Search Results (1,405)

Community detection in complex networks plays a pivotal role in modern scientific research, including in social network analysis and protein structure analysis. Traditional community detection methods face challenges in integrating heterogeneous multi-source information, capturing global semantic relationships, and adapting to dynamic network evolution. This paper proposes a novel unsupervised multimodal community detection algorithm (UMM) based on fractal iteration. The core idea is to design a dual-channel encoder that comprehensively considers node semantic features and network topological structures. Initially, node representation vectors are derived from structural information (using feature vectors when available, or singular value decomposition to obtain feature vectors for nodes without attributes). Subsequently, a parameter-free graph convolutional encoder (PFGC) is developed based on fractal iteration principles to extract high-order semantic representations from structural encodings without requiring any training process. Furthermore, a semantic–structural dual-channel encoder (DC-SSE) is designed, which integrates semantic encodings—reduced in dimensionality via UMAP—with structural features extracted by PFGC to obtain the final node embeddings. These embeddings are then clustered using the K-means algorithm to achieve community partitioning. Experimental results demonstrate that the UMM outperforms existing methods on multiple real-world network datasets. Full article

Plastic pollution is a growing environmental and social concern, particularly in Southeast Asia, where urban rivers serve as key pathways for transporting waste to marine environments. This scoping review examines 110 peer-reviewed studies to understand how plastic pollution in waterways is being researched, addressed, and reconceptualized. Drawing from the literature across environmental science, technology, and social studies, we identify four interconnected areas of focus: urban pollution pathways, innovations in monitoring and methods, community-based interventions, and interdisciplinary perspectives. Our analysis combines qualitative synthesis with visual mapping techniques, including keyword co-occurrence networks, to explore how real-time tools, such as IoT sensors, multi-sensor systems, and geospatial technologies, are transforming the ways plastic waste is tracked and analyzed. The review also considers the growing use of novel theoretical frameworks, such as post-phenomenology and ecological materialism, to better understand the role of plastics as both pollutants and ecological agents. Despite progress, the literature reveals persistent gaps in longitudinal studies, regional representation, and policy translation, particularly across the Global South. We emphasize the value of participatory models and community-led research in bridging these gaps and advancing more inclusive and responsive solutions. These insights inform the development of plastic tracker technologies currently being piloted in Vietnam and contribute to broader sustainability goals, including SDG 6 (Clean Water and Sanitation), SDG 12 (Responsible Consumption and Production), and SDG 14 (Life Below Water). Full article

This article describes the design, methods, and baseline characteristics of the social needs assessment (SNA) of participants enrolled in an ongoing randomized clinical trial implementing a comprehensive approach to improving diabetes self-management and providing an intensive Diabetes Self-Management Education and Support (iDSMES) Program at St. Vincent’s House Clinic, a primary care practice serving resource-challenged diverse populations in Galveston, Texas. Standardized SNA was conducted to collect information on financial needs, psychosocial well-being, and other chronic health conditions. Based on their identified needs, participants were referred to non-medical existing community resources. A series of in-depth interviews were conducted with a subset of participants. A team member independently categorized these SNA narratives and aggregated them into two overarching groups: medical and social needs. Fifty-nine participants (with a mean age of 53 years and equal representation of men and women) completed an SNA. Most (71%) did not have health insurance. Among 12 potential social needs surveyed, the most frequently requested resources were occupational therapy (78%), utility assistance (73%), and food pantry services (71%). SNA provided data with the potential to address barriers that may hinder participation, retention, and outcomes in diabetes self-management. SNA findings may serve as tertiary prevention to mitigate diabetes-related complications and disparities. Full article

Urban infrastructure, such as water supply networks, sewage systems, and electricity networks, is essential for the functioning of cities and, consequently, for the well-being of citizens. Despite its essentiality, the distribution of infrastructure in urban areas is not homogeneous, especially in cities in developing countries. Socially vulnerable areas often face significant deficiencies in sewage and road paving, exacerbating urban inequalities. In this regard, urban planners must consider the multiple elements of urban infrastructure and assess the compensation levels between them to reduce inequality effectively. In particular, the complexity of the problem necessitates considering the multidimensionality and heterogeneity of urban infrastructure. This complexity qualifies the operational framework of composite indicators as the natural solution to the problem. This study develops a new weighting system for the balanced expansion of urban infrastructures through composite indicators constructed by the Ordered Weighted Average operator. Implementing these weighting systems provides an opportunity to analyze urban infrastructure from different perspectives, offering transparency regarding the weaknesses and strengths of each perspective. This prevents unreliable representations from being used in decision-making and provides a solid basis for allocating investments in urban infrastructure. In particular, the study suggests that adopting weighting systems that prioritize intermediate values and avoid extreme values can lead to better resource allocation, helping to identify areas with deficient infrastructure and promoting more equitable urban development. Full article

