Design Modeling Research Articles

Artificial intelligence applications in smile design dentistry: A scoping review.

Artificial intelligence (AI) applications are growing in smile design and aesthetic procedures. The current expansion and performance of AI models in digital smile design applications have not yet been systematically documented and analyzed. The purpose of this review was to assess the performance of AI models in smile design, assess the criteria of points of reference using AI analysis, and assess different AI software performance. An electronic review was completed in five databases: MEDLINE/PubMed, EMBASE, World of Science, Cochrane, and Scopus. Studies that developed AI models for smile design were included. The search strategy included articles published until November 1, 2024. Two investigators independently evaluated the quality of the studies by applying the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi-Experimental Studies and Textual Evidence: Expert Opinion Results. The search resulted in 2653 articles. A total of 2649 were excluded according to the exclusion criteria after reading the title, abstract, and/or full-text review. Four articles published between 2023 and 2024 were included in the present investigation. Two articles compared 2D and 3D points while one article compared the outcome of satisfaction between dentists and patients, and the last article emphasized the ethical components of using AI. The results of the studies reviewed in this paper suggest that AI-generated smile designs are not significantly different from manually created designs in terms of esthetic perception. 3D designs are more accurate than 2D designs and offer more advantages. More articles are needed in the field of AI and smile design.

A review on inverse analysis models in steel material design

AbstractThis paper reviews various inverse analysis models used in steel material design, with a focus on integrating process, microstructure, and properties through advanced machine learning techniques. The study underscores the importance of establishing comprehensive models that effectively link these elements for enhanced materials engineering. Key models discussed include the convolutional neural network–artificial neural network‐coupled model, which employs convolutional neural networks for feature extraction; the Bayesian‐optimized generative adversarial network–conditional generative adversarial network model, which generates diverse virtual microstructures; the multi‐objective optimization model, which concentrates on process–property relationships; and the microstructure–process parallelization model, which correlates microstructural features with process conditions. Each model is assessed for its strengths and limitations, influencing its practical applicability in material design. The paper concludes by advocating for continued improvements in model accuracy and versatility, with the ultimate goal of enhancing steel properties and expanding the scope of data‐driven material development.

Microwave-derivatized enhanced-spectrofluorophotometric characterization of nateglinide-loaded solid dispersion adsorbate and greenness profile assessment of method.

Nateglinide is an oral meglitinide that treats diabetes. Nateglinide was determined in several commercial formulations using chromatographic and spectrophotometric methods in published research. Quality-by-design approach was applied to develop the in-house nateglinide-loaded Solid Dispersion Adsorbate (SDA) for improving bioavailability and solubility. To determine nanogram-level nateglinide concentration forthe in-house developed nateglinide-SDA pharmacokinetics required time-consuming and expensive hyphenated analytical techniques such as LC-MS or UPLC-MS. Hence, asensitive spectrofluorophotometric method was developed to estimate nateglinide using NBD-Cl (7-chloro-4-nitrobenzoxadiazole) as the fluorophore. Microwave-aided chemical derivatization of nateglinide with NBD-Cl sped up sample analysis. Chemical reaction solvents were eco-friendly to safeguard aquatic life and environment. The chemical process was optimized by quality by design approach using screening design and response surface modelling. The fluorescence intensity of nateglinide was found to be linear over the concentration range of 20-250 ng/mL with a correlation coefficient of 0.9978. The limits of detection and quantification were found to be 10 and 20 ng/mL respectively. Precision and robustness investigations of the spectrofluorimetric method showed less than 2% relative standard deviations. Nateglinide-SDA drug-release study and pharmacokinetics were performed using the sensitive spectrofluorophotometric method. The in-house developed nateglinide-SDA released 95% of the drug in 30 min time duration. In-house-developed nateglinide-SDA has 1.5 times the oral bioavailability of commercial formulations due to in-vivo absorption. The published analytical methods were compared to the enhanced-spectrofluorophotometric method for user-friendliness, efficiency, cost-of-analysis, and environmental friendliness. The proposed sensitive-spectrofluorophotometric method was found to be robust, rapid, eco-friendly, cost-effective, and simple for the estimation of nateglinide.

AlphaFold3 versus experimental structures: assessment of the accuracy in ligand-bound G protein-coupled receptors.

