Human Action Research Articles

Re-interpreting intangible cultural heritage through immersive live performances to enhance memory-based institutions and foster community engagement

ABSTRACT Recent studies about heritage as a cultural process confirm the importance of a multidisciplinary approach to enhancing heritage sites through perception and emotion. Heritage sites, with their peculiar identity and character, provoke intense spatial experience for visitors. These experiences have the power to focus on human experiences and actions within specific environments. This paper examines a research project at Nottingham Castle (UK), where a site-specific performance, inspired by historical facts and characters, was aimed at enhancing visitor engagement with this heritage site. The project investigated the performative attributes of Nottingham Castle, emphasising community engagement and the reinterpretation of its heritage through the lens of gender equality and LGBTQI+ inclusion. The project incorporated scenarchitecture methodology, using theatre as a medium to blend historical research with perceptual and emotional responses to the site, fostering a dialectic relationship between the space and the visitors. The project’s final performance was aimed not only to enhance awareness of these themes but also to reveal the intangible cultural heritage of Nottingham Castle, reimagining alternate histories connected to gender equality and LGBTQI+ identities. This approach, deeply rooted in both heritage-as-performance and the theory of living heritage allows for a more inclusive interpretation of the Castle’s past.

From donors to networks: analyzing the “donation of the century” in South Korea through actor-network theory

Abstract This study explores the act of giving, a topic underexplored in semiotic research. To develop a semiotic perspective on gift-giving, it investigates the “donation of the century,” an unprecedented contribution of 23,000 items by the late Samsung Group chairman Lee Kun-hee to South Korea’s national and public museums, which demonstrates how donation transcends material exchange and carries symbolic, social, and cultural significance. Based on actor-network theory, the study analyses the complex networks of donation. It challenges the traditional human-centric perspective framing donation solely as the actions of donors or heirs, rather revealing it as the outcome of intricate and dynamic interactions between human and non-human actors, including laws, artefacts, and recipient institutions. This study recognizes musealization as a process that creates new meanings and knowledge through material-semiotic interactions and that a collection’s defining characteristic lies in its transmissibility. Accordingly, the study highlights how the musealization of the donated collection – grounded in the materiality of artefacts – fosters new relationships and redefines the collection as “dynamic national treasures.” This study presents an exemplary case of art donation, exploring its semiotic, epistemological, and ontological dimensions and offering insights into the dynamics and significance of gift-giving in contemporary society.

The genomic legacy of aurochs hybridisation in ancient and modern Iberian cattle.

Cattle (Bos taurus) play an important role in the life of humans in the Iberian Peninsula not just as a food source but also in cultural events. When domestic cattle were first introduced to Iberia, wild aurochs (Bos primigenius) were still present, leaving ample opportunity for mating (whether intended by farmers or not). Using a temporal bioarchaeological dataset covering eight millennia, we trace gene flow between the two groups. Our results show frequent hybridisation during the Neolithic and Chalcolithic, likely reflecting a mix of hunting and herding or relatively unmanaged herds, with mostly male aurochs and female domestic cattle involved. This is supported by isotopic evidence consistent with ecological niche sharing, with only a few domestic cattle possibly being managed. The proportion of aurochs ancestry in domestic cattle remains relatively constant from about 4000 years ago, probably due to herd management and selection against first generation hybrids, coinciding with other cultural transitions. The constant level of wild ancestry (~20%) continues into modern Western European breeds including Iberian cattle selected for aggressiveness and fighting ability. This study illuminates the genomic impact of human actions and wild introgression in the establishment of cattle as one of the most important domestic species today.

Human Action Recognition with Infrared Images Based on Dual-pathway Trans-ResNet Model

Human Action Recognition with Infrared Images Based on Dual-pathway Trans-ResNet Model

Erratum: Latent space improved masked reconstruction model for human skeleton-based action recognition

Erratum: Latent space improved masked reconstruction model for human skeleton-based action recognition

Hourly Discharge Modelling and Forecast for a Run-of-river Dam

Water resources have become a growing concern in society. This is largely due to the scarcity of this natural asset and the realisation that increasing demand could lead to future conflicts. Sometimes, human action limits access to water or alters natural flows. Run-of-river hydropower schemes manage river flows on a short-term basis, altering the natural flow of rivers according to the energy needs of consumers or the risk of flooding. The aim of this work is to show how to model and predict the hourly flow in a run-of-river reservoir, using the Crestuma-Lever dam on the river Douro (Portugal) as a case study. Data collected from 1998 to 2020 will be used. The study focuses on the use of time series models capable of dealing with multiple periodicities, such as the TBATS model. The findings show that the model can be used for 48-hour to weekly forecasting. In general, it captures the large fluctuations in the turbine discharges and most peak discharges. However, it does not capture most zeros and has difficulty in dealing with low flow values. The results of the time-series model are also compared with those obtained using three machine learning algorithms: the Seasonal Naïve, the Neural Network, and the Random Forest.

