Towards model-based design of causal manipulations of brain circuits with high spatiotemporal precision.

Dimensional accuracy of 3D-printed implant models is essential for precise implant-supported restorations. The objective of this study was to evaluate the influence of printing orientation and model base design on the accuracy of implant position transfer. A standardized maxillary model with four implants was scanned using an intraoral scanner. Solid and hollow models were designed and printed using digital light processing (DLP) technology at orientations of 0°, 45°, and 90° (n = 10 per group). All models were digitized with a high-precision industrial scanner, and implant position deviations were determined by comparing corresponding reference points with the master model. Data were analyzed using two-way analysis of variance and post hoc tests (α = 0.05). Printing orientation significantly affected accuracy (p < 0.001). Models printed at 45° showed the highest deviations, whereas those printed at 0° and 90° exhibited comparable and superior accuracy. Model design (solid vs. hollow) had no significant influence at 0° and 90°, but hollow models were more accurate at 45° (p < 0.001). Mean deviations ranged from 131 μm to 382 μm. Printing at 0° or 90° is recommended, while 45° orientations should be avoided. Model design showed minimal effect on accuracy.

Dual agent dose-finding trials study the effect of a combination of more than one agent, where the objective is to find the Maximum Tolerated Dose Combination, the combination of doses of the two agents that is associated with a pre-specified risk of being unsafe. In a Phase I/II setting, the objective is to find a dose combination that is both safe and active, the Optimal Biological Dose, that optimises a criterion based on both safety and activity. Since Oncology treatments are typically given over multiple cycles, both the safety and activity outcome can be considered as late-onset, potentially occurring in the later cycles of treatment. This work proposes two model-based designs for dual-agent dose finding studies with late-onset activity and late-onset toxicity outcomes, the Joint time-to-event (TITE) partial order continual reassessment method and the Joint TITE Bayesian logistic regression model. Their performance is compared alongside a model-assisted comparator in a comprehensive simulation study motivated by a real trial example, with an extension to consider alternative sized dosing grids. It is found that both model-based methods outperform the model-assisted design. Whilst on average the two model-based designs are comparable, this comparability is not consistent across scenarios.

Model-based optimal experimental design for lithium-ion battery aging: Insights from a comprehensive multi-stage study with real-world data

Model-based disturbance-conscious design space exploration for drug substance flow synthesis using the Grignard reaction

Printing accuracy and post-mounting dimensional stability of 3D-printed hollow dental models with varying shell thicknesses and cross-arch bars.

Surrogate model-based design method for auxiliary intake door of tilt electric ducted fans

Research on an integrated hardware and software system concentrated mostly on design and development techniques. The expected result of the research was an integrated system. One of the key objectives of the project was to find a means of organising development activities considering hardware constraints and software capacities. This was essential as the number of initiatives aiming at bringing the digital logic and physical components of systems closer together has clearly increased recently. A careful analysis of co-design approaches revealed the major impact early-stage cooperation between the hardware and software teams had on the performance, efficiency, and scalability of the system. This assessment aimed to show the significant influence this teamwork produced. Among the effective techniques that can enable parallel development and concurrently lower integration risks under explored in this work are iterative prototyping, hardware/software co-simulation, and model-based design. Those were the approaches under investigation as possible, reasonably efficient ones. Among the main development tools and platforms investigated for their possible to increase system validation and speed up the development process were hardware description languages (HDLs), real-time operating systems (RTOS), and FPGA prototype environments. These platforms and technologies were proven to greatly help in development. Real-world case studies covering embedded systems, automotive control units, and Internet of Things applications were assessed in order to show the pragmatic advantages of integrated development approaches. Doing this aimed to show how well development plans might provide benefits.