Emerging markets face growing pressures to integrate sustainable English business practices while maintaining economic growth, particularly in addressing environmental challenges and achieving carbon neutrality goals. English Financial information extraction becomes crucial for supporting green finance initiatives, Environmental, Social, and Governance (ESG) compliance, and sustainable investment decisions in these markets. This paper presents FinATG, an AI-driven autoregressive framework for extracting sustainability-related English financial information from English texts, specifically designed to support emerging markets in their transition toward sustainable development. The framework addresses the complex challenges of processing ESG reports, green bond disclosures, carbon footprint assessments, and sustainable investment documentation prevalent in emerging economies. FinATG introduces a domain-adaptive span representation method fine-tuned on sustainability-focused English financial corpora, implements constrained decoding mechanisms based on green finance regulations, and integrates FinBERT with autoregressive generation for end-to-end extraction of environmental and governance information. While achieving competitive performance on standard benchmarks, FinATG’s primary contribution lies in its architecture, which prioritizes correctness and compliance for the high-stakes financial domain. Experimental validation demonstrates FinATG’s effectiveness with entity F1 scores of 88.5 and REL F1 scores of 80.2 on standard English datasets, while achieving superior performance (85.7–86.0 entity F1, 73.1–74.0 REL+ F1) on sustainability-focused financial datasets. The framework particularly excels in extracting carbon emission data, green investment relationships, and ESG compliance indicators, achieving average AUC and RGR scores of 0.93 and 0.89 respectively. By automating the extraction of sustainability metrics from complex English financial documents, FinATG supports emerging markets in meeting international ESG standards, facilitating green finance flows, and enhancing transparency in sustainable business practices, ultimately contributing to their sustainable development goals and climate action commitments. Full article

I use Shakespeare to teach acting to students. A key to my work is impression management: what Shakespeare called reputation. I view the management of reputation as a route into Shakespearean character, which I present to students as a mask attuned to sacred values. The physical basis from which the actor can discover the mask is what Hamlet calls ‘smoothness’, which I explain with an acting exercise. We discover the force of sacred values by noticing the ubiquity of keywords in the text such as honor, virtue, reason, shame and faith. By holding characters to the fire of their sacred values, I shift the actor’s attention from an individualist idea of authentic representation towards a sense of character as a battleground of mind-shaping. The resulting performance work is scaled up to a more expansive and energized degree than the actor may be used to delivering in a social media-saturated environment in which what is often prioritized is a quasi-confessional self-revelation. The revelation of an inner life then emerges through a committed exploration of antithetical relations, a strategy basic both to mask work and to Shakespeare’s poetics. The actor finds their personal connection to the material by facing the contradiction between the objective standards of behavior demanded of the character and the character’s attempt to control their status, that is, how they are seen. The final value of the performance work is that the actor learns how to manage their reputation so that they come to appear like a giant who is seen from a distance. Full article

This paper creates a global similarity network between city-level dialects of English in order to determine whether external factors like the amount of population contact or language contact influence dialect similarity. While previous computational work has focused on external influences that contribute to phonological or lexical similarity, this paper focuses on grammatical variation as operationalized in computational construction grammar. Social media data was used to create comparable English corpora from 256 cities across 13 countries. Each sample is represented using the type frequency of various constructions. These frequency representations are then used to calculate pairwise similarities between city-level dialects; a prediction-based evaluation shows that these similarity values are highly accurate. Linguistic similarity is then compared with four external factors: (i) the amount of air travel between cities, a proxy for population contact, (ii) the difference in the linguistic landscapes of each city, a proxy for language contact, (iii) the geographic distance between cities, and (iv) the presence of political boundaries separating cities. The results show that, while all these factors are significant, the best model relies on language contact and geographic distance. Full article

The rubber industry is facing major socio-economic and environmental constraints. Rubber-based agroforestry systems represent a more sustainable solution through the diversification of income and the provision of greater ecosystem services than monoculture plantations. Participative approaches are known for their ability to co-construct solutions with stakeholders and to promote a positive impact on smallholders. This study therefore implemented a participatory research process with stakeholders in the natural rubber sector for the purpose of improving inclusion, relevance and impact. Facilitation training sessions were first organised with academic actors to prepare participatory workshops. A working group of stakeholder representatives was set up and participated in these workshops to share a common representation of the value chain and to identify problems and solutions for the sector in Indonesia. By fostering collective intelligence and systems thinking, the process is aimed at enabling the development of adaptive technical solutions and building capacity across the sector for future government replanting programmes. The resulting adaptive technical packages were then detailed and objectified by the academic consortium and are part of a participatory plant breeding approach adapted to the natural rubber industry. On-station and on-farm experimental plans have been set up to facilitate the drafting of projects for setting up field trials based on these outcomes. Research played a dual role as both knowledge provider and facilitator, guiding a co-learning process rooted in social inclusion, equity and ecological resilience. The initiative highlighted the potential of rubber cultivation to contribute to climate change mitigation and food sovereignty, provided that it can adapt through sustainable practices like agroforestry. Continued political and financial support is essential to sustain and scale these innovations. Full article

