Designing suitable shoes to prevent foot frostbite through optimization of the geometric dimensions of the shoe and sock model

To prevent frostbite in cold environments, proper dimensions and materials for different parts of shoes along with the optimal design of shoe geometry were investigated. Furthermore, the optimal geometry of shoes was computed using an optimization algorithm to provide maximum thermal protection for the foot while having the lowest weight. The results showed that the length of the shoe sole and the thickness of the sock are the most effective parameters in foot protection against frostbite. Using thicker socks, which only increased the weight by roughly 11%, enhanced the minimum foot temperature by more than 2.3 times. HIGHLIGHTSOptimal design of shoe geometry is used to prevent frostbite in cold environments.A model of a biothermal nonlinear model is developed for the barefoot.Length of the shoe sole and the thickness of the sock are the most effective parameters in protecting the foot against frostbite.For the selected weather conditions, the toes are most likely to have frostbite.The best shoe for the selected weather conditions is the shoe that has the highest amount of thermal insulation in the toe area.

Statistical considerations in model-based dose finding for binary responses under model uncertainty

The statistical methodology for model-based dose finding under model uncertainty has attracted increasing attention in recent years. While the underlying principles are simple and easy to understand, developing and implementing an efficient approach for binary responses can be a formidable task in practice. Motivated by the statistical challenges encountered in a phase II dose finding study, we explore several key design and analysis issues related to the hybrid testing-modeling approaches for binary responses. The issues include candidate model selection and specifications, optimal design and efficient sample size allocations, and, notably, the methods for dose-response testing and estimation. Specifically, we consider a class of generalized linear models suited for the candidate set and establish D-optimal designs for these models. Additionally, we propose using permutation-based tests for dose-response testing to avoid asymptotic normality assumptions typically required for contrast-based tests. We perform trial simulations to enhance our understanding of these issues.

Planning stepped wedge cluster randomized trials to detect treatment effect heterogeneity

Stepped wedge design is a popular research design that enables a rigorous evaluation of candidate interventions by using a staggered cluster randomization strategy. While analytical methods were developed for designing stepped wedge trials, the prior focus has been solely on testing for the average treatment effect. With a growing interest on formal evaluation of the heterogeneity of treatment effects across patient subpopulations, trial planning efforts need appropriate methods to accurately identify sample sizes or design configurations that can generate evidence for both the average treatment effect and variations in subgroup treatment effects. To fill in that important gap, this article derives novel variance formulas for confirmatory analyses of treatment effect heterogeneity, that are applicable to both cross-sectional and closed-cohort stepped wedge designs. We additionally point out that the same framework can be used for more efficient average treatment effect analyses via covariate adjustment, and allows the use of familiar power formulas for average treatment effect analyses to proceed. Our results further sheds light on optimal design allocations of clusters to maximize the weighted precision for assessing both the average and heterogeneous treatment effects. We apply the new methods to the Lumbar Imaging with Reporting of Epidemiology Trial, and carry out a simulation study to validate our new methods.

Design, optimization, and inference of multiphasic decay of infectious virus particles

The loss of virus particles is typically considered to arise from a first-order kinetic process. Signals of deviations from this exponential decay are often de-prioritized. Here, we propose methods to evaluate if a design is adequate to evaluate evidence for multiphasic virus particle decay and to optimize the sampling times of decay experiments, accounting for uncertainties in viral kinetics. First, we evaluate 1500 synthetic scenarios of biphasic decays, with varying decay rates and initial proportions of subpopulations. Robust inference of multiphasic decay is more likely when the faster decaying subpopulation predominates insofar as early samples are taken to resolve the faster decay rate. Overall, we find that design optimization leads to a better precision of estimation while reducing the number of samples. It helps to estimate adequately the fastest decay in 54% of situations vs. 41% using a non-optimized design. We then apply these methods to infer multiple decay rates associated with the decay of ΦD9, an evolved isolate derived from phage Φ21. A pilot experiment confirmed that ΦD9 decay is multiphasic, but was unable to resolve the rate or proportion of the fast decay subpopulation(s). We then applied optimal design methods to propose new ΦD9 sampling times. Using this strategy, we were able to robustly estimate both decay rates and their respective subpopulations. Notably, we conclude that the vast majority (94%) of the population decays at a rate 16-fold higher than a slow decaying population. Altogether, these results provide methods to quantitatively estimate heterogeneity in viral decay.

Finite element model with realistic bone geometries for the optimal design of internal fixation during the fibula healing process

A finite element model with realistic bone geometries is developed to design optimal internal fixation during the fibula healing process in this study. The effect of bone plate parameters on fibula fracture healing is studied. The relationship between differences in plate length, thickness and working length, and bone healing performance is focused. The optimal combination form of the bone plate parameters was selected by the orthogonal experimental design and fracture block strain to achieve bone healing maximize the performance. The model results show that the maximum equivalent force of the bone plate was below the material yield limit; the higher mean contact stresses in the bone fragments indicate that the bone plate is prone to higher contact stresses when they are long. The working length of the bone plate has a greater effect on callus healing than the thickness and length of the bone plate. The optimal internal fixation option for distal fibula fractures is achieved when it provides the stability required for internal fixation during bone healing. It ensures lower contact stresses in the fibula as well as maximum Young's modulus during callus healing process.

Bayesian optimal stepped wedge design

Recently, there has been a growing interest in designing cluster trials using stepped wedge design (SWD). An SWD is a type of cluster-crossover design in which clusters of individuals are randomized unidirectional from a control to an intervention at certain time points. The intraclass correlation coefficient (ICC) that measures the dependency of subject within a cluster plays an important role in design and analysis of stepped wedge trials. In this paper, we discuss a Bayesian approach to address the dependency of SWD on the ICC and robust Bayesian SWDs are proposed. Bayesian design is shown to be more robust against the misspecification of the parameter values compared to the locally optimal design. Designs are obtained for the various choices of priors assigned to the ICC. A detailed sensitivity analysis is performed to assess the robustness of proposed optimal designs. The power superiority of Bayesian design against the commonly used balanced design is demonstrated numerically using hypothetical as well as real scenarios.

Designing multicenter individually randomized group treatment trials

In an individually randomized group treatment (IRGT) trial, participant outcomes can be positively correlated due to, for example, shared therapists in treatment delivery. Oftentimes, because of limited treatment resources or participants at one location, an IRGT trial can be carried out across multiple centers. This design can be subject to potential correlations in the participant outcomes between arms within the same center. While the design of a single-center IRGT trial has been studied, little is known about the planning of a multicenter IRGT trial. To address this gap, this paper provides analytical sample size formulas for designing multicenter IRGT trials with a continuous endpoint under the linear mixed model framework. We found that accounting for the additional center-level correlation at the design stage can lead to sample size reduction, and the magnitude of reduction depends on the amount of between-therapist correlation. However, if the variance components of therapist-level random effects are considered as input parameters in the design stage, accounting for the additional center-level variance component has no impact on the sample size estimation. We presented our findings through numeric illustrations and performed simulation studies to validate our sample size procedures under different scenarios. Optimal design configurations under the multicenter IRGT trials have also been discussed, and two real-world trial examples are drawn to illustrate the use of our method.

Design, implementation, and analysis of a compressed sensing photoacoustic projection imaging system

Significance

Compressed sensing (CS) uses special measurement designs combined with powerful mathematical algorithms to reduce the amount of data to be collected while maintaining image quality. This is relevant to almost any imaging modality, and in this paper we focus on CS in photoacoustic projection imaging (PAPI) with integrating line detectors (ILDs).

