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Complex robotic systems, such as humanoid robot hands, soft robots, and walking robots, pose a challenging control problem due to their high dimensionality and heavy non-linearities. Conventional model-based feedback controllers demonstrate robustness and stability but struggle to cope with the escalating system design and tuning complexity accompanying larger dimensions. In contrast, data-driven methods such as artificial neural networks excel at representing high-dimensional data but lack robustness, generalization, and real-time adaptiveness. In response to these challenges, researchers are directing their focus to biological paradigms, drawing inspiration from the remarkable control capabilities inherent in the human body. This has motivated the exploration of new control methods aimed at closely emulating the motor functions of the brain given the current insights in neuroscience. Recent investigation into these Brain-Inspired control techniques have yielded promising results, notably in tasks involving trajectory tracking and robot locomotion. This paper presents a comprehensive review of the foremost trends in biomimetic brain-inspired controlmethods to tackle the intricacies associated with controlling complex robotic systems. [ABSTRACT FROM AUTHOR]

Gait recognition is an advanced biometric modality that identifies individuals based on their walking postures. Currently, the majority of gait recognition methods employ silhouette images as the primary representation of gait. However, the inclusion of silhouette information, which incorporates details about clothing and carried items, may interfere with the extraction of gait features. To this end, we present GaitSkeleton, a novel approach that utilizes skeleton sequences as the gait representation. GaitSkeleton models the motor coordination feature of gait and combines three-scale spatial features to obtain discriminative features for gait recognition. To capture the coordination relationship between joints, we design a Coordination Feature Learner (CFL). CFL is embedded into the ST-GCN block to enhance the model's representational capacity. Additionally, we introduce a novel feature cascade module called MSCM, which further enhances the discriminative information by sampling spatial features at three scales. The entire network significantly enhances the discriminative nature of gait features. Experimental results on the CASIA-B dataset demonstrate that our method achieves state-of-the-art performance in model-based gait recognition. [ABSTRACT FROM AUTHOR]

Dopamine release in the nucleus accumbens core (NAcC) is generally considered to be a proxy for phasic firing of the ventral tegmental area dopamine (VTADA) neurons. Thus, dopamine release in NAcC is hypothesized to reflect a unitary role in reward prediction error signaling. However, recent studies reveal more diverse roles of dopamine neurons, which support an emerging idea that dopamine regulates learning differently in distinct circuits. To understand whether the NAcC might regulate a unique component of learning, we recorded dopamine release in NAcC while male rats performed a backward conditioning task where a reward is followed by a neutral cue. We used this task because we can delineate different components of learning, which include sensory-specific inhibitory and general excitatory components. Furthermore, we have shown that VTADA neurons are necessary for both the specific and general components of backward associations. Here, we found that dopamine release in NAcC increased to the reward across learning while reducing to the cue that followed as it became more expected. This mirrors the dopamine prediction error signal seen during forward conditioning and cannot be accounted for temporal-difference reinforcement learning. Subsequent tests allowed us to dissociate these learning components and revealed that dopamine release in NAcC reflects the general excitatory component of backward associations, but not their sensory-specific component. These results emphasize the importance of examining distinct functions of different dopamine projections in reinforcement learning. [ABSTRACT FROM AUTHOR]

Digital engineering and digital twins are increasingly being used in nuclear energy projects with important impacts. At Idaho National Laboratory, these approaches have been applied in a variety of nuclear energy research, development, and demonstration projects, with key lessons and evolutions occurring for each. In this paper, we describe the use of digital engineering and digital twins in the Versatile Test Reactor design, National Reactor Innovation Center test beds, and nonproliferation analysis of the AGN-201 reactor design. We share key lessons learned for these projects related to tool selection, adoption and training, and working with existing assets versus beginning at the design phase. We also share highlights of future potential uses of digital twins and digital engineering, including using artificial intelligence to perform repetitive design tasks and digital twins to move towards semiautonomous nuclear power plant operations. [ABSTRACT FROM AUTHOR]

While previous research has shown that compulsivity is related to an imbalance between goal‐directed and habitual learning systems, very little is known about whether this effect is due to the impairment of a single system or the impairment of the arbitration mechanism that determines which system controls behaviour at any given moment; the current study aims to address this disagreement. Nineteen alcohol use disorder, 30 obsessive‐compulsive disorder (OCD) and 20 major depressive disorder patients and corresponding sex‐ and age‐matched controls performed two‐choice, three‐stage Markov decision‐making paradigm. Model‐based and mode‐free reinforcement learning models were used to independently fitted their behavioural data. Alcohol use disorder and OCD patients showed less model‐based strategy choice than healthy controls in task conditions where the model‐based strategy was optimal. Only OCD patients showed higher behavioural control system switching in task conditions where model‐free use was optimal. Major depressive disorder patients did not differ from the matched control in both. These findings suggest that dysfunction in arbitration control between dual systems may be the basis for diverse disorders involving compulsivity. [ABSTRACT FROM AUTHOR]

