Model Of Human Action Research Articles

Machine Learning-Based In Silico Prediction of the Inhibitory Activity of Chemical Substances Against Rat and Human Cytochrome P450s.

The prediction of cytochrome P450 inhibition by a computational (quantitative) structure-activity relationship approach using chemical structure information and machine learning would be useful for toxicity research as a simple and rapid in silico tool. However, there are few in silico models focusing on the species differences between rat and human in the P450s inhibition. This study aimed to establish in silico models to classify chemical substances as inhibitors or non-inhibitors of various rat and human P450s, using only molecular descriptors. Using the in-house test results from our in vitro experiments, we used 326 substances for model construction and internal validation data. Apart from the 326 substances, 60 substances were used as external validation data set. We focused on seven rat P450s (CYP1A1, CYP1A2, CYP2B1, CYP2C6, CYP2D1, CYP2E1, and CYP3A2) and 11 human P450s (CYP1A1, CYP1A2, CYP1B1, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, and CYP3A4). Most of the models established using XGBoost showed an area under the receiver operating characteristic curve (ROC-AUC) of 0.8 or more in the internal validation. When we set an applicability domain for the models and confirmed their generalization performance through external validation, most of the models showed an ROC-AUC of 0.7 or more. Interestingly, for CYP1A1 and CYP1A2, we discovered that a human P450 inhibitory activity model can predict rat P450 inhibitory activity and vice versa. These models are the first attempts to predict inhibitory activity against a wide variety of P450s in both rats and humans using chemical structure information. Our experimental results and in silico models would be helpful to support information for species similarities and differences in chemical-induced toxicity.

A human digital twin for the M-Machine

Human digital twins are computational models of the human actions involved in interacting and operating technical artifacts. Such models provide a conceptual and practical tool for artificial intelligence designers when they seek to replace human work with intelligent machines. Indeed, digital twins have long served as models of technical and cyber-physical processes. Human digital twins have such models as their foundations but also include models of human actions. As a result, human digital twin models enable technology designers to model how people interact with intelligent technical artifacts. Yet, development of human digital twins is associated with certain conceptual problems. To clarify the basic idea, we constructed a human digital twin for Minsky’s M-Machine. The abstract conceptual structure of this machine and its generality allowed us to analyze the general properties of human digital twins, their design, and their use as tools in designing intelligent technologies.

РЕПРЕЗЕНТАЦІЇ ІГРОВОГО МОДЕЛЮВАННЯ ЖИТТЄДІЯЛЬНОСТІ ЛЮДИНИ В УМОВАХ НЕВИЗНАЧЕНОСТІ

Purpose. Features of modernity that correspond to the features of a world dominated by volatility, uncertainty, complexity and ambiguity, threats of using such features as means of geopolitical influence, conditions of a full-scale russian-ukrainian war, the necessity restoration for a safe environment throughout the territory of Ukraine requires effective proposals from domestic psychology to ensure human life in the given conditions. A well-founded system-forming position for this increasingly appears as a game as a universal socio-cultural phenomenon and an integral factor of human self-realization, and game modeling as a way and method of human life activity in uncertainty. The purpose of the article is a theoretical study of the psychological regularities of game representation in the aspects of modeling human life, substantiation of psychological content, functional essence, classification of representations as an attributive factor of game modeling of human life in uncertainty conditions. Methods. The research uses methods of analysis and systematization for psychological and interdisciplinary approaches in the study of representation and modeling of human life activities, theoretical reconstruction and classification of the psychological content and functional essence for the game modeling representation of human life activities in current living conditions. Results. Based on the results of the research, the categorical and conceptual content and functional essence for game modeling representations of human life activity in uncertainty conditions were specified. The attribution of representation in the game modeling aspects is updated and substantiated. The definition of "game modeling representations of human life activity in uncertainty conditions" and the corresponding classification are proposed. Scientific ideas about representation, game and game modeling are deepened. It was established that the game modeling representations of human life activity in uncertainty conditions is a universal, pragmatic, attributive phenomenon; non-specific mental formation; the way of life of a person and a social group, which occurs simultaneously in two complementary dimensions: a) the intrasubjective space of a person as an interiorizational mental representation and b) in the external activity dimension as a deed, activity representation of exteriorization. Conclusions. The results of the study allow us to draw conclusions about the relevance of continuing psychological explorations for game modeling representations of human life activity in uncertainty conditions. The clarifying regularities tasks of influence for representations on psychological well-being and productivity of individual and group activities are considered promising.