Aim

Our previous research involved rather general CS measurements, where each ILD can contribute to any measurement. In the real world, however, the design of CS measurements is subject to practical constraints. In this research, we aim at a CS-PAPI system where each measurement involves only a subset of ILDs, and which can be implemented in a cost-effective manner.

Approach

We extend the existing PAPI with a self-developed CS unit. The system provides structured CS matrices for which the existing recovery theory cannot be applied directly. A random search strategy is applied to select the CS measurement matrix within this class for which we obtain exact sparse recovery.

Results

We implement a CS PAPI system for a compression factor of 4:3, where specific measurements are made on separate groups of 16 ILDs. We algorithmically design optimal CS measurements that have proven sparse CS capabilities. Numerical experiments are used to support our results.

Conclusions

CS with proven sparse recovery capabilities can be integrated into PAPI, and numerical results support this setup. Future work will focus on applying it to experimental data and utilizing data-driven approaches to enhance the compression factor and generalize the signal class.

Combined translational pharmacometrics approach to support the design and conduct of the first-in-human study of DWP16001

AIMS

The objective of this study was to characterize the pharmacokinetics (PK)/pharmacodynamics (PD) of DWP16001, a novel sodium-glucose cotransporter 2 inhibitor, and predict efficacious doses for the first-in-human study using various translational approaches.

METHODS

A mechanistic PK/PD model was developed for DWP16001 using nonlinear mixed-effect modelling to describe animal PK/PD properties. Using allometry and in silico physiologically based equations, human PK parameters were predicted. Human PD parameters were scaled by applying interspecies difference and in vitro drug-specific factors. Human parameters were refined using early clinical data. Model-predicted PK and PD outcomes were compared to observations before and after parameter refinement.

RESULTS

The PK/PD model of DWP16001 was developed using a 2-compartment model with first-order absorption and indirect response. Efficacious doses of 0.3 and 2 mg of DWP16001 were predicted using human half-maximal inhibitory concentration values translated from Zucker Diabetic Fatty rats and normal rats, respectively. After parameter refinement, doses of 0.2 and 1 mg were predicted to be efficacious for each disease model, which improved the prediction results to within a 1.2-fold difference between the model prediction and observation.

CONCLUSIONS

This study predicted efficacious human doses of DWP16001 using population PK/PD modelling and a combined translational pharmacometrics approach. Early clinical data allowed the methods used to translate in vitro and in vivo findings to clinical PK/PD values for DWP16001 to be optimized. This study has shown that a refinement step can be readily applied to improve model prediction and further support the study design and conduct of a first-in-human study.

Qubit Adoption Method of a Quantum Computing-Based Metaheuristics Algorithm for Truss Structures Analysis

Since the mention of the Fourth Industrial Revolution in 2016, quantum computers and quantum computing (QC) have emerged as key technologies. Many researchers are trying to realize quantum computers and quantum computing. In particular, most of the development and application of metaheuristics algorithms using quantum computing is focused on computer engineering fields. Cases in which the developed algorithm is applied to the optimal design of a building or the optimal design results presented by expanding the algorithm in various directions are very insufficient. Therefore, in this paper, we proposed four methods of adopting qubits to perform pitch adjusting in the optimization process of the QbHS (quantum-based harmony search) algorithm and applied it to TTO (truss topology optimization) using four methods to compare the results. The four methods of adopting qubits have the same or decreased number of qubits adopted as the number of iterations changes. As a result of applying TTO using four methods, convergence performance differed depending on the adoption method, and convergence performance was superior to conventional HS (harmony search) algorithms in all methods. The optimal design of structural engineering using such QC is expected to contribute to the revitalization of future technologies in the architectural field and the field of computer information systems.

Model-Based Design to Enhance Neotissue Formation in Additively Manufactured Calcium-Phosphate-Based Scaffolds

In biomaterial-based bone tissue engineering, optimizing scaffold structure and composition remains an active field of research. Additive manufacturing has enabled the production of custom designs in a variety of materials. This study aims to improve the design of calcium-phosphate-based additively manufactured scaffolds, the material of choice in oral bone regeneration, by using a combination of in silico and in vitro tools. Computer models are increasingly used to assist in design optimization by providing a rational way of merging different requirements into a single design. The starting point for this study was an in-house developed in silico model describing the in vitro formation of neotissue, i.e., cells and the extracellular matrix they produced. The level set method was applied to simulate the interface between the neotissue and the void space inside the scaffold pores. In order to calibrate the model, a custom disk-shaped scaffold was produced with prismatic canals of different geometries (circle, hexagon, square, triangle) and inner diameters (0.5 mm, 0.7 mm, 1 mm, 2 mm). The disks were produced with three biomaterials (hydroxyapatite, tricalcium phosphate, and a blend of both). After seeding with skeletal progenitor cells and a cell culture for up to 21 days, the extent of neotissue growth in the disks' canals was analyzed using fluorescence microscopy. The results clearly demonstrated that in the presence of calcium-phosphate-based materials, the curvature-based growth principle was maintained. Bayesian optimization was used to determine the model parameters for the different biomaterials used. Subsequently, the calibrated model was used to predict neotissue growth in a 3D gyroid structure. The predicted results were in line with the experimentally obtained ones, demonstrating the potential of the calibrated model to be used as a tool in the design and optimization of 3D-printed calcium-phosphate-based biomaterials for bone regeneration.

Optimizing treatment allocation in randomized clinical trials by leveraging baseline covariates

We consider the problem of optimizing treatment allocation for statistical efficiency in randomized clinical trials. Optimal allocation has been studied previously for simple treatment effect estimators such as the sample mean difference, which are not fully efficient in the presence of baseline covariates. More efficient estimators can be obtained by incorporating covariate information, and modern machine learning methods make it increasingly feasible to approach full efficiency. Accordingly, we derive the optimal allocation ratio by maximizing the design efficiency of a randomized trial, assuming that an efficient estimator will be used for analysis. We then expand the scope of optimization by considering covariate-dependent randomization (CDR), which has some flavor of an observational study but provides the same level of scientific rigor as a standard randomized trial. We describe treatment effect estimators that are consistent, asymptotically normal, and (nearly) efficient under CDR, and derive the optimal propensity score by maximizing the design efficiency of a CDR trial (under the assumption that an efficient estimator will be used for analysis). Our optimality results translate into optimal designs that improve upon standard practice. Real-world examples and simulation results demonstrate that the proposed designs can produce substantial efficiency improvements in realistic settings.

Design and Fabrication of a High-Sensitivity and Wideband Cymbal Hydrophone

So far, cymbal transducers have been developed primarily for transmitting purposes, and even when used for receiving, the focus has been mostly on improving the receiving sensitivity. In this study, we developed a cymbal hydrophone with a higher sensitivity and a wider bandwidth than other existing hydrophones. First, the initial structure of the cymbal hydrophone was established, and then the effects of structural variables on the hydrophone's performance were analyzed using the finite element method. Based on the analysis results, the structure having the highest sensitivity and widest bandwidth, with a receiving voltage sensitivity level above a certain threshold, was derived using optimal design techniques. A prototype of the cymbal hydrophone with the designed structure was fabricated, and its performance was measured, validating the effectiveness of the design by comparing the measurement results with the design values. The developed cymbal hydrophone is expected to be utilized in various underwater precision measurements, as it possesses a significantly broader reception frequency bandwidth when compared with other hydrophones used for the same purpose.

