Search Results (34)

Within the context of urban sustainability, the renewal and activation of communities have received growing attention. Public art, as a common approach to community revitalization, has long been regarded as an effective means of addressing urban and community issues. Basic human senses serve as a bridge between residents and community spaces, offering an effective entry point for creating human-oriented spaces. This study addresses the challenge of insufficient spatial vitality in community spaces by examining how sensory interventions can enhance residents’ participation in public art and thereby contribute to the revitalization of communities. To guide this inquiry, a theoretical framework was constructed based on sensory marketing theory and the Stimulus–Organism–Response (SOR) model, focusing on three core dimensions: sensory stimuli, perceptual responses, and behavioral intention. The study further investigated the relationship between public art and residents’ willingness to participate through five types of sensory stimuli, using a measurement scale and Structural Equation Modeling (SEM), with eight public art installations in Shanghai serving as case references. It also assessed the relative strength of each effect. Participant interviews and non-participatory observations were subsequently conducted for validation and supplementary analysis. The results show that residents’ participation willingness in community public art is directly influenced by perceptual responses (emotional fluctuations, cognitive memory, and physiological responses), and indirectly influenced by different sensory stimuli. Cognitive memory, shaped mainly by olfactory and visual stimuli, emerged as the most important factor in encouraging participation. Participation willingness also varies across generations, and different sensory stimuli are associated with distinct participation patterns. Based on empirical data from Shanghai’s community activation practices, the study proposes implementation strategies guided by the Theory of Planned Behavior (TPB) to enhance spatial vitality, promote community activation, and support sustainable development. Full article

In the context of today’s neoliberal governance of schooling, the psychological, emotional, cultural, and spiritual costs of internalized racism remain unaddressed. However, when Youth of Color embody internalized racial oppression (IRO), they become “psychologically homeless,” which affects their wellbeing in devastating ways. This article advances an anti-racist research agenda in education, suggesting that education researchers’ implementation of “healing methodologies” in school is key to tackling issues of IRO. To this end, this paper advocates for education researchers to embrace, incorporate, and combine art-based and walking approaches into participatory “healing methodologies” to provide students with embodied practices that can support them in exploring, reconciling, and repairing hidden injuries while re-establishing inner strength and equilibrium for wellbeing and body restoration. Full article

Customers heavily depend on reviews for product information. Fake reviews may influence the perception of product quality, making online reviews less effective. ChatGPT’s (GPT-3.5 and GPT-4) ability to generate human-like reviews and responses to inquiries across several disciplines has increased recently. This leads to an increase in the number of reviewers and applications using ChatGPT to create fake reviews. Consequently, the detection of fake reviews generated or rephrased by ChatGPT has become essential. This paper proposes a new approach that distinguishes ChatGPT-rephrased reviews, considered fake, from real ones, utilizing a balanced dataset to analyze the sentiment and linguistic patterns that characterize both reviews. The proposed model further leverages Explainable Artificial Intelligence (XAI) techniques, including Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP) for deeper insights into the model’s predictions and the classification logic. The proposed model performs a pre-processing phase that includes part-of-speech (POS) tagging, word lemmatization, tokenization, and then fine-tuned Transformer-based Machine Learning (ML) model DistilBERT for predictions. The obtained experimental results indicate that the proposed fine-tuned DistilBERT, utilizing the constructed balanced dataset along with a pre-processing phase, outperforms other state-of-the-art methods for detecting ChatGPT-rephrased reviews, achieving an accuracy of 97.25% and F1-score of 97.56%. The use of LIME and SHAP techniques not only enhanced the model’s interpretability, but also offered valuable insights into the key factors that affect the differentiation of genuine reviews from ChatGPT-rephrased ones. According to XAI, ChatGPT’s writing style is polite, uses grammatical structure, lacks specific descriptions and information in reviews, uses fancy words, is impersonal, and has deficiencies in emotional expression. These findings emphasize the effectiveness and reliability of the proposed approach. Full article

