Design Tool Research Articles

Prevalence of problematic pharmaceutical opioid use in patients with chronic non-cancer pain: A systematic review and meta-analysis.

Chronic non-cancer pain (CNCP) is one of the most common causes of disability globally. Opioid prescribing to treat CNCP remains widespread, despite limited evidence of long-term clinical benefit and evidence of harm such as problematic pharmaceutical opioid use (POU) and overdose. The study aimed to measure the prevalence of POU in CNCP patients treated with opioid analgesics. A comprehensive systematic literature review and meta-analysis was undertaken using MEDLINE, Embase and PsycINFO databases from inception to 27 January 2021. We included studies from all settings with participants aged ≥ 12 with non-cancer pain of ≥ 3months duration, treated with opioid analgesics. We excluded case-control studies, as they cannot be used to generate prevalence estimates. POU was defined using four categories: dependence and opioid use disorder (D&OUD), signs and symptoms of D&OUD (S&S), aberrant behaviour (AB) and at risk of D&OUD. We used a random-effects multi-level meta-analytical model. We evaluated inconsistency using the I2 statistic and explored heterogeneity using subgroup analyses and meta-regressions. A total of 148 studies were included with > 4.3 million participants; 1% of studies were classified as high risk of bias. The pooled prevalence was 9.3% [95% confidence interval (CI) = 5.7-14.8%; I2= 99.9%] for D&OUD, 29.6% (95% CI = 22.1-38.3%, I2= 99.3%) for S&S and 22% (95% CI = 17.4-27.3%, I2= 99.8%) for AB. The prevalence of those at risk of D&OUD was 12.4% (95% CI = 4.3-30.7%, I2 = 99.6%). Prevalence was affected by study setting, study design and diagnostic tool. Due to the high heterogeneity, the findings should be interpreted with caution. Problematic pharmaceutical opioid use appears to be common in chronic pain patients treated with opioid analgesics,with nearly one in 10 experiencing dependence and opioid use disorder, one in three showing signs and symptoms of dependence and opioid use disorder and one in five showing aberrant behaviour.

Calculation of the inverse involute function and application to measurement over pins

Abstract Measurement over pins plays a crucial role in ensuring the quality of gear manufacturing, which requires the evolution of the inverse involute function. In this paper, an efficient algorithm for calculating the inverse involute function is proposed. Firstly, the initial approximate solution of the pressure angle near 0 rad is established by using Padé approximation and one step of Schröder iteration. Then, the involute equation is transformed into another form by introducing a variable transformation, and the initial approximate solution of the pressure angle near π / 2 rad is constructed by using the same method. A piecewise initial approximate solution over the entire interval of expanded angles is obtained by combining these solutions. Again, the accuracy of the initial approximate solution can be significantly improved by using one step of Schröder iteration. A numerical example of a two-pin measurement is presented to demonstrate the superiority of our method over traditional iterative approaches. The algorithm is highly efficient and accurate, and is a useful tool for designers to accurately calculate gear systems.

Unveiling novel insights into human IL-6 − IL-6R interaction sites through 3D computer-guided docking and systematic site mutagenesis

The cytokine interleukin-6 (IL-6) plays a crucial role in autoimmune and inflammatory diseases. Understanding the precise mechanism of IL-6 interaction at the amino acid level is essential to develop IL-6-inhibiting compounds. In this study, we employed computer-guided drug design tools to predict the key residues that are involved in the interaction between IL-6 and its receptor IL-6R. Subsequently, we generated IL-6 mutants and evaluated their binding affinity to IL-6R and the IL-6R − gp130 complex, as well as monitoring their biological activities. Our findings revealed that the R167A mutant exhibited increased affinity for IL-6R, leading to enhanced binding to IL-6R − gp130 complex and subsequently elevated intracellular phosphorylation of STAT3 in effector cells. On the other hand, although E171A reduced its affinity for IL-6R, it displayed stronger binding to the IL-6R − gp130 complex, thereby enhancing its biological activity. Furthermore, we identified the importance of R178 and R181 for the precise recognition of IL-6 by IL-6R. Mutants R181A/V failed to bind to IL-6R, while maintaining an affinity for the IL-6 − gp130 complex. Additionally, deletion of the D helix resulted in complete loss of IL-6 binding affinity for IL-6R. Overall, this study provides valuable insights into the binding mechanism of IL-6 and establishes a solid foundation for future design of novel IL-6 inhibitors.