Low-CO2 Optimization Design of Quaternary Binder Containing Calcined Clay, Slag, and Limestone

Blended cement is commonly used for producing sustainable concretes. This paper presents an experimental study and an optimization design of a low-CO2 quaternary binder containing calcined clay, slag, and limestone using the response surface method. First, a Box-Behnken design with three influencing factors and three levels was used for the combination design of the quaternary composite cement. The lower limit of the mineral admixtures was 0%. The upper limits of slag, calcined clay, and limestone powder were 30%, 20%, and 10%, respectively. The water-to-binder ratio (water/binder) was 0.5. Experimental works to examine workability and strength (at 3 and 28 days) were performed for the composite cement. The CO2 emissions were calculated considering binder compositions. A second-order polynomial regression was used to evaluate the experimental results. In addition, a low-CO2 optimization design was conducted for the composite cement using a composite desirability function. The objectives of the optimization design were the target 28-day strength (30, 35, 40, and 45 MPa), target workability (160 mm flow), and low CO2 emissions. The trends of the properties of optimal combinations were consistent with those in the test results. In summary, the proposed optimization design can be used for designing composite cement considering strength, workability, and ecological aspects.

Optimal Design of Galvanic Vestibular Stimulation for Patients with Vestibulopathy and Cerebellar Disorders

OBJECTIVES

Galvanic vestibular stimulation (GVS) has shown positive outcomes in various neurological and psychiatric disorders, such as enhancing postural balance and cognitive functions. In order to expedite the practical application of GVS in clinical settings, our objective was to determine the best GVS parameters for patients with vestibulopathy and cerebellar disorders using optimal design calculation.

METHODS

A total of 31 patients (26 males, mean age 57.03 ± 14.75 years, age range 22-82 years) with either unilateral or bilateral vestibulopathy (n = 18) or cerebellar ataxia (n = 13) were enrolled in the study. The GVS intervention included three parameters, waveform (sinusoidal, direct current [DC], and noisy), amplitude (0.4, 0.8, and 1.2 mA), and duration of stimulation (5 and 30 min), resulting in a total of 18 GVS intervention modes as input variables. To evaluate the effectiveness of GVS, clinical vertigo and gait assessments were conducted using the Dizziness Visual Analogue Scale (D-VAS), Activities-specific Balance Confidence Scale (ABC), and Scale for Assessment and Rating of Ataxia (SARA) as output variables. Optimal design and local sensitivity analysis were employed to determine the most optimal GVS modes.

RESULTS

Patients with unilateral vestibulopathy experienced the most favorable results with either noisy or sinusoidal GVS at 0.4 mA amplitude for 30 min, followed by DC GVS at 0.8 mA amplitude for 5 min. Noisy GVS at 0.8 or 0.4 mA amplitude for 30 min demonstrated the most beneficial effects in patients with bilateral vestibulopathy. For patients with cerebellar ataxia, the optimal choices were noisy GVS with 0.8 or 0.4 mA amplitude for 5 or 30 min.

CONCLUSIONS

This study is the first to utilize design optimization methods to identify the GVS stimulation parameters that are tailored to individual-specific characteristics of dizziness and imbalance. A sensitivity analysis was carried out along with the optimal design to offset the constraints of a limited sample size, resulting in the identification of the most efficient GVS modes for patients suffering from vestibular and cerebellar disorders.

An empirical study on the collaborative usability of age-appropriate smart home interface design

Introduction

The smart home has become a popular product, but with the development of the aging population, the differentiated characteristics of the elderly smart home products in terms of demand and use are becoming more and more significant. The existing smart products are complicated to operate and cumbersome to interact with, which increases the cognitive load of the elderly group and hinders the daily use and user experience feeling of the elderly. The purpose of this paper is to study the interface data information and interface visual design starting from hardware and software, interface interaction, to explore the better interface data information and interface visual design, and to output, a new prototype of the operating interface of smart home system for the elderly, so that the smart products can be better used by the elderly.

Methods

Thirty-two participants aged 55-75 were invited to conduct the test, and subjective evaluation was conducted at the end of the test. Through the tests, the operability of the prototype structure for smart furniture systems for the elderly was demonstrated.

Results

In terms of functionality a new task based on a combination of icons and text is proposed. In the control of devices, the switching status of devices, etc., needs to be clearly distinguished visually, eye-protective bright colors are used, paired with low saturation to highlight the focus, and high bright colors with gray to distinguish the device status. In terms of the density of the content, an appropriate proportion of images and text were used to make the information less dense. ln the arrangement of web content, information content relevant to users was placed first as much as possible.

Discussion

Based on this, a secondary optimal design was carried out to improve the interactive design of the smart home for the elderly and output it as a prototype interactive interface. Thus, the operability, rationality, and aesthetic comfort of the prototype design of smart home interaction in an age-friendly scenario are improved, allowing the elderly to have a better experience when using the smart home.

Two-stage response adaptive randomization designs for multi-arm trials with binary outcome

In recent years, adaptive randomization methods have gained significant popularity in clinical research and trial design due to their ability to provide both efficiency and flexibility in adjusting the statistical procedures of ongoing clinical trials. For a study to compare multiple treatments, a multi-arm two-stage design could be utilized to select the best treatment from the first stage and further compare that treatment with control in the second stage. The traditional design used equal randomization in both stages. To better utilize the interim results from the first stage, we propose to develop response adaptive randomization two-stage designs for a multi-arm clinical trial with binary outcome. Two allocation methods are considered: (1) an optimal allocation based on a sequential design; (2) the play-the-winner rule. Optimal multi-arm two-stage designs are obtained under three criteria: minimizing the expected number of failures, minimizing the average expected sample size, and minimizing the expected sample size under the null hypothesis. Simulation studies show that the proposed adaptive design based on the play-the-winner rule has good performance. A phase II trial for patients with pancreas adenocarcinoma and a germline BRCA/ PALB2 mutation was used to illustrate the application of the proposed response adaptive randomization designs.

Effect of the Laying Order of Core Layer Materials on the Sound-Insulation Performance of High-Speed Train Carbody

The design of sound-insulation schemes requires the development of new materials and structures while also paying attention to their laying order. If the sound-insulation performance of the whole structure can be improved by simply changing the laying order of materials or structures, it will bring great advantages to the implementation of the scheme and cost control. This paper studies this problem. First, taking a simple sandwich composite plate as an example, a sound-insulation prediction model for composite structures was established. The influence of different material laying schemes on the overall sound-insulation characteristics was calculated and analyzed. Then, sound-insulation tests were conducted on different samples in the acoustic laboratory. The accuracy of the simulation model was verified through a comparative analysis of experimental results. Finally, based on the sound-insulation influence law of the sandwich panel core layer materials obtained from simulation analysis, the sound-insulation optimization design of the composite floor of a high-speed train was carried out. The results show that when the sound absorption material is concentrated in the middle, and the sound-insulation material is sandwiched from both sides of the laying scheme, it represents a better effect on medium-frequency sound-insulation performance. When this method is applied to the sound-insulation optimization of a high-speed train carbody, the sound-insulation performance of the middle and low-frequency band of 125-315 Hz can be improved by 1-3 dB, and the overall weighted sound reduction index can be improved by 0.9 dB without changing the type, thickness or weight of the core layer materials.