The classification of political inquiry messages is a crucial task in government affairs. However, with the increasing number of inquiry messages on platforms, it is difficult for government departments to accurately and efficiently categorize these messages solely through manual labor. Existing methods find it challenging to achieve excellent performance due to the sparsity of the features and the imbalanced data. In this paper, we propose a new framework (BTCAM) for automatically classifying political inquiry messages. Specifically, we first propose a topic-based data augmentation algorithm (TDA) to improve the diversity and quality of data. In addition, in order to enable the model to focus on the key information in the text, we propose an applicable streamlined convolutional block attention module (SCBAM), which can highlight the salient features on the channel and spatial axes. Extensive experiments show that the accuracy and recall of BTCAM reach 0.939 and 0.931, respectively, outperforming the state-of-the-art methods. Full article

Task-oriented medical dialogue systems face two fundamental challenges: the explosion of state-action space caused by numerous diseases and symptoms and the sparsity of informative signals during interactive diagnosis. These issues significantly hinder the accuracy and efficiency of automated clinical reasoning. To address these problems, we propose MA-HRL, a multi-agent hierarchical reinforcement learning framework that decomposes the diagnostic task into specialized agents. A high-level controller coordinates symptom inquiry via multiple worker agents, each targeting a specific disease group, while a two-tier disease classifier refines diagnostic decisions through hierarchical probability reasoning. To combat sparse rewards, we design an information entropy-based reward function that encourages agents to acquire maximally informative symptoms. Additionally, medical knowledge graphs are integrated to guide decision-making and improve dialogue coherence. Experiments on the SymCat-derived SD dataset demonstrate that MA-HRL achieves substantial improvements over state-of-the-art baselines, including +7.2% diagnosis accuracy, +0.91% symptom hit rate, and +15.94% symptom recognition rate. Ablation studies further verify the effectiveness of each module. This work highlights the potential of hierarchical, knowledge-aware multi-agent systems for interpretable and scalable medical diagnosis. Full article

Glomalin-related soil protein (GRSP), a glycoprotein primarily exuded by arbuscular mycorrhizal fungi (AMF), exerts key roles in ecological processes in terrestrial ecosystems. Nevertheless, the intricate nature of GRSP, coupled with constraints in its extraction and analytical methodologies, impedes a comprehensive understanding of its compositional attributes and ecological functions. Moreover, the scope of current GRSP research has undergone significant expansion, necessitating a comprehensive synthesis in this field. Here, we employed bibliometric analysis to systematically assess research trends and hotspots in the research field of GRSP based on 840 relevant articles indexed in the Web of Science Core Collection database. Among them, key parameters evaluated encompass publications’ quantity, highly cited articles, high-frequency keywords, and historical direct citations. These analyses illuminated the state-of-the-art of GRSP research, delineated emergent trends, and provided future perspectives. Current investigations into GRSP predominantly focus on three major topics: (i) GRSP’s nature, origin, and quantification methodologies; (ii) GRSP’s key influencing factors including agricultural management practices, climate and land use change; and (iii) GRSP’s ecological functions enhancing soil aggregate stability, C sequestration, and contamination remediation. Our findings can serve as a scholarly resource for advancing inquiries into the ecological functionalities of GRSP and its prospective applications in sustainable soil management and ecological restoration. Full article

In this article, we share our findings on patients’ experiences creating digital stories about living with advanced cancer, represented through found poetry. Over a period of 12 months, patients from the program “Managing Cancer and Living Meaningfully” (CALM) completed digital stories about their experiences living with cancer. Digital stories are short, personalized videos that combine photographs, imagery, narration, and music to communicate a personal experience about a topic of inquiry. Patient interviews were conducted about the digital storytelling process. Found poetry guided the analysis technique. It is a form of arts-based research that involves using words and phrases found in interview transcripts to create poems that represent research themes. This article begins with a brief overview of the psychosocial intervention CALM, arts in healthcare, and found poetry, followed by the project background. The found poems represent themes of emotional impact, legacy making, and support and collaboration. Findings also indicate the inherently relational aspect of digital storytelling as participants emphasized the integral role of the digital storytelling facilitator. What follows is a discussion on digital storytelling, which considers the role of found poetry in representing patient voices in the research process. Full article

This paper presents the results of a Participatory Action Research, whose main objective was to validate a pedagogical intervention characterized by adopting an inquiry-based learning methodology (IBL), and the paradigm of Practice as Research focused on musical performativity. This intervention sought to foster autonomy, the relationship between theory and practice, and the development of critical reflexive skills in professional music students. The co-participants (12 students in the first cycle and 9 in the second) belonged to university training programs in vocal and instrumental interpretation, composition, and music pedagogy at different levels. In the intervention cycles, students were able to organize themselves collaboratively to achieve the learning objectives and research products designed in the intervention. During the process, they redefined research as a tool for professional development in their discipline. This empowered the participants, as they became aware that they themselves could build knowledge in their field. The intervention was validated, and the results highlight the potential of inquiry activities focused on musical performativity and Practice as Research in the context of higher music education, playing an integrating role in the construction of knowledge by and for practitioners. Full article