Abstract Wear of the ultra-high molecular-weight polyethylene (UHMWPE) component in total knee arthroplasty contributes to implant failure. It is often detected late, when patients experience pain or instability. Early monitoring could enable timely intervention, preventing implant failure and joint degeneration. This study investigates the accuracy and precision (repeatability) of model-based wear measurement (MBWM), a novel technique that can estimate inlay thickness and wear radiographically. Six inlays were milled from non-crosslinked UHMWPE and imaged via X-ray in anteroposterior view at flexion angles 0°, 30°, and 60° on a phantom knee model. MBWM measurements were compared with reference values from a coordinate measurement machine. Three inlays were subjected to accelerated wear generation and similarly evaluated. MBWM estimated inlay thickness with medial and lateral accuracies of 0.13 ± 0.09 and 0.14 ± 0.09 mm, respectively, and linear wear with an accuracy of 0.07 ± 0.06 mm. Thickness measurements revealed significant lateral differences at 0° and 30° (0.22 ± 0.08 mm vs. 0.06 ± 0.06 mm, respectively; t-test, p = 0.0002). Precision was high, with average medial and lateral differences of − 0.01 ± 0.04 mm between double experiments. MBWM using plain radiographs presents a practical and promising approach for the clinical detection of implant wear.

Introduction The global measles incidence has decreased from 145 to 49 cases per 1 million population from 2000 to 2018, but evaluating the economic benefits of a second measles-containing vaccine (MCV2) is crucial. This study reviewed the evidence and quality of economic evaluation studies to guide MCV2 introduction.Methods The systematic review of model-based economic evaluation studies was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The search yielded 2231 articles, with 876 duplicates removed and 1355 articles screened, with nine studies included for final analysis.Results Six studies reported a positive benefit–cost ratio with one resulting in net savings of $11.6 billion, and two studies estimated a 2-dose MMR vaccination program would save $119.24 to prevent one measles case, and a second dose could prevent 9,200 cases at 18 months, saving $548.19 per case. The most sensitive variables were the discount rate and vaccination administration cost.Conclusions Two MCV doses or a second opportunity with an additional dose of MCV were highly cost-beneficial and resulted in substantial cost savings compared to a single routine vaccine. But further research using high-quality model-based health economic evaluation studies of MCV2 should be made available to decision-makers.PROSPERO Registration CRD42020200669

There are very few integration methods satisfying the explicit displacement formulation, unconditional stability, and higher-order accuracy, simultaneously. This paper proposes a new predictor–corrector explicit (NPCE) integration method with unconditional stability and higher-order accuracy. The NPCE is developed by matching its temporal discrete equation (TDE) with that of a recently developed fourth-order new dual-explicit (NDE) integration method. The accuracy and stability of the NPCE scheme are analyzed. It is found that the NPCE method possesses the same stability property as the NDE method. In addition, the NPCE method can achieve the same accuracy level as the NDE method for the undamped system, and it is more accurate than the NDE method for the damped system. The performance of the NPCE method is illustrated by four numerical examples. Some representative integration methods are adopted for comparison. The results indicate that the NPCE method has excellent accuracy with low relative absolute and squared errors. [ABSTRACT FROM AUTHOR]

Statistical depth, a commonly used analytic tool in nonparametric statistics, has been extensively studied for multivariate and functional observations over the past few decades. Although various forms of depth were introduced, they are mainly procedure based whose definitions are independent of the generative model for observations. To address this problem, we introduce a generative model-based approach to define statistical depth for both multivariate and functional data. The proposed model-based depth framework permits simple computation via a bootstrap sampling and improves the depth estimation accuracy. When applied to functional data, the proposed depth can capture important features such as continuity, smoothness or phase variability, depending on the defining criteria. We propose efficient algorithms to compute the proposed depths and establish estimation consistency. Through simulations and real data, we demonstrate that the proposed functional depths reveal important statistical information such as those captured by the median and quantiles, and detect outliers. [ABSTRACT FROM AUTHOR]

