Design of mechanically intelligent structures: Review of modelling stimuli-responsive materials for adaptive structures.

Smart materials are upcoming in many industries due to their unique properties and wide range of applicability. These materials have the potential to transform traditional engineering practices by enabling the development of more efficient, adaptive, and responsive systems. However, smart materials are characterized by nonlinear behaviour and complex constitutive models, posing challenges in modelling and simulation. Therefore, understanding their mechanical properties is crucial for model-based design. This review aims for advancements in numerically implementing various smart materials, especially focusing on their nonlinear deformation behaviours. Different mechanisms and functionalities, classification, constitutive models and applications of smart materials were analyzed. In addition, different numerical approaches for modelling across scales were investigated. This review also explored the strategies and implementations for mechanically intelligent structures using smart materials. In conclusion, the potential model-based design methodology for the multiple smart material-based structures is proposed, which provides guidance for the future development of mechanically intelligent structures in industrial applications.

Model-based development of cell voltage control system for modified bio-electro-Fenton process.

A modified bio-electro-Fenton (M-BEF) process with a cell voltage control system that improves the efficiency of organic removal and energy savings is demonstrated. The M-BEF process can accomplish bioelectricity generation, H2O2 production, and the Fenton reaction in a continuous-flow reactor. During synthetic wastewater treatment containing biodegradable (glucose) and recalcitrant (biphenyl) organic matter, the effluent chemical oxygen demand (COD) concentration was maintained between 2 and 6 mg L-1. To investigate the impact of different operating schemes on energy usage, model-based design (MBD) modeling and simulations were performed, which showed that COD removal efficiency without an external voltage supply was unstable at < 70 %. The automatic cell voltage control system saved 90 % of the power compared to the continuous cell voltage supply system. Further testing on more environmental samples and pollutants will enable real-time optimization of supplied power and wastewater treatment using the cell voltage control system.

Evolving Improved Sampling Protocols for Dose-Response Modelling Using Genetic Algorithms with a Profile-Likelihood Metric.

Practical limitations of quality and quantity of data can limit the precision of parameter identification in mathematical models. Model-based experimental design approaches have been developed to minimise parameter uncertainty, but the majority of these approaches have relied on first-order approximations of model sensitivity at a local point in parameter space. Practical identifiability approaches such as profile-likelihood have shown potential for quantifying parameter uncertainty beyond linear approximations. This research presents a genetic algorithm approach to optimise sample timing across various parameterisations of a demonstrative PK-PD model with the goal of aiding experimental design. The optimisation relies on a chosen metric of parameter uncertainty that is based on the profile-likelihood method. Additionally, the approach considers cases where multiple parameter scenarios may require simultaneous optimisation. The genetic algorithm approach was able to locate near-optimal sampling protocols for a wide range of sample number (n = 3-20), and it reduced the parameter variance metric by 33-37% on average. The profile-likelihood metric also correlated well with an existing Monte Carlo-based metric (with a worst-case r > 0.89), while reducing computational cost by an order of magnitude. The combination of the new profile-likelihood metric and the genetic algorithm demonstrate the feasibility of considering the nonlinear nature of models in optimal experimental design at a reasonable computational cost. The outputs of such a process could allow for experimenters to either improve parameter certainty given a fixed number of samples, or reduce sample quantity while retaining the same level of parameter certainty.

Model-Based Design of Variable Stiffness Soft Gripper Actuated by Smart Hydrogels.

Soft grippers have shown their ability to grasp fragile and irregularly shaped objects, but they often require external mechanisms for actuation, limiting their use in large-scale situations. Their limited capacity to handle loads and deformations also restricts their customized grasping capabilities. To address these issues, a model-based soft gripper with adaptable stiffness was proposed. The proposed actuator comprises a silicone chamber with separate units containing hydrogel spheres. These spheres exhibit temperature-triggered swelling and shrinking behaviors. In addition, variable stiffness strips embedded in the units are introduced as the stiffness variation method. The validated finite element method model was used as the model-based design approach to describe the hydrogel behaviors and explore the affected factors on the bending performance. The results demonstrate that the actuator can be programmed to respond in a desired way, and the stiffness variation method enhances bending stiffness significantly. Specifically, a direct correlation exists between the bending angle and hydrogel sphere layers, with a maximum of 128° achieved. In addition, incorporating gap configurations into the chamber membrane results in a maximum threefold increase in the bending angle. Besides, the membrane type minimally impacts the bending angle from 21.3° to 24.6°. In addition, the embedded variable stiffness strips substantially increase stiffness, resulting in a 30-fold rise in bending stiffness. In conclusion, the novel soft gripper actuator enables substantial bending and stiffness control through active actuation, showcasing the potential for enhancing soft gripper performance in complex and multiscale grasping scenarios.

