Design Tool Research Articles

Design of a direct rapid tool for hydroforming of metal bellows using additively manufactured polymer die

Design of a direct rapid tool for hydroforming of metal bellows using additively manufactured polymer die

Temperature-dependent damage of magnesium alloy with ratchetting–fatigue interaction effects: Experiments and mesomechanical theory

Fatigue failure is a significant concern for magnesium (Mg) alloy components. However, fatigue damage mechanisms of Mg alloys, particularly in the case of ratchetting–fatigue interaction at elevated temperatures, are still not well understood. In this paper, we combine experiments and theoretical analysis to investigate the high-temperature damage mechanisms of the extruded AZ31 Mg alloy, specifically focusing on the effects of ratchetting–fatigue interaction. We reveal a distinct demonstration of damage, namely the formation of microvoids in the alloy due to significant ratchetting deformation, defined as ratchetting damage. Notably, this ratchetting damage is more prevalent at higher temperatures. Considering the mesomechanics-based energy mechanisms associated with grain boundaries (or twin boundaries), a mesomechanical damage model is established to capture the ratchetting damage under elevated temperatures and large ratchetting deformations. This model can reasonably simulate the intricate process of damage evolution and predict the critical condition of microvoid or microcrack formation. This work has the potential to serve as a theoretical tool for the safety design of structures made from Mg alloys under complex mechanical and thermal conditions.

Coupled responses of thermomechanical waves in functionally graded viscoelastic nanobeams via thermoelastic heat conduction model including Atangana–Baleanu fractional derivative

Accurately characterizing the thermomechanical parameters of nanoscale systems is essential for understanding their performance and building innovative nanoscale technologies due to their distinct behaviours. Fractional thermal transport models are commonly utilized to correctly depict the heat transfer that occurs in these nanoscale systems. The current study presents a novel mathematical thermoelastic model that incorporates a new fractional differential constitutive equation for heat conduction. This heat equation is useful for understanding the effects of thermal memory. An application of a fractional-time Atangana–Baleanu (AB) derivative with a local and non-singular kernel was utilized in the process of developing the mathematical model that was suggested. To deal with effects that depend on size, nonlocal constitutive relations are introduced. Furthermore, in order to take into consideration, the viscoelastic behaviour of the material at the nanoscale, the fractional Kelvin–Voigt model is utilized. The proposed model is highly effective in properly depicting the unusual thermal conductivity phenomena often found in nanoscale devices. The study also considered the mechanical deformation, temperature variations, and viscoelastic characteristics of the functionally graded (FG) nanostructured beams. The consideration was made that the material characteristics exhibit heterogeneity and continuous variation across the thickness of the beam as the nanobeam transitions from a ceramic composition in the lower region to a metallic composition in the upper region. The complicated thermomechanical features of simply supported viscoelastic nanobeams that were exposed to harmonic heat flow were determined by the application of the model that was constructed. Heterogeneity, nonlocality, and fractional operators are some of the important variables that contribute to its success, and this article provides a full study and illustration of the significance of these characteristics. The results that were obtained have the potential to play a significant role in pushing forward the design and development of tools, materials, and nanostructures that have viscoelastic mechanical characteristics and graded functions.

Sensitivity Analysis for Design Parameters of Electric Tilt-Rotor Aircraft

In recent years, there has been rapid development in electric aircraft, particularly electric vertical takeoff and landing (eVTOL) aircraft, as part of efforts to promote green aviation. During the conceptual design stage, it is crucial to select appropriate values for key parameters and conduct sensitivity analysis on these parameters. This study focuses on an electric tilt-rotor aircraft and proposes a performance analysis method for electric aircraft while developing a general design tool specifically for this type of aircraft. Subsequently, the impact of wing incidence angle, sweep angle, span, propeller solidity, battery-specific energy, and battery mass on range, maximum takeoff weight, and hover power are analyzed. The results show that the battery mass, wingspan, and wingtip chord length have great effects on the maximum takeoff weight; among these, battery mass had the greatest influence. In terms of range, the battery energy density has a great positive effect on range, while the increase in wing angle of incidence, wingtip chord length and battery mass have some negative effects on range.

