High-level Components Research Articles

A meta-model for understanding ‘green-red loop’ social-water interactions at a global scale

Within the coupled human and natural systems (CHANS), social-water interactions exhibit the ‘green-loop’ and ‘red-loop’ patterns, where social development is dependent on the local and external environment for resource provision, respectively. Sustainable management requires coordinating the interactions between water and the other socio-environmental systems, which we term ‘systems water management (SYWM)’. To understand the generic social-water interactions and obtain management implications for green-red loop systems, we develop a SYWM meta-model that conceptualises high-level social-environmental components and formulates their interactions into casual loops. This meta-model is evaluated via structural equation modelling using global datasets. Red- and green-loop scenarios are constructed to investigate the SYWM differences, and mediation analysis is conducted to quantify the reciprocal effects through the loops. Results show a critically weak causal link (0.22) from environmental state to quality of life, implying that the current water management performances have not been successfully incorporated for guiding further social development. Green- and red-loop systems have different direct causal effects in each link, which drives the corresponding behaviour. Red-loop systems have stronger total effects in causal links within the human system, depicting their strengths in socio-economic and water management decision-making. In contrast, green-loop systems have stronger total effects within the natural system, which symbolises receiving more environmental feedback. A sustainable SYWM that enhances both decision-making and feedback is recommended for green-red loop systems. The meta-model provides a platform for interdisciplinary collaborations and practical tools development that implements systems thinking in water management for sustainable development.

Thermoviscosimetric characteristics of borosilicate glasses with regard to the promising area of low-melting compositions currently being developed for the removable small-scale melter designed by Mayak Production Association

Relevance. The need to obtain experimental data on dependence of thermoviscosimetric properties of the examined borosilicate glasses on temperature, in order to solve the problem of developing a low-melting composition of borosilicate glass for a small-scale melter designed by Mayak Production Association. Aim. To get the most complete characterization of the glasses under study using thermoviscometric measurements. Methods. Mathematical modelling, simplex-lattice design. Results and conclusions. One of the most effective methods of experiment planning, which allows, with a relatively small number of experiments, obtaining a mathematical model reflecting the dependence of the studied property of the mixture on the content of components in it, is the simplex experiment planning method. To simplify the solution of such a task, a group or groups of components are usually distinguished, in which the ratio of the mass fraction of the components does not change. In this article, three groups of components are taken – simulators of high-level radioactive waste, sodium and boron oxides, and glass frit. The authors have identified the most promising area, where the mass fraction of glass frit is from 67.5 to 75%, the mass fraction of sodium and boron is from 25 to 32.5% and the mass fraction of high-level waste components is from 0 to 20%. The paper presents the results of thermoviscometric measurements of 15 melts of low-melting borosilicate glasses of different compositions at temperatures ranging from 700 to 1,200°C. Using simplex-based mathematical models of experiment planning, domains of dependence of thermoviscosimetric characteristics of glass melts on their compositions were created. The authors identified the compositions of glass melts that are found within the viscosity limits determined as allowable for glass melt discharge during operation of the removable small-scale melter of Mayak Production Association design.

Bridging the Semantic Latent Space between Brain and Machine: Similarity Is All You Need

How our brain encodes complex concepts has been a longstanding mystery in neuroscience. The answer to this problem can lead to new understandings about how the brain retrieves information in large-scale data with high efficiency and robustness. Neuroscience studies suggest the brain represents concepts in a locality-sensitive hashing (LSH) strategy, i.e., similar concepts will be represented by similar responses. This finding has inspired the design of similarity-based algorithms, especially in contrastive learning. Here, we hypothesize that the brain and large neural network models, both using similarity-based learning rules, could contain a similar semantic embedding space. To verify that, this paper proposes a functional Magnetic Resonance Imaging (fMRI) semantic learning network named BrainSem, aimed at seeking a joint semantic latent space that bridges the brain and a Contrastive Language-Image Pre-training (CLIP) model. Given that our perception is inherently cross-modal, we introduce a fuzzy (one-to-many) matching loss function to encourage the models to extract high-level semantic components from neural signals. Our results claimed that using only a small set of fMRI recordings for semantic space alignment, we could obtain shared embedding valid for unseen categories out of the training set, which provided potential evidence for the semantic representation similarity between the brain and large neural networks. In a zero-shot classification task, our BrainSem achieves an 11.6% improvement over the state-of-the-art.

Review of Integrated Chassis Control Techniques for Automated Ground Vehicles.