Background/Objectives: Frailty is a multifactorial clinical syndrome characterized by diminished physiological reserves and increased vulnerability to stressors. It is increasingly recognized as a predictor of poor outcomes in cardiac rehabilitation (CR). However, how frailty is defined, assessed, and addressed across outpatient CR programmes remains unclear. This scoping review aimed to map the extent, range, and nature of research examining frailty in the context of outpatient CR, including how frailty is measured, its impact on CR participation and outcomes, and whether sex and gender considerations or participation barriers are reported. Methods: Following the PRISMA-ScR guidelines, we conducted a comprehensive search across six electronic databases (from inception to 15 May 2025). Eligible peer-reviewed studies included adult participants assessed for frailty using validated tools and enrolled in outpatient CR programmes. Two reviewers independently screened citations and extracted data. Results were synthesized descriptively and narratively across three domains: frailty assessment, sex and gender considerations, and barriers to CR participation. The protocol was registered with the Open Science Framework. Results: Thirty-nine studies met inclusion criteria, all conducted in the Americas, Western Pacific, or Europe. Frailty was assessed using 26 distinct tools, most commonly the Kihon Checklist, Fried’s Frailty Criteria, and Frailty Index. The median pre-CR frailty prevalence was 33.5%. Few studies (n = 15; 38.5%) re-assessed frailty post-CR. Sixteen studies reported sex or gender data, but none applied sex- or gender-based analysis (SGBA) frameworks. Only eight studies examined barriers to CR participation, identifying physical limitations, emotional distress, cognitive concerns, healthcare system-related factors, personal and social factors, and transportation as key barriers. Conclusions: The literature on frailty in CR remains fragmented, with heterogeneous assessment methods, limited global representation, and inconsistent attention to sex, gender, and participation barriers. Standardized frailty assessments and individualized CR programme adaptations are urgently needed to improve accessibility, adherence, and outcomes for frail individuals. Full article

Purpose: Cancer immunotherapy has transformed oncology, but underserved populations face persistent disparities in access and outcomes. This review explores how artificial intelligence (AI) can help mitigate these barriers. Methods: We conducted a narrative review based on peer-reviewed literature selected for relevance to artificial intelligence, cancer immunotherapy, and healthcare challenges, without restrictions on publication date. We searched three major electronic databases: PubMed, IEEE Xplore, and arXiv, covering both biomedical and computational literature. The search included publications from January 2015 through April 2024 to capture contemporary developments in AI and cancer immunotherapy. Results: AI tools such as machine learning, natural language processing, and predictive analytics can enhance early detection, personalize treatment, and improve clinical trial representation for historically underrepresented populations. Additionally, AI-driven solutions can aid in managing side effects, expanding telehealth, and addressing social determinants of health (SDOH). However, algorithmic bias, privacy concerns, and data diversity remain major challenges. Conclusions: With intentional design and implementation, AI holds the potential to reduce disparities in cancer immunotherapy and promote more inclusive oncology care. Future efforts must focus on ethical deployment, inclusive data collection, and interdisciplinary collaboration. Full article

Social network platforms have a big impact on the development of companies by influencing clients’ behaviors and sentiments, which directly affect corporate reputations. Analyzing this feedback has become an essential component of business intelligence, supporting the improvement of long-term marketing strategies on a larger scale. The implementation of powerful sentiment analysis models requires a comprehensive and in-depth examination of each stage of the process. In this study, we present a new comparative approach for several feature extraction techniques, including TF-IDF, Word2Vec, FastText, and BERT embeddings. These methods are applied to three multilingual datasets collected from hotel review platforms in the tourism sector in English, French, and Arabic languages. Those datasets were preprocessed through cleaning, normalization, labeling, and balancing before being trained on various machine learning and deep learning algorithms. The effectiveness of each feature extraction method was evaluated using metrics such as accuracy, F1-score, precision, recall, ROC AUC curve, and a new metric that measures the execution time for generating word representations. Our extensive experiments demonstrate significant and excellent results, achieving accuracy rates of approximately 99% for the English dataset, 94% for the Arabic dataset, and 89% for the French dataset. These findings confirm the important impact of vectorization techniques on the performance of sentiment analysis models. They also highlight the important relationship between balanced datasets, effective feature extraction methods, and the choice of classification algorithms. So, this study aims to simplify the selection of feature extraction methods and appropriate classifiers for each language, thereby contributing to advancements in sentiment analysis. Full article

