Human Action Research Articles

Environmental risk assessment (ERA)

ERA is a robust tool in environmental decision-making, offering a quantitative framework for risk assessment and management. By separating scientific analysis from policymaking, ERA ensures objective evaluations of ecological risks. Whether dealing with prospective risks or retrospective assessments of past environmental damage, ERA provides a means to balance ecological protection with economic and social considerations. Moreover, integrating advanced monitoring techniques enhances ERA's ability to assess and mitigate the risks contaminants pose to ecosystems. ERA is a critical tool for environmental management, offering a structured yet flexible approach to evaluating the consequences of human actions on ecosystems. Its scientific rigor, transparency, and adaptability across scales make it invaluable for decision-makers, allowing for informed, sustainable development while ensuring ecological protection.

Exploring the Relationship between Human and Non-human actors in Cycling Tour of Flores Island

Objectives: Research aimed to recognize the model contestation between human and non-human actors at the arena of cycling tour in the eastern part of Indonesia in Flores Island. Methods: The research was socio-technical paper, qualitative descriptive method was used to follow the contestation value between cycling tourist and its technical actors during the journey. researchers observed and participated on cycling touring at Flores Island. All participants were Jakarta residents that had different variety of age, gender, occupation and living arrangements. variety of participants ranged in age from 44 to52 years old. total of 1 woman and 4 men were interviewed. Results: findings indicated three stages [Pre-departure cycling tour, En route cycling, and Post-cycling tour] that constructed the field of Actor-Network Theory [ANT], contested human and non-human actors, assemblage of the actors and semiotics were performed in this finding. The use of bicycle as main non-human actors to follow by cycling tourist creating the dynamic and interaction between human actors during cycling. Conclusions: research identifies human and non-human relations within cycling tour, then how the process of relation between actors construct tourism product such as cycling tours. The phrases of human and non-human agents, and sociotechnical were employed to investigate semiotics in their relationship. Values of their experiences and their style in cycling related to motives and type of bicycle. ANT approach described cycling in Flores Island, from departure– to destination or during enroute cycling till finish line, and flying back to their home country.

Ecological Anxiety in Liz Jensen’s The Rapture

The main aim of this article is to analyze Liz Jensen’s The Rapture from the perspective of the Anthropocene, focusing on how environmental catastrophes shape both the physical world and human psychology. By portraying the destructive impact of climate change and ecological collapse, Jensen presents a narrative where human actions such as fossil fuel consumption, deforestation, and chemical pollution are recognized as significant geological forces. This article explores the psychological consequences of living in a disintegrating world, emphasizing how natural disasters, rising sea levels, and extreme weather conditions exacerbate anxiety, trauma, and existential dread. In The Rapture, Jensen illustrates that the environment is not a passive backdrop but an active force that reshapes human experiences. Catastrophic events, such as storms, tsunamis, and ecosystem collapse, serve as more than plot devices; they function as material-affective agents that profoundly influence characters’ emotional and mental states. In doing so, the novel highlights the inseparability of human and nonhuman elements in navigating environmental crises. On the contrary, this article argues that The Rapture rejects narratives that treat nature as a distant, abstract threat and instead portrays it as an ever-present reality that demands urgent attention. At the same time, as this article demonstrates, Jensen’s novel decenters human sovereignty by emphasizing humanity’s vulnerability within the Anthropocene.

Роль прагматистського й ціннісного підходу у децентралізації та лібералізації соціуму

Overcoming the ecological, economic, and military-political crisis in which the modern world is located is possible on the path of human development and the creation of a new quality of social relations, expressed in a free consolidated productive interaction between individuals. Such liberalization and decentralization of the political, economic and cultural life of society is achieved through self-organization of society “from below”, when individuals and their groups, through discourse, find understanding in establishing the rules of interaction in society. Decentralization is possible in a society of equal, active and morally responsible individuals who mutually respect each other and ensure stability and consolidation of people through common aspirations (Allen Mendenhall). Individuals and communities achieve a balance of self-interest in order to coordinate actions in the interests of society voluntarily, and not under the influence of institutional violence. Decentralization creates a competitive environment in which a pluralistic approach makes it possible to harmonize the interests and values of interacting social Subjects in order to optimize social relations and improve the lives of individuals and society as a whole. Totalitarian political regimes support centralization and strict universalism in the application of legal norms, generate political and economic polarization of their own societies, interfere in the internal affairs of other societies, and the best way to overcome such regimes is their decentralization. Optimal for understanding the meaning and implementation of decentralization of society is a pragmatist approach, which is well aligned with the concept of self-organization of society. The pragmatist approach with its empirical intentions focuses on the practical verification and implementation of knowledge through information feedback, which is a universal regularity of natural and social self-organizing polycentric processes. In social systems, informational feedback at certain developed levels of social self-organization has a reflexive nature. An important component of the regulation of human social actions is ethical reflection, which aligns these actions with both its current needs and the strategic values of society. Decentralization is one of the effective ways to strengthen self-organizing network connections in society, increase creative activity and well-being of citizens, harmonize social relations and reduce the level of antagonistic tension in society, which reduces the likelihood of aggressive wars.