Projecting Changes in the Frequency and Magnitude of Ozone Pollution Events Under Uncertain Climate Sensitivity

AbstractClimate change is projected to worsen ozone pollution over many populated regions, with larger impacts at higher concentrations. More intense and frequent ozone episodes risk setbacks to human health and environmental policy achievements. However, assessing these changes is complicated by uncertain climate sensitivity, closely related to climate model response, and internal variability in simulations projecting climate's influence on air quality. Here, leveraging a global modeling framework that one‐way couples a human activity model, an Earth system model of intermediate complexity, and an atmospheric chemistry model, we investigate the role of climate sensitivity in climate‐induced changes to high ozone pollution episodes in the United States using multiple greenhouse gas emissions scenarios, representations of climate sensitivity, and initial condition members. We bias correct and evaluate historical model simulations, identifying modeled and observed O3 episodes using extreme value theory, and extend the approach to projections of mid‐ and end‐century climate impacts. Results show that the influence of climate sensitivity can be as significant as that of greenhouse gas emissions scenario absent precursor emissions changes. Climate change is projected to increase the magnitude of the highest annually occurring O3 concentrations by over 2.3 ppb on average across the U.S. at mid‐century under a high climate sensitivity and moderate emissions scenario, but the increase is limited to less than 0.3 ppb under lower climate sensitivity. Further, we show that areas in the U.S. currently meeting air quality standards risk being pushed into non‐compliance due to a climate‐induced increase in frequency of high ozone days.

Evaluating targeted COVID-19 vaccination strategies with agent-based modeling.

We evaluate approaches to vaccine distribution using an agent-based model of human activity and COVID-19 transmission calibrated to detailed trends in cases, hospitalizations, deaths, seroprevalence, and vaccine breakthrough infections in Florida, USA. We compare the incremental effectiveness for four different distribution strategies at four different levels of vaccine supply, starting in late 2020 through early 2022. Our analysis indicates that the best strategy to reduce severe outcomes would be to actively target high disease-risk individuals. This was true in every scenario, although the advantage was greatest for the intermediate vaccine availability assumptions and relatively modest compared to a simple mass vaccination approach under high vaccine availability. Ring vaccination, while generally the most effective strategy for reducing infections, ultimately proved least effective at preventing deaths. We also consider using age group as a practical surrogate measure for actual disease-risk targeting; this approach also outperforms both simple mass distribution and ring vaccination. We find that quantitative effectiveness of a strategy depends on whether effectiveness is assessed after the alpha, delta, or omicron wave. However, these differences in absolute benefit for the strategies do not change the ranking of their performance at preventing severe outcomes across vaccine availability assumptions.

Modeling of cross-scale human activity for digital twin workshop

Digital Twin Workshop(DTW), as an important approach to digitalization and intelligentization of workshop, has gained significant attention in manufacturing industry. Currently, digital twin models for manufacturing resources have progressed from theoretical research to practical implementation. However, as a crucial component of workshop, modeling of human activity in workshop still faces challenges due to the autonomy and uncertainty of human beings. Therefore, we propose a comprehensive approach to the modeling cross-scale human activity in digital twin workshop, which comprises macro activity and micro activity. Macro activity contains human’s occupation and spatial positions in workshop, while micro activity refers to real-time posture and production actions at work. In this paper, we build and integrate macro activity digital twin model and micro activity digital twin model. With the combination of closed-loop interaction between virtual models and physical entities, we achieve semantic mapping and control of production activities, thereby facilitating practical management of human activity in workshop. Finally, we take certain factory’s manufacturing workshop as an example to introduce the application of the proposed approach.