Integrated chassis control systems represent a significant advancement in the dynamics of ground vehicles, aimed at enhancing overall performance, comfort, handling, and stability. As vehicles transition from internal combustion to electric platforms, integrated chassis control systems have evolved to meet the demands of electrification and automation. This paper analyses the overall control structure of automated vehicles with integrated chassis control systems. Integration of longitudinal, lateral, and vertical systems presents complexities due to the overlapping control regions of various subsystems. The presented methodology includes a comprehensive examination of state-of-the-art technologies, focusing on algorithms to manage control actions and prevent interference between subsystems. The results underscore the importance of control allocation to exploit the additional degrees of freedom offered by over-actuated systems. This paper systematically overviews the various control methods applied in integrated chassis control and path tracking. This includes a detailed examination of perception and decision-making, parameter estimation techniques, reference generation strategies, and the hierarchy of controllers, encompassing high-level, middle-level, and low-level control components. By offering this systematic overview, this paper aims to facilitate a deeper understanding of the diverse control methods employed in automated driving with integrated chassis control, providing insights into their applications, strengths, and limitations.

Neurocognitive Models of Aesthetic Perception of Painting: Possibilities, Limitations and Prospects

This article features one of the main trends in the development of neuroaesthetics and cognitive psychology of art, a systematic description of the mechanisms of aesthetic perception expressed in the creation of models. The paper analyzes six models of varying specification and capacity of extrapolating to other types of art. These are the following models: the neural network model, the neuropsychological model, the model of aesthetic perception of painting, the model of the intentions of the artist, the combined model of visual aesthetic experience, the model of aesthetic assessment and aesthetic judgments, and the Vienna integrated model of the processes of perception of art “top-down” and “bottom-up”. The possibilities and limitations of each model are considered according to such parameters as: the dynamics of aesthetic perception, the description of complex intermodal art forms, the extrapolation to different types of art, the measurement of aesthetic experience, the consideration of the interaction between the characteristics of the stimulus and the viewer, the detail of the esthetic experience description, the suitability for mathematical modeling. The most promising according to these criteria are the model of aesthetic assessment and aesthetic judgments, and the Vienna integrated model of the processes of perception of art “top-down” and “bottom-up”. In addition, the article provides an overview of existing approaches and research programs for modeling aesthetic experience. The paradigm of predictive processing (based on Bayesian inference) is considered as one of the most promising research programs. The key difficulties of modeling aesthetic experience are the lack of a unified research paradigm that includes a common understanding of the process of aesthetic perception and tools for its fixation, difficulties in formalizing and quantifying high-level components of aesthetic experience (expectations, schemes, experience, the viewer's “self”, etc.) and its continuity.

Formula omitted]: A template to build verified thread-local interfaces with software scheduler abstractions

This paper presents ThreadAbs, an extension of the layer-based software formal verification toolkit CCAL (Gu et al., 2018). ThreadAbs is specifically designed to provide better expressiveness and proof management for thread abstraction in multithreaded libraries. Thread abstraction isolates the behavior of each thread from others when providing a top-level formal specification for software. Compared to the original CCAL, ThreadAbs offers significant improvements in this regard.CCAL is a verification framework that enables a layered approach to building certified software, as demonstrated by multiple examples (Gu et al. 2016; Li et al. 2021; Shin et al. 2019). Obviously, its main targets usually include multithread libraries, which support significant improvement in the utilization and isolation of system resources. However, it poses new challenges for formal verification. Firstly, it requires a sudden change in the granularity of concurrency during the implementation and verification of the target software. Typically, systems are associated with software schedulers that are built on top of several underlying components in the system (e.g., thread spawn, yield, sleep, and wake-up). Due to the software scheduler, these systems can be divided into low-level components consisting of modules that the software scheduler depends on (e.g., allocators for shared resources and scheduling queues) and high-level components that use software schedulers (e.g., condition variables, semaphores, and IPCs). Therefore, software formal verification on those systems has to provide proper method to deal with those distinct features, which is usually abstracting other threads’ behavior as much as possible to provide an independent thread model and its formal specification. Secondly, it requires handling side effects from other threads, such as dynamic resource allocation from parents with proper isolation of all threads from each other.CCAL has limited support for two crucial aspects of formal verification in multithreaded systems. Firstly, its previous thread abstraction method does not handle the side effects caused by a parent thread during dynamic initial state allocation properly. Secondly, the proofs produced by CCAL are tied to CertiKOS, which makes it challenging to use them for similar proofs that use CCAL as their verification toolkit. To address these issues, we introduce ThreadAbs, a new mechanized methodology that provides proper thread abstraction to reason about multithreaded programs in conjunction with CCAL. We also extend the previous CertiKOS proof with ThreadAbs to demonstrate its usability and expressiveness.