Dual-Domain Triple Contrast for Cross-Dataset Skeleton-Based Action Recognition

Skeleton-based Action Recognition (SAR) is widely recognized for its robustness and efficiency in human action analysis, but its performance in cross-dataset tasks has been limited due to domain shifts between different datasets. To address this challenge, current methods typically approach cross-dataset SAR as an Unsupervised Domain Adaptation (UDA) task, which is tackled using domain adaptation or self-supervised learning strategies. In this article, we propose a Dual-Domain Triple Contrast (D2TC) framework for cross-dataset SAR under the UDA setting. Unlike existing UDA methods that either focus on a single strategy or superficially combine strategies, our D2TC leverages contrastive learning to integrate both strategies into a unified framework. It performs three types of contrastive learning: Self-Supervised Contrastive Learning, Supervised Contrastive Learning, and UDA with Contrastive Learning, across both source and target domains. The triple contrasts go beyond mere summation, effectively bridging the domain gap and enhancing the model’s representational capacity. Additionally, we introduce multi-modal ensemble contrast and extreme skeleton augmentation methods to further enhance the skeleton-based representation learning. Extensive experiments on six cross-dataset settings validate the superiority of our D2TC framework over state-of-the-art methods, demonstrating its effectiveness in reducing domain discrepancies and improving cross-dataset SAR performance. The codes are available on https://github.com/KennCoder7/DualDomainTripleContrast .

Effect of 0.025% atropine on ocular biometry changes during accommodation.

Low concentration atropine is an effective treatment to slow myopia progression and axial elongation and also reduces accommodation. On-axis ocular dimensions of the eye change during accommodation; hence, this study aimed to quantify the effect of 0.025% atropine eye drops on accommodation-induced changes in ocular biometry. Twenty-eight myopic participants with a mean (SD) age of 17.0 (6.0) years (range: 8.0-25.5 years) and spherical equivalent refraction (SER) of -2.03 (1.05) D (range: -0.75 to -4.38 D) were enrolled. Baseline ocular biometry measurements of the left eye were captured using an optical biometer (Zeiss IOLMaster 700) for 0, 2, 4 and 6 D accommodation stimuli, presented via a Badal optometer. The accommodation response (AR) was determined using wavefront aberrometry (Imagine Eyes irx3) for the same accommodation stimuli and following cycloplegia using 1% tropicamide. Participants instilled 0.025% atropine eye drops nightly for 1 week in both eyes, and ocular biometry measurements were repeated on the day after the final atropine dose. Anterior chamber depth (ACD) and corrected vitreous chamber depth (cVCD) decreased, and crystalline lens thickness (LT), anterior segment length (ASL), crystalline lens centre position (LCP) and the AR increased significantly during accommodation (all p ≤ 0.009). Accommodation-induced changes in ACD and LT were reduced following 0.025% atropine use (both p ≤ 0.01), with significant pre- and post-atropine differences for the 4 and 6 D stimuli (all pairwise comparisons, p ≤ 0.004). On average, ACD, ASL and LCP increased, while cVCD, corrected axial length (cAL), and the AR decreased following 1 week of 0.025% atropine use (all p ≤ 0.002). The AR and on-axis ocular biometric changes during accommodation were reduced following 1 week of 0.025% atropine use. These findings may have implications for the association between near work and myopia, and atropine's mechanism of action in humans.

Toward quantitative cognitive-behavioral modeling of psychopathology: An active inference account of social anxiety disorder.