Academic Detailing as a Health Information Technology Implementation Method: Supporting the Design and Implementation of an Emergency Department-Based Clinical Decision Support Tool to Prevent Future Falls.

Clinical decision support (CDS) tools that incorporate machine learning-derived content have the potential to transform clinical care by augmenting clinicians' expertise. To realize this potential, such tools must be designed to fit the dynamic work systems of the clinicians who use them. We propose the use of academic detailing-personal visits to clinicians by an expert in a specific health IT tool-as a method for both ensuring the correct understanding of that tool and its evidence base and identifying factors influencing the tool's implementation. This study aimed to assess academic detailing as a method for simultaneously ensuring the correct understanding of an emergency department-based CDS tool to prevent future falls and identifying factors impacting clinicians' use of the tool through an analysis of the resultant qualitative data. Previously, our team designed a CDS tool to identify patients aged 65 years and older who are at the highest risk of future falls and prompt an interruptive alert to clinicians, suggesting the patient be referred to a mobility and falls clinic for an evidence-based preventative intervention. We conducted 10-minute academic detailing interviews (n=16) with resident emergency medicine physicians and advanced practice providers who had encountered our CDS tool in practice. We conducted an inductive, team-based content analysis to identify factors that influenced clinicians' use of the CDS tool. The following categories of factors that impacted clinicians' use of the CDS were identified: (1) aspects of the CDS tool's design (2) clinicians' understanding (or misunderstanding) of the CDS or referral process, (3) the busy nature of the emergency department environment, (4) clinicians' perceptions of the patient and their associated fall risk, and (5) the opacity of the referral process. Additionally, clinician education was done to address any misconceptions about the CDS tool or referral process, for example, demonstrating how simple it is to place a referral via the CDS and clarifying which clinic the referral goes to. Our study demonstrates the use of academic detailing for supporting the implementation of health information technologies, allowing us to identify factors that impacted clinicians' use of the CDS while concurrently educating clinicians to ensure the correct understanding of the CDS tool and intervention. Thus, academic detailing can inform both real-time adjustments of a tool's implementation, for example, refinement of the language used to introduce the tool, and larger scale redesign of the CDS tool to better fit the dynamic work environment of clinicians.

Integrating artificial intelligence into the work of an educator: Tools for instructional design and development of educational products

Integrating artificial intelligence into the work of an educator: Tools for instructional design and development of educational products

Advancing energy efficiency in livestock building: Simplified building energy simulation tool for geometric design of pigsty

Concerns related to food security and animal welfare have led to increasing interest in analyzing the energy demands of heating, ventilation, and air conditioning (HVAC) systems in livestock buildings. However, building energy simulation (BES) model, which facilitates precise HVAC systems energy analysis, is difficult to use for non-experts, and particularly, its usability drops significantly in livestock buildings, which are often constructed without involvement of building engineers. This study, therefore, developed a tool that can analyze the energy demand of pigsties by changing geometric design variables with only several numerical inputs without separate 3D modeling. Pigsty geometric design types were classified by examining pigsty architectural design manual and web portal map search. A simplified tool, utilizing EnergyPlus and MATLAB graphical user interface, was developed to analyze energy demand in pigsties by leveraging simple user inputs for geometric design modeling. This tool simplifies exploring potential energy savings through geometric design changes in the early design stages of livestock buildings. The methodology introduced in this study lays the groundwork for future enhancements, including the integration of various design-phase variables beyond geometric design.

Structure and Activity of Reconstructed Pseudo-Ancestral Cyclotides.