The Jiyang depression in the southeastern part of the Bohai Bay Basin has a relatively large scale set of shale oil in the Paleogene Shahejie Formation, but the complex internal components lead to narrow frequency bands, low resolution and difficulty in reservoir information extraction. Impedance is important information for reservoir characterization, and how to predict high‐resolution impedance using available information is particularly important. Deep learning, known for its effectiveness in addressing non‐linear problems, has found extensive applications in various fields of oil and gas exploration. However, the challenges of overfitting and poor generalization persist due to the limited availability of training datasets. Besides, existing methods often use networks to solve a single problem in fact, deep learning can deal with a series of problems intelligently. In order to partially solve the above problems, an intelligent storage prediction network framework is proposed in this paper. Physical information is introduced to realize data‐driven and model‐based approaches, thus solving the problem of difficult construction of training datasets. The processing part accomplishes the high‐resolution processing of seismic records, thus solving the problems of narrow bandwidth and low resolution. Initial model constraints are introduced so as to obtain more stable inversion results. Finally, the well data is compared and analysed to identify and predict the lithology and complete the intelligent prediction of unconventional reservoirs. The results are compared with the traditional model‐driven inversion method, revealing that the approach presented in this paper exhibits higher resolution in predicting dolomite. This contributes to the establishment of a robust data foundation for reservoir evaluation. [ABSTRACT FROM AUTHOR]

Person identifications using the ear-based biometric system has become quite popular in recent year due to increasing demands in security and surveillance applications. With limited training data and computing resources, the run time complexity plays an important role in such a biometric system. With the continuous advancement in deep convolutional neural networks, deep learning-based biometric systems consequently achieved huge progress in solving earlier unanswered and/or incomplete challenges. Though ear-based biometric system gives higher accuracy with the help of pre-trained deep learning models like VGG19, VGG16, Xception, etc. Training these models is a cumbersome task and it requires much time. Most of the ear recognition system developed using deep learning models like VGG19, Xception, ResNet101, etc. requires a large memory area due to the huge parameter requirement of the model. Also, they put computational overhead on the system. One of the major challenges in the field of ear recognition is to identify people with the help of electronic devices over time and space. While developing electronic approaches for person identification, it is worth important to consider the factors like simplicity, cost-effectiveness, and portable flexibility. With these motivations, the authors developed three simple lightweight CNN models and ensemble them to get improved recognition accuracy. The work is validated on the IITD-II ear dataset which contains only 793 sample mages for training purposes. To overcome the limitation of the limited dataset, the author performed data augmentation technique which produces a variety of images from different perspectives. By stacking these three CNN models, an optimal architecture is developed that gives the best accuracy of 98.74% which is a good improvement over the individual model. The proposed CNN models can also be ensemble with other pre-trained models like VGG16, VGG19, ResNet, Xception, etc. for a more effective solution. [ABSTRACT FROM AUTHOR]

The development of safety-critical real-time embedded systems (RTESs) is a difficult task. Indeed, in addition to functional requirements, these systems must guarantee timing constraints. Model-driven engineering (MDE) promotes a rise in the level of abstraction during the development process of these systems. This is beneficial since it may help designers manage the increasing complexity of these systems. It may also accelerate the development process through model transformations and enable early verification of the timing properties. Schedulability analysis techniques aim to verify whether the tasks complete their executions within the time limit specified by the real-time application (i.e., the deadline). Unfortunately, software designers find this task difficult since it requires a deep understanding of real-time scheduling theory, as well as the use of dedicated tools (called analysis tools). In this paper, we propose real-time verification design patterns to promote early verification of timing properties in a model-based approach. These patterns aim to assist designers in the verification process. They encapsulate the knowledge of a real-time expert in scheduling theory. To facilitate and fasten the verification process, we propose to automate the generation of the analysis model. Thanks to the provided patterns, this generation phase includes an automatic consistency check of the design model. The consistency check step verifies the design's validity and completeness with respect to the applied pattern. To add further benefits to the proposed patterns, we describe a process that provides context for how these patterns can be applied. We apply and simulate the contribution to real-world case study. The obtained results show the viability of this research. [ABSTRACT FROM AUTHOR]

AbstractIn Phase I/II dose-finding trials, the objective is to find the Optimal Biological Dose (OBD), a dose that is both safe and shows sufficient activity that maximizes some optimality criterion based on safety and activity. In cancer, treatment is typically given over several cycles, complicating the identification of the OBD as both toxicity and activity outcomes may occur at any point throughout the follow up of multiple cycles. In this work we present and assess the Joint TITE-CRM, a model-based design for late onset toxicities and activity based on the well-known TITE-CRM. It is found to be superior to the currently available alternative designs that account for late onset bivariate outcomes, as well as being both intuitive and computationally feasible. [ABSTRACT FROM AUTHOR]

Complex robotic systems, such as humanoid robot hands, soft robots, and walking robots, pose a challenging control problem due to their high dimensionality and heavy non-linearities. Conventional model-based feedback controllers demonstrate robustness and stability but struggle to cope with the escalating system design and tuning complexity accompanying larger dimensions. In contrast, data-driven methods such as artificial neural networks excel at representing high-dimensional data but lack robustness, generalization, and real-time adaptiveness. In response to these challenges, researchers are directing their focus to biological paradigms, drawing inspiration from the remarkable control capabilities inherent in the human body. This has motivated the exploration of new control methods aimed at closely emulating the motor functions of the brain given the current insights in neuroscience. Recent investigation into these Brain-Inspired control techniques have yielded promising results, notably in tasks involving trajectory tracking and robot locomotion. This paper presents a comprehensive review of the foremost trends in biomimetic brain-inspired control methods to tackle the intricacies associated with controlling complex robotic systems.