Understanding psychopathological mechanisms is a central goal in clinical science. While existing theories have demonstrated high research and clinical utility, they have provided limited quantitative explanations of mechanisms. Previous computational modeling studies have primarily focused on isolated factors, posing challenges for advancing clinical theories holistically. To address this gap and leverage the strengths of clinical theories and computational modeling in a synergetic manner, it is crucial to develop quantitative models that integrate major factors proposed by comprehensive theoretical models. In this study, using social anxiety disorder (SAD) as an example, we present a novel approach to formalize conceptual models by combining cognitive-behavioral theory (CBT) with active inference modeling, an innovative computational approach that elucidates human cognition and action. This CBT-informed active inference model integrates multiple mechanistic factors of SAD in a quantitative manner. Through a series of simulations, we systematically examined the effects of these factors on the belief about social threat and tendency of engaging in safety behaviors. The resultant model inherits the conceptual comprehensiveness of CBT and the quantitative rigor of active inference modeling, delineating previously elusive pathogenetic pathways and enabling the formulation of concrete model predictions for future research. Overall, this research presents a novel quantitative model of SAD that unifies major mechanistic factors proposed by CBT and active inference modeling. It highlights the feasibility and potential of integrating clinical theory and computational modeling to advance our understanding of psychopathology. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Does psychic numbing apply to endangered species conservation? The case of the Peregrine Falcon in Berkeley, California

IntroductionMany individuals exhibit compassion towards charismatic animals in distress, yet they are not as motivated to help the thousands of endangered animal species. The foundation of this paradox is psychic numbing, a psychological phenomenon that explains why people are more inclined to donate to help save human lives when presented with accounts of single identifiable victims compared to accounts of mass atrocities like genocide. The impact of psychic numbing on human tragedies has been well documented, but its impact on non-human tragedies, such as the crisis of endangered species conservation, has not been thoroughly assessed.MethodsThis study uses Peregrine Falcon (Falco peregrinus) conservation as a case study, examining whether identifiable lives or statistical lives elicit the greatest concern for Peregrine Falcon preservation and increased donations. Participants are randomly presented with one of three messages: (1) The story of Annie, a celebrity falcon residing in the University of California, Berkeley, (2) Statistical data on Peregrine Falcon decline and history, or (3) A combination of Annie’s story and statistical data.ResultsWe did not find a significant difference in donation amount for identifiable versus statistical lives. However, the three different messaging conditions did evoke significant differences in word association tasks about endangered species.DiscussionOur results demonstrate the importance of further research into messaging conditions that will bring about the greatest level of human action for endangered species conservation.

Elephant corridors in the Kavango-Zambezi transfrontier conservation area (KAZA TFCA): environmental infrastructures for a greener anthropocene in Namibia’s Mudumu landscape

Abstract Wildlife corridors are seen as essential environmental infrastructure guaranteeing species connectivity and biological diversity in contemporary conservation landscapes. Harking back to recent social science literature on infrastructure in general and environmental infrastructure in particular this contribution will analyze one contested wildlife corridor as part of a large conservationist project, the giant Kavango-Zambezi Transfrontier Conservation Area (KAZA TFCA), founded in 2011, and with 520,000 km2 the world’s largest conservation area. Using elephant corridors as an example, we will examine how they come into being and how they are governed. Only at first sight wildlife corridors appear as something entirely natural, constituted by the daily or seasonal movements of large herbivores. Indeed, elephants for example, produce paths through the savannah that are well visible in the landscape. But to make them wildlife corridors in conservation projects human action is needed: we will analyse wildlife corridors as hybrids of wildlife produced paths, administrative action and modern technologies of wildlife monitoring. As we will show in some detail focusing on one important Namibian wildlife corridor, the so-called Sobbe corridor, the human-elephant conflict is a real challenge leading to local resistance to corridors. Surprisingly, though, conflict is more frequent along the linear settlements close to roads, than on wildlife corridors, instances of human-elephant conflict is a prominent topic linked to corridors. The most significant challenge to corridors though comes from the rapid expansion of agricultural fields as well as from local conflicts over land ownership.

FlowerAction: a federated deep learning framework for video-based human action recognition

FlowerAction: a federated deep learning framework for video-based human action recognition

Optimized ST-GCN Model for Suspicious Human Activity Recognition using Edge Computing

Introduction: In automated video surveillance applications, detecting abnormal human activity is incredibly difficult to classify them. In these systems, labeling of human activities relies on the visual aspects and motion patterns observed in videos. But, a significant portion of many traditional methods, as well as traditional neural models, either disregard or face challenges in leveraging temporal features for predicting Human Action Recognition (HAR). Objectives: The main objective is identifying different suspicious human activities from videos and achieving precise and effective HAR. Methods: The automatic detection of aberrant human activity in a surveillance system was resolved by the Deep Learning (DL) based edge computing in our proposed work. The videos are first turned into frames and secondly, the spatio temporal features are retrieved from the key frames using a DL model Spatiotemporal-Graph Convolutional Network (ST-GCN). Finally, to recognize anomalous activity from video, the collected features are loaded into an optimized gated recurrent unit (OGRU). Results: The experimentation is carried out on the two benchmark datasets and achieved better accuracies of 98.1% (Dataset 1) and 98% (Dataset 2) respectively. Conclusions: This work effectively recognizes all kinds suspicious human activities from vast set of video files.