Deep learning modeling of human activity affected wildfire risk by incorporating structural features: A case study in eastern China

Wildfire risk prediction is a critical component of disaster prevention and mitigation, often closely associated with local human activities in most regions. Recent studies demonstrate that employing joint modeling techniques using diverse datasets alongside Convolutional Neural Networks-Long Short-Term Memory Networks (ConvLSTM) produces favorable predictive results. However, previous research inadequately explored the different impact of factors across different categories and spatial orientations, and neglected the impact of fuels and human activities inside the samples. This study focuses on the six eastern provinces of China, utilizing a multi-source dataset comprising satellite-monitored wildfire products from 2012 to 2022, along with various factors indicating terrestrial and human activities, simulated meteorological elements and high-resolution vegetation imagery. By introducing channel and spatial attention mechanisms and visual transformer mode, this research optimizes the ConvLSTM wildfire prediction model. Results indicate a noteworthy enhancement, elevating accuracy, Kappa coefficient, and AUC of ROC curves from 91.15%, 80.87%, and 97.01% to 92.79%, 84.48%, and 97.90%, respectively. Consequently, it reinforces the accuracy by increase of the structural features within samples and quantifying the differences in the importance of different factors, which is also validated by prediction application of the samples in the entire year of 2023. Sensitivity analysis reveals that the current model is still highly dependent on the meteorological factors. Notably, the impact of structural features significantly surpasses the influence of terrain and terrestrial ecology elements, which should be considered in further models. Thus, this study has developed a methodology integrating multiple attention mechanisms and sample structural features, which could furnish high-precision daily kilometer-level wildfire risk prediction products. This method could improve the efficiency of prevention and control by improving the accuracy and narrowing the high-risk areas.

Measuring regularity of human physical activities with entropy models

Regularity is an important aspect of physical activity that can provide valuable insights into how individuals engage in physical activity over time. Accurate measurement of regularity not only advances our understanding of physical activity behavior but also facilitates the development of human activity modeling and forecasting. Furthermore, it can inform the design and implementation of tailored interventions to improve population health outcomes. In this paper, we aim to assess the regularity of physical activities through longitudinal sensor data, which reflects individuals’ all physical activities over an extended period. We explore three entropy models, including entropy rate, approximate entropy, and sample entropy, which can potentially offer a more comprehensive evaluation of physical activity regularity compared to metrics based solely on periodicity or stability. We propose a framework to validate the performance of entropy models on both synthesized and real-world physical activity data. The results indicate entropy rate is able to identify not only the magnitude and amount of noise but also macroscopic variations of physical activities, such as differences on duration and occurrence time. Simultaneously, entropy rate is highly correlated with the predictability of real-world samples, further highlighting its applicability in measuring human physical activity regularity. Leveraging entropy rate, we further investigate the regularity for 686 individuals. We find the composition of physical activities can partially explain the difference in regularity among individuals, and the majority of individuals exhibit temporal stability of regularity.

Generative replay for multi-class modeling of human activities via sensor data from in-home robotic companion pets

Generative replay for multi-class modeling of human activities via sensor data from in-home robotic companion pets

Care does not just belong at home: inclusion in doctoral supervision for a student with traumatic brain injury

This paper presents an examination of the first author’s experiences and unique positioning as a Traumatic Brain Injury (TBI hereafter) survivor in doctorate supervision. Autoethnography was used to connect her personal to her cultural experience in this environment where the voices of both supervisors and student enrich doctoral supervision practices. We explore the complex intersections of the first co-author’s experience relating to neurodiversity, disability/ability, care, assumptions and disclosure. This paper illustrates that while being personally embodied, her experience also intersected with overlapping external factors which prevented or supported her inclusion. This paper demonstrates that it was necessary to perceive, trust and appreciate the care being offered before being able to fully disclose and progress successfully with her studies. Oppong’s (2014) integrated model of human action and conditions offers the use of an integrated approach to explain her experience to the reader and proposes a more respectful approach to inclusion. Points of interest This article is written from a stand -point perspective and explores the experiences of the first author as a person with TBI navigating the increasingly neoliberal environment of higher education (HE). This article illustrates how inclusive policies within the HE system that advocate inclusion, in reality, neurodiverse students are not fully understood, and their needs are not adequately met. An ethics of care and empathy are advocated that recognizes the individuality of the first author and understands the complexity of her experience. Disclosure was a reciprocal process in this supervision process. Higher education needs to be more inclusive of disability by providing a more flexible approach to doctoral supervision, while not reducing the standard of a doctorate. Universal design for Learning (UDL) principles may offer a useful framework for making doctoral research more accessible.

