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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]

In this paper, leak detection and localization in water distribution networks will be reviewed. In particular, the paper presents the evolution of the methods from model-based towards data-based approaches, listing, describing and comparing the main and/or most recent methods of both categories. Besides, the practical applicability in real water utilities of different existing methods is discussed, outlining the advantages and limitations of model-based and data-driven methods for this task. A well-known case study is used to compare some of the more promising methods and illustrate their performances. Perspectives of the future evolution of the current existing methods are also provided. • A review of the most important leak management methods in water networks is presented. • Both model-based and data-driven approaches are applied to the BattLeDIM2020 benchmark. • General conclusions about the current state of the leak management field are extracted. • Guidelines regarding the interest of water utilities are provided. [ABSTRACT FROM AUTHOR]

Model-based reinforcement learning enables an agent to learn in variable environments and tasks by optimizing its actions based on the predicted states and outcomes. This mechanism has also been considered in the brain. However, exactly how the brain selects an appropriate model for confronting environments has remained unclear. Here, we investigated the model selection algorithm in the human brain during a reinforcement learning task. One primary theory of model selection in the brain is based on sensory prediction errors. Here, we compared this theory with an alternative possibility of internal model selection with reward prediction errors. To compare these two theories, we devised a switching experiment from a first-order Markov decision process to a second-order Markov decision process that provides either reward- or sensory prediction error regarding environmental change. We tested two representative computational models driven by different prediction errors. One is the sensory prediction-error-driven Bayesian algorithm, which has been discussed as a representative internal model selection algorithm in the animal reinforcement learning task. The other is the reward-prediction-error-driven policy gradient algorithm. We compared the simulation results of these two computational models with human reinforcement learning behaviors. The model fitting result supports that the policy gradient algorithm is preferable to the Bayesian algorithm. This suggests that the human brain employs the reward prediction error to select an appropriate internal model in the reinforcement learning task. [ABSTRACT FROM AUTHOR]

• The new algorithm is explicit for both displacement and velocity. • The new algorithm is unconditionally stable for linear and nonlinear softening stiffness systems. • The new algorithm has extremely small period error. • The new algorithm has no energy dissipation. • There still exist overshoot of the new algorithm. Several model-based or structure-dependent integration algorithms with explicit displacement formulation and unconditional stability are recently developed. According to the difference equations, these algorithms can be divided into dual-explicit and semi-explicit, where the first category algorithms are explicit for both displacement and velocity, while the second category are only explicit for displacement. The well-known Chen-Ricles (CR) and Chang algorithms are two representatives of dual-explicit and semi-explicit model-based algorithms, respectively. The model-based integration algorithms are promising with excellent computing efficiency, but most of them are second-order accurate. In this paper, a new dual-explicit (NDE) algorithm with higher-order accuracy is developed. The integration parameters of the NDE algorithm are obtained by using the pole mapping and second-order Pade approximation. Numerous important numerical characteristics including the consistency and local truncation error, stability, numerical dispersion and energy dissipation and overshoot are systematically analyzed for the new algorithm. It is found that the NDE algorithm is fourth-order accurate for free vibration without damping. As for forced vibration with/without damping and free vibration with damping, the NDE algorithm is no longer fourth-order accurate, but it is still more accurate than the CR and Chang algorithms. In addition, the period error of the algorithm is extremely small. Three representative numerical examples are finally provided to demonstrate the high accuracy of the NDE algorithm. Several classical and latest model-based integration algorithms and three fourth-order accurate algorithms are also adopted for comparisons. It has been proved that the NDE algorithm has much better performance than other selected model-based algorithms. [ABSTRACT FROM AUTHOR]

In-orbit alignment of the secondary mirror (SM) of deployable space telescopes is crucial for high-resolution imaging. Traditional alignment methods, which typically involve dedicated wavefront sensors or numerous iterations to optimize a metric function, are not well-suited for space applications. In this paper, an improved model-based SM alignment method is proposed. The term model refers to the relationship between misalignments and a metric function. The improved method offers two advantages over the previous model-based method in [12]. Firstly, our method allows for self-calibration of the model without the need for an additional wavefront sensor, which greatly simplifies the calibration process. Secondly, our method enables field-balancing alignment of the SM with several correction cycles. The exposure number is only six in one correction cycle and is regardless of the number of FOV sampling points, making it highly efficient for telescopes with a large FOV. The feasibility and performance of our method are verified by simulation. A comparison with existing methods is also given to show the advantages of our method in convergence speed and accuracy. • A self-calibrated model-based method is proposed for the in-orbit alignment of the secondary mirror of space telescopes. • A field-balancing alignment of secondary mirror can effectively balance the wavefront errors across the full field of view. • The image sampling frequency and image noises have minimal impact on the alignment results. [ABSTRACT FROM AUTHOR]

In this study, we conceptualize two approaches, model-based and text-based, grounded on mental models and discourse comprehension theories, to computerized summary analysis. We juxtapose the model-based approach with the text-based approach to explore shared knowledge dimensions and associated measures from both approaches and use them to examine changes in students' summaries over time. We used 108 cases in which we computed model-based and text-based measures for two versions of students' summaries (i.e., initial and final revisions), resulting in a total of 216 observations. We used correlations, Principal Components Analysis (PCA), and Linear Mixed-Effects models. This exploratory investigation suggested a shortlist of text-based measures, and the findings of the PCA demonstrated that both model-based and text-based measures explained the three-dimensional model (i.e., surface, structure, and semantic). Overall, model-based measures were better for tracking changes in the surface dimension, while text-based measures were descriptive of the structure dimension. Both approaches worked well for the semantic dimension. The tested text-based measures can serve as a cross-reference to evaluate students' summaries along with the model-based measures. The current study shows the potential of using multidimensional measures to provide formative feedback on students' knowledge structure and writing styles along the three dimensions. • This study introduces two types of Automated Summary Evaluation (ASE) approaches. • We explored shareable knowledge dimensions and their measures in both ASE approaches. • We employed PCA and Mixed-Effects models to assess 216 observations across 108 cases. • PCA showed measures from both approaches explained the three-dimensional model. • Both approaches' measures could enable multidimensional assessment and feedback. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of finding the part of the observations that is useful for the diagnosis. We define a sub-observation as an abstraction of the observations. We then argue that a sub-observation is sufficient if it allows a diagnoser to derive the same minimal diagnosis as the original observations; and we define critical observations as a maximally abstracted sufficient sub-observation. We show how to compute a critical observation, and discuss a number of algorithmic improvements that also shed light on the theory of critical observations. Finally, we illustrate this framework on both state-based and event-based observations. [ABSTRACT FROM AUTHOR]