The unanticipated performativity of an observation grid through the evolution of its forms of agency

PurposeThis paper explores the unanticipated performativity of an observation grid during the ideation phase of a large construction project. Performativity is conceptualized as the constitutive capacity (anticipated or not) of theory to bring the practice to life through communicational interactions between various actors.Design/methodology/approachThe research used the action design research (ADR) methodology to design a grid to observe the facilitation of cross-disciplinary collaborative design workshops during the ideation phase. Key points in the grid’s design and data collection activities were analyzed in line with a communicative constitution of organization (CCO) conceptual framework and a process perspective.FindingsOur findings demonstrate how an observation grid, as an other-than-human actor, gives a voice to other other-than-human actors and contributes to the performativity of two practices (research tool design and facilitation), even if the grid did not perform as originally intended. By guiding human actors to understand and resolve what was wrong, in hindsight, the grid worked as intended, even if perceived otherwise initially. Moreover, by considering the grid as a knowledge object, we show that its performativity changes through the evolution of its forms of agency.Originality/valueWhile qualitative research generally perceives observation grids as data collection tools, not as actors, this study focuses on the grid itself and its performativity in the context of two practices: facilitation and research tool design. In addition, we investigate performativity using a Montreal School’s CCO framework that mobilizes knowledge objects.

Metformin-regulated glucose flux from the circulation to the intestinal lumen

Background:Through a retrospective analysis of existing FDG PET-MRI images, we recently demonstrated that metformin increases the accumulation of FDG in the intestinal lumen, suggesting that metformin stimulates glucose excretion into the intestine. However, the details of this phenomenon remain unclear. We here investigate the detailed dynamics of intestinal glucose excretion, including the rate of excretion and the metabolism of excreted glucose, in both the presence and absence of metformin.Methods:We quantified intestinal glucose excretion using newly developed FDG PET-MRI-based bioimaging in individuals with type 2 diabetes, both treated and untreated with metformin. The metabolism of excreted glucose was analyzed through mass spectrometry of fecal samples from mice intravenously injected with 13C-labeled glucose.Results:Continuous FDG PET/MRI image taking reveals that FDG is initially observed in the jejunum, suggesting its involvement in FDG excretion. Metformin-treated individuals excrete a significant amount of glucose (~1.65 g h–1 per body) into the intestinal lumen. In individuals not receiving metformin, a certain amount of glucose (~0.41 g h–1per body) is also excreted into the intestinal lumen, indicating its physiological importance. Intravenous injection of 13C-labeled glucose in mice increases the content of 13C in short-chain fatty acids (SCFAs) extracted from feces, and metformin increased the incorporation of 13C into SCFAs.Conclusions:A previously unrecognized, substantial flux of glucose from the circulation to the intestinal lumen exists, which likely contributes to the symbiosis between gut microbiota and the host. This flux represents a potential target of metformin’s action in humans.

Human Action Recognition Based on 3D Convolution and Multi-Attention Transformer

To address the limitations of traditional two-stream networks, such as inadequate spatiotemporal information fusion, limited feature diversity, and insufficient accuracy, we propose an improved two-stream network for human action recognition based on multi-scale attention Transformer and 3D convolutional (C3D) fusion. In the temporal stream, the traditional 2D convolutional is replaced with a C3D network to effectively capture temporal dynamics and spatial features. In the spatial stream, a multi-scale convolutional Transformer encoder is introduced to extract features. Leveraging the multi-scale attention mechanism, the model captures and enhances features at various scales, which are then adaptively fused using a weighted strategy to improve feature representation. Furthermore, through extensive experiments on feature fusion methods, the optimal fusion strategy for the two-stream network is identified. Experimental results on benchmark datasets such as UCF101 and HMDB51 demonstrate that the proposed model achieves superior performance in action recognition tasks.

A robust two-stage framework for human skeleton action recognition with GAIN and masked autoencoder

A robust two-stage framework for human skeleton action recognition with GAIN and masked autoencoder

Comparison of motion encoding frameworks on human manipulation actions

Comparison of motion encoding frameworks on human manipulation actions

DiffAct++: Diffusion Action Segmentation.