Knit-Mycelium Hybrids: Textile Design Strategies for Biofabrication Systems Based on Mycocrete Injection in 3D Knitted Tubular Formworks

This paper investigates the design potential of monolithically grown Mycelium-based Composites (MBCs), focusing on integrating 3D knitted formworks within the fabrication process. Mycelium has gained attention as a sustainable, biodegradable construction material with insulating and self-healing capabilities. While there have been advancements in MBC, challenges remain regarding geometrical complexity and design potential at scale through in situ practices. Recent interest has emerged in digital fabrication methods and mould creation using textile logic and CNC knitting. However, fabricating mycelium within tubular soft moulds present a lack of stability and uneven growth distribution. The novel technique using injection filling of Mycocrete, a viscous mycelium paste, overcomes this structural disintegration. This method enables the creation of complex designs within a two-staged fabrication process while providing high structural performance. The paper presents three case studies demonstrating the impact of hanging, draping, and internal shape integration as fabrication steps as design tools for geometrical possibilities and diversity of expressions. It examines the relationship between knitted soft preform design and biofabrication parameters in relation to design expression, scale and mycelium growth. The paper concludes by emphasising the significance of 3D knitted formworks in scaling up MBCs in the built environment and its potential for implementing textile design parameters in MBC expressions and geometries. The fabrication steps provide newfound design opportunities, opening possibilities for aesthetical, material-saving and functionally graded design approaches. Further research in this area holds promise for advancing sustainable construction and textile design practices.

A design thinking approach to developing an online curriculum in interprofessional education in health in South Africa

Background. The design of contemporary curricula in health professions education in South Africa (SA) is constantly evolving, as there is a need to ensure that students are adequately prepared to work in complex healthcare environments. The World Health Organization proposes that a way to accomplish this is through interprofessional education (IPE), where students from two or more professions learn from one another to improve health outcomes. Objectives. To reflect on the design of an introductory module of a postgraduate diploma in IPE in health at a historically disadvantaged institution in SA, where a student-centred approach to learning and teaching was adopted. Methods. Through an auto-ethnographic methodology, a design thinking approach was used to carefully construct and align an introductory module in IPE in the postgraduate programme. Considering the remote learning environment, this article also examines the importance of educational technologies in the design process of this online curriculum. Results. The results of this study demonstrated the process used in the design of an IPE module supported by the five stages of design thinking to critically appraise the student-centred approach to learning and teaching. The instructional design tools used for this module included the university’s learning management system, Google Suite, Flip, Google Jamboard, webinars, presentations, elevator speeches, quizzes and guest speakers. The end product was an interactive online module that was intentionally designed to be interactive and provide students with a core understanding of IPE. Conclusion. This article demonstrates how an IPE module with a focus on theories, models and concepts can provide the foundation for students to understand the importance of IPE to transform the future of healthcare in SA.

Embodied immersive virtual reality to enhance the conceptual understanding of charged particles: A qualitative study

Learning in science, technology, engineering, and mathematics (STEM) is often challenging due to the abstract and counterintuitive nature of some concepts. Computer-based learning has emerged as an alternative method to help improve students' comprehension of these complex topics, even though technological tools must be supported with pedagogical strategies, technology affordances, sound design, and structured activities to teach scientific concepts properly. In that sense, we propose the design of an immersive virtual reality (IVR) experience, including visual and haptic cues to facilitate learning about electric fields (EFs) and charged particles (CPs) concepts. We scaffolded our design tool based on embodied design principles and cognition. The IVR experience allows learners to manipulate the components of point charges (e.g., particles, distance between particles, and charges) to learn electricity concepts. We conducted a qualitative study (N = 8) to assess the designed application. The sample included undergraduate students (five male and three female) from technology-related fields with some or no prior knowledge of high school or higher education physics. We assessed study participants' conceptual understanding through a pretest-posttest and conducted a brief interview to identify their expected interaction with the designed affordances. Screen recording and the System Usability Scale (SUS) are the other metrics of interest in defining study participants’ performance and experience. The collected data and thematic analysis suggested that participants recognized the included affordances based on the embodied design principles and used them to interact, link previous knowledge, and identify the different factors to explain the physics phenomenon. Additionally, we provided insights for designing IVR experiences to promote conceptual understanding of complex STEM topics based on embodied learning principles.