This paper describes the design and implementation of intelligent dynamic models for fault detection and isolation of V94.2(5)/MGT-70(2) single-axis heavy-duty gas turbine system. The series–parallel structure of nonlinear autoregressive exogenous (NARX) models are used for fault detection, which initiate greater robustness and stability against uncertainties and perturbations. Moreover, to improve the fault detection robustness against uncertainties, the Monte Carlo technique is used in the proposed fault detection structure to select the best threshold. The analysis of fault detectability and fault detection sensitivity are accomplished to analyze the performance of the suggested technique. The fault isolation process is also achieved by using the residual classification approach. The results show the feasibly, robustness, and performance of the presented approach for fault diagnosis of nonlinear systems in the presence of uncertainties. • The series–parallel structure of NARX models is used for gas turbine fault detection. • A comprehensive comparative study was performed for different NARX models. • The Monte Carlo technique is used to select the best threshold. • Fault detectability and sensitivity analysis are performed. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular magnetic resonance T1ρ mapping may detect myocardial injuries without exogenous contrast agent. However, multiple co-registered acquisitions are required, and the lack of robust motion correction limits its clinical translation. We introduce a single breath-hold myocardial T1ρ mapping method that includes model-based non-rigid motion correction. Methods: A single-shot electrocardiogram (ECG)-triggered balanced steady state free precession (bSSFP) 2D adiabatic T1ρ mapping sequence that collects five T1ρ-weighted (T1ρw) images with different spin lock times within a single breath-hold is proposed. To address the problem of residual respiratory motion, a unified optimization framework consisting of a joint T1ρ fitting and model-based non-rigid motion correction algorithm, insensitive to contrast change, was implemented inline for fast (~ 30 s) and direct visualization of T1ρ maps. The proposed reconstruction was optimized on an ex vivo human heart placed on a motion-controlled platform. The technique was then tested in 8 healthy subjects and validated in 30 patients with suspected myocardial injury on a 1.5T CMR scanner. The Dice similarity coefficient (DSC) and maximum perpendicular distance (MPD) were used to quantify motion and evaluate motion correction. The quality of T1ρ maps was scored. In patients, T1ρ mapping was compared to cine imaging, T2 mapping and conventional post-contrast 2D late gadolinium enhancement (LGE). T1ρ values were assessed in remote and injured areas, using LGE as reference. Results: Despite breath holds, respiratory motion throughout T1ρw images was much larger in patients than in healthy subjects (5.1 ± 2.7 mm vs. 0.5 ± 0.4 mm, P < 0.01). In patients, the model-based non-rigid motion correction improved the alignment of T1ρw images, with higher DSC (87.7 ± 5.3% vs. 82.2 ± 7.5%, P < 0.01), and lower MPD (3.5 ± 1.9 mm vs. 5.1 ± 2.7 mm, P < 0.01). This resulted in significantly improved quality of the T1ρ maps (3.6 ± 0.6 vs. 2.1 ± 0.9, P < 0.01). Using this approach, T1ρ mapping could be used to identify LGE in patients with 93% sensitivity and 89% specificity. T1ρ values in injured (LGE positive) areas were significantly higher than in the remote myocardium (68.4 ± 7.9 ms vs. 48.8 ± 6.5 ms, P < 0.01). Conclusions: The proposed motion-corrected T1ρ mapping framework enables a quantitative characterization of myocardial injuries with relatively low sensitivity to respiratory motion. This technique may be a robust and contrast-free adjunct to LGE for gaining new insight into myocardial structural disorders. [ABSTRACT FROM AUTHOR]

To develop a fast volumetric T 1 mapping technique. A stack-of-stars (SOS) Look Locker technique based on the acquisition of undersampled radial data (>30× relative to Nyquist) and an efficient multi-slab excitation scheme is presented. A principal-component based reconstruction is used to reconstruct T 1 maps. Computer simulations were performed to determine the best choice of partitions per slab and degree of undersampling. The technique was validated in phantoms against reference T 1 values measured with a 2D Cartesian inversion-recovery spin-echo technique. The SOS Look Locker technique was tested in brain (n = 4) and prostate (n = 5). Brain T 1 mapping was carried out with and without k z acceleration and results between the two approaches were compared. Prostate T 1 mapping was compared to standard techniques. A reproducibility study was conducted in brain and prostate. Statistical analyses were performed using linear regression and Bland Altman analysis. Phantom T 1 values showed excellent correlations between SOS Look Locker and the inversion-recovery spin-echo reference (r2 = 0.9965; p < 0.0001) and between SOS Look Locker with slab-selective and non-slab selective inversion pulses (r2 = 0.9999; p < 0.0001). In vivo results showed that full brain T 1 mapping (1 mm3) with k z acceleration is achieved in 4 min 21 s. Full prostate T 1 mapping (0.9 × 0.9 × 4 mm3) is achieved in 2 min 43 s. T 1 values for brain and prostate were in agreement with literature values. A reproducibility study showed coefficients of variation in the range of 0.18–0.2% (brain) and 0.15–0.18% (prostate). A rapid volumetric T 1 mapping technique was developed. The technique enables high-resolution T 1 mapping with adequate anatomical coverage in a clinically acceptable time. [ABSTRACT FROM AUTHOR]

Incipient faults almost occur gradually at a low rate in systems and usually are unnoticeable during their early stages. If diagnostic tools or proper monitoring systems ignore them, they could not be detectable until their effects become severe and cause catastrophic damages to systems. This paper presents a survey on model-based (incipient) fault diagnosis approaches to show the significance of the incipient faults diagnosis in nonlinear closed-loop systems and, by taking a glance through data-based incipient fault diagnosis advancements, a picture of their present state of the art is also briefly discussed for completeness. Moreover, a classification of the most used state estimation filters is also provided. Consequently, recent works on incipient fault diagnosis approaches are reviewed, and an incipient fault diagnosis case study is investigated for a discrete-time nonlinear open-loop system affected by stochastic noise and disturbances. Specifically, a numerical example of a closed-loop three-tank system is considered, and simulations are accomplished, to demonstrate the inability of open-loop incipient fault diagnosis approaches in detecting incipient faults in the proposed closed-loop system. • Survey and classification of linear/nonlinear and open-loop/closed-loop incipient fault diagnosis approaches. • Application-based classification of applied estimation filters in the presence of incipient faults. • Problem formulation and solution of a nonlinear closed-loop system for model-based incipient fault diagnosis. • Discussion on a incipient fault diagnosis approach; nonlinear open-loop vs. closed-loop systems. [ABSTRACT FROM AUTHOR]

The cooling of the open-cathode proton exchange membrane fuel cell (PEMFC) is critical for the operational safety and overall efficiency. However, its control is challenging because of the model uncertainties and frequent disturbances caused by the power adjustment. To this end, this paper proposes a hybrid cooling control strategy by combining the merits of the model-based and data-driven methods. Firstly, a simplified nonlinear mechanistic model is used to exhibit the dynamic perturbations in terms of the different fan speeds and power conditions. Secondly, a modified active disturbance rejection control (ADRC) is developed by incorporating an identified nominal linear model into extended state observer. The external disturbances and the internal uncertainties beyond the nominal model are lumped as a total term, which will be estimated and mitigated in a real-time data-driven manner. The simulation results show that the proposed hybrid method is able to give a faster response with stronger robustness and less noise sensitivity against the uncertainties than the conventional PI and ADRC methods. The experimental test on a 500W open-cathode PEMFC verifies the simulation merits in both set-point tracking and disturbance rejection, depicting a promising prospect of the proposed hybrid method in the open-cathode PEMFC cooling control practice. • The nonlinearity of the fuel cell stack temperature is exhibited. • The uncertainties are accommodated by the data-driven estimation. • The effect of measurement noise is reduced by incorporating model information. • Experiments validate the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