Modeling of analytical, preparative and industrial scale counter-current chromatography separations.

Analytical, preparative and industrial scale counter-current chromatography (CCC) processes differ in the volumes of the loaded solution of components to be separated and in the design of the equipment. Preliminary mathematical modeling is necessary for selection of the optimal design and operation mode of these CCC separations. This study aims to compare simulations of CCC separations at different scales, using an exact description based on the model of equilibrium cells and a much simpler approximate solution based on the Gaussian distribution. Equations for modeling CCC separations of different scales and examples of simulation these separations are presented. It is shown that the discrepancy between the two simulations increases with an increase in the volume of the loaded solution of the components and a decrease in the number of equilibrium cells of a CCC device. In analytical and preparative separations, which are based on complex centrifugal devices, and relatively small sample volumes are injected, approximate equations can be used to simulate various options of CCC separation. In industrial-scale CCC separations, large volumes of the solution of components may be loaded, and as we have proposed previously, these separations can be based on a cascade of mixer-settler extractors. In this case, a more accurate mathematical description based on the cell model equations should be used for modeling.

AIDE: Adaptive intrapatient dose escalation designs to accelerate Phase I clinical trials.

Designing Phase I clinical trials is challenging when accrual is slow or sample size is limited. The corresponding key question is: how to efficiently and reliably identify the maximum tolerated dose (MTD) using a sample size as small as possible? We propose model-assisted and model-based designs with adaptive intrapatient dose escalation (AIDE) to address this challenge. AIDE is adaptive in that the decision of conducting intrapatient dose escalation depends on both the patient's individual safety data, as well as other enrolled patient's safety data. When both data indicate reasonable safety, a patient may perform intrapatient dose escalation, generating toxicity data at more than one dose. This strategy not only provides patients the opportunity to receive higher potentially more effective doses, but also enables efficient statistical learning of the dose-toxicity profile of the treatment, which dramatically reduces the required sample size. Simulation studies show that the proposed designs are safe, robust, and efficient to identify the MTD with a sample size that is substantially smaller than conventional interpatient dose escalation designs. Practical considerations are provided and R code for implementing AIDE is available upon request.

Automated Firmware Generation for Compressive Sensing on Heterogeneous Hardware.

In this paper, a model-based firmware generator is presented towards complex sampling schemes. The framework is capable of automatically generating a fixed-rate Shannon-compliant acquisition scheme, as well as a variable-rate compressive sensing acquisition scheme. The generation starts from a model definition, which consists of two main components, namely an acquisition sequence to implement and the platform on which the sequence should be implemented. This model is then combined with the specifications to be transformed into a functional firmware. When generating firmware for compressive sensing (CS) purposes, the defined acquisition sequence is automatically generated to implement a pseudo-random sampling scheme in agreement with the defined undersampling factor. The evaluation of the generated firmware is done by means of an example use-case, including a proposed strategy for synchronization between CS setups. This research attempts to reduce the development complexity for embedded CS to lower the threshold towards effective usage in the field.

Model-Based Design of a Synthetic Oscillator Based on an Epigenetic Methylation Memory System.

Oscillations are an important component in biological systems; grasping their mechanisms and regulation, however, is difficult. Here, we use the theory of dynamical systems to support the design of oscillatory systems based on epigenetic control elements. Specifically, we use results that extend the Poincaré-Bendixson theorem for monotone control systems that are coupled to a negative feedback circuit. The methodology is applied to a synthetic epigenetic memory system based on DNA methylation that serves as a monotone control system, which is coupled to a negative feedback. This system is generally able to show sustained oscillations according to its structure; however, a first experimental implementation showed that fine-tuning of several parameters is required. We provide design support by exploring the experimental design space using systems-theoretic analysis of a computational model.