A Multi-Modal Under-Sensorized Wearable System for Optimal Kinematic and Muscular Tracking of Human Upper Limb Motion

Wearable sensing solutions have emerged as a promising paradigm for monitoring human musculoskeletal state in an unobtrusive way. To increase the deployability of these systems, considerations related to cost reduction and enhanced form factor and wearability tend to discourage the number of sensors in use. In our previous work, we provided a theoretical solution to the problem of jointly reconstructing the entire muscular-kinematic state of the upper limb, when only a limited amount of optimally retrieved sensory data are available. However, the effective implementation of these methods in a physical, under-sensorized wearable has never been attempted before. In this work, we propose to bridge this gap by presenting an under-sensorized system based on inertial measurement units (IMUs) and surface electromyography (sEMG) electrodes for the reconstruction of the upper limb musculoskeletal state, focusing on the minimization of the sensors' number. We found that, relying on two IMUs only and eight sEMG sensors, we can conjointly reconstruct all 17 degrees of freedom (five joints, twelve muscles) of the upper limb musculoskeletal state, yielding a median normalized RMS error of 8.5% on the non-measured joints and 2.5% on the non-measured muscles.

Optimal Design of Bubble Deck Concrete Slabs: Sensitivity Analysis and Numerical Homogenization

The use of layered or hollow floors in the construction of buildings obviously reduces the self-weight of the slab, and their design requires some expertise. In the present work, a sensitivity analysis and numerical homogenization were used to select the most important characteristics of bubble deck floors that have a direct or indirect impact on their load capacity. From the extensive case study, conclusions were drawn regarding the optimal selection of geometry, materials, and the arrangement and size of air voids in such a way as to ensure high stiffness of the cross-section and at the same time maximally reduce the self-weight of the slabs. The conducted analyses showed that the height of the slab and the geometry of the voids had the greatest impact on the load-bearing capacity. The concrete class and reinforcement used are of secondary importance in the context of changes in load-bearing capacity. Both the type of steel and the amount of reinforcement has a rather small or negligible influence on the bubble deck stab stiffness. Of course, the geometry of the voids and their arrangement and shape have the greatest influence on the drop in the self-weight of the floor slabs. Based on the presented results of the sensitivity analysis combined with numerical homogenization, a set of the most important design parameters was ordered and selected for use in the optimization procedure.

Bayesian optimal phase II designs with dual-criterion decision making

The conventional phase II trial design paradigm is to make the go/no-go decision based on the hypothesis testing framework. Statistical significance itself alone, however, may not be sufficient to establish that the drug is clinically effective enough to warrant confirmatory phase III trials. We propose the Bayesian optimal phase II trial design with dual-criterion decision making (BOP2-DC), which incorporates both statistical significance and clinical relevance into decision making. Based on the posterior probability that the treatment effect reaches the lower reference value (statistical significance) and the clinically meaningful value (clinical significance), BOP2-DC allows for go/consider/no-go decisions, rather than a binary go/no-go decision. BOP2-DC is highly flexible and accommodates various types of endpoints, including binary, continuous, time-to-event, multiple, and coprimary endpoints, in single-arm and randomized trials. The decision rule of BOP2-DC is optimized to maximize the probability of a go decision when the treatment is effective or minimize the expected sample size when the treatment is futile. Simulation studies show that the BOP2-DC design yields desirable operating characteristics. The software to implement BOP2-DC is freely available at www.trialdesign.org.

The optimal design of 3D-printed lattice bone plate by considering fracture healing mechanism

The biomechanical stimulus is the most important factor for fracture healing and mainly determined by the structural stiffness of bone plate. Currently, the materials commonly used in bone plates are stainless steel and titanium, which often lead to stress shielding effects because of their higher elastic modulus compared with the bone. This article suggests an optimal design method of lattice bone plate based on fracture healing theory. First, the mechanical regulation model with deviatoric strain is established to simulate the tissue differentiation process during fracture healing process. The ratio of the average elastic modulus of callus at the 120th day to the elastic modulus of mature bone is used to characterize the fracture healing rate. Second, the optimal elastic modulus of the design domain is obtained by the optimization mathematical model with the maximum fracture healing rate. Then, the design domain is filled with microstructures, the porosity of which is adjusted to make it possible that the equivalent elastic modulus is equal to the optimized value. And the finite element analysis of the bone plate with microstructure is executed. Finally, the designed lattice bone plates are manufactured through 3D printing, and the mechanical test is carried out. The simulation results indicate that the fracture healing rate is maximum when the elastic modulus of material in design domain is 38 GPa under the constraints of fixation stability. And both the finite element analysis and experiment results show that the designed lattice bone plate meet the strength requirements of fracture internal fixation implants.

Optimization of the two-stage group sequential three-arm gold-standard design for non-inferiority trials

If design parameters are chosen appropriately, group sequential trial designs are known to be able to reduce the expected sample size under the alternative hypothesis compared to single-stage designs. The same holds true for the so-called 'gold-standard' design for non-inferiority trials, a design involving an experimental group, an active control group, and a placebo group. However, choosing design parameters that maximize the advantages of a two-stage approach for the three-arm gold-standard design for non-inferiority trials is not a straightforward task. In particular, optimal choices of futility boundaries for this design have not been thoroughly discussed in existing literature. We present a variation of the hierarchical testing procedure, which allows for the incorporation of binding futility boundaries at interim analyses. We show that this procedure maintains strong control of the family-wise type I error rate. Within this framework, we consider the futility and efficacy boundaries as well as the sample size allocation ratios as optimization parameters. This allows the investigation of the efficiency gain from including the option to stop for futility in addition to the ability to stop for efficacy. To analyze the extended designs, optimality criteria that include the design's performance under the alternative as well as the null hypothesis are introduced. On top of this, we discuss methods to limit the allocation of placebo patients in the trial while maintaining relatively good operating characteristics. The results of our numerical optimization procedure are discussed and a comparison of different approaches to designing a three-arm gold-standard non-inferiority trial is provided.

Mechanistic PK/PD modeling to address early-stage biotherapeutic dosing feasibility questions

Early assessment of dosing requirements should be an integral part of developability assessments for a discovery program. If a very high dose is required to achieve the desired pharmacological effect, it may not be clinically feasible or commercially desirable to develop the biotherapeutic for the selected target unless extra measures are taken to develop a high concentration formulation or maximize yield during manufacturing. A quantitative understanding of the impact of target selection, biotherapeutic format, and optimal drug properties on potential dosing requirements to achieve efficacy can affect many early decisions. Early prediction of dosing requirements for biotherapeutics, as opposed to small molecules, is possible due to a strong influence of target biology on pharmacokinetics and dosing. Mechanistic pharmacokinetic/pharmacodynamic (PK/PD) models leverage knowledge and competitor data available at an early stage of drug development, including biophysics of the target(s) and disease physiology, to rationally inform drug design criteria. Here we review how mathematical mechanistic PK/PD modeling can and has been applied to guide early drug development decisions.