Human-environment interactions in the Mesolithic – The case of site Paliwodzizna 29, a lakeside site in central Poland

Human-environmental relations before agriculture can be revealed at archaeological sites through multi-method palaeoenvironmental studies, particularly at sites which have both dryland and wetland areas. Paliwodzizna 29 is such as site, being a series of pits and hearths at the edge of Lake Grodno in Central Poland. A combination of faunal analysis, lithostratigraphy, pollen, diaspores, charcoal and sedaDNA from the site revealed seasonal collecting-type occupation based on the catching of pike (a superfood) in the spring in the early Mesolithic, but a different foraging-type pattern of occupation probably in the summer in the late Mesolithic. Subtle human impacts on the local environment are also seen, including probably burning of some woodland and reed-beds as well as localized soil erosion. This analysis reveals the potential of both such sites, and the combined methodology, for the testing of theoretical models of human mobility and activities in the Mesolithic.

Wavelet-based temporal models of human activity for anomaly detection in smart robot-assisted environments1

Detecting anomalies in patterns of sensor data is important in many practical applications, including domestic activity monitoring for Active Assisted Living (AAL). How to represent and analyse these patterns, however, remains a challenging task, especially when data is relatively scarce and an explicit model is required to be fine-tuned for specific scenarios. This paper, therefore, presents a new approach for temporal modelling of long-term human activities with smart-home sensors, which is used to detect anomalous situations in a robot-assisted environment. The model is based on wavelet transforms and used to forecast smart sensor data, providing a temporal prior to detect unexpected events in human environments. To this end, a new extension of Hybrid Markov Logic Networks has been developed that merges different anomaly indicators, including activities detected by binary sensors, expert logic rules, and wavelet-based temporal models. The latter in particular allows the inference system to discover deviations from long-term activity patterns, which cannot be detected by simpler frequency-based models. Two new publicly available datasets were collected using several smart-sensors to evaluate the approach in office and domestic scenarios. The experimental results demonstrate the effectiveness of the proposed solutions and their successful deployment in complex human environments, showing their potential for future smart-home and robot integrated services.

Human Activity Recognition Using Cascaded Dual Attention CNN and Bi-Directional GRU Framework.

Vision-based human activity recognition (HAR) has emerged as one of the essential research areas in video analytics. Over the last decade, numerous advanced deep learning algorithms have been introduced to recognize complex human actions from video streams. These deep learning algorithms have shown impressive performance for the video analytics task. However, these newly introduced methods either exclusively focus on model performance or the effectiveness of these models in terms of computational efficiency, resulting in a biased trade-off between robustness and computational efficiency in their proposed methods to deal with challenging HAR problem. To enhance both the accuracy and computational efficiency, this paper presents a computationally efficient yet generic spatial-temporal cascaded framework that exploits the deep discriminative spatial and temporal features for HAR. For efficient representation of human actions, we propose an efficient dual attentional convolutional neural network (DA-CNN) architecture that leverages a unified channel-spatial attention mechanism to extract human-centric salient features in video frames. The dual channel-spatial attention layers together with the convolutional layers learn to be more selective in the spatial receptive fields having objects within the feature maps. The extracted discriminative salient features are then forwarded to a stacked bi-directional gated recurrent unit (Bi-GRU) for long-term temporal modeling and recognition of human actions using both forward and backward pass gradient learning. Extensive experiments are conducted on three publicly available human action datasets, where the obtained results verify the effectiveness of our proposed framework (DA-CNN+Bi-GRU) over the state-of-the-art methods in terms of model accuracy and inference runtime across each dataset. Experimental results show that the DA-CNN+Bi-GRU framework attains an improvement in execution time up to 167× in terms of frames per second as compared to most of the contemporary action-recognition methods.

Dimension-wise Sequential Update for Learning a Multidimensional Environment in Humans.