Impact of Artificial Intelligence on Mechanical Engineering: A Comprehensive Overview

Artificial Intelligence (AI) is coming to mainstream and as emerged as a transformative force in various fields not just limited including mechanical engineering. This paper provides an overview of the profound impact of AI on the practice and evolution of mechanical engineering. The usage of AI technologies in the field of mechanical engineering has potential to revolutionize traditional design, manufacturing, and maintenance processes. With AI-powered design tools engineers now can generate optimized designs faster with greater efficiency, leading to enhanced product performance and reduced development cycles. Further, Predictive/forecasting method of AI in maintenance systems facilitate early detection of equipment failures, thereby minimizing downtime and maintenance costs.

A Dataset Generation Framework for Symmetry-Induced Mechanical Metamaterials

Abstract The surge in machine learning research and recent advancements in 3D printing technologies have significantly enriched materials science and engineering, particularly in the domain of mechanical metamaterials, which commonly consist of periodic truss materials. Despite the extensive exploration of their tailorable properties, truss-based metamaterial design has predominantly adhered to cubic and orthotropic unit-cells, a limitation arising from the conventional design method, where the type of symmetry related to the designed truss-based material is determined after the design process is done. To overcome this issue, this work introduces a groundbreaking 3D truss material designing framework that departs from this constraint by employing six distinctive material symmetries (cubic, hexagonal, tetragonal, orthotropic, trigonal, and monoclinic) within the design process. This innovative approach represents a versatile paradigm shift compared to previous design approaches. Furthermore, we are able to integrate anisotropy into the design framework, thus enhancing the property space exploration capability of the proposed design framework. Probing materials property space using our design framework demonstrates its capacity to achieve a diverse range of mechanical properties, surpassing even the most extensive datasets available in the literature. The proposed method facilitates the generation of a comprehensive truss dataset, which can be represented in a trainable continuous format suitable for machine learning and data-driven approaches. This advancement paves the way for the development of robust inverse design tools for truss materials, marking a significant contribution to the mechanical metamaterial community.

Fuzzy cognitive network process for software reliability and quality measurement: comparisons with fuzzy analytic hierarchy process

Software reliability and quality measurement has a long-lasting impact on the final products and user experiences. The analytic hierarchy process (AHP) and its various hybrid models, including fuzzy AHP, have been applied to software reliability and quality measurement in various aspects. Related studies indicate that limitations in the paired ratio scales of AHP may lead to misapplications, which most AHP users might not be aware of. To address this issue, the fuzzy cognitive network process (FCNP) is proposed as a promising alternative applied for software reliability and quality measurement. One application based Fuzzy AHP is revisited to demonstrate the feasibility and usability of the FCNP. The paper discusses conversion from the FAHP to the FCNP, examining their reproducibility, comparability, merits, and limitations. The proposed fuzzy CNP method can a valuable tool for software customers, designers, developers, testers, and purchasers, to evaluate the level of software reliability and quality in general applications within intelligent environment.

ACEGEN: Reinforcement Learning of Generative Chemical Agents for Drug Discovery.