Surrogate Ensemble-Assisted Hyper-Heuristic Algorithm for Expensive Optimization Problems

This paper proposes a novel surrogate ensemble-assisted hyper-heuristic algorithm (SEA-HHA) to solve expensive optimization problems (EOPs). A representative HHA consists of two parts: the low-level and the high-level components. In the low-level component, we regard the surrogate-assisted technique as a type of search strategy and design the four search strategy archives: exploration strategy archive, exploitation strategy archive, surrogate-assisted estimation archive, and mutation strategy archive as low-level heuristics (LLHs), each archive contains one or more search strategies. Once the surrogate-assisted estimation archive is activated to generate the offspring individual, SEA-HHA first selects the dataset for model construction from three principles: All Data, Recent Data, and Neighbor, which correspond to the global and the local surrogate model, respectively. Then, the dataset is randomly divided into training and validation data, and the most accurate model built by polynomial regression (PR), support vector regression (SVR), and Gaussian process regression (GPR) cooperates with the infill sampling criterion is employed for solution estimation. In the high-level component, we design a random selection function based on the pre-defined probabilities to manipulate a set of LLHs. In numerical experiments, we compare SEA-HHA with six optimization techniques on 5-D, 10-D, and 30-D CEC2013 benchmark functions and three engineering optimization problems with only 1000 fitness evaluation times (FEs). The experimental and statistical results show that our proposed SEA-HHA has broad prospects for dealing with EOPs.

Digital Marketing Tactics for Startups: Building a Strong Online Presence

Abstract: The reason for considering it is to recognize the role of front demonstrations in startups. This document provides a comprehensive analysis of the computerised startup demonstration procedure. He examines the statistical information of budget projections. This also distinguishes the main goal of social networking technologies. It outlines how startups can leverage social media to reach large numbers of people and potential customers. We also talk about what materials to use for which deals, how to leverage social causes to build your brand and how to do it. recognition device. An extended demonstration is the promotion of a product through electronic media. It differs from the regular demo in that it uses different channels and strategies to help businesses analyse their marketing campaigns. Most of the startups have standard arrangements to get closer to their customers. Extended demos play an important role in today's competitive marketplace. A forward marketing team needs to be robust enough to meet customer needs. This paper throws light on the concept of computerised showcasing and computerised promoting techniques embraced by start-ups.A promoting methodology for start-ups is in general amusement arranged for coming to individuals and turning them into aspirants of the Plot that the start-ups give. The showcasing technique for start-ups contains the Scheme’s valueposition, key showcasing messages, data on the target group of onlookers, and other high-level components. The Marketing methodology advises the marketing plan, which may be a record that lays out the sorts and timing of marketing activities.

BEYONDPLANCK

We describe the computational infrastructure for end-to-end Bayesian cosmic microwave background (CMB) analysis implemented by the BeyondPlanck Collaboration. The code is calledCommander3. It provides a statistically consistent framework for global analysis of CMB and microwave observations and may be useful for a wide range of legacy, current, and future experiments. The paper has three main goals. Firstly, we provide a high-level overview of the existing code base, aiming to guide readers who wish to extend and adapt the code according to their own needs or re-implement it from scratch in a different programming language. Secondly, we discuss some critical computational challenges that arise within any global CMB analysis framework, for instance in-memory compression of time-ordered data, fast Fourier transform optimization, and parallelization and load-balancing. Thirdly, we quantify the CPU and RAM requirements for the current BEYONDPLANCKanalysis, finding that a total of 1.5 TB of RAM is required for efficient analysis and that the total cost of a full Gibbs sample for LFI is 170 CPU-hrs, including both low-level processing and high-level component separation, which is well within the capabilities of current low-cost computing facilities. The existing code base is made publicly available under a GNU General Public Library (GPL) license.