This study examined how incorporating art into an upper-level undergraduate field-based ecology research course influenced students’ communication and collaboration skills, their career goals, and how they conceptualized the scientific method. Student pairs designed an independent research study that used artwork and a scientific research poster to disseminate their findings at an end-of-term exhibit. Students enrolled in either a local or a (subsidized) travel abroad section of the course. Students in both sections found new or deeper connections between art and science, developed a more sophisticated understanding of the science method, became more confident with their science skills, and reported an expanded perspective on their future careers (often including field work and a wider geographic job search). Science–art student teams indicated they wanted more opportunities for collaborative work in the future, and that their final products were more professional due to their collaborations, as compared to science–science teams. Additionally, the travel abroad students benefitted from experiencing new ecosystems and cultures, from working with science and art professionals from other countries, and from working in an isolated field station without distractions. Full article

This article describes the development and public performances of La Liga de la Decencia, a new play presented as part of the 2023 New Works Festival at the University of Texas at Austin. Inspired by the cabaret scene and teatro de revista of the 1940s in Mexico City, La Liga de la Decencia combines live performance and video art to explore how hegemonic gender and social norms shaped by the emergent nationalism of postrevolutionary Mexico continue to oppress femme and queer bodies today across the US–Mexico border. Through satire, parody, and dance, La Liga de la Decencia problematizes the social, class, and gender norms as established by the cultural elite and the state. Following research-based theatre as an inquiry process, this article describes how writing and directing this play allowed for a deeper understanding of the dynamics of a historical period. By mixing facts, fiction, and critical commentary, La Liga de la Decencia investigates history through embodiment. Full article

This article explores the implementation of Connected Art Practice in diverse learning environments, serving as an immersive entry point for students and researchers to develop collaborative transdisciplinary skills. This innovative approach integrates audio, educational, and sustainability research, employing sound-interaction methods applied to tangible objects. Participants engage in exploring the interplay between objects representing interests or values, fostering the creation of a visual and linguistic network of interconnectedness. Inspired by artistic research, particularly Dérive, the practice provides experiences of connectedness to others and the environment, intertwined with reflections and discussions that foster a community of inquiry. This community collaboratively designs shared practices or projects, encouraging a holistic approach to transformative learning, addressing heterogeneity, complexity, authenticity, critical awareness, and emotional connectedness. All three case studies utilized qualitative analysis in artistic and academic settings. Datasets were collected in case study two from group discussion, participant observation, press releases and documentary photographs. In case studies one and three, audio–visual recordings, participant observation, field notes, and photo-documentation were collected. This study demonstrates that “Connected Art Practice” enhances competences in artistic expression, communication, and collaboration across disciplinary, social, and cultural boundaries. Specifically, it contributes to creative reinvention, personal sharing, self-reflection, and the capacity to co-design diverse projects. The paper concludes by discussing findings and pointing out the essential qualities of Connected Art, providing insights and resources for educational and research institutions seeking to foster transdisciplinary engagement and transformative learning in their curricular activities. Full article

When navigating the environmental exigencies precipitated by global pandemics, the escalation of mask waste presents a multifaceted dilemma. In this avant-garde research, we unveil a novel approach: harnessing the sterilized shredded mask residues (SMRs), predominantly composed of 100 wt. % polypropylene, as pioneering modifiers for asphalt. Distinct proportions of SMR (e.g., 3, 6, and 9 wt. %) were judiciously integrated with fresh–virgin base AP-5 asphalt and subjected to an extensive suite of state-of-the-art examinations, encompassing thin-layer chromatography-flame ionization detection (TLC-FID), Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and specific rheological metrics. The TLC-FID diagnostic trajectories highlighted the nuanced rejuvenating influence of SMR on the binder, a facet reinforced by a pronounced elevation in the thermodynamic stability index (I_C). The FT-IR spectra elucidated SMR’s preeminent role as a filler, negating notions of chemical reactivity. The TGA analyses unveiled an elevated thermal onset of degradation, signposting enhanced thermal resilience, whereas the DSC readings illuminated a superior thermal comportment at lower extremities. The SEM evaluations rendered a clearer panorama: there was heightened textural perturbation at escalated SMR incorporations, yet the 3 wt. % concoction showcased an optimal, coherent microtexture symbiosis with asphalt. The rheological scrutinies revealed a systematic trajectory: a diminishing penetration and ductility countered by ascending softening points and viscosity metrics. The coup de maître stemmed from the DSR analyses, unequivocally validating SMR’s unparalleled prowess in curtailing rutting distress. This seminal inquiry not only posits a blueprint for refined pavement longevity but also champions a sustainable countermeasure to pandemic-propelled waste, epitomizing the confluence of environmental prudence an d infrastructural fortitude. Full article