Cyclotides are cyclic peptides that are promising scaffolds for the design of drug candidates and chemical tools. However, despite there being hundreds of reported cyclotides, drug design studies have commonly focussed on a select few prototypic examples. Here, we explored whether ancestral sequence reconstruction could be used to generate new cyclotides for further optimization. We show that the reconstructed 'pseudo-ancestral' sequences, named Ancy-m (for the ancestral cyclotide of the Möbius sub-family) and Ancy-b (for the bracelet sub-family), have well-defined structures like their extant members, comprising the core structural feature of a cyclic cystine knot. This motif underpins efforts to re-engineer cyclotides for agrochemical and therapeutic applications. We further show that the reconstructed sequences are resistant to temperatures approaching boiling, bind to phosphatidyl-ethanolamine lipid bilayers at micromolar affinity, and inhibit the growth of insect cells at inhibitory concentrations in the micromolar range. Interestingly, the Ancy-b cyclotide had a higher oxidative folding yield than its comparator cyclotide cyO2, which belongs to the bracelet cyclotide subfamily known to be notoriously difficult to fold. Overall, this study provides new cyclotide sequences not yet found naturally that could be valuable starting points for the understanding of cyclotide evolution and for further optimization as drug leads.

Redistrict: Online Public Deliberation Support that Connects and Rebuilds Inclusive Communities

Public deliberations are often a staple ingredient in community decision-making. However, traditional, time-constrained, in-person debates can become highly polarized, eroding trust in authorities, and leaving the community divided. This is the case in redistricting deliberations for public school zoning. Seeking alternative ways of support, we evaluated the potential introduction of an online platform that combines multiple streams of data, visualizes school attendance boundaries, and enables the manipulation of representations of land parcels. To capture multiple stakeholders' values about the potential to enhance public engagement in school rezoning decision-making through an online platform, we conducted interviews with 12 participants with previous experiences in traditional, in-person deliberations. Insights from the interviews highlight the several roles an online platform could take, especially as it provides alternative means of participation (online, synchronous, and asynchronous). Additionally, we discuss the potential for technology to increase the visibility and participation of multiple community actors in public deliberations and present implications for the design of future tools to support public decision-making.

Breaking Boundaries in Malaria Research: Design of a Genetic Tool for High-Throughput Gametocidal Drug Screening

Breaking Boundaries in Malaria Research: Design of a Genetic Tool for High-Throughput Gametocidal Drug Screening

Estimation by capillary water absorption method to building natural stones’ durability against freeze-thaw

In construction, the proper selection of natural building stones and the prediction of their pathology are significant, with freeze-thaw action being a primary cause of deterioration. Testing for freeze-thaw durability, being labour-intensive and time-consuming, often conflicts with construction timelines. The proposed methodology estimates natural stone behaviour against frost damage using quicker laboratory tests (e.g., capillary water absorption). The use of natural stones with an open porosity exceeding 2% is not advisable, as exceeding this threshold leads to losses in compressive strength of more than 10% after 168 freeze-thaw cycles. This alternative evaluation through the capillary water absorption method provides a precise estimation of freeze-thaw durability, proving to be an instrumental tool for architects when selecting natural stones.

"I felt like I was doing grown-up things": Young Adult Reflections on their Childhood Experiences of Online Searching and Brokering in Immigrant Families

Online search and brokering (OSB) is the phenomenon in which youth search for information for their immigrant families on the Internet. Few studies exist on reflecting on the past as youth conduct OSB from childhood to adolescence to young adulthood. We conducted a retrospective study using semi-structured interviews of 27 young adults (ages 19 - 24) to examine their experience and perceptions of OSB over time (primary schooling, secondary schooling, undergraduate). We found that although all the interviewees shared a common experience of being an online searcher and information broker for their immigrant families, their stories exist on various dimensions which are constantly shifting and changing over time, as ecological factors influence searching behaviors. We discuss how the results from our study can serve as future references for researchers doing work in search research and designers who are currently working on improving design tools for searching.

Engineering Small HOMO-LUMO Gaps in Polycyclic Aromatic Hydrocarbons with Topologically Protected States.