Flexible reuse of cortico-hippocampal representations during encoding and recall of naturalistic events

Although every life event is unique, there are considerable commonalities across events. However, little is known about whether or how the brain flexibly represents information about different event components at encoding and during remembering. Here, we show that different cortico-hippocampal networks systematically represent specific components of events depicted in videos, both during online experience and during episodic memory retrieval. Regions of an Anterior Temporal Network represented information about people, generalizing across contexts, whereas regions of a Posterior Medial Network represented context information, generalizing across people. Medial prefrontal cortex generalized across videos depicting the same event schema, whereas the hippocampus maintained event-specific representations. Similar effects were seen in real-time and recall, suggesting reuse of event components across overlapping episodic memories. These representational profiles together provide a computationally optimal strategy to scaffold memory for different high-level event components, allowing efficient reuse for event comprehension, recollection, and imagination.

Green location routing problem with flexible multi-compartment for source-separated waste: A Q-learning and multi-strategy-based hyper-heuristic algorithm

In this paper, we extend a novel model for source-separated waste collection and transportation, the green location routing problem with multi-compartment (GLRPFMC), for which we design a Q-learning and multi-strategy-based hyper-heuristic algorithm (QLMSHH). The remarkable merits of this paper can be highlighted as the following threefold: (1) The GLRPFMC is novel in that it constructs a variant of the location routing problem with carbon emissions and flexible multi-compartment sizes that occurs in a source-separated waste transportation context. (2) For the methodological contribution, the QLMSHH is presented to design a hyper-heuristic model by intelligently selecting appropriate high-level heuristic components during different stages of the optimization process. (3) The proposed method incorporates the design of solution representations, evolution-acceptance pairs for high-level heuristic construction, the repairing solution scheme, and the local search strategy. Finally, sufficient experiments are conducted on the benchmark, new instances, and simulation data of GLRPFMC and draw some managerial insights. The satisfactory results highlight the efficiency and universality of the proposed model and method.

MoCLORA—An Architecture for Legged-and-Climbing Modular Bio-Inspired Robotic Organism

MoCLORA (Modular Climbing-and-Legged Robotic Organism Architecture) is a software framework for climbing bio-inspired robotic organisms composed of modular robots (legs). It is presented as a modular low-level architecture that coordinates the modules of an organism with any morphology, at the same time allowing exchanges between the physical robot and its digital twin. It includes the basic layers to control and coordinate all the elements, while allowing adding new higher-level components to improve the organism's behavior. It is focused on the control of both the body and the legs of the organism, allowing for position and velocity control of the whole robot. Similarly to insects, which are able to adapt to new situations after the variation on the capacity of any of their legs, MoCLORA allows the control of organisms composed of a variable number of modules, arranged in different ways, giving the overall system the versatility to tackle a wide range of tasks in very diverse environments. The article also presents ROMERIN, a modular climbing and legged robotic organism, and its digital twin, which allows the creation of different module arrangements for testing. MoCLORA has been tested and validated with both the physical robot and its digital twin.

Characterizing variability of electronic health record-driven phenotype definitions.

The aim of this study was to analyze a publicly available sample of rule-based phenotype definitions to characterize and evaluate the variability of logical constructs used. A sample of 33 preexisting phenotype definitions used in research that are represented using Fast Healthcare Interoperability Resources and Clinical Quality Language (CQL) was analyzed using automated analysis of the computable representation of the CQL libraries. Most of the phenotype definitions include narrative descriptions and flowcharts, while few provide pseudocode or executable artifacts. Most use 4 or fewer medical terminologies. The number of codes used ranges from 5 to 6865, and value sets from 1 to 19. We found that the most common expressions used were literal, data, and logical expressions. Aggregate and arithmetic expressions are the least common. Expression depth ranges from 4 to 27. Despite the range of conditions, we found that all of the phenotype definitions consisted of logical criteria, representing both clinical and operational logic, and tabular data, consisting of codes from standard terminologies and keywords for natural language processing. The total number and variety of expressions are low, which may be to simplify implementation, or authors may limit complexity due to data availability constraints. The phenotype definitions analyzed show significant variation in specific logical, arithmetic, and other operators but are all composed of the same high-level components, namely tabular data and logical expressions. A standard representation for phenotype definitions should support these formats and be modular to support localization and shared logic.