Mathematical Modelling in Plant Synthetic Biology.

Mathematical modelling techniques are integral to current research in plant synthetic biology. Modelling approaches can provide mechanistic understanding of a system, allowing predictions of behaviour and thus providing a tool to help design and analyse biological circuits. In this chapter, we provide an overview of mathematical modelling methods and their significance for plant synthetic biology. Starting with the basics of dynamics, we describe the process of constructing a model over both temporal and spatial scales and highlight crucial approaches, such as stochastic modelling and model-based design. Next, we focus on the model parameters and the techniques required in parameter analysis. We then describe the process of selecting a model based on tests and criteria and proceed to methods that allow closer analysis of the system's behaviour. Finally, we highlight the importance of uncertainty in modelling approaches and how to deal with a lack of knowledge, noisy data, and biological variability; all aspects that play a crucial role in the cooperation between the experimental and modelling components. Overall, this chapter aims to illustrate the importance of mathematical modelling in plant synthetic biology, providing an introduction for those researchers who are working with or working on modelling techniques.

Real time control and fabrication of a soft robotic glove by two parallel sensors with MBD approach.

Soft robotic gloves were designed to aid the rehabilitation process with hand pathologies and coordination of gripping exercises. The main issue in soft robotic actuators is to design a control strategy to overcome deformation in grasping exercises. In this paper, a new soft robotic actuator is developed to be protected against swell and deformation. This soft robotic glove is equipped with two sensors; these sensors make the robotic glove more intelligent. In the hardware, it was used two sensors in the new closed-loop method which include an air pressure sensor in the figure tip and a flex sensor to measure finger flexion rate. Two closed-loop control system is developed to regulate inlet air pressure and regulate the angle of the fingers for the soft robotic actuator. A Model-Based Design (MBD) method is presented as a very cost-effective, favorable, and robust method. PID programming on an embedded controller is applied by MBD approach. The soft actuator process contains a molded wooden chamber and fiber reinforcement. Experimental results show that the proposed soft robotic has a soft gripping mechanism, accurate gripping against various objects during daily activities.

Streamlining tablet lubrication design via model-based design of experiments.

In oral solid dosage production through direct compression powder lubrication must be carefully selected to facilitate the manufacturing of tablets without degrading product manufacturability and quality (e.g. dissolution). To do so, several semi-empirical models relating compression performance to process operating conditions have been developed. Among them, we consider an extension of the Kushner and Moore model (Kushner and Moore, 2010, International Journal Pharmaceutics, 399:19) that is useful for the purpose, but requires an extensive experimental campaign for parameters identification. This implies the preparation and compression of multiple powder blends, each one with a different lubrication extent. In turn, this translates into a considerable consumption of Active Pharmaceutical Ingredient (API), and into time-consuming experiments. We tackled this issue by proposing a novel model-based design of experiments (MBDoE) approach, which minimizes the number of optimal blends for model calibration, while obtaining statistically sound parameters estimates and model predictions. Both sequential and parallel MBDoE configurations were compared. Experimental results involving two placebo blends with different lubrication sensitivity showed that this methodology is able to reduce the experimental effort by 60-70% with respect to the standard industrial practice independently of the formulation considered and configuration (i.e. parallel vs. sequential) adopted.

Trade-Offs in Biosensor Optimization for Dynamic Pathway Engineering.

Recent progress in synthetic biology allows the construction of dynamic control circuits for metabolic engineering. This technology promises to overcome many challenges encountered in traditional pathway engineering, thanks to its ability to self-regulate gene expression in response to bioreactor perturbations. The central components in these control circuits are metabolite biosensors that read out pathway signals and actuate enzyme expression. However, the construction of metabolite biosensors is a major bottleneck for strain design, and a key challenge is to understand the relation between biosensor dose-response curves and pathway performance. Here we employ multiobjective optimization to quantify performance trade-offs that arise in the design of metabolite biosensors. Our approach reveals strategies for tuning dose-response curves along an optimal trade-off between production flux and the cost of an increased expression burden on the host. We explore properties of control architectures built in the literature and identify their advantages and caveats in terms of performance and robustness to growth conditions and leaky promoters. We demonstrate the optimality of a control circuit for glucaric acid production in Escherichia coli, which has been shown to increase the titer by 2.5-fold as compared to static designs. Our results lay the groundwork for the automated design of control circuits for pathway engineering, with applications in the food, energy, and pharmaceutical sectors.