Accurately estimating the state-of-charge (SOC) of lithium-ion batteries under complicated temperature conditions is crucial in all-climate battery management systems. This paper proposes a model-based SOC estimation method for series-connected battery pack with time-varying cell temperature. Systematic battery experiments are conducted to investigate the influences of changing temperature on both cell characteristics and cell-to-cell inconsistencies. A normalized open-circuit voltage (OCV) model is developed and applied in cell Thevenin model to describe the temperature-dependent OCV-SOC characteristic. The battery pack SOC is analyzed considering the effect of passive balance control. Then, a lumped parameter battery pack model is established by connecting cell models in series. To reduce computational complexity, a dual time-scale parameter identification framework is proposed which is supported by an online filtering process of selecting variable reference cell (VRC). An adaptive co-estimator is presented to update pack parameters in dual time-scale using an optimized recursive least squares algorithm, and to estimate the battery pack SOC using an extended Kalman filter. Experimental verifications are conducted under time-varying environmental temperature ranging from −40 °C to 40 °C. Results indicate the established model can well describe the dynamic behavior of battery pack, and the proposed method can estimate the battery pack SOC with considerably high precision. • Investigate cell-to-cell inconsistencies experimentally at different temperatures. • Establish a temperature-dependent normalized OCV model. • Select VRC online and develop a dual time-scale identification framework. • Propose a VFFRLS-EKF based co-estimator considering fast-varying cell temperature. • Prove the accuracy and robustness of proposed method experimentally. [ABSTRACT FROM AUTHOR]

The problem of workpiece tolerance verification is of high interest in the field of nondestructive testing and evaluation. In this paper, reflection mode immersion 2D laser-ultrasound tomography is proposed for control of dimensions of solid objects with piecewise linear surface profiles. Pulsed laser radiation is used for generation of short ultrasonic probe pulse providing high spatial resolution. Wideband array of piezofilm sensors is used for detection of the waves reflected by the external and internal surfaces of the object. The image reconstruction algorithm accounts for refraction of acoustic waves at the liquid-solid interface. The contrast-enhanced reconstructed image is processed using the virtual model of the inspected part and the least-squares method to measure its actual dimensions. The accuracy obtained in the experiments with PMMA and duralumin samples is better than 100 μ m in axial direction and is better than the size of the point-spread function in the material of the inspected part. [ABSTRACT FROM AUTHOR]

Under the premise that model-centric engineering enterprises of the future will necessitate specialized competencies and leadership, ongoing research is investigating both model curation as a practice and model curator as a new leadership role. Specific needs and approaches for model curation have been generated through research investigation using primary and secondary sources. Four notional stages are used to frame the digital engineering paradigm shift, describing the characteristics of the enterprise and the respective leadership. This investigation suggests seven alternative forms for implementing model curation leadership roles and responsibilities, based on unique needs and organizational characteristics. Related research on human-model interaction, model-centric decision making, and model trust and integrity provide evidence of the importance of model curation for the envisioned future of digital engineering. Interim research findings have been shared with research stakeholders as a preliminary validation of needs, as well as capturing emerging practices related to strategic management of models. [ABSTRACT FROM AUTHOR]

Due to the increased complexity of machines and plants service technicians have to be more trained to be able to maintain them. In many service operations it is still common to use simple service manuals like checklists. With the strong growth of the information and communication technology it is possible to create interactive and context-sensitive tools to support service technicians during operations. One problem is that these tasks are always in change by new knowledge. To solve this issue a model-based engine is being developed to dynamically generate instruction-tools for service operations for direct machine communication. [ABSTRACT FROM AUTHOR]

We survey the state-of-the-art on model-based formalisms for safety and security joint analysis, where safety refers to the absence of unintended failures, and security to absence of malicious attacks. We conduct a thorough literature review and – as a result – we consider fourteen model-based formalisms and compare them with respect to several criteria: (1) Modeling capabilities and Expressiveness: which phenomena can be expressed in these formalisms? To which extent can they capture safety-security interactions? (2) Analytical capabilities: which analysis types are supported? (3) Practical applicability: to what extent have the formalisms been used to analyze small or larger case studies? Furthermore, (1) we present more precise definitions for safety-security dependencies in tree-like formalisms; (2) we showcase the potential of each formalism by modeling the same toy example from the literature and (3) we present our findings and reflect on possible ways to narrow highlighted gaps. In summary, our key findings are the following: (1) the majority of approaches combine tree-like formal models; (2) the exact nature of safety-security interaction is still ill-understood and (3) diverse formalisms can capture different interactions; (4) analyzed formalisms merge modeling constructs from existing safety- and security-specific formalisms, without introducing ad hoc constructs to model safety-security interactions, or (5) metrics to analyze trade offs. Moreover, (6) large case studies representing safety-security interactions are still missing. [ABSTRACT FROM AUTHOR]

Abstract Software-intensive embedded systems have become an essential aspect of our lives. To cope with its growing complexity, modeling and model-driven engineering (MDE) are widely used for analysis, design, implementation, and testing of these systems. Since a large variety of software modeling practices is used in the domain of embedded software, it is important to understand and characterize the-state-of-the-practices and also the benefits, challenges and consequences of using software modeling approaches in this domain. The goal of this study is to investigate those practices in the embedded software engineering projects by identifying to what degree, why and how software modeling and MDE are used. To achieve this objective, we designed and conducted an online survey. Opinions of 627 practicing embedded software engineers from 27 different countries are included in the survey. The survey results reveal important and interesting findings about the state of software modeling and MDE practices in the worldwide embedded software industry. Among the results: (1) Different modeling approaches (from informal sketches to formalized models) are widely used in the embedded software industry with different needs and all of the usages could be effective depending on the various modeling characteristics; (2) The majority of participants use UML; and the second most frequently selected response is "Sketch/No formal modeling language", which shows the wide-spread informal usage of modeling; (3) In model-driven approaches, it is not so important to have a graphical syntax to represent the model (as in UML) and depending on the type of target embedded industrial sector, modeling stakeholders prefer models, which can be represented in a format that is readable by a machine (as in DSL); (4) Sequence diagrams and state-machines are the two most popular diagram types; (5) Top motivations for adopting MDE are: cost savings, achieving shorter development time, reusability and quality improvement. The survey results will shed light on the state of software modeling and MDE practices and provide practical benefits to embedded software professionals (e.g., practitioners, researchers and also educators). [ABSTRACT FROM AUTHOR]

Abstract The search for interpretable reinforcement learning policies is of high academic and industrial interest. Especially for industrial systems, domain experts are more likely to deploy autonomously learned controllers if they are understandable and convenient to evaluate. Basic algebraic equations are supposed to meet these requirements, as long as they are restricted to an adequate complexity. Here we introduce the genetic programming for reinforcement learning (GPRL) approach based on model-based batch reinforcement learning and genetic programming, which autonomously learns policy equations from pre-existing default state–action trajectory samples. GPRL is compared to a straightforward method which utilizes genetic programming for symbolic regression, yielding policies imitating an existing well-performing, but non-interpretable policy. Experiments on three reinforcement learning benchmarks, i.e., mountain car, cart–pole balancing, and industrial benchmark, demonstrate the superiority of our GPRL approach compared to the symbolic regression method. GPRL is capable of producing well-performing interpretable reinforcement learning policies from pre-existing default trajectory data. [ABSTRACT FROM AUTHOR]