Understanding long-form videos requires precise temporal action segmentation. While existing studies typically employ multi-stage models that follow an iterative refinement process, we present a novel framework based on the denoising diffusion model that retains this core iterative principle. Within this framework, the model iteratively produces action predictions starting with random noise, conditioned on the features of the input video. To effectively capture three key characteristics of human actions, namely the position prior, the boundary ambiguity, and the relational dependency, we propose a cohesive masking strategy for the conditioning features. Moreover, a consistency gradient guidance technique is proposed, which maximizes the similarity between outputs with or without the masking, thereby enriching conditional information during the inference process. Extensive experiments are performed on four datasets, i.e., GTEA, 50Salads, Breakfast, and Assembly101. The results indicate that our proposed method outperforms or is on par with existing state-of-the-art techniques, underscoring the potential of generative approaches for action segmentation.

Quantitation of new arbitrary view dynamic human action recognition framework

<p>Dynamic action recognition has attracted many researchers due to its applications. Nevertheless, it is still a challenging problem because the diversity of camera setups in the training phases are not similar to the testing phases, and/or the arbitrary view actions are captured from multiple viewpoints of cameras. In fact, some recent dynamic gesture approaches focus on multiview action recognition, but they are not resolved in novel viewpoints. In this research, we propose a novel end-to-end framework for dynamic gesture recognition from an unknown viewpoint. It consists of three main components: (i) a synthetic video generation with generative adversarial network (GAN)-based architecture named ArVi-MoCoGAN model; (i) a feature extractor part which is evaluated and compared by various 3D CNN backbones; and (iii) a channel and spatial attention module. The ArVi-MoCoGAN generates the synthetic videos at multiple fixed viewpoints from a real dynamic gesture at an arbitrary viewpoint. These synthetic videos will be extracted in the next component by various three-dimensional (3D) convolutional neural network (CNN) models. These feature vectors are then processed in the final part to focus on the attention features of dynamic actions. Our proposed framework is compared to the SOTA approaches in accuracy that is extensively discussed and evaluated on four standard dynamic action datasets. The experimental results of our proposed method are higher than the recent solutions, from 0.01% to 9.59% for arbitrary view action recognition.</p>

ROCIP: robust continuous inertial position tracking for complex actions emerging from the interaction of human actors and environment

Inertial navigation is advancing rapidly due to improvements in sensor technology and tracking algorithms, with consumer-grade inertial measurement units (IMUs) becoming increasingly compact and affordable. Despite progress in pedestrian dead reckoning (PDR), IMU-based positional tracking still faces significant noise and bias issues. While traditional model-based methods and recent machine learning approaches have been employed to reduce signal drift, error accumulation remains a barrier to long-term system performance. Inertial tracking’s self-contained nature offers broad applicability but limits integration with a global reference frame. To solve this problem, a system that could “introspect its error” and “learn from the past” is proposed. It consists of a neural statistical motion model that regresses both poses and uncertainties with DenseNet, which are then fed into Rao-Blackwellised particle filter (RBPF) for calibration with a probabilistic transition map. An inertial tracking dataset with head-mounted IMUs was collected, including walking and running with different speeds while allowing participants to rotate their heads in a self-selected manner. The dataset consisted of 19 volunteers that generated 151 sequences in 4 scenarios with a total time of 929.8 min. It was shown that our proposed method (ROCIP) outperformed the leading methods in the field, with a relative trajectory error (RTE) of 4.94m and absolute trajectory error (ATE) of 4.36m. ROCIP could also solve the problem of error accumulation in dead reckoning and maintain a small and consistent error during long-term tracking.

Values in Education: A Conceptual Study

Values play a fundamental role in shaping human behavior, societal norms, and the educational landscape. This paper examines the necessity of values, particularly in the context of teaching and learning, by exploring their philosophical, ethical, and educational significance. Rooted in philosophical traditions, values are moral principles that guide human actions and influence decision-making. They encompass ethical standards, social obligations, and cultural ideals, forming the foundation of a meaningful life. The study highlights the critical role of value education in fostering moral development, ethical reasoning, and social responsibility among individuals. Education serves as a vital medium for instilling values, with schools and teachers playing a pivotal role in shaping students' moral and intellectual growth. The report underscores the importance of an integrated approach, involving families, educators, and curricula, to ensure holistic value formation. The anthroposophical approach to education further highlights the need for spiritual, emotional, and intellectual development in learners. In conclusion, the report asserts that value education is indispensable in addressing contemporary ethical dilemmas and social challenges. By embedding values in educational institutions, societies can foster responsible citizens, ethical decision-makers, and individuals committed to social justice and collective well-being.