In recent years, reinforcement learning (RL) has emerged as a valuable tool in drug design, offering the potential to propose and optimize molecules with desired properties. However, striking a balance between capabilities, flexibility, reliability, and efficiency remains challenging due to the complexity of advanced RL algorithms and the significant reliance on specialized code. In this work, we introduce ACEGEN, a comprehensive and streamlined toolkit tailored for generative drug design, built using TorchRL, a modern RL library that offers thoroughly tested reusable components. We validate ACEGEN by benchmarking against other published generative modeling algorithms and show comparable or improved performance. We also show examples of ACEGEN applied in multiple drug discovery case studies. ACEGEN is accessible at https://github.com/acellera/acegen-open and available for use under the MIT license.

A valid and reliable explanatory model of learning processes in heritage education

BackgroundThe main challenge in heritage education is to identify the verbs—and their hierarchical relations—that explain heritage learning as based on empirical evidence. The Heritage Learning Sequence (HLS) selects seven verbs (Knowing-Understanding-Respecting-Valuing-Caring-Enjoying-Transmitting) on the basis of (a) theoretical studies, (b) analyses of international standards, and (c) evaluation of heritage education programs. The study has the following objectives: (a) to clarify the heritage learning process; (b) to test a theoretical model that groups the verbs that make up the Heritage Learning Sequence (HLS), as well as the relationships between them; (c) to identify possible sub-models that explain the different heritage learning itineraries.MethodsThe Q-Herilearn scale (previously calibrated using SEM and IRT models) was administered to N=1454\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N = 1454$$\\end{document} individuals, focusing on seven factors (corresponding to each HLS verb) that measure heritage learning. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used as a general analytical strategy.FindingsThe results obtained provided sufficient guarantees to validate the HLS and showed the adequate explanatory and predictive power and general fit of the proposed model (Heritage Learning Model); all twelve hypothesized direct influence relations between the main verbs that define heritage learning were confirmed. The statistical significance values suggested the existence of other internal subsequences that could be explored in further studies.ContributionLearning modeling provides a key structural framework for (a) the design of effective, efficient, and comprehensive tools to measure heritage learning and (b) their operationalization in heritage education designs.

Software for Bioprinting.

The bioprinting of heterogeneous organs is a crucial issue. To reach the complexity of such organs, there is a need for highly specialized software that will meet all requirements such as accuracy, complexity, and others. The primary objective of this review is to consider various software tools that are used in bioprinting and to reveal their capabilities. The sub-objective was to consider different approaches for the model creation using these software tools. Related articles on this topic were analyzed. Software tools are classified based on control tools, general computer-aided design (CAD) tools, tools to convert medical data to CAD formats, and a few highly specialized research-project tools. Different geometry representations are considered, and their advantages and disadvantages are considered applicable to heterogeneous volume modeling and bioprinting. The primary factor for the analysis is suitability of the software for heterogeneous volume modeling and bioprinting or multimaterial three-dimensional printing due to the commonality of these technologies. A shortage of specialized suitable software tools is revealed. There is a need to develop a new application area such as computer science for bioprinting which can contribute significantly in future research work.

Charlotte: A modern tool for cave surveying

The future of cave surveying is a full 3D scan, as automatic as possible, capturing all morphologically relevant details. However, the vast majority of cavers are still using tools designed more than 15 years ago. The advancements in tools for architects, CAD designers, programmers of VR and robotics could be helpful also for the caving community. That’s why Charlotte, a cheap DIY 2.5D scanner designed for cave surveying, was developed and built. To demonstrate its capabilities, a new survey of Grotta Regina del Carso cave (cad. nr. 2328/4760VG), the biggest cave in the Gorizia Karst, has been realized and published. The new scanner is based on the same traditional cave surveying techniques, familiar to all cavers, and features a LiDAR to capture the exact cave morphology. The results are so realistic they can be used for 3D animations, physics simulations, or even 3D printing. The prototype of the new tool costs less than 400€ (EURO; as of December 2022). All the source code and schematics are released as Free and Open Source. Cavers are encouraged to fork the project on Github and provide new functionalities, to keep up with new technology available and the needs of caving scientific research all around the world.

