Diverse Movements Research Articles

Activist burnout in No Borders: The case of a highly diverse movement.

Activist burnout is a common threat to activists' personal sustainability and to a movement's effectiveness. Compared to related fields such as humanitarian aid or social work we know relatively little about mental health risks in activists or how a specific activist environment may contribute to mental health outcomes. This study examines the case of the No Borders movement in Europe, a grassroots movement fighting for migrant rights. The movement's groups are highly diverse in terms of nationality, ethnicity, culture, and religion because they are composed of refugees, migrants, and local populations. Following the vulnerability-stress-model, the article asks: which specific stressors occur in the No Borders movement? The analysis is exploratory and based on ethnographic research and qualitative interviews (N = 26). Situational Analysis (SitA) shows that: a) activists have to navigate a complex environment in which radical grassroots activism meets humanitarian emergencies, and b) in dealing with diversity and intergroup conflicts they are under pressure to live up to their political ideals. These insights led to the identification of three stressors: prefigurative betrayal, inadequate expectations, and split of life-worlds. Understanding these stressors can contribute to informing preventive measures in No Borders and in other migrant or antiracist movements.

Tracking animal movements via collaborative acoustic telemetry networks: Multiscale habitat use, phenology, and management insights.

Estuaries support diverse fish and invertebrate communities, including resident species that rely on estuarine habitats year-round and transient migratory species. The unique movement patterns of these animals connect habitats within and far beyond the estuary and are integrally linked to fisheries management objectives. With a focus on Chesapeake Bay, this study leveraged data from collaborative acoustic telemetry networks in the northwest Atlantic to assess habitat use and phenology of movements for seven species of fish (cownose rays, dusky sharks, smooth dogfish, alewife, striped bass, common carp, and blue catfish) and one invertebrate (horseshoe crabs). A total of 288 acoustically tagged individuals were detected >3.2 million times (6,743 to 2,095,717 detections per species) on receivers across ~20.5 degrees of latitude spanning the North American Atlantic seaboard from Florida, USA, to New Brunswick, Canada. Common metrics of movement and phenology grouped these species as resident (common carp, blue catfish, horseshoe crabs), primarily resident in estuaries (juvenile striped bass), and coastal migrant (cownose rays, dusky sharks, smooth dogfish, alewife); maximum distance traveled varied by three orders of magnitude among these species. Further analysis of phenology for coastal migrants elucidated the timing and duration of these species' use of Chesapeake Bay. Collectively, movements linked habitats within Chesapeake Bay and connected the estuary to coastal ecosystems both to the north (e.g., alewife) and south (e.g., cownose rays), creating networks of fisheries management jurisdictions that varied in complexity and identified opportunities for enhancement to current management or co-management of some species. Our results elucidate the importance of estuaries to species with diverse movement behaviors, identify scales and pathways of habitat connectivity via animal movements, and highlight the utility of collaborative acoustic telemetry networks for quantifying movements relevant to both ecological research and fisheries management.

Busier, Happier, and Good(er) – 40 Years on from ‘Busy, Happy, and Good’ as Success in Teaching Physical Education

ABSTRACT In 1983, Judith Placek published “Conceptions of success in teaching: Busy, happy, and good?” Placek’s arguments have been picked up widely in research and are often used as a catchphrase to represent bad teaching. Our purpose in this paper is to revisit Placek’s argument and “update” it for modern times. We first contextualize “Busy, Happy, and Good” by exploring the sociopolitical “setting” in which it came from and examine the literature to investigate how the concept has been used. We then outline three challenges physical education faces regarding success in teaching. Lastly, we propose a framework using a revised version of Placek’s concept that engages in the complexities of student learning. In doing so, we discuss five learning “bodies”: (a) Moving Body, (b) Thinking Body, (c) Social Body, (d) Emotional Body, and (e) Cultural Body, and propose educational aims that can guide successful teaching across diverse movement cultures and reposition Placek’s arguments as more positive outcomes of physical education.