Storage-Heterogeneity Aware Task-based Programming Models to Optimize I/O Intensive Applications

Task-based programming models have enabled the optimized execution of the computation workloads of applications. These programming models can take advantage of large-scale distributed infrastructures by allowing the parallel and distributed execution of applications in high-level work components called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tasks</i> . Nevertheless, in the era of Big Data and Exascale, the amount of data produced by modern scientific applications has already surpassed terabytes and is rapidly increasing. Hence, I/O performance became the bottleneck to overcome in order to achieve more total performance improvement. New storage technologies offer higher bandwidth and faster solutions than traditional Parallel File Systems (PFS). Such storage devices are deployed in modern day infrastructures to boost I/O performance by offering a fast layer that absorbs the generated data. Therefore, it is necessary for any programming model targeting more performance to manage this heterogeneity and take advantage of it to improve the I/O performance of applications. Towards this goal, we propose in this article a set of programming model capabilities that we refer to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Storage-Heterogeneity Awareness</i> . Such capabilities include: (i) abstracting the heterogeneity of storage systems, and (ii) optimizing I/O performance by supporting dedicated I/O schedulers and an automatic data flushing technique. The evaluation section of this article presents the performance results of different applications on the MareNostrum CTE-Power heterogeneous storage cluster. Our experiments demonstrate that a storage-heterogeneity aware programming model can achieve up to almost 5x I/O performance speedup and 48% total time improvement compared to the reference PFS-based usage of the execution infrastructure.

Research on Common Mode Analysis Method in System Safety Analysis

In system safety analysis, the existence of Common Mode Failure (CMF) greatly undermines the independence of components and poses great threat to safety of the system. In Common Mode Analysis (CMA), whether CMF needs to be considered for basic events under different types of logic gates in Fault Tree, it lacks strict reasoning and comprehensive consideration. This paper takes CMF of the same and different levels under "AND gate" and "OR gate" in fault tree as an example, and uses reasoning analysis and mathematical calculation methods to compare the influence of the existence of CMF on failure rate of the system. The results show that whether CMF need to be considered for the component depends on the type of logic gate correspond to the higher-level component. The research results have certain reference value for the analysis of CMF in CMA.

Analisis Perawatan Mesin dengan Menggunakan Metode Reability Centered Maintenance dan Fuzzy Fuzzy Failure Mode and Effect Analysis

In the company there is a problem, namely the high number of downtime that occurs in the SEYI SN-110 Ton press machine at PT. Padma Soode Indonesia. The purpose of this research is to find out the right maintenance action on the system that has been selected using the RCM Reability Centered Maintenance method, to find out which components are prioritized (critical) using Fuzzy FMEA (Failure Mode and Effect Analysis), and provide policy proposals in the form of SOPs. Both methods are types of quantitative methods. The results of data collection are the number of damage to the engine inside that is as much as 40 times in 1 year with a critical percentage of 77%. This has a very big impact on the company where in 1 day the production time / shift is 8 hours, with the downtime level reaching 8 hours which can be said in 1 day there is no production. From the results of data processing using RCM to overcome downtime, namely by doing machine maintenance for 47 days so that there is no more severe downtime later. 47 days to maintain the machine starting from cleaning, replacing components whose economic value is already low and looking at the level of the machine's ability to operate, the results of the research that obtained high critical level components are engine parts with a defuzzication rate of 155. The SOP for machine maintenance uses machine maintenance time from RCM worksheet results.

LC-MS Metabolite Profiling and the Hypoglycemic Activity of Morus alba L. Extracts.

Morus alba L. is used in traditional Chinese medicine for its anti-diabetic activity; however, the part of the hypoglycemic activity and related active metabolites are still not fully clarified. In this study, the metabolites in the M. alba roots, leaves, twigs, and fruits extracts (70% ethanol extracts) were systematically identified, and their hypoglycemic activity was evaluated by the high-fat diet/streptozotocin-induced 2 diabetes mellitus (T2D) mouse model. A total of 60 high-level compounds, including 16 polyphenols, 43 flavonoids, and one quinic acid, were identified by high-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (HPLC-Q-TOF-MS) combined with the fragmentation pathways of standards and the self-established database. Among them, 23 metabolites were reported for the first time from this plant. In contrast to the extracts of M. alba leaves and fruits, the extracts of roots and twigs displayed significant hypoglycemic activity The glycemia was significantly reduced from 32.08 ± 1.27 to 20.88 ± 1.82 mmol/L and from 33.32 ± 1.98 to 24.74 ± 1.02 mmol/L, respectively, after 4 weeks of treatment with roots and twigs extracts. Compound 46 (morusin), which is a high-level component identified from the extracts of M. alba roots, also displayed significant activity in decreasing the blood glucose level of T2D mice reduced from 31.45 ± 1.23 to 23.45 ± 2.13 mmol/L. In addition, the extracts of roots and twigs displayed significant activity in reducing postprandial glycemia. This work marks the first comparison of the metabolites and hypoglycemic activity of M. alba roots, leaves, twigs, and fruits extracts, and provides a foundation for further development of M. alba extracts as anti-diabetic drugs.