In this essay, I discuss the art of Missionary Mary Proctor, a contemporary folk artist from Tallahassee, Florida, in the context of the literary aesthetics of the renowned twentieth-century anthropologist and writer Zora Neale Hurston. In comparing these Southern-born African American women artists, I argue that both are rooted in an aesthetic praxis deriving from their shared womanist ethics. My goal in this inquiry is to highlight the faith-based aesthetic traditions of African American women and reveal the manner in which discourses of freedom intertwine with literary and visual aesthetics and faith-based practices in African American folk art and literature. To that end, I analyze the prevalence of themes of liberation within the spiritual discourses of Southern African American women artists such as Missionary Mary Proctor and theorize the manner in which a landscape of Black female liberation is envisioned within their works. Full article

In the face of ongoing interpersonal and structural violence towards sex workers in Canada, this research inquiry explores nine women and gender-diverse sex workers’ experiences of place identity within their workplaces. Employing multisensory and arts-based ethnographic fieldwork, the co-researchers storied their embodied place-based experiences of identity. The research findings illuminate place-identity processes within sex workplaces, suggesting that the context, materiality, and multisensory atmospheres of the co-researchers’ work environments were entwined with internal and external self-concepts. The co-researchers created personalized multisensory atmospheres in their workplaces through the use of colour, visual art, and music. Having workplaces that positively supported place identity fostered workplace comfort, control, and empowerment. Ultimately, this research suggests that place-identity processes in sex workplaces have the possibility to resist and shift sex work stigma. Full article

With the swift pace of the development of artificial intelligence (AI) in diverse spheres, the medical and healthcare fields are utilizing machine learning (ML) methodologies in numerous inventive ways. ML techniques have outstripped formerly state-of-the-art techniques in medical and healthcare practices, yielding faster and more precise outcomes. Healthcare practitioners are increasingly drawn to this technology in their initiatives relating to the Internet of Behavior (IoB). This area of research scrutinizes the rationales, approaches, and timing of human technology adoption, encompassing the domains of the Internet of Things (IoT), behavioral science, and edge analytics. The significance of ML in medical and healthcare applications based on the IoB stems from its ability to analyze and interpret copious amounts of complex data instantly, providing innovative perspectives that can enhance healthcare outcomes and boost the efficiency of IoB-based medical and healthcare procedures and thus aid in diagnoses, treatment protocols, and clinical decision making. As a result of the inadequacy of thorough inquiry into the employment of ML-based approaches in the context of using IoB for healthcare applications, we conducted a study on this subject matter, introducing a novel taxonomy that underscores the need to employ each ML method distinctively. With this objective in mind, we have classified the cutting-edge ML solutions for IoB-based healthcare challenges into five categories, which are convolutional neural networks (CNNs), recurrent neural networks (RNNs), deep neural networks (DNNs), multilayer perceptions (MLPs), and hybrid methods. In order to delve deeper, we conducted a systematic literature review (SLR) that examined critical factors, such as the primary concept, benefits, drawbacks, simulation environment, and datasets. Subsequently, we highlighted pioneering studies on ML methodologies for IoB-based medical issues. Moreover, several challenges related to the implementation of ML in healthcare and medicine have been tackled, thereby gradually fostering further research endeavors that can enhance IoB-based health and medical studies. Our findings indicated that Tensorflow was the most commonly utilized simulation setting, accounting for 24% of the proposed methodologies by researchers. Additionally, accuracy was deemed to be the most crucial parameter in the majority of the examined papers. Full article