Purpose. The purpose of the article is to develop a methodology for creating cone crusher adaptive control system based on the model-based design method for automated software generation of microprocessor controllers. Methodology. A method based on a block-oriented predictive model was used to generate control signals for the cone crusher. The parameters and structure of this model were identified in real time using measured data from the plant. A prototype of the control system was created in MATLAB/Simulink. Then, the model-based design method was used to generate software for digital signal processors. Mathematical statistics methods were employed to analyze the experimental results. Findings. A method of model-based design of an adaptive control system for a cone crusher has been developed. This system uses a predictive model of a block-oriented structure. This model adjusts the predictive controller’s structure and parameters directly during operation. This approach makes it possible to divide the functions of identifying the process model and generating controls between the two digital controllers. Consequently, the average computational time is reduced while ensuring the stabilization of the degree of homogeneity of ore crushing and separate output of the control size class, with respective standard deviation coefficients not exceeding 3.42 and 1.83 %, respectively. Originality. The regularity of the effect of the closed-side setting and the eccentric speed on the particle size distribution of crushed ore has been established. This shows that high homogeneity of the crushed product is ensured by simultaneously adjusting these input coordinates. We propose a new method for synthesizing an adaptive cone crusher control system based on a model-based design approach. This method provides automated real-time generation of software for microprocessor-based controllers, allowing the system to quickly adjust to changes in rock mass characteristics and other disturbances. Practical value. A hardware and software implementation of an adaptive control system for a cone crusher is proposed. This system is based on a block-oriented predictive model. The model ensures the stabilization of the required ore particle size distribution. This stabilization is achieved by adjusting the closed-side setting and the eccentric speed. The system is based on 16-bit, low-cost digital signal processors. A prototype of the system was tested in a crushing plant at a metallurgical enterprise.

This study presents the design of a high-efficiency pulse width modulation (PWM) power amplifier for marine biological sound reproduction. Due to the capacitive nature of underwater transducers and step-up transformers, output LC filter design is constrained, making it difficult to achieve a flat frequency response and low total harmonic distortion (THD). To address this, the electrical characteristics of these components were measured and modeled to construct equivalent circuits for the PSPICE simulator. Based on these models, an optimized LC filter was designed, and its performance was validated through simulation and experiments. The cause of THD occurring in specific frequency bands was analyzed, and two types of notch filters were applied to improve THD and switching signal attenuation. The proposed methodology offers a practical approach to improving PWM amplifier performance in underwater acoustic systems, supporting the development of compact, efficient, and reliable SONAR transmitters.

Abstract The interest in fast and robust scenario design tools is increasing more and more in view of the operation of future large tokamaks like ITER. In fact, an efficient design procedure can reduce costs and risks, and, in particular, a well designed ramp-up can have a positive influence on the quality of the entire pulse. Large part of the ramp-up success is due to the way magnetic control acts, and in particular to the design of current waveforms in the active control coils. Model based intra-shot optimization tools have been proved to be useful for the magnetic design of plasma initiation and early ramp-up scenarios with recent experiments on TCV and MAST-U. These are based on the assumption that plasma is nearly circular and the focus is on the evolution of plasma current and position. The possibility to extend such procedures to design an entire ramp-up with a focus on shape evolution including X-point formation is the subject of this paper. The proposed algorithm is based on iterative learning control concepts. After a first model-based design obtained with classical tools, the scenario is corrected step by step solving a linearly constrained quadratic optimization problem which makes use of the results of previous experiments, rapidly converging to an optimal solution shot after shot. The procedure has been applied to MAST-U demonstrating advantages also on the plasma performance, and the possibility to implement it with a reasonable computational time to be used every time a new ramp-up is proposed or in the early operations of new or refurbished tokamaks.