Highlights • Three simpler, lower workload and thus potentially more clinically desirable alternative nutrition protocols used in conjunction with the STAR insulin Glycaemic Control (GC) protocol are investigated. • Each nutrition protocol is simulated on a 221 patient virtual cohort, and GC performance, safety and workload assessed. • A fixed nutrition rate that varies in steps by day over the first three days of GC can offer similar performance and potentially enhanced safety, while still delivering world leading nutrition delivery to hyperglycemic, insulin resistant patients. • The result is relatively general and could be adapted by STAR or any form of GC to reduce clinical workload without affecting GC safety and quality, and thus make it more clinically acceptable. • The results delineate clear, more general tradeoffs between the safety and performance of glycemic control, workload, and nutrition delivery for hyperglycemic, insulin resistant ICU patients. Abstract Background and Objective Hyperglycaemia is commonplace in the adult intensive care unit (ICU), and has been associated with increased morbidity and mortality. Effective glycaemic control (GC) can reduce morbidity and mortality, but has proven difficult. STAR is a model-based GC protocol that uniquely maintains normoglycaemia by changing both insulin and nutrition interventions, and has been proven effective in controlling blood glucose (BG) in the ICU. However, most ICU GC protocols only change insulin interventions, making the variable nutrition aspect of STAR less clinically desirable. This paper compares the performance of STAR modulating only insulin, with three simpler alternative nutrition protocols in clinically evaluated virtual trials. Methods Alternative nutrition protocols are fixed nutrition rate (100% caloric goal), CB (Cahill et al. best) stepped nutrition rate (60%, 80% and 100% caloric goal for the first 3 days of GC, and 100% thereafter) and SLQ (STAR lower quartile) stepped nutrition rate (65%, 75% and 85% caloric goal for the first 3 days of GC, and 85% thereafter). Each nutrition protocol is simulated with the STAR insulin protocol on a 221 patient virtual cohort, and GC performance, safety and total intervention workload are assessed. Results All alternative nutrition protocols considerably reduced total intervention workload (14.6–19.8%) due to reduced numbers of nutrition changes. However, only the stepped nutrition protocols achieved similar GC performance to the current variable nutrition protocol. Of the two stepped nutrition protocols, the SLQ nutrition protocol also improved GC safety, almost halving the number of severe hypoglycaemic cases (5 vs. 9, P = 0.42). Conclusions Overall, the SLQ nutrition protocol was the best alternative to the current variable nutrition protocol, but either stepped nutrition protocol could be adapted by STAR to reduce workload and make it more clinically acceptable, while maintaining its proven performance and safety. [ABSTRACT FROM AUTHOR]

To meet the rising demands for safety and reliability of modern industrial control systems, the model-based fault diagnosis problem has attracted much attention in the past few decades both from the academic communities and in practical applications. Among the rich body of literature, the majority of the results are proposed for linear time invariant systems despite the fact that most practical processes are inherently time-varying by nature and discretized for online implementations. Recently, with the ever-increasing system complexity and scale, the model-based fault diagnosis issue for linear discrete time-varying (LDTV) systems has become a hot research topic of both theoretical importance and practical significance, and a great number of results have been reported with many open problems deserving further investigation. To reflect the latest progress in this emerging research branch, this survey aims to provide a timely reference to readers interested in this topic. More specifically, in this survey, the techniques for model-based fault diagnosis for LDTV systems are classified into observer-based methods, parity space-based approaches and parameter estimation schemes, which constitute the three main parts of this survey. The background and the latest progress for each class of these techniques are comparatively discussed with particular focuses on the fault detection and the fault estimation issues. Additionally, an overview on their practical applications to specific industrial plants is provided. Furthermore, the possible future research directions concerning the model-based fault diagnosis for LDTV processes are pointed out, followed by comprehensive concluding remarks. [ABSTRACT FROM AUTHOR]

Recommender systems have emerged in the e-commerce domain and have been developed to actively recommend appropriate items to online users. The use of recently developed hybrid recommendation systems has helped overcome the main drawbacks of Content-Based Filtering (CBF) and Collaborative Filtering (CF). In hybrid recommendation systems that combine CF and CBF, the CF part uses two methods, including memory- and model-based approaches. Both approaches have some advantages and disadvantages for item recommendation. Sparsity has been one of the main difficulties associated with these approaches, whereas recommendation with high accuracy has been one of the important advantages of the memory-based approach. However, this approach is not scalable for current recommendation systems as their databases include huge numbers of items and users. In contrast, the model-based approach generates recommendations with low accuracy but is scalable for large databases in e-commerce recommender systems. Accordingly, to address this problem and take advantage of both approaches, in this work, we propose a new hybrid recommendation method and evaluate it using a real-world dataset. The aim is to improve efficiency and accuracy by designing a heuristic hybrid recommender method that combines memory-based and model-based approaches. Specifically, we use ontology in the CF part and improve ontology structure by eliminating uniformity of edges of the hierarchical relation between concepts (IS-A relation) in item ontology in the CBF part. Ontology structure is considered for improving accuracy; according to this, a new method for measuring semantic similarity that is more accurate than the traditional methods is presented. This new method can enhance the accuracy of CF and CBF in our method. In addition, the number of searches required to find similar clusters and neighbor users of the target user is decreased significantly using ontology, enhanced clustering and the new proposed algorithm. We evaluate the proposed method using a real-world dataset. The experimental results show that our method is more scalable and accurate than the benchmark k -Nearest Neighbor ( k -NN) and model-based recommendation methods. [ABSTRACT FROM AUTHOR]

Accurate 3-D surface segmentation is a challenging task in echocardiography because of the relatively low image quality. We introduce a new method for 3-D segmentation of the endocardium involving temporal decorrelation of echo signals originating from tissue and blood using radiofrequency (RF) signals acquired in 3-D Doppler mode. Temporal features were extracted in 3-D Doppler mode, where a sequence of RF lines is recorded for each image line. Each set of RF lines is highly correlated because of the high pulse repetition frequency. However, for high blood flow, the RF signals will decorrelate over time in contrast to the endocardium, which will remain relatively highly correlated over time. These decorrelation features permit differentiation between myocardial tissue and blood flow. We describe an implementation of a 3-D segmentation model in which temporal information is used as external constraint. The model was validated in a phantom and in vivo in healthy volunteers (n = 5). The phantom study revealed that the model successfully segmented the artificial blood lumen even for low flow velocity and illustrated the sensitivity of the segmentations to flow rate. In healthy volunteers, high Dice similarity indices indicate that 3-D segmentation of the endocardial border in vivo is feasible. [ABSTRACT FROM AUTHOR]