Yo Cuento: transmedia testimonios and Latinx participation in the outdoor diversity movement

Yo Cuento: transmedia testimonios and Latinx participation in the outdoor diversity movement

A mindful body approach to creating architecture: movement, models and spatial experiences.

If architecture is an expression of human creativity through multi-sensory embodiment, then learning, creating and experiencing architecture should also be multi-sensory and embodied. In this article, we challenge the separation of mind and body through Sheets-Johnstone's mindful bodies concept. We define a mindful body in architecture as one that documents, analyses and memory maps the moving body in different qualities of movement to create diverse spatial experiences. A mindful body approach to creating architecture involves: (i) engaging in meaningful movement and documenting the body, (ii) documenting embodied interactions with dynamic, animate elements in the built environment, (iii) connecting our body's movements with emotions and memories, (iv) designing spaces that produce diverse movements and atmospheres, and (v) designing architecture based on these spaces. We hypothesize that if designers engage in a mindful body approach to design, they can create spatial experiences that help us make sense of ourselves, others and the world. A mindful body approach to design can result in architectural spaces that activate our attentional switches, connect haptic experiences and memories and reveal wonders. This article is part of the theme issue 'Minds in movement: embodied cognition in the age of artificial intelligence'.

The use of intersectional tactics by Tunisia’s multicultural movement to build social capital and sustain collective action: It is not what we say or think that defines us but what we do

ABSTRACT Multicultural movements can pursue strategies, like utilizing intersectionality, to overcome collective action problems created by diversity. Scholarship has shown how intersectionality has inspired the creation of common ideological frames among marginalized groups who come to see their causes as intertwined, but such rhetoric is insufficient for sustaining collective action when social capital is lacking. This is relevant in contexts where activists have never collaborated such as a democratic transition. Activists need to see that others practice what they preach so that trust can develop across communal networks. Intersectionality, as I will demonstrate, also inspires social-movement tactics that target intersectional minorities as beneficiaries and encourages activists from different backgrounds to coordinate resources. This builds social capital by strengthening trust and reciprocity across social networks – thereby, sustaining collective action. To demonstrate this, I analysed collective action processes within Tunisia’s multicultural movement after the revolution in 2010 by using historical process tracing and elite interviews. I show how a diverse movement representing women, racial minorities, migrants, and the LGBTQ community utilized intersectional tactics to build social capital and sustain collective action during a democratic transition.

Machine learning-based bee recognition and tracking for advancing insect behavior research

The study of insect behavior, particularly that of honey bees, has a broad scope and significance. Tracking bee flying patterns grants much helpful information about bee behavior. However, tracking a small yet fast-moving object, such as a bee, is difficult. Hence, we present artificial intelligence, machine-learning-based bee recognition, and tracking systems to assist the researcher in studying the bee’s behavior. To develop a machine learning system, a labeled database is required for model training. To address this, we implemented an automated system for analyzing and labeling bee videos. This labeled database served as the foundation for two distinct bee-tracking solutions. The first solution (planar bee tracking system) tracked individual bees in closed mazes using a neural network. The second solution (spatial bee tracking system) utilized a neural network and a tracking algorithm to identify and track flying bees in open environments. Both systems tackle the challenge of tracking small-bodied creatures with rapid and diverse movement patterns. Although we applied these systems to entomological cognition research in this paper, their relevance extends to general insect research and developing tracking solutions for small organisms with swift movements. We present the complete architecture and detailed methodologies to facilitate the utilization of these models in future research endeavors. Our approach is a simple and inexpensive method that contributes to the growing number of image-analysis tools used for tracking animal movement, with future potential applications under less sterile field conditions. The tools presented in this paper could assist the study of movement ecology, specifically in insects, by providing accurate movement specifications. Following the movement of pollinators or natural enemies, for example, greatly contributes to the study of pollination or biological control, respectively, in natural and agro-ecosystems.