G protein-coupled receptors (GPCRs) are critical drug targets involved in numerous physiological processes, yet many of their structures remain unresolved due to inherent flexibility and diverse ligand interactions. This study systematically evaluates the accuracy of AlphaFold3-predicted GPCR structures compared to experimentally determined structures, with a primary focus on ligand-bound states. Our analysis reveals that while AlphaFold3 shows improved performance over AlphaFold2 in predicting overall GPCR backbone architecture, significant discrepancies persist in ligand-binding poses, particularly for ions, peptides, and proteins. Despite advancements, these limitations constrain the utility of AlphaFold3 models in functional studies and structure-based drug design, where high-resolution details of ligand interactions are crucial. We assess the accuracy of predicted structures across various ligand types, quantifying deviations in binding pocket geometries and ligand orientations. Our findings highlight specific challenges in the computational prediction of ligand-bound GPCR structures, emphasizing areas where further refinement is needed. This study provides valuable insights for researchers using AlphaFold3 in GPCR studies, underscores the ongoing necessity for experimental structure determination, and offers direction for improving protein-ligand interaction predictions in future computational models.

Large Language Models in Design and Manufacturing

Large Language Models in Design and Manufacturing

Design and dynamic modeling of a balance rehabilitation cable (BaReCa) robot: integration with patients

Design and dynamic modeling of a balance rehabilitation cable (BaReCa) robot: integration with patients

Structural Design and Kinematic Modeling of Highly Biomimetic Flapping-Wing Aircraft with Perching Functionality

Birds use their claws to perch on branches, which helps them to recover energy and observe their surroundings; however, most biomimetic flapping-wing aircraft can only fly, not perch. This study was conducted on the basis of bionic principles to replicate birds’ claw and wing movements in order to design a highly biomimetic flapping-wing aircraft capable of perching. First, a posture conversion module with a multi-motor hemispherical gear structure allows the aircraft to flap, twist, swing, and transition between its folded and unfolded states. The perching module, based on helical motion, converts the motor’s rotational movement into axial movement to extend and retract the claws, enabling the aircraft to perch. The head and tail motion module has a dual motor that enables the aircraft’s head and tail to move as flexibly as a bird’s. Kinematic models of the main functional modules are established and verified for accuracy. Functional experiments on the prototype show that it can perform all perching actions, demonstrating multi-modal motion capabilities and providing a foundation upon which to develop dynamics models and control methods for highly biomimetic flapping-wing aircraft with perching functionality.

AI-assisted design of lightweight and strong 3D-printed wheels for electric vehicles.

The automotive industry is undergoing a transformative shift towards electric vehicles (EVs), driven by environmental concerns and technological advancements. One critical aspect of EV design is the development of lightweight yet robust components, including 3D vehicle wheels. This research explores the implementation of generative models in Computer-Aided Design (CAD) systems to optimize the design of 3D vehicle wheels for electric vehicles. Through the use of generative design and additive manufacturing, we aim to create vehicle wheels that are energy-efficient, aesthetically pleasing, and structurally sound. Electric vehicles are gaining popularity due to their environmental benefits and reduced operating costs, making lightweight and strong wheels an important design goal. This research proposes a novel approach for designing lightweight and strong 3D vehicle wheels for EVs using generative models. The proposed approach involves the following steps: collect and prepare data, choose a generative model architecture, train the generative model, and generate new wheel designs. The approach methods show potential to revolutionize the design and manufacturing of lightweight and strong 3D-printed wheels for electric vehicles. In conclusion, generative models can be used to design and optimize wheel designs, making it possible to create safer, more efficient, and more cost-effective wheels.

Design and Analytical Modeling of a Dumbbell-Shaped Balloon Anchoring Actuator for Safe and Efficient Locomotion Inside Gastrointestinal Tract.