Reinforcement learning theory distinguishes “model-free” learning, which fosters reflexive repetition of previously rewarded actions, from “model-based” learning, which recruits a mental model of the environment to flexibly select goal-directed actions. Whereas model-free learning is evident across development, recruitment of model-based learning appears to increase with age. However, the cognitive processes underlying the development of model-based learning remain poorly characterized. Here, we examined whether age-related differences in cognitive processes underlying the construction and flexible recruitment of mental models predict developmental increases in model-based choice. In a cohort of participants aged 9–25, we examined whether the abilities to infer sequential regularities in the environment (“statistical learning”), maintain information in an active state (“working memory”) and integrate distant concepts to solve problems (“fluid reasoning”) predicted age-related improvements in model-based choice. We found that age-related improvements in statistical learning performance did not mediate the relationship between age and model-based choice. Ceiling performance on our working memory assay prevented examination of its contribution to model-based learning. However, age-related improvements in fluid reasoning statistically mediated the developmental increase in the recruitment of a model-based strategy. These findings suggest that gradual development of fluid reasoning may be a critical component process underlying the emergence of model-based learning. [ABSTRACT FROM AUTHOR]

The purpose of this study was to quantify the accuracy and precision of a biplane fluoroscopy system for model-based tracking of in vivo hindfoot motion during over-ground gait. Gait was simulated by manually manipulating a cadaver foot specimen through a biplane fluoroscopy system attached to a walkway. Three 1.6-mm diameter steel beads were implanted into the specimen to provide marker-based tracking measurements for comparison to model-based tracking. A CT scan was acquired to define a gold standard of implanted bead positions and to create 3D models for model-based tracking. Static and dynamic trials manipulating the specimen through the capture volume were performed. Marker-based tracking error was calculated relative to the gold standard implanted bead positions. The bias, precision, and root-mean-squared (RMS) error of model-based tracking was calculated relative to the marker-based measurements. The overall RMS error of the model-based tracking method averaged 0.43 ± 0.22 mm and 0.66 ± 0.43° for static and 0.59 ± 0.10 mm and 0.71 ± 0.12° for dynamic trials. The model-based tracking approach represents a non-invasive technique for accurately measuring dynamic hindfoot joint motion during in vivo , weight bearing conditions. The model-based tracking method is recommended for application on the basis of the study results. [ABSTRACT FROM AUTHOR]

This paper presents a model based approach for defining automotive functional safety requirements and provides a solution to ensure functional safety through model-based diagnosis and fault tolerant control. This model-based approach is consistent with ISO 26262 – functional safety standard. In particular, this paper presents the necessary steps for defining and implementing functional safety requirements, including item and function definition, Hazard Analysis and Risk Assessment, as well as the design of a model-based diagnostic and fault tolerant control (FTC) system that can lead to a systematic solution to automotive functional safety problems. The methodology proposed in this paper is applied to the problem of torque functional safety of pedal-by-wire systems. [ABSTRACT FROM AUTHOR]

In this paper the impact of different distance metrics as well as different sortings of the parameter space on the shape of the objective function for model-based leakage localization is investigated. Leakage localization is formulated as an optimization problem solved with a differential evolution algorithm. Distance metrics and sortings are evaluated through the convergence speed of the algorithm and the quality of the results in terms of a topological distance from the leak found by the algorithm to the real leak. The algorithm is tested on a hydraulic model of a real-world network and has shown that a Cuthill-McKee ordering of the search space together with correlation distance metric performs the best. [ABSTRACT FROM AUTHOR]

Existing national forest inventory plots, an airborne lidar scanning (ALS) system, and a space profiling lidar system (ICESat-GLAS) are used to generate circa 2005 estimates of total aboveground dry biomass (AGB) in forest strata, by state, in the continental United States (CONUS) and Mexico. The airborne lidar is used to link ground observations of AGB to space lidar measurements. Two sets of models are generated, the first relating ground estimates of AGB to airborne laser scanning (ALS) measurements and the second set relating ALS estimates of AGB (generated using the first model set) to GLAS measurements. GLAS then, is used as a sampling tool within a hybrid estimation framework to generate stratum-, state-, and national-level AGB estimates. A two-phase variance estimator is employed to quantify GLAS sampling variability and, additively, ALS-GLAS model variability in this current, three-phase (ground-ALS-space lidar) study. The model variance component characterizes the variability of the regression coefficients used to predict ALS-based estimates of biomass as a function of GLAS measurements. Three different types of predictive models are considered in CONUS to determine which produced biomass totals closest to ground-based national forest inventory estimates - (1) linear (LIN), (2) linear-no-intercept (LNI), and (3) log-linear. For CONUS at the national level, the GLAS LNI model estimate (23.95 ± 0.45 Gt AGB), agreed most closely with the US national forest inventory ground estimate, 24.17 ± 0.06 Gt, i.e., within 1%. The national biomass total based on linear ground-ALS and ALS-GLAS models (25.87 ± 0.49 Gt) overestimated the national ground-based estimate by 7.5%. The comparable log-linear model result (63.29 ± 1.36 Gt) overestimated ground results by 261%. All three national biomass GLAS estimates, LIN, LNI, and log-linear, are based on 241,718 pulses collected on 230 orbits. The US national forest inventory (ground) estimates are based on 119,414 ground plots. At the US state level, the average absolute value of the deviation of LNI GLAS estimates from the comparable ground estimate of total biomass was 18.8% (range: Oregon, − 40.8% to North Dakota, 128.6%). Log-linear models produced gross overestimates in the continental US, i.e., > 2.6x, and the use of this model to predict regional biomass using GLAS data in temperate, western hemisphere forests is not appropriate. The best model form, LNI, is used to produce biomass estimates in Mexico. The average biomass density in Mexican forests is 53.10 ± 0.88 t/ha, and the total biomass for the country, given a total forest area of 688,096 km 2 , is 3.65 ± 0.06 Gt. In Mexico, our GLAS biomass total underestimated a 2005 FAO estimate (4.152 Gt) by 12% and overestimated a 2007/8 radar study's figure (3.06 Gt) by 19%. [ABSTRACT FROM AUTHOR]

• Behavioral addictions share neurobiological and behavioral characteristics with SUDs. • Computational modeling may help clarify underlying mechanisms. • Aberrant model-based & -free control are presented as common computational principles. • Reinforcement learning, Bayesian inference, and neural models are introduced. • Possible extensions of models for SUDs, as well as new perspectives, are discussed. Non-pharmacological behavioral addictions, such as pathological gambling, videogaming, social networking, or internet use, are becoming major public health concerns. It is not yet clear how behavioral addictions could share many major neurobiological and behavioral characteristics with substance use disorders, despite the absence of direct pharmacological influences. A deeper understanding of the neurocognitive mechanisms of addictive behavior is needed, and computational modeling could be one promising approach to explain intricately entwined cognitive and neural dynamics. This review describes computational models of addiction based on reinforcement learning algorithms, Bayesian inference, and biophysical neural simulations. We discuss whether computational frameworks originally conceived to explain maladaptive behavior in substance use disorders can be effectively extended to non-substance-related behavioral addictions. Moreover, we introduce recent studies on behavioral addictions that exemplify the possibility of such extension and propose future directions. [ABSTRACT FROM AUTHOR]