Digital engineering and digital twins are increasingly being used in nuclear energy projects with important impacts. At Idaho National Laboratory, these approaches have been applied in a variety of nuclear energy research, development, and demonstration projects, with key lessons and evolutions occurring for each. In this paper, we describe the use of digital engineering and digital twins in the Versatile Test Reactor design, National Reactor Innovation Center test beds, and nonproliferation analysis of the AGN-201 reactor design. We share key lessons learned for these projects related to tool selection, adoption and training, and working with existing assets versus beginning at the design phase. We also share highlights of future potential uses of digital twins and digital engineering, including using artificial intelligence to perform repetitive design tasks and digital twins to move towards semiautonomous nuclear power plant operations.

In the automotive industry, the accurate estimation of wheel displacements is crucial for optimizing vehicle suspension systems. Traditional model-based approaches often face challenges in accurately predicting these displacements due to the complex dynamics of the road-vehicle interaction. To address this limitation, this study, conducted in the frame of the OWHEEL project, proposes the integration of a multi-output neural network capable of compensating for estimation errors inherent in model-based approaches, specifically those arising from road inputs. Leveraging only vertical acceleration measurements, the neural network operates in parallel with the model-based estimator, enhancing the overall accuracy of displacement estimation. Experimental validation using a sports vehicle demonstrates the efficacy of the proposed methodology, showcasing its ability to improve estimation accuracy beyond the capabilities of the model-based approach alone.

This study proposes a hybrid fault detection methodology for detecting epoxy faults in hairpin-based stator windings of electric motors. The hybrid methodology integrates a model-based approach for feature extraction and a data-driven approach for fault classification. A new lumped-parameter equivalent circuit model specifically for hairpin windings is developed. It can accurately simulate the high-frequency impedance behaviors of hairpin windings and physically interpret the distinction of the measurement curves under different epoxy configurations. Using system identification, the parameters of this new model are identified to extract the features of phase windings, reflecting different fault conditions by varying the parameters in distinct ranges. Fault classification is implemented using a data-driven method to distinguish the underlying patterns, which is difficult to achieve by conventional threshold limit checking due to the inevitably introduced noise and uncertainties. Principal Component Analysis (PCA) is applied to refine the features, followed by a Support Vector Machine (SVM) performing fault classification. The proposed hybrid methodology successfully detects epoxy-related fault conditions, providing a new strategy for fault detection.

When a facial image is blurred, it significantly affects high-level vision tasks such as face recognition. The purpose of facial image deblurring is to recover a clear image from a blurry input image, which can improve the recognition accuracy, etc. However, general deblurring methods do not perform well on facial images. Therefore, some face deblurring methods have been proposed to improve performance by adding semantic or structural information as specific priors according to the characteristics of the facial images. In this paper, we survey and summarize recently published methods for facial image deblurring, most of which are based on deep learning. First, we provide a brief introduction to the modeling of image blurring. Next, we summarize face deblurring methods into two categories: model-based methods and deep learning-based methods. Furthermore, we summarize the datasets, loss functions, and performance evaluation metrics commonly used in the neural network training process. We show the performance of classical methods on these datasets and metrics and provide a brief discussion on the differences between model-based and learning-based methods. Finally, we discuss the current challenges and possible future research directions. [ABSTRACT FROM AUTHOR]