Microstructure and Wear Resistance of TiCp/Ti6Al4V Composite Coatings by Follow-Up Ultrasonic-Assisted Laser Additive Manufacturing

With the increasing demand for the high agility and fast response of high-level equipment in the aerospace and energy power fields, it is increasingly urgent to improve the performance of the high-temperature and wear resistance of the corresponding high-level components. Ceramic-reinforced titanium matrix composites have excellent high-temperature and wear resistance, but, in laser additive manufacturing, the primary ceramic phase is coarse, and the morphology of the ceramic phase is difficult to control, which limits their further development. In this investigation, a follow-up ultrasonic-assisted laser-additive-manufacturing method was proposed to prepare a 30 wt.% TiC/Ti6Al4V composite coating on a Ti6Al4V surface. Under the effects of ultrasonic cavitation and acoustic streaming, the content of the unmelted TiC was reduced, the dendritic primary TiC in the solidification process was broken and the distribution uniformity of the primary TiC was improved. The content of the unmelted TiC in the composite coating decreased significantly under ultrasonic action, and it was only 50.23% of that without ultrasonic action. At the same time, the average size of the dendritic primary TiC in the composite coating decreased from 61.59 μm to 27.04 μm, which was 56.10% smaller than that without ultrasonic action. The average microhardness of the composite coating reached the maximum of 656.70 HV0.2 under ultrasonic power, and it was 83.21% higher than that of the Ti6Al4V substrate, and 26.44% higher than that of the composite coating without ultrasonic power. Due to the ultrasonic-cavitation and acoustic-streaming effects, the content of the unmelted TiC obviously decreased, so that the average friction coefficient of the composite coating increased, and the wear mechanism changed from abrasive wear to adhesive wear.

Controlling the Stage: A High-Level Control System for Virtual Audiences in Virtual Reality

This article presents a novel method for controlling a virtual audience system (VAS) in Virtual Reality (VR) application, called STAGE, which has been originally designed for supervised public speaking training in university seminars dedicated to the preparation and delivery of scientific talks. We are interested in creating pedagogicalnarratives: narratives encompass affective phenomenon and rather than organizing events changing the course of a training scenario, pedagogical plans using our system focus on organizing the affects it arouses for the trainees. Efficiently controlling a virtual audience towards a specific training objective while evaluating the speaker’s performance presents a challenge for a seminar instructor: the high level of cognitive and physical demands required to be able to control the virtual audience, whilst evaluating speaker’s performance, adjusting and allowing it to quickly react to the user’s behaviors and interactions. It is indeed a critical limitation of a number of existing systems that they rely on a Wizard of Oz approach, where the tutor drives the audience in reaction to the user’s performance. We address this problem by integrating with a VAS a high-level control component for tutors, which allows using predefined audience behavior rules, defining custom ones, as well as intervening during run-time for finer control of the unfolding of the pedagogical plan. At its core, this component offers a tool to program, select, modify and monitor interactive training narratives using a high-level representation. The STAGE offers the following features: i) a high-level API to program pedagogical narratives focusing on a specific public speaking situation and training objectives, ii) an interactive visualization interface iii) computation and visualization of user metrics, iv) a semi-autonomous virtual audience composed of virtual spectators with automatic reactions to the speaker and surrounding spectators while following the pedagogical plan V) and the possibility for the instructor to embody a virtual spectator to ask questions or guide the speaker from within the Virtual Environment. We present here the design, and implementation of the tutoring system and its integration in STAGE, and discuss its reception by end-users.

An efficient, modular controller for flapping flight composing model-based and model-free components

We present a controller that combines model-based methods with model-free data-driven methods hierarchically, utilizing the predictive power of template models with the strengths of model-free methods to account for model error, such as due to manufacturing variability in the RoboBee, a 100 mg flapping-wing micro aerial vehicle (FWMAV). Using a large suite of numerical trials, we show that the model-predictive high-level component of the proposed controller is more performant, easier to tune, and able to stabilize more dynamic tasks than a baseline reactive controller, while the data-driven inverse dynamics controller is able to better compensate for biases arising from manufacturing variability. At the same time, the formulated controller is very computationally efficient, with the MPC implemented at 5 KHz on a Simulink embedded target, via which we empirically demonstrate controlled hovering on a RoboBee.