Volcanic islands host exceptional geological features that illustrate complex endogenic processes and interactions with climatic and marine forces, while also being particularly vulnerable to the impacts of climate change. Despite their scientific, educational, touristic, and aesthetic values, such islands remain underrepresented within the UNESCO Global Geoparks (UGGp). This study reviews current volcanic island geoparks and evaluates territories with potential for future designation, based on documented geoheritage, geosite inventories, and geoconservation frameworks. Geoparks are categorized according to their dominant narratives—ranging from recent Quaternary volcanism to broader tectonic, sedimentary, and metamorphic histories. Through an analysis of their distribution, management strategies, and integration into territorial planning, this work highlights the challenges that insular territories face, including vulnerability to global environmental change, limited legal protection, and structural inequalities in access to international resources recognition. It concludes that volcanic island geoparks represent strategic platforms for implementing sustainable development models, especially in ecologically and socially fragile contexts. Enhancing their global representation will require targeted efforts in ecologically and socially fragile contexts. Enhancing their global representation will require targeted efforts in capacity building, funding access, and regional cooperation—particularly across the Global South. Full article

Caring for the lives and welfare of animals is central to veterinary and animal sanctuary work, yet the meaning remains a subject of complex debates. Different stakeholders negotiate what constitutes appropriate care, leading to conflicting demands and expectations from internal and external sources. This article is based on two qualitative studies: Study I explores the multifaceted aspects of death work in farm animal medicine, emphasising the practical, emotional and ethical challenges involved. Study II examines human–animal interaction in sanctuaries, which reveal tensions between instrumental and relational care in animal-centred work. Relational care represents a subjectifying approach with individual attention to animals, while instrumental care is a more objectifying perspective based on species representation. These demands can often be contradictory, complicating day-to-day decision making under pressure. To analyse these complexities, this study employs Clarke’s situational analysis (social worlds/arenas mapping), providing a means of comparing care work across different fields. This approach highlights how actor constellations, institutional settings, and structural constraints influence the negotiation of care. Addressing these issues provides a more nuanced understanding of the professional challenges of animal-centred care and the necessary skills to navigate its inherent contradictions. Full article

Prejudices, particularly those related to social biases, are shaped by various cognitive and sensory mechanisms. This study investigates the interaction between olfactory perception and propensity and implicit or explicit prejudices through three experimental protocols in a metaverse condition. Experiment 1 examines the impact of five different odors on proxemic behavior when interacting with individuals from stigmatized social groups. Experiment 2 integrates electroencephalography (EEG) to analyze the neural correlates of prejudice-related responses to olfactory stimuli. Experiment 3 explores implicit biases through the Implicit Association Test (IAT) in relation to different fragrances, without employing virtual reality. The proposed protocol is expected to demonstrate a significant relationship between olfactory cues, linked to social relationships, and implicit or explicit prejudices, with variations based on individual differences. These insights will contribute to psychological, neuroscientific, and social interventions, offering new perspectives on the unconscious mechanisms of bias formation. Additionally, this study highlights the potential of virtual reality and olfactory stimuli as innovative tools for studying and addressing social biases in controlled environments. Full article

This essay examines two hybrid poetic texts that emerged from a period of feminist activism in U.S. and global poetry communities from 2014 to 2017: the collaboratively, anonymously authored “No Manifesto” (2015) and the radically revised second edition of the book of poetry and visual art YOU DA ONE by Jennif(f)er Tamayo. “No Manifesto” and YOU DA ONE embrace the hybrid tactics of collectivity, incongruity, and nonresolution as ways of protesting sexism and sexual violence in poetry communities. Synthesizing theories of hybridity from poetry criticism as well as immigrant and borderlands studies, the essay defines hybridity as a literary representation of cultural positions forcefully imposed upon subjects. Born out of the domination of sexual and state violence, hybridity marks the wound that remakes the subject, who develops strategies for resistance. By refusing to play by the rules of poetic or social discourse—the logics of domination that would have them be singular, cohesive, and compliant—Tamayo and the authors of “No Manifesto” insist on alternative ways of performing activism, composing literature, and entering the public sphere. These socially engaged, hybrid poetic texts demonstrate the power of the collective to disrupt the social and literary status quo. Full article