This paper describes and demonstrates a model-based sensor selection and controller design framework for robust control of air–fuel-ratio, air flow and EGR flow for turbocharged stoichiometric engines using low pressure EGR, waste-gate turbo-charging, intake throttling and variable valve timing. Model uncertainties, disturbances, transport delays, and sensor- and actuator-characteristics are considered in this framework. Based on the required control performance and candidate sensor sets, the framework synthesizes an H ∞ feedback controller and evaluates the viability of the candidate sensor set through analysis of the singular structured value μ of the closed-loop system in the frequency domain. The framework can also be used to understand if relaxing the controller performance requirements enable the use of a simpler (less costly) sensor set. The sensor selection and controller co-design approach is applied here, for the first time, to turbo-charged engines using exhaust gas circulation. High fidelity GT-Power simulations are used to validate the approach. The computed optimal controllers were able to maintain the AFR within 14.7 ± 0.3, control the torque within 8.2% error and the EGR ratio within 1.2% error at different drive cycles with sensor inaccuracies and actuator dynamics. • Sensor architecture and controller are designed simultaneously. • Actuator/sensor performance, uncertainties and control targets are modeled. • The method is applied to a high-fidelity SI engine model for AFR and flow control. [ABSTRACT FROM AUTHOR]

Local and regional energy systems are becoming increasingly entangled. Therefore, models for optimizing these energy systems are becoming more and more complex and the required computing resources (run-time and random access memory usage) are increasing rapidly. The computational requirements can basically be reduced solver-based (mathematical optimization of the solving process) or model-based (simplification of the real-world problem in the model). This paper deals with identifying how the required computational requirements for solving optimization models of multi-energy systems with high spatial resolution change with increasing model complexity and which model-based approaches enable to reduce the requirements with the lowest possible model deviations. A total of 12 temporal model reductions (reduction of the number of modeled time steps), nine techno-spatial model reductions (reduction of possible solutions), and five combined reduction schemes were theoretically analyzed and practically applied to a test case. The improvement in reducing the usage of computational resources and the impact on the quality of the results were quantified by comparing the results with a non-simplified reference case. The results show, that the run-time to solve a model increases quadratically and memory usage increases linearly with increasing model complexity. The application of various model adaption methods have enabled a reduction of the run-time by over 99% and the memory usage by up to 88%. At the same time, however, some of the methods led to significant deviations of the model results. Other methods require a profound prior knowledge and understanding of the investigated energy systems to be applied. In order to reduce the run-time and memory requirements for investment optimization, while maintaining good quality results, we recommend the application of (1) a pre-model that is used to (1a) perform technological pre-selection and (1b) define reasonable technological boundaries, (2) spatial sub-modeling along network nodes, and 3) temporal simplification by only modeling every n th day (temporal slicing), where at least 20% of the original time steps are modeled. Further simplifications such as spatial clustering or larger temporal simplification can further reduce the computational effort, but also result in significant model deviations. • Focus on model-based methods for run-time and memory (RAM) usage reduction. • Run-time increases quadratically, RAM usage linearly with increasing model complexity. • Numerous model-based methods were analyzed theoretically and tested practically. • A step-by-step guide for reducing computational requirements is provided. • Run-time was reduced by over 99 % and memory usage by up to 88 % for a test case. [ABSTRACT FROM AUTHOR]

During the design of complex mechatronic systems, multiple engineering teams covering disciplines such as mechanics, electronics, control and safety must cooperate and exchange information. Today's state-of-the-practice systems engineering methodologies rely on document-based approaches to elicit and manage requirements, describe the system architecture, log design decisions and exchange this information between the project members. Unfortunately, the informal character of the documents causes misinterpretations, generates inconsistencies and prohibits the use of computer power for validation. Model-based Systems Engineering techniques promise to alleviate some of these issues. SysML is a formal graphical modeling language that aims to support this model-based design of complex multidisciplinary systems. Yet, as SysML is a general-purpose language intended to cover a broad range of systems, applying SysML to a specific domain such as production lines is not a trivial task. In this paper, we present some experiences and limitations encountered when applying and tailoring SysML 1.4 to support the systems engineering process of an industrial use case: the design of a production line by a multi-disciplinary team across several companies, consisting of process experts, control engineers, thermal designers, software engineers, mechanical designers, signal processing experts, roboticists and embedded hardware designers. [ABSTRACT FROM AUTHOR]

Schizomid female flagellum, including the number of “flagellomeres” and the setae, are among the most important taxonomic characters within the order. It is widely used to diagnose genera within both extant families. Despite the traditional use in schizomid taxonomy, the phylogenetic signal of these characters has been only once tested in the systematics of Protoschizomidae. However, our understanding of the pseudosegmentation of the flagellum, along with different terminologies of the relative position of certain setae, creates conflict in terms of homology assessments. In the present contribution, homology hypotheses and the phylogenetic importance of these characters across the only two extant families Protoschizomidae and Hubbardiidae were established and tested. The analyses were based on 44 morphological characters of 22 schizomid exemplar species, and two thelyphonid species as outgroup. Parsimony, Bayesian inference analyses, and the ancestral state reconstruction of the “annuli” characters were conducted. The resulting topology revealed Hubbardiidae as monophyletic with the two subfamilies currently recognized (Hubbardiinae + Megaschizominae). However, Protoschizomidae was not recovered as monophyletic. The ancestral state reconstruction of the number of annuli in the common ancestor of Schizomida suggested gain and loss of the different annuli at different hierarchical levels. Finally, the flagellum contains robust taxonomic characters, but caution needs to be taken when used to diagnose genera. [ABSTRACT FROM AUTHOR]

Dual-systems frameworks propose that moral judgments are derived from both an immediate emotional response, and controlled/rational cognition. Recently Cushman (2013) proposed a new dual-system theory based on model-free and model-based reinforcement learning. Model-free learning attaches values to actions based on their history of reward and punishment, and explains some deontological, non-utilitarian judgments. Model-based learning involves the construction of a causal model of the world and allows for far-sighted planning; this form of learning fits well with utilitarian considerations that seek to maximize certain kinds of outcomes. I present three concerns regarding the use of model-free reinforcement learning to explain deontological moral judgment. First, many actions that humans find aversive from model-free learning are not judged to be morally wrong. Moral judgment must require something in addition to model-free learning. Second, there is a dearth of evidence for central predictions of the reinforcement account-e.g., that people with different reinforcement histories will, all else equal, make different moral judgments. Finally, to account for the effect of intention within the framework requires certain assumptions which lack support. These challenges are reasonable foci for future empirical/theoretical work on the model-free/model-based framework. [ABSTRACT FROM AUTHOR]