Moving to mate? Migration strategy does not predict genetic structure or diversity in bats (Chiroptera)

Abstract How, when, and where animals move during mating periods directs gene-flow patterns across landscapes. Traits associated with movement, such as movement ability and migratory behaviour, are sometimes correlated with population genetic structure, but this relationship depends on where and when mating occurs relative to annual movements. With their wide diversity in behaviours and life-history strategies, bats provide a testing ground for hypotheses about population structure related to migration and mating. We used a global sample of microsatellite data (N = 233 sampling locations from 17 bat species) associated with published studies to examine links between genetic variation and short-distance, long-distance, or non-migratory strategies that also relate to varied mating strategies. The genetic measures we tested were population-specific differentiation, gene diversity, and allelic richness. Using Bayesian models that accounted for phylogenetic distances among species and spatial autocorrelation, we identified no correlations between migration strategy and genetic variation. Our results suggest that hypotheses about genetic structure being mediated by migration might not hold, in general, for bat species. We discuss the need for continued research into the complex association of ecological, biogeographical, and behavioural factors that facilitate gene flow among populations, especially in species with diverse movement patterns.

Movement escalation and mobilization for resistance: From anti-coup protest to ‘People's War’ in Myanmar

Protest campaigns have proven an effective form of contention to challenge authoritarian rule yet can at times escalate into armed conflict. This article aims to explain how a protest movement can escalate to an uprising by examining the case of Myanmar, where the 2021 military coup prompted the emergence of a diverse movement of strike groups, civil society, ousted Burman-majority parliamentarians, and ethnic minority resistance organizations. Based on original interviews with movement members, the article traces how repression triggered escalation and spurred increased cooperation among movement actors that resulted in an armed uprising. Building primarily on social movements and contentious politics studies, this article argues effective campaign framing was critical in maintaining movement unity and legitimizing violence as a form of resistance. Mobilization of resources in social networks and coalition networks was key to organizing armed resistance. The article contributes novel insights by showing that a diverse movement can strengthen cooperation to mount armed resistance, instead of fragment during escalation as studies have often found. The article also deepens our understanding of how movements with links to established armed actors may escalate, as it details how cooperation between mainstream protestors and armed actors can transform a movement into armed resistance.

Marker Data Enhancement For Markerless Motion Capture.

Human pose estimation models can measure movement from videos at a large scale and low cost; however, open-source pose estimation models typically detect only sparse keypoints, which leads to inaccurate joint kinematics. OpenCap, a freely available service for researchers to measure movement from videos, addresses this issue using a deep learning model-the marker enhancer-that transforms sparse keypoints into dense anatomical markers. However, OpenCap performs poorly on movements not included in the training data. Here, we create a much larger and more diverse training dataset and develop a more accurate and generalizable marker enhancer. We compiled marker-based motion capture data from 1176 subjects and synthesized 1433 hours of keypoints and anatomical markers to train the marker enhancer. We evaluated its accuracy in computing kinematics using both benchmark movement videos and synthetic data representing unseen, diverse movements. The marker enhancer improved kinematic accuracy on benchmark movements (mean error: 4.1°, max: 8.7°) compared to using video keypoints (mean: 9.6°, max: 43.1°) and OpenCap's original enhancer (mean: 5.3°, max: 11.5°). It also better generalized to unseen, diverse movements (mean: 4.1°, max: 6.7°) than OpenCap's original enhancer (mean: 40.4°, max: 252.0°). Our marker enhancer demonstrates both accuracy and generalizability across diverse movements. We integrated the marker enhancer into OpenCap, thereby offering its thousands of users more accurate measurements across a broader range of movements.