Colorectal cancer stands as one of the most prevalent cancers globally, representing 9.8% of total cases and contributing to 9.2% of mortalities annually. Robotic "front-wheel" navigating colonoscopes mitigate aggressive stretching against the long and tortuous colonic wall, alleviating associated discomfort and pain typically experienced by patients inspected by conventional "back-wheel" navigating colonoscopes. The anchoring unit of most "front-wheel" navigating colonoscopes plays a crucial role in ensuring effective locomotion by preventing slipping during elongation/contraction of the central actuation part. The soft balloon anchoring actuator emerges as a promising solution due to its high compliance. This study introduces a dumbbell-shaped balloon anchoring actuator (DBAA) integrating an "inflation and suction" mechanism to address the inherent conflict between achieving sufficient anchoring force and minimizing expansion and potential trauma of the colonic wall, commonly encountered in current balloon anchoring actuators. Analytical modeling of DBAA and soft external lumen, encompassing geometric deformation and anchoring force, were proposed to characterize the actuator and provide guidelines for designing and controlling DBAA in further applications, enabling autonomous anchoring within different diameter lumens and achieving the expected anchoring force. A comprehensive set of validation experiments was conducted, and the outcomes revealed high consistency with analytical predictions, confirming the effectiveness of the proposed analytical modeling approach. Furthermore, the results demonstrated a significant enhancement in anchoring force with the proposed actuator and corresponding mechanism while concurrently maintaining low-lumen expansion. For instance, in a lumen sample with the anchoring force reaches 14.5 N with 50 kPa negative pressure, which is 12.4 times of the force (1.17 N) observed without applying negative pressure.

FEASIBILITY STUDY OF A FIXED OFFSHORE OTEC PLANT FOR COST-EFFECTIVE POWER GENERATION IN MALAYSIA

Ocean Thermal Energy Conversion (OTEC) is a renewable energy technology that harnesses the temperature differential between warm surface water and cold deep ocean water to generate electricity and freshwater. Technological advancements have made OTEC increasingly viable, positioning it as a potential solution for sustainable energy generation in Malaysia. Despite its promise, OTEC development in Malaysia is challenged by the need for suitable sites and the high capital costs associated with traditional floating OTEC platforms. Additionally, efforts to attract anchor partners for OTEC projects have been met with limited success, slowing progress in implementation. This study assessed the feasibility of OTEC plant development in Malaysia through the analysis of sea temperature profiles, identifying five potential sites. A multi-criteria analysis was conducted considering factors such as cold water intake pipe length, proximity to power transmission lines, and environmental impact. The study also proposes a fixed offshore OTEC platform with a single-legged caisson structure, incorporating a braced substructure and dual-level production decks to optimize space for equipment and reduce CAPEX. Kuala Baram (Location C) was identified as the most favorable site due to its optimal conditions for cold water intake and proximity to transmission infrastructure. The proposed fixed platform design offers significant CAPEX reductions compared to floating alternatives, enhancing OTEC’s economic potential. The study’s findings support the immediate application of OTEC in Malaysia, particularly in ongoing projects off the coast of Sabah. Further research, including modeling and testing of the platform design, is necessary to advance commercialization and establish OTEC as a key component of Malaysia's renewable energy strategy.

Manufacture of a 4-Degree-of-Freedom Robot to Support an IRB 120 Robot

In this work, we present the construction and control of a four-degrees-of-freedom (DOF) manipulator aimed at addressing one of the key challenges faced by the Academy-Industry Cooperation Center (CCAI): the need for mechatronic equipment to support and facilitate the development of advanced robotic cells. We begin by designing the robot’s structure and components using SolidWorks software for computer-aided design (CAD) modeling. This ensures that all the links and parts fit together properly without collisions. The robot links are then manufactured using 3D printing. Additionally, we performed kinematic modeling, dynamic analysis, and PI-V control, along with control using a trigonometric function (hyperbolic tangent). To evaluate the robot’s movement, we simulate these processes using Matlab R2019a/Simulink software, focusing on key parameters such as position, velocity, and acceleration, which inform the design of PI-V control for each link. We also present the electrical and electronic designs, followed by system implementation. The kinematics of the robot play a crucial role in the dynamics and controller design. We validate the kinematics using Peter Corke’s libraries based on the Denavit–Hartenberg parameters. The results show that the controller based on the trigonometric function improves the response time, particularly enhancing the performance of axes 2 and 3.