Traditional model-based approaches are based on periodic iterations, where the continuous-time model is discretized with a fixed period. Despite the easiness in analysis and design, such periodic approximation model may be undesirable from the computation-efficiency point of view. This paper presents the Lebesgue-approximation model (LAM) of continuous-time nonlinear systems, where the iteration is activated on an “as-needed” basis, but not periodically. We show that the proposed LAM behaves exactly the same as a specific event-triggered feedback system, through which the properties of the LAM can be studied. We provide a sufficient condition to ensure asymptotic stability of the LAM and derive theoretical bounds on the difference between the states of the LAM and the original continuous-time system. The LAM is then integrated in the particle-filtering approach for fault prognosis. Simulation results show that the LAM can dramatically reduce the number of iterations in prognosis without sacrificing accuracy and precision. [ABSTRACT FROM AUTHOR]

The state-of-energy of lithium-ion batteries is an important evaluation index for energy storage systems in electric vehicles and smart grids. To improve the battery state-of-energy estimation accuracy and reliability, an online model-based estimation approach is proposed against uncertain dynamic load currents and environment temperatures. Firstly, a three-dimensional response surface open-circuit-voltage model is built up to improve the battery state-of-energy estimation accuracy, taking various temperatures into account. Secondly, a total-available-energy-capacity model that involves temperatures and discharge rates is reconstructed to improve the accuracy of the battery model. An extended-Kalman-filter and particle-filter based dual filters algorithm is then developed to establish an online model-based estimator for the battery state-of-energy. The extended-Kalman-filter is employed to update parameters of the battery model using real-time battery current and voltage at each sampling interval, while the particle-filter is applied to estimate the battery state-of-energy. Finally, the proposed approach is verified by experiments conducted on a LiFePO 4 lithium-ion battery under different operating currents and temperatures. Experimental results indicate that the battery model simulates battery dynamics robustly with high accuracy, and the estimates of the dual filters converge to the real state-of-energy within an error of ±4%. [ABSTRACT FROM AUTHOR]

This work reveals the applicability of a relatively new optimization technique, Leapfrogging, for both nonlinear regression modeling and a methodology for nonlinear model-predictive control. Both are relatively simple, yet effective. The application on a nonlinear, pilot-scale, shell-and-tube heat exchanger reveals practicability of the techniques. [ABSTRACT FROM AUTHOR]

Many applications including object reconstruction, robot guidance, and. scene mapping require the registration of multiple views from a scene to generate a complete geometric and appearance model of it. In real situations, transformations between views are unknown and it is necessary to apply expert inference to estimate them. In the last few years, the emergence of low-cost depth-sensing cameras has strengthened the research on this topic, motivating a plethora of new applications. Although they have enough resolution and accuracy for many applications, some situations may not be solved with general state-of-the-art registration methods due to the signal-to-noise ratio (SNR) and the resolution of the data provided. The problem of working with low SNR data, in general terms, may appear in any 3D system, then it is necessary to propose novel solutions in this aspect. In this paper, we propose a method, μ -MAR, able to both coarse and fine register sets of 3D points provided by low-cost depth-sensing cameras, despite it is not restricted to these sensors, into a common coordinate system. The method is able to overcome the noisy data problem by means of using a model-based solution of multiplane registration. Specifically, it iteratively registers 3D markers composed by multiple planes extracted from points of multiple views of the scene. As the markers and the object of interest are static in the scenario, the transformations obtained for the markers are applied to the object in order to reconstruct it. Experiments have been performed using synthetic and real data. The synthetic data allows a qualitative and quantitative evaluation by means of visual inspection and Hausdorff distance respectively. The real data experiments show the performance of the proposal using data acquired by a Primesense Carmine RGB-D sensor. The method has been compared to several state-of-the-art methods. The results show the good performance of the μ -MAR to register objects with high accuracy in presence of noisy data outperforming the existing methods. [ABSTRACT FROM AUTHOR]

Accurate estimations of battery energy and available power capability are of great of importance for realizing an efficient and reliable operation of electric vehicles. To improve the estimation accuracy and reliability for battery state of energy and power capability, a novel model-based joint estimation approach has been proposed against uncertain external operating conditions and internal degradation status of battery cells. Firstly, it proposes a three-dimensional response surface open circuit voltage model to calibrate the estimation inaccuracies of battery state of energy. Secondly, the adaptive unscented Kalman filter (AUKF) is employed to develop a novel model-based joint state estimator for battery state of energy and power capability. The AUKF algorithm utilizes the well-known features of the Kalman filter but employs the method of unscented transform (UT) and adaptive error covariance matching technology to improve the state estimation accuracy. Thirdly, the proposed joint estimator has been verified by a LiFePO 4 lithium-ion battery cell under different operating temperatures and aging levels. The result indicates that the estimation errors of battery voltage and state-of-energy are less than 2% even if given a large erroneous initial value, which makes the state of available power capability predict more accurate and reliable for the electric vehicles application. [ABSTRACT FROM AUTHOR]

Despite being considered primarily a mood disorder, major depressive disorder (MDD) is characterized by cognitive and decision making deficits. Recent research has employed computational models of reinforcement learning (RL) to address these deficits. The computational approach has the advantage in making explicit predictions about learning and behavior, specifying the process parameters of RL, differentiating between model-free and model-based RL, and the computational model-based functional magnetic resonance imaging and electroencephalography. With these merits there has been an emerging field of computational psychiatry and here we review specific studies that focused on MDD. Considerable evidence suggests that MDD is associated with impaired brain signals of reward prediction error and expected value (‘wanting’), decreased reward sensitivity (‘liking’) and/or learning (be it model-free or model-based), etc., although the causality remains unclear. These parameters may serve as valuable intermediate phenotypes of MDD, linking general clinical symptoms to underlying molecular dysfunctions. We believe future computational research at clinical, systems, and cellular/molecular/genetic levels will propel us toward a better understanding of the disease. [ABSTRACT FROM AUTHOR]

Gaining a better understanding of the biological mechanisms underlying the individual variation observed in response to rewards and reward cues could help to identify and treat individuals more prone to disorders of impulsive control, such as addiction. Variation in response to reward cues is captured in rats undergoing autoshaping experiments where the appearance of a lever precedes food delivery. Although no response is required for food to be delivered, some rats (goal-trackers) learn to approach and avidly engage the magazine until food delivery, whereas other rats (sign-trackers) come to approach and engage avidly the lever. The impulsive and often maladaptive characteristics of the latter response are reminiscent of addictive behaviour in humans. In a previous article, we developed a computational model accounting for a set of experimental data regarding sign-trackers and goal-trackers. Here we show new simulations of the model to draw experimental predictions that could help further validate or refute the model. In particular, we apply the model to new experimental protocols such as injecting flupentixol locally into the core of the nucleus accumbens rather than systemically, and lesioning of the core of the nucleus accumbens before or after conditioning. In addition, we discuss the possibility of removing the food magazine during the inter-trial interval. The predictions from this revised model will help us better understand the role of different brain regions in the behaviours expressed by sign-trackers and goal-trackers. [ABSTRACT FROM AUTHOR]