Textile‐based triboelectric nanogenerators integrated with 2D materials

AbstractThe human body continuously generates ambient mechanical energy through diverse movements, such as walking and cycling, which can be harvested via various renewable energy harvesting mechanisms. Triboelectric Nanogenerator (TENG) stands out as one of the most promising emerging renewable energy harvesting technologies for wearable applications due to its ability to harness various forms of mechanical energies, including vibrations, pressure, and rotations, and convert them into electricity. However, their application is limited due to challenges in achieving performance, flexibility, low power consumption, and durability. Here, we present a robust and high‐performance self‐powered system integrated into cotton fabric by incorporating a textile‐based triboelectric nanogenerator (T‐TENG) based on 2D materials, addressing both energy harvesting and storage. The proposed system extracts significant ambient mechanical energy from human body movements and stores it in a textile supercapacitor (T‐Supercap). The integration of 2D materials (graphene and MoS2) in fabrication enhances the performance of T‐TENG significantly, as demonstrated by a record‐high open‐circuit voltage of 1068 V and a power density of 14.64 W/m2 under a force of 22 N. The developed T‐TENG in this study effectively powers 200+ LEDs and a miniature watch while also charging the T‐Supercap with 4‐5 N force for efficient miniature electronics operation. Integrated as a step counter within a sock, the T‐TENG serves as a self‐powered step counter sensor. This work establishes a promising platform for wearable electronic textiles, contributing significantly to the advancement of sustainable and autonomous self‐powered wearable technologies.image

‘Buddhism(s) for this World’ and ‘Engaged Buddhism’: Some Key Differences

Abstract This article highlights the key differences between two approaches to Buddhism adopted worldwide, ‘Buddhism(s) for this World’ and ‘Engaged Buddhism.’ Although these two terms seem interchangeable, the article presents some differences arising from specific historical circumstances. While the former originated in a reform movement within Chinese Buddhism at the beginning of the twentieth century, the second one, which emerged later, encompasses a much wider variety of Buddhist movements. While ‘Buddhism(s) for this world’ started as a reformist movement, it has become in some cases the vehicle of nationalism. Meanwhile, ‘Engaged Buddhism,’ a somewhat more diverse movement indirectly inspired by his predecessor, has embraced many social justice causes but struggles to institutionalize. The article presents the geopolitical context of international rivalry in which these two trends seek to affirm their respective perspectives.

Multipopulation Evolution-Based Dynamic Constrained Multiobjective Optimization Under Diverse Changing Environments

Dynamic constrained multiobjective optimization involves irregular changes in the distribution of the true Pareto-optimal fronts, drastic changes in the feasible region caused by constraints, and the movement directions and magnitudes of the optimal distance variables due to diverse changing environments. To solve these problems, we propose a multi-population evolution based dynamic constrained multiobjective optimization algorithm. In this algorithm, we design a tribe classification operator to divide the population into different tribes according to a feasibility check and the objective values, which is beneficial for driving the population toward the feasible region and Pareto-optimal fronts. Meanwhile, a population selection strategy is proposed to identify promising solutions from tribes and exploit them to update the population. The optimal values of the distance variables vary differently with dynamic environments, thus, we design a dynamic response strategy for solutions in different tribes that estimates their distances to approach the Pareto-optimal fronts and regenerates a promising population when detecting environmental changes. In addition, a scalable generator is designed to simulate diverse movement directions and magnitudes of the optimal distance variables in real-world problems under dynamic environments, obtaining a set of improved test problems. Experimental results show the effectiveness of test problems, and the proposed algorithm is impressively competitive with several chosen state-of-the-art competitors.

Machine Learning-Based Hand Pose Generation Using a Haptic Controller

In this study, we present a novel approach to derive hand poses from data input via a haptic controller, leveraging machine learning techniques. The input values received from the haptic controller correspond to the movement of five fingers, each assigned a value between 0.0 and 1.0 based on the applied pressure. The wide array of possible finger movements requires a substantial amount of motion capture data, making manual data integration difficult. This challenge is primary due to the need to process and incorporate large volumes of diverse movement information. To tackle this challenge, our proposed method automates the process by utilizing machine learning algorithms to convert haptic controller inputs into hand poses. This involves training a machine learning model using supervised learning, where hand poses are matched with their corresponding input values, and subsequently utilizing this trained model to generate hand poses in response to user input. In our experiments, we assessed the accuracy of the generated hand poses by analyzing the angles and positions of finger joints. As the quantity of training data increased, the margin of error decreased, resulting in generated poses that closely emulated real-world hand movements.

Integrative analysis of plasma and substantia nigra in Parkinson's disease: unraveling biomarkers and insights from the lncRNA-miRNA-mRNA ceRNA network.