Topological phases in laterally confined low-dimensional nanographenes have emerged as versatile design tools that can imbue otherwise unremarkable materials with exotic band structures ranging from topological semiconductors and quantum dots to intrinsically metallic bands. The periodic boundary conditions that define the topology of a given lattice have thus far prevented the translation of this technology to the quasi-zero-dimensional (0D) domain of small molecular structures. Here, we describe the synthesis of a polycyclic aromatic hydrocarbon (PAH) featuring two localized zero modes (ZMs) formed by the topological junction interface between a trivial and nontrivial phase within a single molecule. First-principles density functional theory calculations predict a strong hybridization between adjacent ZMs that gives rise to an exceptionally small HOMO-LUMO gap. Scanning tunneling microscopy and spectroscopy corroborate the molecular structure of 9/7/9-double quantum dots and reveal an experimental quasiparticle gap of 0.16 eV, corresponding to a carbon-based small molecule long-wavelength infrared (LWIR) absorber.

Integration of the Butina algorithm and ensemble learning strategies for the advancement of a pharmacophore ligand-based model: an in silico investigation of apelin agonists.

Introduction: 3D pharmacophore models describe the ligand's chemical interactions in their bioactive conformation. They offer a simple but sophisticated approach to decipher the chemically encoded ligand information, making them a valuable tool in drug design. Methods: Our research summarized the key studies for applying 3D pharmacophore models in virtual screening for 6,944 compounds of APJ receptor agonists. Recent advances in clustering algorithms and ensemble methods have enabled classical pharmacophore modeling to evolve into more flexible and knowledge-driven techniques. Butina clustering categorizes molecules based on their structural similarity (indicated by the Tanimoto coefficient) to create a structurally diverse training dataset. The learning method combines various individual pharmacophore models into a set of pharmacophore models for pharmacophore space optimization in virtual screening. Results: This approach was evaluated on Apelin datasets and afforded good screening performance, as proven by Receiver Operating Characteristic (AUC score of 0.994 ± 0.007), enrichment factor of (EF1% of 50.07 ± 0.211), Güner-Henry score of 0.956 ± 0.015, and F-measure of 0.911 ± 0.031. Discussion: Although one of the high-scoring models achieved statistically superior results in each dataset (AUC of 0.82; an EF1% of 19.466; GH of 0.131 and F1-score of 0.071), the ensemble learning method including voting and stacking method balanced the shortcomings of each model and passed with close performance measures.

Eliciting the views of left breast cancer patients' receiving deep inspiration breath hold radiation therapy to inform the design of multimedia education and improve patient-centred care for prospective patients.

The currently accepted best practice radiation treatment for left breast cancer patients is Deep Inspiration Breath Hold (DIBH) where patients hold a deep breath to reduce late cardiac and pulmonary effects from treatment. DIBH can be challenging and induce or exacerbate anxiety in patients due to the perceived pressure to reduce radiation treatment side effects. This study explored the experiences of patients treated with Deep Inspiration Breath Hold Radiation Therapy (DIBH-RT) to improve patient-centred care and inform the design of multimedia educational tools for future patients undergoing DIBH. This descriptive qualitative study was underpinned by a social constructivist approach to create new educational and patient care approaches based on previous patients' experiences. Semi-structured interviews were conducted with patients who had completed DIBH-RT for breast cancer. Data was analysed with reflexive thematical analysis. Twenty-two patients were interviewed with five key themes identified: (1) informational needs, (2) care needs, (3) autonomy, (4) DIBH performance influencers and (5) other centredness. Recommendations were derived from these themes to improve future treatments of DIBH patients. These recommendations revolved around improvements to education, patient-centred care and strategies to improve self-efficacy with breath holding. Patients offer a wealth of knowledge regarding their lived experiences with treatment which can enhance future patients' experiences if incorporated into their education and care. Eliciting patients' views of their DIBH-RT treatment highlighted the need to improve patient self-efficacy with DIBH through familiarity with their planned treatment from new multimedia education, and foster patient care to enhance their experience.