Marshall’s scissors and a post-classical human organisation and praxis theory of value

Abstract We revisit Marshall’s scissors analogy to the theory of value in economics and his discussion of human actors and other determinants of value creation. We build critically upon Marshall and related literature in economics and organisations to propose a novel post-classical human organisation and praxis theory of value. The theory reinstates the role of entrepreneurs, managers and workers discussed by Marshall and incorporates and develops key Marshallian ideas on organisation, knowledge and innovation, increasing returns to scale, inter-firm cooperation and (in) industrial districts, and their subsequent developments. In so doing the post-classical theory of value helps address limitations of extant theories and provides opportunities for theory development and implications for public policy.

Smart Video Monitoring: Advanced Deep Learning for Activity and Object Recognition

This study explores the integration of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks for the real-time recognition of human activities in video data. By harnessing the advantages of these two approaches, the system achieves high accuracy in detecting complex human actions. Specifically, CNNs address the spatial aspects of the task, while LSTMs handle the temporal sequences. A notable feature of the system is its categorization module, which enables users to select an action and identify similar actions, thereby enhancing productivity and usability. Existing models often face challenges related to real-time inter- action capabilities and resilience to environmental disturbances. This study tackles these shortcomings by refining the CNN-LSTM framework to support real-time functionality and incorporating preprocessing techniques, such as frame extraction and normal- ization, to improve input data quality. The system’s effectiveness is measured using indicators like accuracy, recall, and latency, demonstrating its advantages over traditional rule-based and basic deep learning approaches. The early findings are optimistic, demonstrating significant improvements in performance. Nevertheless, challenges remain, particularly in tracking per- formance under occlusion or in cluttered environments. Future research should explore the integration of multi-modal data and advanced architectures, such as spatio- temporal graph con- volutional networks (STGCN), to further enhance recognition accuracy and system robustness. In conclusion, the proposed CNN-LSTM hybrid architecture for activity recognition demonstrates potential for applications in video surveillance and beyond, including fields like healthcare and sports analytics. The system offers improved automated monitoring capabilities through enhanced accuracy, scalable human action detection, and user- friendly design.

Fixed‐Time Adaptive Neural Control for Physical Human–Robot Collaboration With Time‐Varying Workspace Constraints

ABSTRACTEffective physical human–robot collaboration (pHRC) requires strict safety guarantees since robots coordinate with human actions in a shared workspace. Moreover, compliance is essential, enabling robots to adjust their stiffness and behavior in response to human interaction forces. This paper presents a novel fixed‐time adaptive neural control method for handling safety constraints that occur in physical human–robot collaboration while also guaranteeing compliance during intended force interactions. The proposed method combines the benefits of compliance control, time‐varying integral barrier Lyapunov function (TVIBLF), and fixed‐time techniques, which not only achieve compliance during physical contact with human operators but also guarantee time‐varying workspace constraints and fast tracking error convergence without any restriction on the initial conditions. Furthermore, a neural adaptive control law is designed to compensate for the unknown dynamics and disturbances of the robot manipulator such that the proposed control framework is overall fixed‐time‐converged and capable of online learning without any prior knowledge of robot dynamics and disturbances. The proposed approach is finally validated on a simulated two‐link robot manipulator and then extended to the simulated UR10 robot. Simulation results show that the proposed controller is superior in the sense of both the tracking error and convergence time compared with the existing barrier Lyapunov function‐based controllers while simultaneously guaranteeing compliance and safety.

Demonstrating Tactical Combat Casualty Care in Simulated Environments to Enable Passive, Autonomous Documentation: Protocol for a Prospective Simulation-Based Study.

The Telemedicine & Advanced Technology Research Center (TATRC) commenced a new research portfolio specifically addressing Autonomous Casualty Care (AC2) in 2023. The first project within this portfolio addresses the current and historical challenges of capturing tactical combat casualty care (TCCC) data in operational settings. The initial autonomous casualty care effort, the Passive Data Collection using Autonomous Documentation research project, conducts systematic, simulated patient and casualty care scenarios, leveraging suites of passive sensor inputs to populate a data repository that will automate future combat care. To obtain the required datasets, TATRC will engage care provider participants who provided consent in one of 6 randomized simulated TCCC scenarios leveraging an institutional review board-approved office protocol (#M-11057). These simulations will leverage mannikins (low and high fidelity) and live simulated patients (eg, human actors who provided consent). All consenting participants (eg, both the care providers and live simulated patients) will be equipped with suites of sensors that will passively collect data on care delivery actions and patient physiology. Simulated data is being collected at Fort Detrick, Maryland; Fort Sam Houston, Texas; Fort Indiantown Gap, Pennsylvania; Fort Liberty, North Carolina; and a commercial site in Greenville, North Carolina. Across all research locations, TATRC will collect and annotate approximately 2500 simulation procedures tasks by March 2025. These study data will generate the first machine learning and artificial intelligence algorithms to populate Department of Defense (DD) Form 1380 fields accurately and reliably. Additional data collected past March 2025 will be used to continue to refine and mature the algorithm. The military health care system (MHS) lacks real-world datasets for TCCC care at the point of injury. Developing a data repository of simulated TCCC data is required as an essential step toward automating TCCC care. If TATRC's research efforts result in the ability to automate care delivery documentation, this will alleviate the cognitive burden of TCCC care providers in austere, chaotic environments. By generating a TCCC data repository through this Autonomous Documentation research project, TATRC will have opportunities to leverage this research data to create machine learning and artificial intelligence models to advance passive, automated medical documentation across the health care continuum. DERR1-10.2196/67673.

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