Parkinson's disease (PD) is a rapidly growing neurological disorder characterized by diverse movement symptoms. However, the underlying causes have not been clearly identified, and accurate diagnosis is challenging. This study aimed to identify potential biomarkers suitable for PD diagnosis and present an integrative perspective on the disease. We screened the GSE7621, GSE8397-GPL96, GSE8397-GPL97, GSE20163, and GSE20164 datasets in the NCBI GEO database to identify differentially expressed (DE) mRNAs in the substantia nigra (SN). We also screened the GSE160299 dataset from the NCBI GEO database to identify DE lncRNAs and miRNAs in plasma. We then constructed 2 lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) regulatory networks based on the ceRNA hypothesis. To understand the biological function, we performed Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology analyses for each ceRNA network. The receiver operating characteristic analyses (ROC) was used to assess ceRNA results. We identified 7 upregulated and 29 downregulated mRNAs as common DE mRNAs in the 5 SN datasets. In the blood dataset, we identified 31 DE miRNAs (9 upregulated and 22 downregulated) and 332 DE lncRNAs (69 upregulated and 263 downregulated). Based on the determined interactions, 5 genes (P2RX7, HSPA1, SLCO4A1, RAD52, and SIRT4) appeared to be upregulated as a result of 10 lncRNAs sponging 4 miRNAs (miR-411, miR-1193, miR-301b, and miR-514a-2/3). Competing with 9 genes (ANK1, CBLN1, RGS4, SLC6A3, SYNGR3, VSNL1, DDC, KCNJ6, and SV2C) for miR-671, a total of 26 lncRNAs seemed to function as ceRNAs, influencing genes to be downregulated. In this study, we successfully constructed 2 novel ceRNA regulatory networks in patients with PD, including 36 lncRNAs, 5 miRNAs, and 14 mRNAs. Our results suggest that these plasma lncRNAs are involved in the pathogenesis of PD by sponging miRNAs and regulating gene expression in the SN of the brain. We propose that the upregulated and downregulated lncRNA-mediated ceRNA networks represent mechanisms of neuroinflammation and dopamine neurotransmission, respectively. Our ceRNA network, which was associated with PD, suggests the potential use of DE miRNAs and lncRNAs as body fluid diagnostic biomarkers. These findings provide an integrated view of the mechanisms underlying gene regulation and interactions in PD.

Exploring neural motion transfer for unsupervised remote physiological measurement: A practicality study

Remote Photoplethysmography (rPPG) is a method to measure cardiac activity without the need for any contact-based sensors, garnering attention due to its non-invasive and convenient nature. Addressing the issue of substantial missing data in large-scale datasets, current research is inclined towards unsupervised learning methods. However, prevailing unsupervised learning methods often prioritize inter-sample comparative learning while neglecting individual sample features, severely impacting performance and generalizability. Meanwhile, body movement is one of the most important influencing factors when attempting to extract the rPPG signal from videos. Hence, we embarked on the inaugural exploration of integrating Neural Motion Transfer into unsupervised learning methods for remote physiological measurements, aiming to enrich the inherent motion characteristics and diversity of samples. Employing Neural Motion Transfer, we synthesized videos encompassing diverse movements as a means of data augmentation. Through an in-depth comparison with other synthetic video methods, we extensively analyzed the physiological information within motion-enhanced synthetic videos. We investigated the applicability of these motion-enhanced synthetic videos to unsupervised learning methods and various associated impacts. We conducted extensive experiments on four benchmark datasets. After utilizing motion-enhanced synthetic videos to aid unsupervised training, our approach achieved an improvement of approximately 52% in intra-dataset testing and up to 97% in cross-dataset testing compared to the baseline unsupervised method, achieving the state-of-the-art (SOTA) performance among current unsupervised methods. Our research underscores the importance of neural motion networks in enhancing unsupervised learning methods and highlights the significant potential of synthetic videos.