Packing optimization of practical systems using a dynamic acceleration methodology

System design is a challenging and time-consuming task which often requires close collaboration between several multidisciplinary design teams to account for complex interactions between components and sub-systems. As such, there is a growing demand in industry to create better performing, efficient, and cost-effective development tools to assist in the system design process. Additionally, the ever-increasing complexity of systems today often necessitates a shift away from manual expertise and a movement towards computer-aided design tools. This work narrows the scope of the system design process by focusing on one critical design aspect: the packaging of system components. The algorithm presented in this paper was developed to optimize the packaging of system components with consideration of practical, system-level functionalities and constraints. Using a dynamic acceleration methodology, the algorithm packages components from an initial position to a final packed position inside of a constrained volume. The motion of components from initial to final positions is driven by several acceleration forces imposed on each component. These accelerations are based on physical interactions between components and their surrounding environment. Various system-level performance metrics such as center of mass alignment and rotational inertia reduction are also considered throughout optimization. Results of several numerical case studies are also presented to demonstrate the functionality and capability of the proposed packaging algorithm. These studies include packaging problems with known optimal solutions to verify the efficacy of the algorithm. Finally, the proposed algorithm was used in a more practical study for the packaging of an urban air mobility nacelle to demonstrate the algorithm’s prospective capabilities in solving real-world packaging problems.

Efficient Sizing and Layout Optimization of Truss Benchmark Structures Using ISRES Algorithm

This paper presents a comprehensive investigation into the application of the Improved Stochastic Ranking Evolution Strategy (ISRES) algorithm for the sizing and layout optimization of truss benchmark structures. Truss structures play a crucial role in engineering and architecture, and optimizing their designs can lead to more efficient and cost-effective solutions. The ISRES algorithm, known for its effectiveness in multi-objective optimization, is adapted for the single-objective optimization of truss designs with multiple design constraints. This study encompasses a wide range of truss benchmark structures, including 10-bar, 15-bar, 18-bar, 25-bar, and 72-bar configurations, each subjected to distinct loading conditions and stress constraints. The objective is to minimize the truss weight while ensuring stress and displacement limits are met. Through extensive experimentation, the ISRES algorithm demonstrates its ability to efficiently explore the solution space and converge to optimal solutions for each truss benchmark structure. The algorithm effectively handles the complexity of the problems, which involve numerous design variables, stress constraints, and nodal displacement limits. A comparative analysis is conducted to assess the performance of the ISRES algorithm against other state-of-the-art optimization methods reported in the literature. The comparison evaluates the quality of the solutions and the computational efficiency of each method. Furthermore, the optimized truss designs are subjected to finite element analysis to validate their structural integrity and stability. The verification process confirms that the designs adhere to the imposed constraints, ensuring the safety and reliability of the final truss configurations. The results of this study demonstrate the efficacy of the ISRES algorithm in providing practical and reliable solutions for the sizing and layout optimization of truss benchmark structures. The algorithm’s competitive performance and robustness make it a valuable tool for structural engineers and designers, offering a versatile and powerful approach for complex engineering optimization tasks. Overall, the findings contribute to the advancement of optimization techniques in structural engineering, promoting the development of more efficient and cost-effective truss designs for a wide range of engineering and architectural applications. The study’s insights empower practitioners to make informed decisions in selecting appropriate optimization strategies for complex truss-design scenarios, fostering advancements in structural engineering and sustainable design practices.