When confronted with multidimensional environment problems, humans may need to jointly update multiple state-action-outcome associations across various dimensions. Computational modeling of human behavior and neural activities suggests that such updates are implemented based upon Bayesian update principle. However, it is unclear whether humans perform these updates individually or sequentially. If the update occurs sequentially, the order in which the associations are updated matters and can influence the updated results. To address this question, we tested a few computational models with different update orders using both human behavior and EEG data. Our results indicated that a model undertaking dimension-wise sequential updates was the best fit to human behavior. In this model, ordering the dimensions was decided using entropy, which indexed the uncertainty of associations. Simultaneously collected EEG data revealed evoked potentials that were correlated to the proposed timing of this model. These findings provide new insights into the temporal processes underlying Bayesian update in multidimensional environments.

Кинематические профили целенаправленных действий человека в зависимости от социального контекста деятельности (обзор)

The review analyses studies into the influence of social factors on the kinematic profiles (velocity, amplitude, trajectory, grip aperture and other characteristics) of human actions. Laboratory models of human joint sensorimotor activity in different social contexts are described, exemplified by reach kinematics, complementary actions, sensorimotor communication, imitation tasks and motor interference tasks. Research into the influence of the goals of interaction (cooperation, competition, communication) and the presence of an observer on the kinematic profiles of human movements is analysed. Further, the paper demonstrates the role of social factors in the performance of complementary actions and describes the effect of the mutual influence of the physical requirements of the task and social context on the kinematic profile of movements. Results are presented showing that co-actors modulate their actions to eliminate ambiguity of their motor intentions for the other person (sensorimotor communication). Contextual factors influencing the degree of motor interference and imitative behaviour are described, such as: the type of stimuli observed, co-agent’s status, group membership, and neurotypicality of the subjects. The possibility of reducing the effect of visuomotor interference by presenting incongruent actions as interdependent components of an overall activity plan is shown. In addition, neurophysiological mechanisms of interpersonal coordination are described. It is concluded that findings on human movement patterns in different social contexts can be used to increase teamwork efficiency in various professional fields, rehabilitate people with movement disorders, optimize working conditions, and improve human interactions with robotic systems.

Characterizing and quantifying uncertainty in projections of climate change impacts on air quality

Climate change can aggravate air pollution, with important public health and environmental consequences. While major sources of uncertainty in climate change projections—greenhouse gas (GHG) emissions scenario, model response, and internal variability—have been investigated extensively, their propagation to estimates of air quality impacts has not been systematically assessed. Here, we compare these uncertainties using a coupled modeling framework that includes a human activity model, an Earth system model of intermediate complexity, and a global atmospheric chemistry model. Uncertainties in projections of U.S. air quality under 21st century climate change are quantified based on a climate-chemistry ensemble that includes multiple initializations, representations of climate sensitivity, and climate policy scenarios, under constant air pollution emissions. We find that climate-related uncertainties are comparable at mid-century, making it difficult to distinguish the impact of variations in GHG emissions on ozone and particulate matter pollution. While GHG emissions scenario eventually becomes the dominant uncertainty based on the scenarios considered, all sources of uncertainty are significant through the end of the century. The results provide insights into intrinsically different uncertainties in projections of air pollution impacts and the potential for large ensembles to better capture them.

Formation of sign and symbolic activity in junior schoolchildren in the process of acquaintance with information processes

Currently, we are witnessing a digital transformation in various areas of life, including education. Educational institutions are equipped with new equipment, connected to high-speed Internet, a digital document management system is being created, etc. New educational technologies are being developed and existing ones are being modernized, taking into account the possibility of using digital equipment, which contribute to the development of new personal qualities in students, "digital" thinking. The article discusses the impact of the digital transformation of education on the peculiarities of the perception of information by students, on the development of sign-symbolic activity. The functions of sign-symbolic means in activity (communicative, cognitive, substitute), types of sign-symbolic human activity (modeling, coding, schematization, substitution), the influence of social networks and instant messengers on the perception of information by students, the process of change in the perception of written (digital) information are described in the article. Two series of exercises are presented: 1) to familiarize younger students with information processes using the emoji language, 2) to familiarize them with editing information in "distorted texts".