Superconducting motors offer high power density, compactness, and efficiency for hydrogen-powered cryo-electric aircraft, but AC operation in cryogenic temperatures produces thermal losses that must be estimated accurately and rapidly at the design stage to optimize efficiency, minimize cryogenic heat load, and maximize specific power density. Traditional modeling approaches fall short-Finite-element is too slow/costly for system-level models, analytical models and look-up tables lack accuracy/flexibility, and earlier intelligent models gave only cycle-averaged (static) losses. Here we demonstrate AI can rapidly and accurately predict dynamic AC losses for superconducting propulsion motors. Using a large dataset of motor configurations, our AI-driven approach both predicts cycle-averaged and time-dependent morphology of instantaneous AC loss waveforms across various operating conditions and generalizes to unseen designs. Integrated into system-level model-based design, these AI-surrogatemodels enable rapid model trials, compliance checks, and the discovery of integration issues within simulated environments before propulsion motor deployment. Our deep learning-based model achieves a prediction time of less than 9 ms with a 99.97% accuracy (R2), making it suitable for system-level modeling of electric powertrains in hydrogen-powered cryo-electric aircraft. Furthermore, we benchmarked 14 AI and 2 mathematical fitting techniques for estimating average AC losses, providing comparative performance analysis. The results highlightthat AI-based surrogate models enable high-accuracy, low-latency loss predictions to achieve optimal performancein superconducting propulsion motors in aircraft powertrain design.

Boosting Productivity in Scalable Continuous Grignard Reagent Formation via Model-Based Design of Experiments and Optimization

In response to modern-day learning and working demands, virtual studio learning environments (VsLEs) offer opportunities for idea exchange, collaborative reflection, and feedback that support self-directed learning, autonomous thinking, higher-order thinking, and flexibility. This study investigates technology acceptance among secondary school students to inform the instructional design of VsLEs that enhance scientific creativity. An extended Technology Acceptance Model (TAM) was employed, incorporating technological complexity (TC), perceived ease of use (PEU), perceived usefulness (PU), and social relationships (SR) as predictors of attitude toward use (ATU). Data were collected from 396 Thai upper secondary students. Results revealed that SR and TC significantly influenced PEU (β = 0.61, −0.54), SR and PEU influenced PU (β = 0.37, 0.39), and SR and PU influenced ATU (β = 0.19, 0.75). The findings provide empirical insights for designing student-centered digital learning environments that foster scientific creativity and cognitive engagement in upper secondary education.

Considering issues such as homogeneity among existing language learning apps and diminished user novelty, this study integrates emotional design principles into the innovative design of language learning apps to optimize their functionality and user experience. User needs for language learning apps were summarized through surveys. Following affective design principles, these needs were categorized into visceral, behavioral, and reflective levels. The Kano model was applied to classify and identify needs across these levels, clarify user demand attributes, assess importance, and conduct preliminary screening. Based on joint analysis, innovative design strategies were proposed for language learning apps that effectively address user learning needs and foster long-term emotional value binding. The Kano model-based affective design strategy for language learning apps provides a conceptual framework for designing such applications.

This study presents the modeling and simulation of shift control for the U340 automatic transmission using MATLAB–Simulink and Stateflow software based on a model-based design approach. The objective of this study is to build a system-level model combining physical blocks (engine, hydraulic torque converter, CR–CR planetary gearbox, powertrain – vehicle) and electro-hydraulic control blocks to evaluate the shift pattern, performance, and smoothness of the shifting process. The torque converter is modeled through dimensionless characteristics in the form of lookup tables in Simulink, while the transmission is simulated with discrete gear ratios corresponding to the closed/open states of the clutches and brakes. Simulation scenarios were performed in two cases: changing throttle opening while the vehicle was running on a flat road and fixing throttle opening but changing the simulated rolling resistance torque to simulate steep road conditions. Simulation results show that the model accurately simulates the shifting pattern of the U340 transmission; turbine shaft torque is significantly amplified in the low speed ratio range, improving acceleration capability, while pressure regulation and torque converter lock-up reduce speed fluctuations, enhancing transmission efficiency and smoothness during shifting. The proposed model can be used as an effective tool in research, teaching, and optimization of automatic transmission control strategies, as well as extended to more modern power transmission systems.

Model-based pre-compensator design for piezoelectric actuators based on physics guided neural network and physics-precision balanced training

Dynamical modeling of the thermal stress in the nozzle corner of reactor pressure vessels for model-based power maneuvering control design

System Identification of an Omnidirectional Test Vehicle for Model-Based Function Design

Mobility data for reduced uncertainties in model-based WWTP design