Comparative Analysis of Deep Generative Model for Industrial Enzyme Design

Abstract: Although enzymes have the advantage of efficient catalysis, natural enzymes lack stability in industrial environments and do not even meet the required catalytic reactions. This prompted us to urgently de novo design new enzymes. Computational design is a powerful tool, allowing rapid and efficient exploration of sequence space and facilitating the design of novel enzymes tailored to specific conditions and requirements. It is beneficial to de novo design industrial enzymes using computational methods. Currently, only one tool explicitly designed for the enzyme-only generation performs unsatisfactorily. We have selected several general protein sequence design tools and systematically evaluated their effectiveness when applied to specific industrial enzymes. We investigated the literature related to protein generation. We summarized the computational methods used for sequence generation into three categories: structure-conditional sequence generation, sequence generation without structural constraints, and co-generation of sequence and structure. To effectively evaluate the ability of six computational tools to generate enzyme sequences, we first constructed a luciferase dataset named Luc_64. Then we assessed the quality of enzyme sequences generated by these methods on this dataset, including amino acid distribution, EC number validation, etc. We also assessed sequences generated by structure-based methods on existing public datasets using sequence recovery rates and root-mean-square deviation (RMSD) from a sequence and structure perspective. In the functionality dataset, Luc_64, ABACUS-R, and ProteinMPNN stood out for producing sequences with amino acid distributions and functionalities closely matching those of naturally occurring luciferase enzymes, suggesting their effectiveness in preserving essential enzymatic characteristics. Across both benchmark datasets, ABACUS-R and ProteinMPNN, have also exhibited the highest sequence recovery rates, indicating their superior ability to generate sequences closely resembling the original enzyme structures. Our study provides a crucial reference for researchers selecting appropriate enzyme sequence design tools, highlighting the strengths and limitations of each tool in generating accurate and functional enzyme sequences. ProteinMPNN and ABACUS-R emerged as the most effective tools in our evaluation, offering high accuracy in sequence recovery and RMSD and maintaining the functional integrity of enzymes through accurate amino acid distribution. Meanwhile, the performance of protein general tools for migration to specific industrial enzymes was fairly evaluated on our specific industrial enzyme benchmark.

Custom orthotic insoles with gradual variable stiffness using 3D printed spacer technique

ABSTRACT 3D-printing enables fabricating insoles with locally variable mechanical properties that can better redistribute plantar pressure by altering the mechanical structure. However, current approaches have limitations in terms of speed, strand thickness, and gradual stiffness transitions. To address this, our research proposes utilising the 3D-printed Spacer technique. Our approach involves 3D-printing the insole on their lateral side, with gradual stiffness transitions achieved by modulating the feed speed and material extrusion, enabling strands thinner than the nozzle diameter, while eliminating travel movements. We present a workflow for customisation and fabrication of insole geometry utilising 3D foot scans and automatic g-code generation directly from our design tool. Additionally, we introduce a novel automated support removal process using a hot-wire cutter mounted on a 6-axis robotic arm. We evaluated the printed structures’ mechanical properties and durability through compression tests and assessed the insoles’ performance using a user wear test that included pressure distribution analysis.

Application of an active learning method for cumulative fatigue damage assessment of floating wind turbine mooring lines

Long-term cumulative fatigue damage of mooring lines is crucial for the design of floating wind turbine structures (FWTs). Although many efforts are carried out for the offshore floating platforms, there still needs to be an efficient approach for assessing the long-term fatigue damage of the floating wind turbine mooring lines due to the complex loading in FWTs. An active learning approach named AK-DA (Adaptive Kriging Damage Assessment) was recently proposed for the cumulative fatigue damage assessment of wind turbine structures. However, in the original work, the AK-DA approach was only tested on fatigue damage assessment of a 5MW wind turbine tower with monopile support structures. It is unclear whether it is applicable to other parts of the wind turbine system, especially considering the complex loading of FWTs. Therefore, in this work, the AK-DA approach is used to assess the cumulative fatigue damage of mooring lines. The Gaussian process regression (Kriging) model is used to predict the fatigue damage of the mooring line under different wind-wave cases. The cumulative fatigue damage of the mooring lines in the IEA 15MW semi-submersible wind turbines is assessed with the AK-DA approach. The numerical simulation results show that the AK-DA approach can efficiently and accurately estimate the cumulative fatigue damage of the mooring lines. Compared to the traditional simulation approach, the AK-DA approach can increase efficiency by more than 45 times, and the absolute error is less than 1%. This active learning approach could serve as a helpful tool for offshore mooring system designers, facilitating the cumulative fatigue damage assessment during the design process.