Environmental insights of bioethanol production and phenolic compounds extraction from apple pomace-based biorefinery

Food waste is one of the main challenges of solid waste management throughout the food supply chain. Apples are one of the most widely consumed fruits worldwide, the production of which is accompanied by the generation of pomace as a by-product with valuable nutrients. This research presents the first environmental insights of a multiproduct apple pomace-based biorefinery through a life cycle perspective. The design and process modelling of this platform aims to produce bioethanol and extract total phenolic compounds (TPC) towards an efficient use of the pomace obtained from the apple manufacturing industry (juice production). Bioethanol production consist of pressing, fermentation, distillation as the main processes; while in the case of TPC, two extraction techniques were evaluated: i) solvent extraction (mainly water), and ii) the Soxhlet method. The life cycle analysis followed an attributional cradle-to-gate approach considering different midpoint impact categories from the ReCipe 2016 method such as Global Warming (GW), Eutrophication, Human Toxicity, Fossil Scarcity, among others. The results indicate that bioethanol production encompasses a GW profile of 3.17 kg of CO2 eq per kg of product, being the vinasse treatment (subproduct after distillation) the most impactful stage. Phenolic compounds extraction with water achieves a total value of 5.8 kg CO2 eq per g of TPC, while 0.22 kg CO2 eq per g of TPC is obtained with Soxhlet technique. Furthermore, a sensitivity analysis is addressed to improve the environmental profile of bioethanol and TPC. This research demonstrated the relevance of process design on the environmental performance of bioethanol and TPC, where stillage treatment has a key contribution to the results and the use of solvents in TPC extraction, although improving extraction yield, leads to a higher environmental impact.

AI-generated VR: Leveraging AI and VR for Rapid Ideation and Concept Modeling of Interior Design

AI-generated VR: Leveraging AI and VR for Rapid Ideation and Concept Modeling of Interior Design

Beyond Traditional Computer-Aided Design Parameterization, Feature Engineering for Improved Surrogate Modeling in Engineering Design

Beyond Traditional Computer-Aided Design Parameterization, Feature Engineering for Improved Surrogate Modeling in Engineering Design

Using Cure Models for Trial Analysis and Design

Using Cure Models for Trial Analysis and Design

МАТЕМАТИЧНА КОМПЕТЕНТНІСТЬ МАЙБУТНЬОГО ВЧИТЕЛЯ МАТЕМАТИКИ: ТЕОРЕТИЧНИЙ АНАЛІЗ

In the context of modernization of contemporary education on the basis of a competence approach, the problem of professional training of future mathematics teachers, who are able to be actively implemented in the information society, have professional competences that would meet the needs of today, becomes especially relevant. It is emphasized that an important task of training future mathematics teachers is the formation of their mathematical competence. The hierarchy of competences is characterized (according to N. Bibik): key super subjective (basic) competences, which are based on cognitive processes and displayed in different contexts; general subject competences, which belong to a certain set of subjects or branches of knowledge; subject competences - partial to the above, which are acquired in the process of studying a particular subject. The article describes the various definitions of mathematical competence proposed in the State Standard of Basic and Complete General Education (2011), the Concept of the New Ukrainian School (2018), the European Reference Framework of Key Competences for Lifelong Learning (2018), and the PISA International Education Quality Assessment (2022). Based on the analysis, it is determined that mathematical competence is defined by modern scientists as an integrated characteristic of the qualities of a mathematics teacher’s personality, which is expressed in the ability and willingness to apply mathematical knowledge and skills in order to solve professional problems using mathematical methods, applying modern methods of design, mathematical and computer modelling if necessary. Mathematical competence as a result of mathematical training of a graduate of a pedagogical education institution implies a high level of knowledge and experience of independent activity based on this knowledge for the effective professional activity of a future mathematics teacher. The structural components of mathematical competence are allocated: motivational, cognitive, value, activity, and reflexive.

Thermo-economic investigation of transcritical Carbon Dioxide solar-thermal power plants, Part 1: System modeling and simulation for design with validation of results

This study comprises the engineering design and economic assessment of a novel trans-critical-point Carbon Dioxide cycle integrated with a solar Parabolic Trough Collector (PTC) power plant. A 1-MW facility with a Rankine transcritical CO2 cycle providing combined heat and power for a small industrial facility in Texas was proposed as the baseline power plant to perform the analysis. A design and modeling effort was performed in Engineering Equation Solver (EES) for the many systems and components of the power plant, including the solar field, the power block, and sub-components such as turbomachinery and heat exchangers. The model created enabled the simulation of plant performance for the median day of each month in the year by inputting meteorological TMY3 data, and results were extrapolated to estimate the annual energy and monetary income yield. Data processing was performed with MATLAB. Economic analysis included estimation of capital and O&M costs for the baseline plant. Results were verified through empirical performance simulation, using System Advisor Model (SAM), a software provided by NREL. The levelized cost of energy (LCOE) for the modeled plant was $0.2915/kWh, as calculated with EES/MATLAB, and $0.305/kWh, as calculated with SAM, resulting in a difference of less than 5%, between the two different simulation approaches. The design and simulation work presented herein provides the means to perform an optimization analysis, which reveals important clues as to how the energy cost could be reduced so as to make this technology a competitive alternative within the energy market.