Multiaxial fatigue life assessment of dental implants

As screwed joints, dental restorations may suffer mechanical failures such as screw loosening and implant or prosthetic screw failure due to fatigue. This work is focused on the failure of the implant and develops a numerical methodology to predict its fatigue life under cyclic loading conditions. This methodology is based on the combination of Critical Plane Methods and the Theory of Critical Distances to account for stress multiaxiality and notch effects. The obtained predictions were validated experimentally, which can be used to identify the main geometrical, assembly and operational factors affecting the fatigue behavior of dental implants. As a result, a powerful and efficient design tool for fatigue life prediction of dental implants is presented. This methodology complements a previously presented one focused on the fatigue life prediction of the prosthetic screw, thereby, offering now a complete design tool package regardless the critical component of the dental restoration, predicting accurately the fatigue response of the restoration, with no need for long-term fatigue test campaigns. This is a pioneering work since no other fatigue design methodology for dental implants with such a solid foundation and experimental validation has been published to date.

Creating Real Situations, Developing Thinking Tools Design and Implementation of the Interdisciplinary Project Autumn Museum for Lower Grades of Primary School

Childrens expressive abilities need to be gradually built. In the lower grades of primary school, real situational tasks should be used as the teaching environment, students task inquiry activities as the main line of learning, and language interaction between teachers and students as the teaching technique. The ultimate goal is to enhance individual language experience centered on students pragmatic abilities. By using situational tasks as the main line, continuously providing students with tools for expression, and achieving the learning objective of childrens creative expression through a structured language construction process. This paper develops and designs an expressive literacy course based on the first semester Chinese textbook for first graders.

Polycaprolactone-based shape memory foams as self-fitting vaginal stents

There is an urgent critical need for a patient-forward vaginal stent that can prevent debilitating vaginal stenosis that occurs after pelvic radiation treatments and vaginal reconstruction. To this end, we developed a self-fitting vaginal stent based on a shape-memory polymer (SMP) foam that can assume a secondary, compressed shape for ease of deployment. Upon insertion, the change in temperature and hydration initiates foam expansion to shape fit to the individual patient and restore the lumen of the stent to allow egress of vaginal secretions. To achieve rapid actuation at physiological temperature, we investigated the effect of architecture of two photocurable, polycaprolactone (PCL) macromers. Star-PCL-tetraacrylate displayed a reduced melting temperature as compared to a linear-PCL-diacrylate. Upon fabrication into high porosity foams with emulsion-templating, both compositions displayed shape fixity (>90 %) in a crimped, temporary shape. However, only the PCL star-foams displayed shape recovery (∼84 %) at 37 °C with expansion back to its permanent shape. A custom mold and curing system were then used to fabricate the PCL star-foams into hollow, cylindrical stents. The stent was crimped to its temporary insertion shape (50 % reduction in diameter, OD ∼ 11 mm) with a custom radial crimper and displayed excellent shape fixity for deployment (> 95 %) and shape recovery (∼ 100 %). To screen vaginal stents, we developed a custom benchtop pelvic model that simulated vaginal anatomy, temperatures, and pressures with an associated computational model. The crimped SMP vaginal stent was deployed in the model and expanded to walls of the canal (∼70 % increase in cross-sectional area) in less than 5 min after irrigation with warm water. The vaginal stent demonstrated retention of vaginal caliber with less than 10 % decrease in cross-sectional area under physiological pressures. Collectively, this work demonstrates the potential for SMP foams as self-fitting vaginal stents to prevent stenosis and provides new open-source tools for the iterative design of other gynecological devices. Statement of significanceVaginal stenosis, a painful narrowing of the vaginal canal, is a common complication after pelvic radiation therapy or reconstructive surgery. To address this clinical need, we have created a self-fitting vaginal stent from a shape-memory polymer foam. The stent compresses for easy insertion and then expands to adapt to each patient's anatomy to maintain an open vaginal canal and prevent stenosis. This innovative stent provides a patient-friendly solution that could make a significant difference for women undergoing pelvic treatments by reducing pain, aiding recovery, and improving quality of life.