Evaluation of Objective Functions for the Optimal Design of an Assistive Robot

The number of individuals with upper or lower extremities dysfunction (ULED) has considerably increased in the past few decades, resulting in a high economic burden for their families and society. Individuals with ULEDs require assistive robots to fulfill all their activities of daily living (ADLs). However, a theory for the optimal design of assistive robots that reduces energy consumption while increasing the workspace is unavailable. Thus, this research presents an algorithm for the optimal link length selection of an assistive robot mounted on a wheelchair to minimize the torque demands of each joint while increasing the workspace coverage. For this purpose, this research developed a workspace to satisfy a list of 18 ADLs. Then, three torque indices from the literature were considered as performance measures to minimize; the three torque measures are the quadratic average torque (QAT), the weighted root square mean (WRMS), and the absolute sum of torques (AST). The proposed algorithm evaluates any of the three torque measures within the workspace, given the robot dimensions. This proposed algorithm acts as an objective function, which is optimized using a genetic algorithm for each torque measure. The results show that all tree torque measures are suitable criteria for assistance robot optimization. However, each torque measures yield different optimal results; in the case of the QAT optimization, it produces the least workspace with the minimum overall torques of all the joints. Contrarily, the WRMS and AST optimization yield similar results generating the maximum workspace coverage but with a greater overall torque of all joints. Thus, the selection between the three methods depends on the designer's criteria. Based on the results, the presented methodology is a reliable tool for the optimal dimensioning of assistive robots.

Tribological Investigation of Textured Surfaces in Starved Lubrication Conditions

The present work investigates the friction reduction capability of two types of micro-textures (grooves and dimples) created on steel surfaces using a vertical milling machine. The wear studies were conducted using a pin-on-disc tribometer, with the results indicating a better friction reduction capacity in the case of the dimple texture as compared to the grooved texture. The microscopic images of the pin surface revealed deep furrows and significant damage on the pin surfaces of the groove-textured disc. An optimization of the textured surfaces was performed using an artificial neural network (ANN) model, predicting the influence of the surface texture as a function of the load, depth of cut and distance between the micro-textures.

An Efficient Method for Optimizing HPC-FRP Retrofit Systems of Flexural Strengthened One-Way Continuous Slabs Based on ACI 440.2R

An innovative retrofit system consisting of fiber-reinforced polymers (FRP) and high-performance concrete (HPC) considering the difficulty of the accessibility and installation of FRP on the underside of reinforced concrete (RC) slabs was found to be efficient in the flexural strengthening of existing RC slabs. It is important to note that continuous slabs using the FRP-HPC retrofit systems are less effective in exploiting FRP tensile strength and can cause sudden failure once excessively enhanced flexural strength exceeds shear strength. A design method to ensure ductile failure mode was also proposed for strengthened continuous RC slabs in the previous literature. Thus, it is necessary to optimize retrofit systems in terms of mechanical performance aspects to improve the efficiency of retrofitted slabs in serviceability. This study proposes a design method for optimizing the strength of materials and inducing ductile failure of continuous slab retrofitting FRP-HPC systems. The proposed approach demonstrated its effectiveness for strengthening a continuous RC slab with various FRP-HPC retrofit systems through a case study. The results show that the design factored load in the serviceability limit state does not change appreciably from a decrease in carbon fiber-reinforced polymers (CFRP) of 38%; the design factored load decreased only by 9% and the ultimate failure load by 13% while reducing CFRP by 20% and HPC by 25%.

Hybrid Polymer Composites Based on Polystyrene (PS) Used in the Melted and Extruded Manufacturing Technology

As part of the work, innovative hybrid polymer composites dedicated to rapid prototyping, especially for 3D printing with the melted and extruded manufacturing (MEM) technique, were developed. For this purpose, the influence of modified fillers, such as alumina-modified silica, bentonite modified with quaternary ammonium salt, and lignin/silicon dioxide hybrid filler, on the functional properties of polystyrene-based composites was investigated. The introduced additives were selected to improve the processing properties of polystyrene (PS), in particular its thermal stability, while maintaining good mechanical properties. In the first part of the work, using the proprietary technological line, filaments from unfilled PS and its composites were obtained, which contain modified fillers in the amount of 1.5% to 3.0% by weight. Samples for testing functional properties were obtained by 3D printing in MEM technology and injection technique. The rheological properties-mass melt flow rate (MFR), viscosity, and mechanical properties-are presented in the further part of the work. The size and the respective dispersion in the polystyrene polymer matrix of the fillers used were determined by scanning electron microscopy with energy dispersion spectroscopy (SEM/EDS). The correct dispersion of additives in PS was also confirmed by wide-angle X-ray analysis (WAXS). A significant improvement in the thermal stability of the obtained composites after the introduction of fillers into the polymer matrix was confirmed on the basis of thermogravimetric analysis (TGA). The remaining tests of physicochemical properties, differential scanning calorimetry (DSC), and infrared spectroscopy with Fourier transform (FT-IR) allowed us to state no significant changes in relation to polystyrene. The obtained test results allowed us to conclude that the amount and type of fillers used in the PS polymer matrix significantly affect the performance properties of the tested hybrid composites. The composites obtained as part of the work can be successfully used in rapid prototyping technologies, especially for the production of details originally designed from PS, which are required to have higher thermal stability than is guaranteed only by the polymer matrix.

Design of Improved Flow-Focusing Microchannel with Constricted Continuous Phase Inlet and Study of Fluid Flow Characteristics

This paper proposed an improved flow-focusing microchannel with a constricted continuous phase inlet to increase microbubble generation frequency and reduce microbubbles' diameter. The design variables were obtained by Latin hypercube sampling, and the radial basis function (RBF) surrogate model was used to establish the relationship between the objective function (microbubble diameter and generation frequency) and the design variables. Moreover, the optimized design of the nondominated sorting genetic algorithm II (NSGA-II) algorithm was carried out. Finally, the optimization results were verified by numerical simulations and compared with those of traditional microchannels. The results showed that dripping and squeezing regimes existed in the two microchannels. The constricted continuous phase inlet enhanced the flow-focusing effect of the improved microchannel. The diameter of microbubbles obtained from the improved microchannel was reduced from 2.8141 to 1.6949 μm, and the generation frequency was increased from 64.077 to 175.438 kHz at the same capillary numbers (Ca) compared with the traditional microchannel. According to the fitted linear function, it is known that the slope of decreasing microbubble diameter with increasing Ca number and the slope of increasing generation frequency with increasing Ca number are greater in the improved microchannel compared with those in the traditional microchannel.

Revealing Angiopep-2/LRP1 Molecular Interaction for Optimal Delivery to Glioblastoma (GBM)

Background: The family of synthetic peptide angiopeps, and particularly angiopep-2 (ANG-2) demonstrated the ability preclinically and clinically to shuttle active molecules across the blood–brain barrier (BBB) and selectively toward brain tumor cells. The literature has also proved that the transport occurs through a specific receptor-mediated transcytosis of the peptide by LRP-1 receptors present both on BBB and tumor cell membranes. However, contradictory results about exploiting this promising mechanism to engineer complex delivery systems, such as nanoparticles, are being obtained. Methodology: For this reason, we applied a molecular docking (MD)-based strategy to investigate the molecular interaction of ANG-2 and the LRP-1 ligand-binding moieties (CR56 and CR17), clarifying the impact of peptide conjugation on its transport mechanism. Results: MD results proved that ANG-2/LRP-1 binding involves the majority of ANG-2 residues, is characterized by high binding energies, and that it is site-specific for CR56 where the binding to 929ASP recalls a transcytosis mechanism, resembling the binding of the receptor to the receptor-associated protein. On the other hand, ANG-2 binding to CR17 is less site-specific but, as proved for apolipoprotein internalization in physiological conditions, it involves the ANG-2 lysin residue. Conclusions: Overall, our results proved that ANG-2 energetic interaction with the LRP-1 receptor is not hindered if specific residues of the peptide are chemically crosslinked to simple or complex engineered delivery systems.