Mobilność młodych dorosłych z krajów Europy Środkowo-Wschodniej – polsko-czesko-słowacko-ukraińskie studium porównawcze

The young generation is treated as the future of society. This generation’s size, skills and knowledge, as well as economic activity, socio-cultural involvement, marital intentions and family (procreation) plans determine the quality of society. Thus, an important issue is the outflow of young people who decide to migrate abroad. It is therefore important to recognize both the current experiences and the life plans of the young generation in this field. In post-communist countries, the fall of the communist system had a major impact on the increase in mobility. Political, sociocultural and economic changes have contributed to the emergence of new migration-related phenomena and the so-called fluid migration, characterized by free movement of labour, massiveness, diversity and plasticity. The surveyed youth from Poland, the Czech Republic and Slovakia have experiences related to functioning within open borders, unlike the youth from Ukraine, where the borders are closed. Wioletta Danilewicz notices the existence of the so-called migration culture, i.e. the acceptance (by the participants of migration processes) that these are phenomena constituting an inherent element of their lives, despite their awareness of the unfavourable co-occurring elements. The research results presented in the text indicate that the surveyed young adults do not fully follow this trend.

Navigating the Currents: A Comprehensive Exploration of Migration Dynamics and Global Impacts

Migration is a complex and ever-evolving process that impacts societies globally, presenting both positive and negative impacts. The African continent, with its rich history, has been a significant force in migration, driven by exploration, trade, and colonialism. The continent's vastness and diverse regions complicate migration dynamics, with geological factors contributing to the continent's ever-evolving landscape. European migration to Africa is significant, with diverse movements within and outside Africa. The impact of African migration on the global community is profound, affecting labour markets, demographics, and presenting challenges and opportunities. The study advocates for a comprehensive approach to address employment, healthcare, education, and security issues, creating jobs, improving local conditions, and fostering global cooperation to ensure a more equitable and sustainable future. Keywords: Migration dynamics, Globalization and migration, European migration in Africa.

Time-resolved quantification of fine hand movements as a proxy for evaluating bradykinesia-induced motor dysfunction

Bradykinesia is a behavioral manifestation that contributes to functional dependencies in later life. However, the current state of bradykinesia indexing primarily relies on subjective, time-averaged categorizations of motor deficits, which often yield poor reliability. Herein, we used time-resolved analyses of accelerometer recordings during standardized movements, data-driven factor analyses, and linear mixed effects models (LMEs) to quantitatively characterize general, task- and therapy-specific indices of motor impairment in people with Parkinson’s disease (PwP) currently undergoing treatment for bradykinesia. Our results demonstrate that single-trial, accelerometer-based features of finger-tapping and rotational hand movements were significantly modulated by divergent therapeutic regimens. Further, these features corresponded well to current gold standards for symptom monitoring, with more precise predictive capacities of bradykinesia-specific declines achieved when considering kinematic features from diverse movement types together, rather than in isolation. Herein, we report data-driven, sample-specific kinematic profiles of diverse movement types along a continuous spectrum of motor impairment, which importantly, preserves the temporal scale for which biomechanical fluctuations in motor deficits evolve in humans. Therefore, this approach may prove useful for tracking bradykinesia-induced motor decline in aging populations the future.

Impact of Globalization and Digitalization on Social Movements and Collective Action Across Different Regions and Contexts in Nigeria

Purpose: The aim of the study was to investigate impact of globalization and digitalization on social movements and collective action across different regions and contexts
 Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.
 Findings: Globalization and digitalization have profoundly affected social movements and collective action. They enable global connectivity, amplify marginalized voices, and give rise to diverse movements. However, challenges include government control, digital inequality, and the need for new tactics. Activists now use digital platforms to mobilize globally, but also face increased state repression. The digital age has led to innovative strategies and ongoing evolution of the global activist landscape.
 Unique Contribution to Theory, Practice and Policy: Resource mobilization theory, network theory & transnational activism theory may be used to anchor future studies on impact of globalization and digitalization on social movements and collective action across different regions and contexts. Movements should prioritize digital literacy and security training for their members. Policymakers should address the digital divide by implementing policies and initiatives that ensure equitable access to digital resources, particularly in marginalized regions