions regardless of the slenderness ratio of the steel tube for all grades of steel and concrete.

Non-fungible tokens (NFTs) in healthcare: a thematic analysis and research agenda.

In the big data era, where corporations commodify health data, non-fungible tokens (NFTs) present a transformative avenue for patient empowerment and control. NFTs are unique digital assets on the blockchain, representing ownership of digital objects, including health data. By minting their data as NFTs, patients can track access, monetize its use, and build secure, private health information systems. However, research on NFTs in healthcare is in its infancy, warranting a comprehensive review. This study conducted a systematic literature review and thematic analysis of NFTs in healthcare to identify use cases, design models, and key challenges. Five multidisciplinary research databases (Scopus, Web of Science, Google Scholar, IEEE Explore, Elsevier Science Direct) were searched. The approach involved four stages: paper collection, inclusion/exclusion criteria application, screening, full-text reading, and quality assessment. A classification and coding framework was employed. Thematic analysis followed six steps: data familiarization, initial code generation, theme searching, theme review, theme definition/naming, and report production. Analysis of 19 selected papers revealed three primary use cases: patient-centric data management, supply chain management for data provenance, and digital twin development. Notably, most solutions were prototypes or frameworks without real-world implementations. Four overarching themes emerged: data governance (ownership, tracking, privacy), data monetization (commercialization, incentivization, sharing), data protection, and data storage. The focus lies on user-controlled, private, and secure health data solutions. Additionally, data commodification is explored, with mechanisms proposed to incentivize data maintenance and sharing. NFTs are also suggested for tracking medical products in supply chains, ensuring data integrity and provenance. Ethereum and similar platforms dominate NFT minting, while compact NFT storage options are being explored for faster data access. NFTs offer significant potential for secure, traceable, decentralized healthcare data exchange systems. However, challenges exist, including dependence on blockchain, interoperability issues, and associated costs. The review identified research gaps, such as developing dual ownership models and data pricing strategies. Building an open standard for interoperability and adoption is crucial. The scalability, security, and privacy of NFT-backed healthcare applications require further investigation. Thus, this study proposes a research agenda for adopting NFTs in healthcare, focusing on governance, storage models, and perceptions.

Statistical Procedures Used in Pretest-Posttest Control Group Design: A Review of Papers in Five Iranian Journals

The pretest-posttest control group design is one of the most widely used quantitative experimental design models for evaluating the efficacy of programs, treatments, and interventions. Despite the prevalence and utility of this research design, best practices for data analytical procedures are not clearly defined. Invalid results decrease the chance of generalization. Given that Iranian Journals are interested in publishing pretest-posttest control group design studies, it is important to denote the accuracy of them. The aim of the current study is to explore the correct procedure for using ANCOVA in pretest-posttest control group designs to mitigate the potential limitations of this approach. This study explores the use of ANCOVA in pretest-posttest control group design. It has been done by analyzing data from experimental studies published in five Iranian journals indexed in PubMed or Scopus between 2011 and 2018. The results indicate that among the 280 published experimental studies in these journals, 53 papers (18.9 percent) used ANCOVA as the statistical test in pretest-posttest studies. The power of the test represents the probability of detecting differences between the groups being compared when such differences exist. Our analysis concludes that ANCOVA, which runs a multiple linear regression, is a suitable method for comparing and examining pretest-posttest study designs. Implications of this study have potential utility for researchers employing the use of pretest-posttest control group designs in various fields in and outside of Iran.

Student-teachers’ professional agency development through ADDIE model of CALL teacher preparation

Developing teachers’ professional agency through standard models of instructional designs is critical in helping them deal with the barriers in designing and implementing online learning environments. Therefore, the purpose of this study was to explore the role of an ADDIE (Analysis–Design–Development–Implementation–Evaluation) model of the computer-assisted language learning (CALL) teacher preparation program (TPP) in developing TEFL (teaching English as a foreign language) student-teachers’ professional agency (PA). We conducted an instrumental (exploratory) case study to explore the PA development of 18 TEFL student-teachers who took a CALL course that was designed based on ADDIE. The deductive thematic analysis of student-teachers’ autobiographical narratives, unstructured interviews, and course projects showed that each stage of ADDIE contributed to developing the dimensions of the PA. We concluded that the constructive role of the ADDIE model of CALL TPP in developing TEFL student-teachers’ PA might be due to ADDIE’s capability to construct a context for developing student-teachers’ dynamic capacity to tackle educational concerns and provide a context of playing different professional roles. The study bears implications for TEFL teacher educators to consider the action-based nature of PA when the ADDIE model is implemented.

Participatory and Inclusive Design Models from the Perspective of Universal Design for Children with Autism: A Systematic Review

Participatory and Inclusive Design Models from the Perspective of Universal Design for Children with Autism: A Systematic Review

Inverse interaction of shear between steel-stirrups and externally bonded carbon fiber-reinforced polymers in shear-strengthened reinforced concrete beams: Analytical and numerical models

Shear failure in reinforced concrete (RC) beams have always been a serious concern due to its brittle fracture mode. In addition, many questions are raised about the accuracy of current design guidelines for predicting the shear resistance contribution of externally bonded carbon fiber-reinforced polymers (EB-CFRPs) to the ultimate shear strength of strengthened RC beams, particularly with regard to the inverse interaction of the shear contribution between steel-stirrups and EB-CFRPs. The main objective of the present study is to implement experimental and numerical tests to develop analytical and numerical models for RC beams strengthened in shear using EB-CFRPs. Emphasis is placed on the negative inverse interaction between the steel-stirrups and the EB-CFRPs as the ratio of EB-CFRP-to-steel-stirrups increases with increasing the CFRP rigidity (CFRP thickness and CFRP width). The inverse interaction is not included in the design models proposed by most current guidelines, although it has a considerable effect on shear resistance as predicted by the guidelines. In fact, the shear contribution associated with EB-CFRP decreases as the ratio of EB-CFRP-to-steel-stirrups increases. Therefore, proposing reliable effective strains by including these parameters improve the calculated shear contribution of EB-CFRPs. First, an analytical model is proposed for CFRP effective strain considering the inverse interaction between EB-CFRPs and steel-stirrups. Afterward, a validation of the proposed model with experimental data is done by conducting a parametric study of the increasing trends with respect to the ratio of EB-CFRP-to-steel-stirrups. A numerical finite-element model for the reduction factor and the corresponding effective strain based on the inverse interactions between EB-CFRPs and steel-stirrups is also proposed, and the results are compared with various current guidelines. The results are presented in terms of shear crack patterns, load-midspan deflections, shear stresses, strain responses along the fibers, maximum strain profiles for all the CFRPs and specimens, applied shear forces and strains for all the steel-stirrups and EB-CFRPs, and interactions between steel-stirrups and EB-CFRPs based on their maximum strain contributions at the maximum shear forces and the maximum strain they experience after shear failure.