People with methamphetamine use disorder (MUD) struggle to shift their behaviour from methamphetamine‐orientated habits to goal‐oriented choices. The model‐based/model‐free framework is well suited to understand this difficulty by unpacking the computational mechanisms that support experienced‐based (model‐free) and goal‐directed (model‐based) choices. We aimed to examine whether 1) participants with MUD differed from controls on behavioural proxies and/or computational mechanisms of model‐based/model‐free choices; 2) model‐based/model‐free decision‐making correlated with MUD symptoms; and 3) model‐based/model‐free deficits improved over six weeks in the group with MUD. Participants with MUD and controls with similar age, IQ and socioeconomic status completed the Two‐Step Task at treatment commencement (MUD n = 30, Controls n = 31) and six weeks later (MUD n = 23, Controls n = 26). We examined behavioural proxies of model‐based/model‐free decisions using mixed logistic regression, and their underlying mechanisms using computational modelling. At a behavioural level, participants with MUD were more likely to switch their choices following rewarded actions, although this pattern improved at follow up. At a computational level, groups were similar in their use of model‐based mechanisms, but participants with MUD were less likely to apply model‐free mechanisms and less likely to repeat rewarded actions. We did not find evidence that individual differences in model‐based or model‐free parameters were associated with greater severity of methamphetamine dependence, nor did we find that group differences in computational parameters changed between baseline and follow‐up assessment. Decision‐making challenges in people with MUD are likely related to difficulties in pursuing choices previously associated with positive outcomes. [ABSTRACT FROM AUTHOR]

Students often learn about measurement uncertainty as an isolated topic, with a focus on generalizable strategies to manage uncertainty in scientific investigation. In this study, we report and analyze a case of emergent learning about measurement and uncertainty, in which students in a Chinese elementary school science class explored and reconciled discrepancies in hypotheses by constructing and using measures and making inferences. Adopting a model‐based view of measurement, we show that when allowed to take on emergent measurement uncertainty while inquiring into causes for phenomena, late elementary students with no prior experience can engage in sophisticated reasoning characterized by a variety of theoretical modeling practices. Developing and aligning models of the phenomenon, the measure and the measurement data supported students in constructing an intuitive solution to their discrepancies. Our analysis also identified (1) a pattern of thinking and some common assumptions students adopted in their modeling practice that were productive, and (2) contextual elements affording and constraining emergent learning on measurement and uncertainty. In our discussion, we reflect on the educational potential of adopting a model‐based account of measurement and of treating measurement and uncertainty as integrated into investigative practice. We also discuss the necessary contexts for realizing the potential of the model‐based account. [ABSTRACT FROM AUTHOR]

Applying remote sensing models outside the temporal range of their training data, referred to as temporal model transfer, has become common practice for large area monitoring projects that extrapolate models for hindcasting or forecasting to time periods lacking reference data. However, the development of appropriate validation methods for temporal transfer has lagged behind its rapid adoption. Breaking temporal transfer's assumption of temporal consistency in both remote sensing and reference data and their relationship to each other could lead to biased pixel-level predictions and small area estimators, compromising the operational validity of large area monitoring products. Few studies using temporal transfer have evaluated its effects on model accuracy at the pixel/plot level, and the propensity for biased small area estimators and trends resulting from temporal transfer remains unexplored. We present a framework for evaluating temporal transferability by combining temporal cross-validation with a multiscale map assessment to aid in identifying where and when biased model predictions could scale to small area estimates and affect predicted trends.This validation framework is demonstrated in a case study of annual percent tree canopy cover mapping in Michigan. We tested and compared temporal transferability of random forest models of canopy cover derived from 2010 to 2016 systematic dot-grid photo-interpretations at Forest Inventory and Analysis plots with Landsat spectral indices fit with the LandTrendr temporal segmentation algorithm serving as the primary predictor variables. The temporal cross-validation error (mean RMSE = 13.9% cover) was higher than the common validation approach of considering all time periods of testing data together (RMSE = 13.6% cover) and lower than models trained and tested within the same year (mean RMSE = 14.2% cover). However, the bias of model predictions and small area estimators for individual years was higher with temporal transfer models than when applying models within the same year as their training data. We also evaluated how training models using different temporal subsets and with and without LandTrendr fitting affected predictions of Michigan's 1984–2020 predicted annual mean cover. The mean cover from LandTrendr-based models followed expected and consistent trends and had less difference between models trained with different temporal subsets (max difference = 5.8% cover). While those from Landsat had high interannual variations and greater difference between temporal models (max difference = 11.2% cover). The results of this case study demonstrate that evaluation of temporal transferability is necessary for establishing the operational validity of large area monitoring products, even when using time series algorithms that improve temporal consistency.

Abstract When a facial image is blurred, it significantly affects high-level vision tasks such as face recognition. The purpose of facial image deblurring is to recover a clear image from a blurry input image, which can improve the recognition accuracy, etc. However, general deblurring methods do not perform well on facial images. Therefore, some face deblurring methods have been proposed to improve performance by adding semantic or structural information as specific priors according to the characteristics of the facial images. In this paper, we survey and summarize recently published methods for facial image deblurring, most of which are based on deep learning. First, we provide a brief introduction to the modeling of image blurring. Next, we summarize face deblurring methods into two categories: model-based methods and deep learning-based methods. Furthermore, we summarize the datasets, loss functions, and performance evaluation metrics commonly used in the neural network training process. We show the performance of classical methods on these datasets and metrics and provide a brief discussion on the differences between model-based and learning-based methods. Finally, we discuss the current challenges and possible future research directions.