Optimal Design of Acoustic Metamaterial of Multiple Parallel Hexagonal Helmholtz Resonators by Combination of Finite Element Simulation and Cuckoo Search Algorithm

To achieve the broadband sound absorption at low frequencies within a limited space, an optimal design of joint simulation method incorporating the finite element simulation and cuckoo search algorithm was proposed. An acoustic metamaterial of multiple parallel hexagonal Helmholtz resonators with sub-wavelength dimensions was designed and optimized in this research. First, the initial geometric parameters of the investigated acoustic metamaterials were confirmed according to the actual noise reduction requirements to reduce the optimization burden and improve the optimization efficiency. Then, the acoustic metamaterial with the various depths of the necks was optimized by the joint simulation method, which combined the finite element simulation and the cuckoo search algorithm. The experimental sample was prepared using the 3D printer according to the obtained optimal parameters. The simulation results and experimental results exhibited excellent consistency. Compared with the derived sound absorption coefficients by theoretical modeling, those achieved in the finite element simulation were closer to the experimental results, which also verified the accuracy of this optimal design method. The results proved that the optimal design method was applicable to the achievement of broadband sound absorption with different low frequency ranges, which provided a novel method for the development and application of acoustic metamaterials.

Nonmyopic and pseudo-nonmyopic approaches to optimal sequential design in the presence of covariates

In sequential experiments, subjects become available for the study over a period of time, and covariates are often measured at the time of arrival. We consider the setting where the sample size is fixed but covariate values are unknown until subjects enrol. Given a model for the outcome, a sequential optimal design approach can be used to allocate treatments to minimize the variance of the estimator of the treatment effect. We extend existing optimal design methodology so it can be used within a nonmyopic framework, where treatment allocation for the current subject depends not only on the treatments and covariates of the subjects already enrolled in the study, but also the impact of possible future treatment assignments within a specified horizon. The nonmyopic approach requires recursive formulae and suffers from the curse of dimensionality. We propose a pseudo-nonmyopic approach which has a similar aim to the nonmyopic approach, but does not involve recursion and instead relies on simulating trajectories of future possible decisions. Our simulation studies show that, for the simple case of a logistic regression with a single binary covariate and a binary treatment, and a more realistic case with four binary covariates, binary treatment and treatment-covariate interactions, the nonmyopic and pseudo-nonmyopic approaches provide no competitive advantage over the myopic approach, both in terms of the size of the estimated treatment effect and also the efficiency of the designs. Results are robust to the size of the horizon used in the nonmyopic approach, and the number of simulated trajectories used in the pseudo-nonmyopic approach.

Analysis and Optimization of a Microgripper Driven by Linear Ultrasonic Motors

This paper presents the vibration response analysis and optimal structural design of a microgripper driven by linear ultrasonic motors (LUMs) dedicated to improving end-point positioning accuracy. Based on structural vibration theory, a parametric vibration response model of the microgripper finger was established, and the relative sensitivities of the structural and material parameters that affect the vibration amplitude of the fingertip were calculated within the structural and material constraints. Then, according to the sensitivity calculation results, a multidimensional constrained nonlinear optimization model was constructed to suppress the vibration of the end-effector. The improved internal penalty function method combined with Newton iteration was adopted to obtain the optimal structural parameters. Finally, the vibration experimental results show that the vibration amplitude of the initial microgripper fingertip is 16.31 μm, and the value measured after optimization was 2.49 μm, exhibiting a reduction of 84.7%. Therefore, the proposed optimal design method can effectively restrain the vibration of the microgripper end-effector and improve manipulation stability.

Optimal Design for 3-PSS Flexible Parallel Micromanipulator Based on Kinematic and Dynamic Characteristics

This paper proposes two optimal design schemes for improving the kinematic and dynamic performance of the 3-PSS flexible parallel micromanipulator according to different application requirements and conditions. Firstly, the workspace, dexterity, frequencies, and driving forces of the mechanism are successively analyzed. Then, a progressive optimization design is carried out, in which the scale parameters of this mechanism are firstly optimized to maximize the workspace, combining the constraints of the minimum global dexterity of the mechanism. Based on the optimized scale parameters, the minimum thickness and the cutting radius of the flexure spherical hinge are further optimized for minimizing the required driving forces, combined with constraints of the minimum first-order natural frequency of the mechanism and the maximum stress of the flexure spherical hinge during the movement of the mechanism. Afterward, a synchronous optimization design is proposed, in which the scale parameters are optimized to maximize the first-order natural frequency of the mechanism, combined with the constraints of a certain inscribed circle of the maximum cross-section of the workspace, the maximum stroke of the selected piezoelectric stages, and the maximum ultimate angular displacement of the flexure spherical hinge. The effectiveness of both optimization methods is verified by the comparison of the kinematic and dynamic characteristics of the original and optimized mechanism. The advantage of the progressive optimization method is that both the workspace and the driving forces are optimized and the minimum requirements for global dexterity and first-order natural frequency are ensured. The merit of the synchronous optimization method is that only the scale parameters of the mechanism need to be optimized without changing the structural parameters of the flexible spherical hinge.

Optimal cooling of an internally heated disc

Motivated by the search for sharp bounds on turbulent heat transfer as well as the design of optimal heat exchangers, we consider incompressible flows that most efficiently cool an internally heated disc. Heat enters via a distributed source, is passively advected and diffused, and exits through the boundary at a fixed temperature. We seek an advecting flow to optimize this exchange. Previous work on energy-constrained cooling with a constant source has conjectured that global optimizers should resemble convection rolls; we prove one-sided bounds on energy-constrained cooling corresponding to, but not resolving, this conjecture. In the case of an enstrophy constraint, our results are more complete: we construct a family of self-similar, tree-like 'branching flows' whose cooling we prove is within a logarithm of globally optimal. These results hold for general space- and time-dependent source-sink distributions that add more heat than they remove. Our main technical tool is a non-local Dirichlet-like variational principle for bounding solutions of the inhomogeneous advection-diffusion equation with a divergence-free velocity. This article is part of the theme issue 'Mathematical problems in physical fluid dynamics (part 1)'.

Optimal unplanned design modification in adaptive two-stage trials

Adaptive planning of clinical trials allows modifying the entire trial design at any time point mid-course. In this paper, we consider the case when a trial-external update of the planning assumptions during the ongoing trial makes an unforeseen design adaptation necessary. We take up the idea to construct adaptive designs with defined features by solving an optimization problem and apply it to the situation of unplanned design reassessment. By using the conditional error principle, we present an approach on how to optimally modify the trial design at an unplanned interim analysis while at the same time strictly protecting the type I error rate. This linking of optimal design planning and the conditional error principle allows sound reactions to unforeseen events that make a design reassessment necessary.