Accurate estimations of cell state-of-charge for series-connected battery pack are remaining challenge due to the inhabited inconsistency characteristic. This paper tries to make three contributions. (1) A parametric modeling method is proposed for developing model-based SoC estimation approach. Based on the analysis for the mapping relationship between battery parameters and its SoC, a three-dimensional response surface open circuit voltage model is proposed for correcting erroneous SoC estimation. (2) An improved battery model considering model and parameter uncertainties is developed for modeling multiple cells in battery pack. A filtering process for selecting cell having “average capacity” and “average resistance” of battery pack has been developed to build the nominal battery model. Then a bias correction for single cells based on an average cell model is proposed for improving the expansibility of the nominal battery model. (3) A novel model-based dual-scale cell SoC estimator has been proposed. It uses micro and macro time scale to estimate the SoC of the selected cell and unselected cells respectively. Lastly, the proposed approach has been verified by two lithium-ion battery packs. The results show that the maximum estimation errors for cell voltage and SoC are less than 30 mV and 1% respectively against uncertain diving cycles and battery packs. [ABSTRACT FROM AUTHOR]

The control system of aeroengine design is a complex system engineeringincludingmany procedure of different subsystems. To improve the efficiency of areoengine control system design and reduce the cost and development period of research procedure, model-based areoengine control system design method can be used for integrating systems, collaborative design and optimizing design. Model-based aeroengine control system design workflow consists of the phases from analysis of system requirements to semi-physics simulation verification. The management approach and design procedure can help to separate the whole design workflow into different sub-phases which can help to scheme the concurrent design procedure of complex control system in order to improve the [ABSTRACT FROM AUTHOR]

A minimal recruitment model can be used to guide mechanical ventilation PEEP selection for acute respiratory distress syndrome (ARDS) patients. However, implementation of this model requires a specific clinical protocol and is computationally expensive, and thus not suitable for bedside application. This work aims to improve the performance and bedside utility of the minimal recruitment model through simplifying the model, and improving the identification algorithm without compromising the model's physiological relevance to the disease. Identifying the model requires fitting of 8 unique parameters to pressure–volume data at multiple levels of positive end-expiratory pressure (PEEP). A minimal algorithm is proposed to improve the model's performance. The algorithm utilises a non-linear least-squares solver to estimate a global set of the parameters to a pressure–volume curve at one PEEP level. These global parameters were then subsequently used to estimate other parameters at different pressure–volume curves. The accuracy and computational performance of the minimal algorithm is compared to the grid search algorithm for 2 ARDS patient cohorts. The median [IQR] absolute percentage curve fitting error over all patients for grid search algorithm is 1.40% [0.55–3.75], and for the minimal algorithm is 2.43% [0.83–8.09] ( p < 0.005). The median [IQR] computational time for all patients for the grid search algorithm is 394.51s [284.79–630.45], and for the minimal algorithm is 0.72s [0.39–1.46], where a 600% of reduction of computational time was found for the minimal algorithm. The estimated parameters using the minimal algorithm are correlated with the grid search algorithm with median person's correlation of R 2 > 0.9. The model fitting error for the minimal algorithm is higher than the grid search algorithm. However the model was able capture similar trends in physiologically relevant parameters without the loss of important clinical information. The minimal algorithm is less computationally intensive than the grid search algorithm, whilst still providing a means of selecting PEEP with only a small increase in model fitting error. The minimal algorithm is able to improve computational performance while maintaining the ability to capture physiological parameters as the grid search algorithm. The significant reduction in computational time encourages its clinical application at the bedside for decision making. [ABSTRACT FROM AUTHOR]

Abstract: Nonlinear, adaptive, process-model based control is demonstrated in a cascaded single-input–single-output mode for pressure drop control in a pilot-scale packed absorption column. The process is shown to be nonlinear. Control is demonstrated in both servo and regulatory modes, for no wind-up in a constrained situation, and for bumpless transfer. Model adaptation is demonstrated and shown to provide process insight. The application procedure is revealed as a design guide to aid others in implementing process-model based control. [Copyright &y& Elsevier]

Highlights: [•] Boltzmann sigmoid function was incorporated into the model-based 2D correlation IR. [•] Model-based 2D IR was used to study the mechanism of T g and T ll of monodisperse aPS. [•] Flip motions of the phenyl rings are earlier than main chains creep of aPS during T g. [•] T ll is a consequence of the melts motions and the slippage of chain entanglements. [Copyright &y& Elsevier]

The time-course response after a single injection of botulinum toxin (BoNT) for post-stroke spasticity is debated. We addressed this issue by conducting a systematic review and a pharmacodynamic model-based meta-analysis. We searched Medline, PeDro and Google Scholar databases up to March 2020, selecting randomized controlled trials of post-stroke and traumatic brain injury patients with arm or leg muscle hypertonia, comparing BoNT to placebo, or different BoNT preparations. The main outcome was change in Modified Ashworth Scale (MAS) score. A non-linear mixed effect model was used to estimate maximal toxin and placebo effects (Emax and EPlacebo), the effect disappearance half-life (T1/2off) of BoNT and the doses achieving 50 and 80% of Emax (D50 and D80). The equivalence ratios between different BoNT preparations were calculated from D50 values. Adverse events were recorded. Altogether, 2,236 unique records were screened by 2 independent reviewers: 35 eligible trials including 3011 patients (95% post-stroke) were identified. For all BoNT preparations, the BoNT Emax of -1.11 (95% credible interval -1.31; -0.29) was reached at 5 weeks; the maximal placebo effect was -0.30 (-0.37; -0.22). Both D50 and D80 differed significantly by muscle volume. At D50, the equivalence ratio was significantly higher for abobotulinumtoxinA (3.35) than onabotulinumtoxinA and lower for letibotulinumtoxinA (0.41). T1/2off was longer for abobotulinumtoxinA than for onabotulinumtoxinA and the other preparations (13.1 weeks [95% credible interval 7.7; 19.3] vs 8.6 weeks [7.1; 10.1]). Adverse events were minor, with a weak, but significant, dose–response relation for muscle weakness. This first pharmacodynamic model-based meta-analysis of individuals with stroke revealed that for all BoNT-A preparations, BoNT-A injections to treat spasticity have maximal effect at 5 weeks. The T1/2off was longer for abobotulinumtoxinA than other preparations. Differences between certain BoNT unit scales were also confirmed. [ABSTRACT FROM AUTHOR]

Highlights: [•] We synthesize sensor faults/failures diagnosis method for polytopic LPV system. [•] A novel bank of filters is designed and synthesized with respect to a quality factor definition in order to guarantee the fault diagnosis performance. [•] We provided the stability conditions of the polytopic LPV filter based on LMI. [•] Effect of sensor faults on a real closed-loop winding machine are presented on the whole operating conditions. [Copyright &y& Elsevier]

Abstract: The proton exchange membrane fuel cell systems (PEMFC)s are interesting devices for energy conversion. Recent researches are aimed at developing new monitoring and diagnosis techniques; a good management of these systems would allow optimizing the performance and reducing their degradation. The objective of a suitable diagnostic tool is to identify and isolate the different faults that may occur in the system being monitored in real time. Therefore, the main features of computational methods are accuracy, reliability and high computational speed. In order to perform the diagnosis, it is necessary to evaluate different approaches. In this work different model-based approaches are investigated as well as their validation and applications. An overview of different methodologies available in the literature is proposed, which is oriented to help in developing suitable diagnostic tool for PEMFC monitoring and fault detection and isolation (FDI). [Copyright &y& Elsevier]