A biometric system is a technology that utilizes an individual's unique physiological and behavioral characteristics to identify and authenticate them. It falls under the category of pattern recognition. Gait recognition, specifically the identification of individuals based on their walking patterns, has garnered significant attention from researchers due to its potential to accurately identify individuals from a distance. Gait recognition systems involve a complex integration of technical, operational and definitional choices and have been applied in a variety of contexts such as security, medical examinations, identity management, and access control. The utilization of gait recognition methods and tools has led to the development of various useful and widely accepted applications. This article provides an overview of the various techniques and approaches employed in gait recognition, including the framework, history, and parameters utilized. The article also delves into the different classifiers, both traditional and deep learning-based, used in the field. Additionally, it examines the different types of datasets utilized in experimental research and the methodology for evaluating articles on gait recognition. With its potential in security applications, gait recognition is expected to have a wide range of future applications. [ABSTRACT FROM AUTHOR]

Abstract This article presents the development of the Control‐oriented, Reconfigurable, and Acausal Floating Turbine Simulator (CRAFTS). CRAFTS has a modular, hierarchical model architecture that enables rapid and accurate simulation of wind turbines. The architecture facilitates the incorporation of model variants, and its system reconfiguration features help simulate multiple design variants. CRAFTS also supports the integration of models developed on a causality‐free platform (e.g., Modelica®) with existing causal models. This article focuses on the validation of a land‐based wind turbine models against OpenFAST, an industry‐standard platform, for several test cases. Closed‐loop scenarios are also tested using the standard ROSCO controller and compared against OpenFAST. In addition, a nonlinear controller developed in our prior work is also evaluated. The test cases demonstrate the user‐friendly and computationally efficient capabilities of CRAFTS to facilitate control co‐design, assist in incorporating multiphysics models, be adaptable to design variants, and allow for rapid simulations to validate models and evaluate controllers.

The application of FPGA is becoming wider because of its advantages of reconfigurability, high performance, low power consumption, and strong real-time performance. However, its shortcomings such as high development threshold, low efficiency, and long development cycle become more prominent as the increase of system size and complexity. This paper uses model-based development methods to study the correlation algorithm of FPGA, including Simulink model building, test validation, code generation and simulation. The results of comparison with traditional manual coding show that the FPGA software development cycle using model-based design methods is only one fifth of the original method and the error in calculation results of the correlation coefficient is also under 0.3%. Although the length of generated code far exceeds that of handwritten code, the resource utilization of logic units, register, storage units, and multiplier units, etc. at runtime are less than those of handwritten code after code synthesis. It can be seen that model-based development methods not only meet the accuracy requirements (not exceeding ±5‰), but also greatly shorten the development cycle and lower the coding threshold.

This article presents the development of the Control‐oriented, Reconfigurable, and Acausal Floating Turbine Simulator (CRAFTS). CRAFTS has a modular, hierarchical model architecture that enables rapid and accurate simulation of wind turbines. The architecture facilitates the incorporation of model variants, and its system reconfiguration features help simulate multiple design variants. CRAFTS also supports the integration of models developed on a causality‐free platform (e.g., Modelica®) with existing causal models. This article focuses on the validation of a land‐based wind turbine models against OpenFAST, an industry‐standard platform, for several test cases. Closed‐loop scenarios are also tested using the standard ROSCO controller and compared against OpenFAST. In addition, a nonlinear controller developed in our prior work is also evaluated. The test cases demonstrate the user‐friendly and computationally efficient capabilities of CRAFTS to facilitate control co‐design, assist in incorporating multiphysics models, be adaptable to design variants, and allow for rapid simulations to validate models and evaluate controllers. [ABSTRACT FROM AUTHOR]