Optimal Design of a Novel Composite Anchorage for Carbon-Fiber-Reinforced Polymer (CFRP) Tendons

In this study, we proposed a novel composite anchorage that considers the anchoring performance and dimension simultaneously. The design concept of this composite anchorage was first introduced, followed by comparison with the traditional inner-cone bond-type anchorage and traditional composite anchorage through theoretical and experimental methods. Then, a parametric study was conducted to determine the influence of different parameters on the anchoring performance, and the optimal design parameters were recommended according to the finite element (FE) and test results. Finally, the practicability of the optimal design parameters were validated through experiments on the anchorage with multiple CFRP tendons. Results showed that the novel composite anchorage could improve the anchoring performance compared with the traditional inner-cone bond-type anchorage by promoting increased anchorage efficiency by 60.4% and, with an ideal failure mode of tendon rupture. Moreover, the novel composite anchorage had smaller dimensions and avoided the presence of a vulnerable position at the junction of the mechanical and bond parts compared with the traditional composite anchorage. In addition, a group of optimal design parameters of this composite anchorage with a pre-tightening force of 130 kN, an inclinational differential angle of 0.1°, an inclination angle of 2.9°, and an embedded length of 30 d~40 d were proposed. The composite anchorage with five CFRP tendons designed with the proposed parameters failed with the rupture of the tendons and exhibited an anchoring efficiency of 1.05. This result showed that the optimal parameters were suitable for this novel composite anchorage to grip multiple tendons. This study can provide an experimental and theoretical basis for designing large-tonnage anchorage for multiple FRP tendons used as hangers or cables in real bridges.

Study on the Design, Preparation, and Performance Evaluation of Heat-Resistant Interlayer-Polyimide-Resin-Based Neutron-Shielding Materials

Polymers have an excellent effect in terms of moderating fast neutrons with rich hydrogen and carbon, which plays an indispensable role in shielding devices. As the shielding of neutrons is typically accompanied by the generation of γ-rays, shielding materials are developed from monomers to multi-component composites, multi-layer structures, and even complex structures. In this paper, based on the typical multilayer structure, the integrated design of the shield component structure and the preparation and performance evaluation of the materials is carried out based on the design sample of the heat-resistant lightweight polymer-based interlayer. Through calculation, the component structure of the polymer-based materials and the three-layer thickness of the shield are obtained. The mass fraction of boron carbide accounts for 11% of the polymer-based material. Since the polymer-based material is the weak link of heat resistance of the multilayer shield, in terms of material selection and modification, the B₄C/TiO₂/polyimide molded plate was prepared by the hot-pressing method, and characterization analysis was conducted for its structure and properties. The results show that the ball milling method can mix the materials well and realize the uniform dispersion of B₄C and TiO₂ in the polyimide matrices. Boron carbide particles are evenly distributed in the material. Except for Ti, the other elemental content of the selected areas for mapping is in good agreement with the theoretical values of the elemental content of the system. The prepared B₄C/TiO₂/polyimide molded plate presents excellent thermal properties, and its glass transition temperature and initial thermal decomposition temperature are as high as 363.6 °C and 572.8 °C, respectively. In addition, the molded plate has good toughness performs well in compression resistance, shock resistance, and thermal aging resistance, which allows it to be used for a long time under 300 °C. Finally, the prepared materials are tested experimentally on an americium beryllium neutron source. The experimental results match the simulation results well.

Optimal sampling for design-based estimators of regression models

Two-phase designs measure variables of interest on a subcohort where the outcome and covariates are readily available or cheap to collect on all individuals in the cohort. Given limited resource availability, it is of interest to find an optimal design that includes more informative individuals in the final sample. We explore the optimal designs and efficiencies for analyses by design-based estimators. Generalized raking is an efficient class of design-based estimators, and they improve on the inverse-probability weighted (IPW) estimator by adjusting weights based on the auxiliary information. We derive a closed-form solution of the optimal design for estimating regression coefficients from generalized raking estimators. We compare it with the optimal design for analysis via the IPW estimator and other two-phase designs in measurement-error settings. We consider general two-phase designs where the outcome variable and variables of interest can be continuous or discrete. Our results show that the optimal designs for analyses by the two classes of design-based estimators can be very different. The optimal design for analysis via the IPW estimator is optimal for IPW estimation and typically gives near-optimal efficiency for generalized raking estimation, though we show there is potential improvement in some settings.

Optimization of adaptive designs with respect to a performance score

Adaptive designs are an increasingly popular method for the adaptation of design aspects in clinical trials, such as the sample size. Scoring different adaptive designs helps to make an appropriate choice among the numerous existing adaptive design methods. Several scores have been proposed to evaluate adaptive designs. Moreover, it is possible to determine optimal two-stage adaptive designs with respect to a customized objective score by solving a constrained optimization problem. In this paper, we use the conditional performance score by Herrmann et al. (2020) as the optimization criterion to derive optimal adaptive two-stage designs. We investigate variations of the original performance score, for example, by assigning different weights to the score components and by incorporating prior assumptions on the effect size. We further investigate a setting where the optimization framework is extended by a global power constraint, and additional optimization of the critical value function next to the stage-two sample size is performed. Those evaluations with respect to the sample size curves and the resulting design's performance can contribute to facilitate the score's usage in practice.

Design of a Gough-Stewart Platform Based on Visual Servoing Controller

Designing a robot with the best accuracy is always an attractive research direction in the robotics community. In order to create a Gough-Stewart platform with guaranteed accuracy performance for a dedicated controller, this paper describes a novel advanced optimal design methodology: control-based design methodology. This advanced optimal design method considers the controller positioning accuracy in the design process for getting the optimal geometric parameters of the robot. In this paper, three types of visual servoing controllers are applied to control the motions of the Gough-Stewart platform: leg-direction-based visual servoing, line-based visual servoing, and image moment visual servoing. Depending on these controllers, the positioning error models considering the camera observation error together with the controller singularities are analyzed. In the next step, the optimization problems are formulated in order to get the optimal geometric parameters of the robot and the placement of the camera for the Gough-Stewart platform for each type of controller. Then, we perform co-simulations on the three optimized Gough-Stewart platforms in order to test the positioning accuracy and the robustness with respect to the manufacturing errors. It turns out that the optimal control-based design methodology helps get both the optimum design parameters of the robot and the performance of the controller {robot + dedicated controller}.

Optimization of Ultrasonic-Assisted Extraction of Chlorogenic Acid from Tobacco Waste

Using tobacco waste as raw material, the ultrasonic-assisted extraction of chlorogenic acid was optimized by response surface methodology (RSM). After repeated freezing and thawing of tobacco waste twice, the effect of pH value, ethanol volume fraction, temperature and extraction time on the extraction rate of chlorogenic acid was investigated by a single factor experiment. On the basis of this, the factors affecting the yield of chlorogenic acid were further optimized by using RSM. The optimum extraction conditions for chlorogenic acid were set at pH = 4.1, ethanol volume fraction was 49.57% and extraction time was 2.06 h. Under the above conditions, the extraction rate of chlorogenic acid could reach 0.502%, which was higher than traditional extraction and unpretreated ultrasonic extraction. All these results can be used as a reference for the extraction of effective ingredients in tobacco waste.

Conditional power and friends: The why and how of (un)planned, unblinded sample size recalculations in confirmatory trials

Adapting the final sample size of a trial to the evidence accruing during the trial is a natural way to address planning uncertainty. Since the sample size is usually determined by an argument based on the power of the trial, an interim analysis raises the question of how the final sample size should be determined conditional on the accrued information. To this end, we first review and compare common approaches to estimating conditional power, which is often used in heuristic sample size recalculation rules. We then discuss the connection of heuristic sample size recalculation and optimal two-stage designs, demonstrating that the latter is the superior approach in a fully preplanned setting. Hence, unplanned design adaptations should only be conducted as reaction to trial-external new evidence, operational needs to violate the originally chosen design, or post hoc changes in the optimality criterion but not as a reaction to trial-internal data. We are able to show that commonly discussed sample size recalculation rules lead to paradoxical adaptations where an initially planned optimal design is not invariant under the adaptation rule even if the planning assumptions do not change. Finally, we propose two alternative ways of reacting to newly emerging trial-external evidence in ways that are consistent with the originally planned design to avoid such inconsistencies.