Feature-reweighted representational similarity analysis: A method for improving the fit between computational models, brains, and behavior

Representational Similarity Analysis (RSA) has emerged as a popular method for relating representational spaces from human brain activity, behavioral data, and computational models. RSA is based on the comparison of representational (dis-)similarity matrices (RDMs or RSMs), which characterize the pairwise (dis-)similarities of all conditions across all features (e.g. fMRI voxels or units of a model). However, classical RSA treats each feature as equally important. This ‘equal weights’ assumption contrasts with the flexibility of multivariate decoding, which reweights individual features for predicting a target variable. As a consequence, classical RSA may lead researchers to underestimate the correspondence between a model and a brain region and, in case of model comparison, may lead them to select an inferior model. The aim of this work is twofold: First, we sought to broadly test feature-reweighted RSA (FR-RSA) applied to computational models and reveal the extent to which reweighting model features improves RSM correspondence and affects model selection. Previous work suggested that reweighting can improve model selection in RSA but it has remained unclear to what extent these results generalize across datasets and data modalities. To draw more general conclusions, we utilized a range of publicly available datasets and three popular deep neural networks (DNNs). Second, we propose voxel-reweighted RSA, a novel use case of FR-RSA that reweights fMRI voxels, mirroring the rationale of multivariate decoding of optimally combining voxel activity patterns. We found that reweighting individual model units markedly improved the fit between model RSMs and target RSMs derived from several fMRI and behavioral datasets and affected model selection, highlighting the importance of considering FR-RSA. For voxel-reweighted RSA, improvements in RSM correspondence were even more pronounced, demonstrating the utility of this novel approach. We additionally show that classical noise ceilings can be exceeded when FR-RSA is applied and propose an updated approach for their computation. Taken together, our results broadly validate the use of FR-RSA for improving the fit between computational models, brain, and behavioral data, allowing us to better adjudicate between competing computational models. Further, our results suggest that FR-RSA applied to brain measurement channels could become an important new method to assess the correspondence between representational spaces.

Optimized hybrid RNN model for human activity recognition in untrimmed video

Human activity recognition is a field of video processing that requires restricted temporal analysis of video sequences for estimating the existence of different human actions. Designing an efficient human activity model requires credible implementations of keyframe extraction, preprocessing, feature extraction and selection, classification, and pattern recognition methods. In the real-time video, sequences are untrimmed and do not have any activity endpoints for effective recognition. Thus, we propose a hybrid gated recurrent unit and long short-term memory-based recurrent neural network model for high-efficiency human action recognition in untrimmed video datasets. The proposed model is tested on the TRECVID dataset, along with other online datasets, and is observed to have an accuracy of over 91% for untrimmed video-based activity recognition. This accuracy is compared with various state-of-the-art models and is found to be higher when evaluated on multiple datasets.

TU Delft COVID-app: A tool to democratize CFD simulations for SARS-CoV-2 infection risk analysis

This work describes a modelling approach to SARS-CoV-2 dispersion based on experiments. The main goal is the development of an application integrated in Ansys Fluent to enable computational fluid dynamics (CFD) users to set up, in a relatively short time, complex simulations of virion-laden droplet dispersion for calculating the probability of SARS-CoV-2 infection in real life scenarios. The software application, referred to as TU Delft COVID-app, includes the modelling of human expiratory activities, unsteady and turbulent convection, droplet evaporation and thermal coupling. Data describing human expiratory activities have been obtained from selected studies involving measurements of the expelled droplets and the air flow during coughing, sneezing and breathing. Particle Image Velocimetry (PIV) measurements of the transient air flow expelled by a person while reciting a speech have been conducted with and without a surgical mask. The instantaneous velocity fields from PIV are used to determine the velocity flow rates used in the numerical simulations, while the average velocity fields are used for validation. Furthermore, the effect of surgical masks and N95 respirators on particle filtration and the probability of SARS-CoV-2 infection from a dose-response model have also been implemented in the application. Finally, the work includes a case-study of SARS-CoV-2 infection risk analysis during a conversation across a dining/meeting table that demonstrates the capability of the newly developed application.