Purpose: To improve time‐resolved reconstructions by training auto‐encoders to learn compact representations of Bloch‐simulated signal evolution and inserting the decoder into the forward model. Methods: Building on model‐based nonlinear and linear subspace techniques, we train auto‐encoders on dictionaries of simulated signal evolution to learn compact, nonlinear, latent representations. The proposed latent signal model framework inserts the decoder portion of the auto‐encoder into the forward model and directly reconstructs the latent representation. Latent signal models essentially serve as a proxy for fast and feasible differentiation through the Bloch equations used to simulate signal. This work performs experiments in the context of T2‐shuffling, gradient echo EPTI, and MPRAGE‐shuffling. We compare how efficiently auto‐encoders represent signal evolution in comparison to linear subspaces. Simulation and in vivo experiments then evaluate if reducing degrees of freedom by incorporating our proxy for the Bloch equations, the decoder portion of the auto‐encoder, into the forward model improves reconstructions in comparison to subspace constraints. Results: An auto‐encoder with 1 real latent variable represents single‐tissue fast spin echo, EPTI, and MPRAGE signal evolution to within 0.15% normalized RMS error, enabling reconstruction problems with 3 degrees of freedom per voxel (real latent variable + complex scaling) in comparison to linear models with 4–8 degrees of freedom per voxel. In simulated/in vivo T2‐shuffling and in vivo EPTI experiments, the proposed framework achieves consistent quantitative normalized RMS error improvement over linear approaches. From qualitative evaluation, the proposed approach yields images with reduced blurring and noise amplification in MPRAGE‐shuffling experiments. Conclusion: Directly solving for nonlinear latent representations of signal evolution improves time‐resolved MRI reconstructions. [ABSTRACT FROM AUTHOR]

Decarbonization of the transport sector could be achieved through fuel cell technology. The candidature of this technology is motivated by its high current density and lack of emissions. However, its widespread deployment is restrained by durability and reliability constraints. During normal operation, the fuel cell system supplies stable power to the load. Contrarily, when it is operated under faulty conditions, the system's output power deteriorates, leading to low durability. It is therefore of paramount importance to ensure that the system is operated in a non-faulty condition. In this paper, we provide a critical review of the analytical-redundancy-based fault diagnosis methods for proton exchange membrane fuel cells (PEMFCs). An in-depth analysis of the various methods has been presented in terms of accuracy, complexity, implementability, and robustness to aging and dynamic operating conditions. [ABSTRACT FROM AUTHOR]

The bacterium Escherichia coli is a widely used organism in biotechnology. For high space-time yields, glucose-limited fed-batch technology is the industry standard; this is because an overflow metabolism of acetate occurs at high glucose concentrations. As an interesting alternative, various strains with limited glucose uptake have been developed. However, these have not yet been characterized under process conditions. To demonstrate the efficiency of our previously developed high-throughput robotic platform, in the present work, we characterized three different exemplary E. coli knockout (KO) strains with limited glucose uptake capacities at three different scales (microtiter plates, 10 mL bioreactor system and 100 mL bioreactor system) under excess glucose conditions with different initial glucose concentrations. The extensive measurements of growth behavior, substrate consumption, respiration, and overflow metabolism were then used to determine the appropriate growth parameters using a mechanistic mathematical model, which allowed for a comprehensive comparative analysis of the strains. The analysis was performed coherently with these different reactor configurations and the results could be successfully transferred from one platform to another. Single and double KO mutants showed reduced specific rates for substrate uptake qSmax and acetate production qApmax; meanwhile, higher glucose concentrations had adverse effects on the biomass yield coefficient YXSem. Additional parameters compared to previous studies for the oxygen uptake rate and carbon dioxide production rate indicated differences in the specific oxygen uptake rate qOmax. This study is an example of how automated robotic equipment, together with mathematical model-based approaches, can be successfully used to characterize strains and obtain comprehensive information more quickly, with a trade-off between throughput and analytical capacity. [ABSTRACT FROM AUTHOR]

Selective catalytic reduction (SCR) is diesel aftertreatment using a reduction agent to reduce nitrogen oxides. Diesel engine regulations are being tightened; therefore, the diesel aftertreatment system should be operated efficiently. In the urea-SCR system, there is a possibility of various faults, e.g., catalyst deactivation by sulfur or hydrothermal aging and fault in urea injection system. These faults interfere with normal system operation and result in increase of NOx concentration at the tailpipe. To prevent this situation, it is necessary to detect system faults. In this study, a first-principle model for SCR system is presented based on mass and energy balance equations. Using the one-dimensional urea-SCR model, this research introduces a model-based fault detecting strategy for SCR system. The residuals are calculated as the difference between the model calculation and the actual catalyst system measurement with the system faults. The results of this research are used in fault diagnosis and fault tolerant control studies to meet diesel vehicle nitrogen oxide regulations even in the presence of catalyst faults. [ABSTRACT FROM AUTHOR]

This letter presents a new decomposition approach with independent physical scattering models for odd- and even-bounce mechanisms. The rationale behind the method is to provide orthogonality between the odd- and even-bounce scattering components by removing their correlation from a rank-deficient residue coherency matrix by two successive unitary rotations. The rotated residue coherency matrix is then decomposed into the sum of two rank-1 orthogonal Hermitian positive semi-definite matrices. We show that these two orthogonal matrices consistently depict a single-type scattering phenomenon. Therefore, without branching criteria, one can independently compute the nonnegative odd- and even-bounce scattering powers. We used two full polarimetric SAR data to validate the effectiveness of the proposed method. We first affirm the orthogonality of the proposed scattering models. Then, we perform decomposition to derive the scattering power components and compare them with conventional and state-of-the-art methods. The quantitative analysis supports the merit of the proposed method. [ABSTRACT FROM AUTHOR]