Closed-Loop Recycling Dual-Mode Counter-Current Chromatography with Specified Sample Loading Durations: Modeling of Preparative and Industrial-Scale Separations.

We previously reported on a new counter-current chromatography (CCC) operating mode called closed-loop recycling dual-mode counter-current chromatography (CLR DM CCC), which incorporates the advantages of closed-loop recycling (CLR) and dual-mode (DM) counter-current chromatography and includes sequential separation of compounds in the closed-loop recycling mode with the mobile x-phase and in the inverted-phase counter-current mode with the mobile y-phase. The theoretical analysis of several implementations of this separation method was carried out under impulse sample injection conditions. This study is dedicated to the further development of CLR DM CCC theory applied to preparative and industrial separations, where high-throughput operation is required. Large sample volumes can be loaded via continuous loading within a specified time. To simulate CLR DM CCC separations with specified sample loading durations, equations are developed and presented in "Mathcad" software.

Early completion of phase I cancer clinical trials with Bayesian optimal interval design.

Phase I cancer clinical trials have been proposed novel designs such as algorithm-based, model-based, and model-assisted designs. Model-based and model-assisted designs have a higher identification rate of maximum tolerated dose (MTD) than algorithm-based designs, but are limited by the fact that the sample size is fixed. Hence, it would be very attractive to estimate the MTD with sufficient accuracy and complete the trial early. O'Quigley proposed the early completion of a trial with the continual reassessment method among model-based designs when the MTD is estimated with sufficient accuracy. However, the proposed early completion method based on the binary outcome trees has a problem that the calculation cost is high when the number of remaining patients is large. Among model-assisted designs, the Bayesian optimal interval (BOIN) design provides the simplest approach for dose adjustment. We propose the novel early completion method for the clinical trials with the BOIN design when the MTD is estimated with sufficient accuracy. This completion method can be easily calculated. In addition, the method does not require many more patients treated for the determination of early completion. We confirm that the BOIN design applying the early completion method has almost the same MTD identification rate compared to the BOIN design through simulations conducted based on over 30 000 scenarios.

Incorporating patient concerns into design requirements for IoMT-based systems: The fall detection case study.

Internet of Medical Things (IoMT) systems are envisioned to provide high-quality healthcare services to patients in the comfort of their home, utilizing cutting-edge Internet of Things (IoT) technologies and medical sensors. Patient comfort and willingness to participate in such efforts is a prominent factor for their adoption. As IoT technology has provided solutions for all technical issues, patient concerns are those that seem to restrict their wider adoption. To enhance patient awareness of the system properties and enhance their willingness to adopt IoMT solutions, this paper presents a novel methodology to integrate patient concerns in the design requirements of such systems. It comprises a number of straightforward steps that an IoMT designer can follow, starting from identifying patient concerns, incorporating them in system design requirements as criticalities, proceeding to system implementation and testing, and finally, verifying that it fulfills the concerns of the patients. To showcase the effectiveness of the proposed methodology, the paper applies it in the design and implementation of a fall detection system for elderly patients remotely monitored in their homes.

Model-assisted approaches for continuous chromatography: Current situation and challenges.

Continuous bioprocessing is a promising trend in biopharmaceutical production, and multi-column continuous chromatography shows advantages of high productivity, high resin capacity utilization, small footprint, low buffer consumption and less waste. Due to the complexity and dynamic nature of continuous processing, traditional experiment-based approaches are often time-consuming and inefficient. In this review, model-assisted approaches were focused and their applications in continuous chromatography process development, validation and control were discussed. Chromatographic models are useful in describing particular process performances of continuous capture and polishing with multi-column chromatography. Model-assisted tools showed powerful ability in evaluating multiple operating parameters and identifying optimal points over the entire design space. The residence time distribution models, model-assisted process analytical technologies and model-predictive control strategies were also developed to reveal the propagation of disturbances, enhance real time monitor and achieve adaptive control to match the transient disturbances and deviations of continuous processes. Moreover, artificial neural networks and machine learning concepts were integrated into modeling approaches to improve data treatment. In general, further development in research and applications of model-assisted approaches for continuous chromatography are needed urgently to support the continuous manufacturing.