All you need is rotation: Construction of developable strips

We present a novel approach to generate developable strips along a space curve. The key idea of the new method is to use the rotation angle between the Frenet frame of the input space curve, and its Darboux frame of the curve on the resulting developable strip as a free design parameter, thereby revolving the strip around the tangential axis of the input space curve. This angle is not restricted to be constant but it can be any differentiable function defined on the curve, thereby creating a large design space of developable strips that share a common directrix curve. The range of possibilities for choosing the rotation angle is diverse, encompassing constant angles, linearly varying angles, sinusoidal patterns, and even solutions derived from initial value problems involving ordinary differential equations. This enables the potential of the proposed method to be used for a wide range of practical applications, spanning fields such as architectural design, industrial design, and papercraft modeling. In our computational and physical examples, we demonstrate the flexibility of the method by constructing, among others, toroidal and helical windmill blades for papercraft models, curved foldings, triply orthogonal structures, and developable strips featuring a log-aesthetic directrix curve.

Hybrid regret-based p-robust and distributionally robust optimization models for electric vehicle charging station network design

The rise of electric vehicles (EVs) in recent years has underscored the need for well-established Electric Vehicle Charging Station (EVCS) infrastructure. The lack of a reliable network of EVCS is a significant obstacle to the widespread adoption of EVs. Also, one often overlooked aspect of electric vehicles is the importance of using renewable energy sources to recharge their batteries. Without sustainable energy, EVs could produce more greenhouse gas emissions than traditional vehicles. Therefore, it seems essential to design networks of EVCSs that depend on renewable sources to meet their energy needs and ensure convenient access for customers while reducing environmental impact. This study proposes a two-stage stochastic optimization model to determine the optimal locations and capacities for EVCSs and supporting sustainable energy plants. The model considers uncertain factors such as vehicle flows and renewable energy supply. The model combines a hybrid adjusted p-robust and min–max regret approach to address uncertainties to ensure robustness in the network design process. Two approaches including Sample Average Approximation (SAA) and Fuzzy C-means Clustering are employed to solve the model efficiently. Additionally, the Distributionally Robust Optimization (DRO) approach accounts for uncertainty in the probability distribution function of scenarios. The results of these solution approaches are validated through parameter realization, which involves simulating uncertain parameters and verifying the robustness of the solutions obtained from each approach. This validation process is done by comparing the obtained results with the optimal values based on two criteria: the sum of deviation from optimality and the maximum deviation from optimality. By adopting these solution methods, the proposed methodology contributes to developing efficient and sustainable EV infrastructure. This methodology gives investors and city officials robust decision support for establishing EVCS.

Co-designing transport models as a heuristic planning tool.

Recently the transportation sector has witnessed several new technologically driven disruptions that have amplified the complexity of city planning and policymaking. Traditional well-established processes of decision-making in urban planning and transportation are proving insufficient to deal with this degree of complexity and uncertainty. This paper proposes an alternative approach, combining qualitative and normative urban design, with quantitative and predictive transport modelling. This requires urban designers and transport modellers to co-create goal-driven and agile transport models that act as a heuristic tool to guide planning decisions in early design stages. Heuristic modelling is informed by design optioning and vice versa in an iterative loop. A case study is presented to demonstrate how this approach is operationalized to study the impacts of automated vehicles on urban planning. Design workshops are used as a method to elicit responses from stakeholders, which are used to co-create the simulation models. This collaborative process grounds the research in real-world practice and enhances the communication of design proposals and research findings across disciplines. By integrating design thinking methods with agent-based transport simulations, this approach provides a better understanding of emergent effects in complex urban systems and improves stakeholder engagement in the planning process.This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'.