Background: The use of 'backfilling', assigning additional patients to doses deemed safe, in phase I dose-escalation studies has been used in practice to collect additional information on the safety profile, pharmacokinetics and activity of a drug. These additional patients help ensure that the maximum tolerated dose is reliably estimated and give additional information to determine the recommended phase II dose. Methods: In this article, we study the effect of employing backfilling in a phase I trial on the estimation of the maximum tolerated dose and the duration of the study. We consider the situation where only one cycle of follow-up is used for escalation as well as the case where there may be delayed onset toxicities. Results: We find that, over a range of scenarios, the use of backfilling gives an increase in the percentage of correct selections by up to 9%. On average, for a treatment with a cycle length of 6 weeks, each additional backfilling patient reduces the trial duration by half a week. Conclusions: Backfilling in phase I dose-escalation studies can substantially increase the accuracy of estimation of the maximum tolerated dose, with a larger impact in the setting with a dose-limiting toxicity event assessment period of only one cycle. This increased accuracy and reduction in the trial duration are at the cost of increased sample size. [ABSTRACT FROM AUTHOR]

Copyright of Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao is the property of NUDT Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This short communication supports that rule‐based study designs such as the '3 + 3' study design are still being used in early phase oncology development programs despite their inferior performance to model‐based and model‐assisted designs. Statisticians have an opportunity to shape and improve early phase oncology drug development programs by introducing newer, more efficient study designs that estimate the Optimal Biological dose to their oncology trialist colleges. [ABSTRACT FROM AUTHOR]

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Compulsive behaviors are central to addiction and obsessive-compulsive disorder and can be understood as a failure of adaptive decision making. Particularly, they can be conceptualized as an imbalance in behavioral control, such that behavior is guided predominantly by learned rather than inferred outcome expectations. Inference is a computational process required for adaptive behavior, and recent work across species has identified the neural circuitry that supports inference-based decision making. This includes the orbitofrontal cortex, which has long been implicated in disorders of compulsive behavior. Inspired by evidence that modulating orbitofrontal cortex activity can alter inference-based behaviors, here we discuss noninvasive approaches to target these circuits in humans. Specifically, we discuss the potential of network-targeted transcranial magnetic stimulation and real-time neurofeedback to modulate the neural underpinnings of inference. Both interventions leverage recent advances in our understanding of the neurocomputational mechanisms of inference-based behavior and may be used to complement current treatment approaches for behavioral disorders. [ABSTRACT FROM AUTHOR]

Sample efficiency, which refers to the number of samples required for a learning agent to attain a specific level of performance, is central to developing practical reinforcement learning (RL) for complex and large-scale decision-making problems. The ability to transfer and generalize knowledge gained from previous experiences to downstream tasks can significantly improve sample efficiency. Recent research indicates that successor feature (SF) RL algorithms enable knowledge generalization between tasks with different rewards but identical transition dynamics. It has recently been hypothesized that combining model-based (MB) methods with SF algorithms can alleviate the limitation of fixed transition dynamics. Furthermore, uncertainty-aware exploration is widely recognized as another appealing approach for improving sample efficiency. An agent can efficiently explore to better understand an environment by tracking uncertainty about the value of each available action. Putting together two ideas of hybrid model-based successor feature (MB-SF) and uncertainty leads to an approach to the problem of sample efficient uncertainty-aware knowledge transfer across tasks with different transition dynamics or/and reward functions. In this paper, the uncertainty of the value of each action is approximated by a Kalman filter (KF)-based multiple-model adaptive estimation. This KF-based framework treats the parameters of a model as random variables. To the best of our knowledge, this is the first attempt at formulating a hybrid MB-SF algorithm capable of generalizing knowledge across large or continuous state space tasks with various transition dynamics while requiring less computation at decision time than MB methods. We highlight why previous SF-based methods are constrained to knowledge generalization across same transition dynamics, present our novel approach on a firm theoretical foundation, and design a set of demonstration tasks to empirically validate the effectiveness of our proposed approach. The number of samples required to learn the tasks was compared to recent SF and MB baselines. The results show that our algorithm generalizes its knowledge across different transition dynamics, learns downstream tasks with significantly fewer samples than starting from scratch, and outperforms existing approaches. We believe that our proposed framework can account for the computationally efficient behavioural flexibilities observed in the empirical literature and can also serve as a solid theoretical foundation for future experimental work. [ABSTRACT FROM AUTHOR]