A Model-Based Design Floating-Point Accumulator. Case of Study: FPGA Implementation of a Support Vector Machine Kernel Function.

Recent research in wearable sensors have led to the development of an advanced platform capable of embedding complex algorithms such as machine learning algorithms, which are known to usually be resource-demanding. To address the need for high computational power, one solution is to design custom hardware platforms dedicated to the specific application by exploiting, for example, Field Programmable Gate Array (FPGA). Recently, model-based techniques and automatic code generation have been introduced in FPGA design. In this paper, a new model-based floating-point accumulation circuit is presented. The architecture is based on the state-of-the-art delayed buffering algorithm. This circuit was conceived to be exploited in order to compute the kernel function of a support vector machine. The implementation of the proposed model was carried out in Simulink, and simulation results showed that it had better performance in terms of speed and occupied area when compared to other solutions. To better evaluate its figure, a practical case of a polynomial kernel function was considered. Simulink and VHDL post-implementation timing simulations and measurements on FPGA confirmed the good results of the stand-alone accumulator.

Use of Contemporary Groups in the Construction of Multi-Environment Trial Datasets for Selection in Plant Breeding Programs.

Plant breeding programs use multi-environment trial (MET) data to select superior lines, with the ultimate aim of increasing genetic gain. Selection accuracy can be improved with the use of advanced statistical analysis methods that employ informative models for genotype by environment interaction, include information on genetic relatedness and appropriately accommodate within-trial error variation. The gains will only be achieved, however, if the methods are applied to suitable MET datasets. In this paper we present an approach for constructing MET datasets that optimizes the information available for selection decisions. This is based on two new concepts that characterize the structure of a breeding program. The first is that of "contemporary groups," which are defined to be groups of lines that enter the initial testing stage of the breeding program in the same year. The second is that of "data bands," which are sequences of trials that correspond to the progression through stages of testing from year to year. MET datasets are then formed by combining bands of data in such a way as to trace the selection histories of lines within contemporary groups. Given a specified dataset, we use the A-optimality criterion from the model-based design literature to quantify the information for any given selection decision. We demonstrate the methods using two motivating examples from a durum and chickpea breeding program. Datasets constructed using contemporary groups and data bands are shown to be superior to other forms, in particular those that relate to a single year alone.

The i3+3 design for phase I clinical trials.

The traditional rule-based design, 3 + 3, has been shown to be less likely to achieve the objectives of dose-finding trials when compared with model-based designs. We propose a new rule-based design called i3 + 3, which is based on simple but more advanced rules that account for the variabilities in the observed data. We compare the operating characteristics for the proposed i3 + 3 design with other popular phase I designs by simulation. The i3 + 3 design is far superior than the 3 + 3 design in trial safety and the ability to identify the true MTD. Compared with model-based phase I designs, i3 + 3 also demonstrates comparable performances.

Modeling of closed-loop recycling dual-mode counter-current chromatography based on non-ideal recycling model.

Closed-loop recycling dual-mode counter-current chromatography (CLR DM CCC) includes two separation stages: 1 - closed-loop recycling separation of solutes with mobile x-phase (CLR CCC); 2 - separation of solutes with the mobile y-phase in the opposite flow direction. Previous analysis of CLR DM CCC separations has been limited to the ideal recycling model, which neglects extra-column dispersion. In this study, the analysis of CLR CCC separations is based on the non-ideal recycling model, which takes into account the extra-column dispersion caused by the recycling system. This is of great practical importance, since by selecting the optimal parameters of the recycling system the separation can be significantly improved. Comparative analysis of CLR CCC and CLR DM CCC separations has shown that at low separations factors compounds with low partition coefficients can be separated by CLR CCC using recycling systems with a long recycling line; the separation of compounds with high partition coefficients and the separation of complex mixtures can be performed by CLR DM CCC. Simple equations for simulation and design of CLR DM CCC separations are developed. Several variants of the implementation of this separation method are discussed; examples of simulation are presented in "Mathcad" program.