Optimization Design of Surface Wave Electromagnetic Acoustic Transducers Based on Simulation Analysis and Orthogonal Test Method

The energy conversion of electromagnetic acoustic transducers (EMATs) is typically lower, which seriously restricts the application of EMATs in the field of non-destructive testing and evaluation. In this work, parameters of surface wave EMATs, including structural parameters and electrical parameters, are investigated using the orthogonal test method to improve the transducer’s energy conversion efficiency. Based on the established finite element 2-D model of EMATs, the amplitude of the displacement components at the observation point of a plate is the optimization objective to be maximized with five parameters pertaining to the magnets, meander-line coils, and excitation signal as design variables. Results show that the signal amplitude of EMATs is 3.48 times on in-plane and 3.49 times on out-of-plane, respectively, compared with the original model. Furthermore, a new material (amorphous nanocrystalline material of type 1K107) is applied to optimize the magnetic circuit of EMATs and enhance the eddy current in an aluminum plate to increase the signal amplitude. Finally, the signal amplitudes obtained from the three types of models, that is, the original one, the optimization one after an orthogonal test, and the optimization one with the addition of magnetic concentrators, are analyzed and compared, indicating that the signal amplitude, compared with the original one, is 6.02 times on in-plane and 6.20 times on out-of-plane, respectively.

Optimal adaptive allocation using deep reinforcement learning in a dose-response study

Estimation of the dose-response curve for efficacy and subsequent selection of an appropriate dose in phase II trials are important processes in drug development. Various methods have been investigated to estimate dose-response curves. Generally, these methods are used with equal allocation of subjects for simplicity; nevertheless, they may not fully optimize performance metrics because of nonoptimal allocation. Optimal allocation methods, which include adaptive allocation methods, have been proposed to overcome the limitations of equal allocation. However, they rely on asymptotics, and thus sometimes cannot efficiently optimize the performance metric with the sample size in an actual clinical trial. The purpose of this study is to construct an adaptive allocation rule that directly optimizes a performance metric, such as power, accuracy of model selection, accuracy of the estimated target dose, or mean absolute error over the estimated dose-response curve. We demonstrate that deep reinforcement learning with an appropriately defined state and reward can be used to construct such an adaptive allocation rule. The simulation study shows that the proposed method can successfully improve the performance metric to be optimized when compared with the equal allocation, D-optimal, and TD-optimal methods. In particular, when the mean absolute error was set to the metric to be optimized, it is possible to construct a rule that is superior for many metrics.

Optimal one-stage design and analysis for efficacy expansion in Phase I oncology trials

BACKGROUND

Contemporary Phase I oncology trials often include efficacy expansion in various tumor indications post dose finding. Preliminary anti-tumor activity from efficacy expansion can aid Go/No-Go decision for Phase 2 or Phase 3 initiation. Tumor cohorts in efficacy expansion are commonly analyzed independently in practice, which are often underpowered due to small sample size. Pooled analysis is also sometimes conducted, but it ignores the heterogeneity of the anti-tumor activity across cohorts.

METHODS

We propose an optimal one-stage design and analysis strategy for the efficacy expansion to assess whether the treatment is effective. Allowing heterogeneous anti-tumor effects across tumor cohorts, inactive cohorts are pruned, and the potentially active cohorts are pooled together to gain study power. For a prospective design with a target power, the total sample size across all cohorts is minimized; or for an ad hoc analysis with pre-specified sample size for each cohort, the pruning criteria are optimized to achieve maximum power. The global type I error is controlled after proper multiplicity adjustment, and a penalty adjusted significance level is used for the pooled test.

RESULTS

Simulation studies show that the proposed optimal design has desirable operating characteristics in increasing the overall power and detecting more true positive tumor cohorts.

CONCLUSION

The proposed optimal design and analysis strategy provides a practical approach to design and analyze heterogeneous efficacy expansion cohorts in a basket setting with global type I and type II error being controlled.

Maximin design of cluster randomized trials with heterogeneous costs and variances

Cluster randomized trials evaluate the effect of a treatment on persons nested within clusters, with clusters being randomly assigned to treatment. The optimal sample size at the cluster and person level depends on the study cost per cluster and per person, and the outcome variance at the cluster and the person level. The variances are unknown in the design stage and can differ between treatment arms. As a solution, this paper presents a Maximin design that maximizes the minimum relative efficiency (relative to the optimal design) over the variance parameter space, for trials with two treatment arms and a quantitative outcome. This maximin relative efficiency design (MMRED) is compared with a published Maximin design which maximizes the minimum efficiency (MMED). Both designs are also compared with the optimal designs for homogeneous costs and variances (balanced design) and heterogeneous costs and homogeneous variances (cost-conscious design), for a range of variances based upon three published trials. Whereas the MMED is balanced under high uncertainty about the treatment-to-control variance ratio, the MMRED then tends towards a balanced budget allocation between arms, leading to an unbalanced sample size allocation if costs are heterogeneous, similar to the cost-conscious design. Further, the MMRED corresponds to an optimal design for an intraclass correlation (ICC) in the lower half of the assumed ICC range (optimistic), whereas the MMED is the optimal design for the maximum ICC within the ICC range (pessimistic). Attention is given to the effect of the Welch-Satterthwaite degrees of freedom for treatment effect testing on the design efficiencies.

[Optimization Design of Emergency Quick Fixation Device Based on ANSYS Workbench]

OBJECTIVE

A rapid fixation device is developed to solve the problems of emergency fixation and transportation of patients with spinal injury.

METHODS

Through the analysis of the function,3D modeling design, finite element analysis and optimization design based on ANSYS Workbench, tensile strength verification experiment, we produced the prototype, and tested it, conducted a simulated rescue experiment.

RESULTS

The fixation device designed can meet the demand of spinal injury patients for safe rescue after accidents, and the quality of the rapid fixation device was lighten by about 30% without reducing the intensity.

CONCLUSIONS

The method based on optimal design can obviously improve the structure design, and has reference significance for other related rescue equipment design.

Design of a Broadband Array Pattern of Underwater Cymbal Transducers

Cymbal transducers are frequently used as an array rather than a single element because of their high quality factor and low energy conversion efficiency. When used as an array, cymbal transducers are likely to have a big change in their frequency characteristics due to the interaction with neighboring elements. In this study, we designed an array pattern of cymbal transducers to achieve a wide frequency bandwidth using this property. First, cymbal transducers with specific center frequencies were designed. Next, a 2 × 2 planar array was constructed with the designed transducers, where dielectric polarity directions of the transducers were divided into two cases (i.e., same and different). For the array, the effect of the difference in the center frequencies and the spacing between the transducers on the acoustic characteristics of the entire array was analyzed. Based on the results, the structural pattern of the array was optimized to have the maximum fractional bandwidth while maintaining the transmitting voltage response over a given requirement. The design validity was verified by making cymbal array prototypes, followed by measuring their performances and comparing them with that of the design.