Circular Systems Navigator: A Process Tool for Circular Systems Design and Rebound Effect Mitigation

Circular Systems Navigator: A Process Tool for Circular Systems Design and Rebound Effect Mitigation

A multi reaction kinetic model to describe the enzymatic transesterification reaction of jatropha oil using a fermented solid containing lipases

The advancement of more precise tools for sustainable process design in enzymatic biodiesel synthesis from renewable sources is crucial. Kinetics of solvent-free transesterification reactions were conducted across a temperature spectrum from 30 °C to 60 °C, utilizing Jatropha curcas oil (TG) and ethanol as substrates, alongside a fermented solid by Rhizopus homothallicus as the biocatalyst. The dynamics of chemical species concentrations were monitored through High-performance Thin-Layer Chromatography. Maximum productivities were achieved at 35 °C and 60 °C for biodiesel (293.24 and 299.02 g kg biocat−1 h−1, respectively), at 40 °C for diglycerides (1018.36 g kg biocat−1 h−1), and at 35 °C for monoglycerides (560.75 g kg biocat−1 h−1). Maximum yields were determined at 30 °C for fatty acid ethyl esters (0.56 g gTG−1), and at 40 °C for diglycerides (0.53 g gTG−1) and monoglycerides (0.30 g gTG−1). Based on the experimental findings, a kinetic model was formulated encompassing three reversible transesterification reactions. Individual reactions were structured following classical biochemical kinetics, inclusive of ethanol inhibition. Model fitting was executed through non-linear multivariable regression techniques, with the minimum of the average coefficient of variation of the residuals (ACVR) serving as the objective function. The resulting fit of the kinetic model to the experimental data proved satisfactory, with an ACVR of less than 5 % across all instances. Notably, the maximum biodiesel productivity, obtained in this work, represented the highest value, compared to other related studies, using a fermented solid as a biocatalyst.

Computational Approaches: A New Frontier in Cancer Research.

Cancer is a broad category of disease that can start in virtually any organ or tissue of the body when aberrant cells assault surrounding organs and proliferate uncontrollably. According to the most recent statistics, cancer will be the cause of 10 million deaths worldwide in 2020, accounting for one death out of every six worldwide. The typical approach used in anti-cancer research is highly time-consuming and expensive, and the outcomes are not particularly encouraging. Computational techniques have been employed in anti-cancer research to advance our understanding. Recent years have seen a significant and exceptional impact on anticancer research due to the rapid development of computational tools for novel drug discovery, drug design, genetic studies, genome characterization, cancer imaging and detection, radiotherapy, cancer metabolomics, and novel therapeutic approaches. In this paper, we examined the various subfields of contemporary computational techniques, including molecular docking, artificial intelligence, bioinformatics, virtual screening, and QSAR, and their applications in the study of cancer.

Effects of TiO2 nanobelts addition on microstructure and physical properties of vitrified bonds

Titanium dioxide (TiO2) is commonly used additive to improve the strength, hardness and coefficient of expansion properties of vitrified bonds. In this work, TiO2 nanobelts were prepared through hydrothermal method, and the effect of TiO2 nanobelts content on the microstructure and physical properties of vitrified bonds and vitrified bond CBN composites were systematically investigated. When the content of 0.2 wt% TiO2 nanobelts and sintered at 660°C, as-prepared nano-vitrified bonds exhibited the beneficial performance improvement. Specifically, the obtained coefficient of thermal expansion, flexural strength, microindentation hardness, and mobility are 5.37×10-6 °C-1, 85.96 MPa, 853.19 MPa, and 140.6 %, respectively. Moreover, the vitrified bonds with 0.2 wt% TiO2 nanobelts addition is further used to optimize the performance of cubic boron nitride (CBN) composites. When sintered at 720 °C, the CBN composite samples depicted the excellent flexural strength, rockwell, porosity, and grinding ratio of 63.27 MPa, 61.5 HRB, 48.9 %, and 22.3, respectively. It can be attributed to the positive contribution of nano-vitrified bonds on the physical properties of CBN composites. This work is expected to contribute to the research on improving the performance of vitrified bonds with nano additives, and further obtain excellent performance nano-vitrified bond materials for the design and manufacturing of high-performance superhard abrasive tools.