Search Results (1,381)

Background: Family caregivers provide most (75–90%) of the essential unpaid care and support for individuals living with chronic conditions, disabilities, and age-related needs in the community, with about half performing medical tasks traditionally performed by professionals. Caregivers also assist with 15 to 35% of the care in congregate care settings. Yet despite their critical contributions to patient care, caregivers face stress, declining well-being, and insufficient recognition in healthcare systems. Addressing these challenges requires innovative, person-centered approaches to training healthcare providers. Co-design or co-production are participatory research methods that involve individuals with lived experience to ensure relevance and impact. Objective: This study sought to understand how participatory co-design principles influenced learning, collaboration, and engagement among diverse participants in developing a caregiver-centered education program for healthcare providers. Actionable recommendations for optimizing co-design processes are provided. Methods: Eighty-five participants from a team of 155 collaborators, including caregivers, healthcare providers, educators, policymakers, and leaders, participated in ten focus group sessions conducted in Zoom breakout rooms. Interviews were transcribed verbatim and analyzed using Thorne’s interpretive description and Braun and Clarke’s reflexive thematic analysis. Results: Participants described the co-design process as fostering collaboration, inclusivity, and skill enhancement. Exposure to diverse perspectives expanded transformative understanding and prompted reflection on caregiver support within professional practices. Skilled facilitation ensured equitable engagement. Challenges included information overload and personal time constraints. Participants liked using breakout rooms to mitigate the dynamics of large group management. Still, they recommended pre-meeting materials, flexible scheduling, and expanding stakeholder diversity (e.g., rural, Indigenous, and immigrant caregivers). Conclusions: Co-design fosters meaningful, caregiver-centered education through collaboration and inclusivity. Addressing logistical challenges and representation gaps can further enhance the impact of co-design and empower multi-level, interdisciplinary partners to inform equitable healthcare education. Full article

Urban green-blue spaces (UGBS) are increasingly recognized for their benefits to physical and mental well-being. However, research on real-time gender-specific emotional responses to UGBS remains limited. To address this gap, a dual-method approach combining facial expression recognition (FER) and self-reported measures to investigate gender differences in real-time emotional evaluations of UGBS was developed. Using static images from Google Street View as stimuli, a self-reporting experiment involving 108 participants provided insights into subjective emotional experiences. Subsequently, a FER experiment, utilizing 360-degree video stimuli, captured over two million data points, validating the feasibility and advantages of real-time emotion monitoring. The findings revealed distinct gender-specific emotional patterns: women experienced stronger pleasant emotions and preferred scenes evoking higher arousal, while men demonstrated sharper responses and rated scenes with peak valence emotions more favorably. Grass elicited relaxation and delight in women and arousal in men, whereas blue spaces induced calmness across genders, with men reporting greater relaxation as water content increased. The study underscores the potential of FER technology in assessing real-time emotional responses, providing actionable insights for inclusive urban planning. By integrating advanced tools and participatory design approaches, urban planners can develop strategies that enhance emotional well-being and create livable cities that support diverse user needs. Full article

Chemotherapy-induced peripheral neuropathy and the accompanying affective disorders are serious side effects, and their resolution is not guaranteed. Oxidative stress and elevated levels of Nod-like receptor protein 3 (NLRP3) have been detected in the peripheral and central nervous systems of animals with neuropathic pain provoked by several antineoplastic drugs, such as paclitaxel (PTX). Several studies have further indicated that NLRP3 inflammasome inhibition could be an approach for treating chronic pain, but its impact on the anxiodepressive-like behaviors and memory deficits related to PTX-provoked neuropathy has not yet been investigated. MCC950 is a potent and specific inhibitor of the NLRP3 pathway that acts through inhibiting NLRP3 activation and inflammasome formation. We hypothesized that the administration of MCC950 could alleviate the affective and cognitive disorders accompanying PTX-provoked neuropathy. Using male C57BL/6 mice, we assessed the effects of MCC950 on the mechanical and thermal allodynia, anxiodepressive-like behavior, and memory deficits incited by this taxane. The results indicated that the intraperitoneal administration of 10 mg/kg of MCC950 twice daily for three consecutive days fully reversed the PTX-induced mechanical and thermal allodynia. This treatment also completely attenuated the anxiolytic (p < 0.004) and depressive-like behaviors (p < 0.022) and memory deficits (novel object recognition test; p < 0.0018) incited by PTX. These actions were mainly achieved through blocking NLRP3 inflammasome activation in the sciatic nerve, amygdala, and hippocampus, and oxidative stress in the amygdala and hippocampus. MCC950 also normalized the p-ERK 1/2 overexpression in the sciatic nerve and apoptotic responses in the sciatic nerve and the amygdala. This study suggests that MCC950 might be a promising treatment for PTX-induced mental illnesses and neuropathy. Full article

The hospitality sector’s corporate social responsibility (CSR) is dynamic and constantly evolving. This article examines CSR implementation in the hospitality industry and investigates the growing prevalence of CSR initiatives. This research examines the implementation and challenges of CSR in the hospitality sector through a qualitative literature review methodology. The study highlights trends such as community engagement, ethical labour practices, and sustainable resource utilisation while identifying barriers like financial constraints and stakeholder resistance. Moreover, it examines the determinants that influence these patterns, including consumer inclinations, governmental policies, and industry recognition of the social and ecological repercussions. This research enhances the field of theory by consolidating and expanding upon current knowledge regarding CSR, building on Archie Carroll’s Pyramid theory’s focus on economic, legal, ethical, and philanthropic responsibilities and R. Edward Freeman’s Stakeholder Theory’s emphasis on business ethics and corporate governance. Modifications are made to these frameworks to adhere to the precise requirements of the hospitality industry. This research presents an alternative perspective on the intricate relationship between environmental sustainability, social accountability, and financial prosperity within the hospitality sector. This study questions the idea that CSR is either a mandatory obligation or an optional behaviour. Key findings reveal that integrating CSR into business strategies enhances operational efficiency, stakeholder trust, and financial performance. By building on established theoretical frameworks, this research provides actionable insights. It contributes to the global discourse on sustainability, offering a nuanced perspective on the hospitality industry’s evolving role in advancing environmental, social, and financial prosperity. Full article

Currently, when the role of biodiversity in maintaining and restoring ecosystems is widely discussed, rot fungi are far from being integrated into common policies, conservation laws, or risk assessment frameworks. Despite the widespread recognition of the natural role of rot fungi as decomposers and their capabilities for various industrial purposes (the treatment of effluents rich in organic or inorganic substances), their peculiar characteristics are poorly understood and investigated. Highlighting the potential of rot fungi is of paramount importance because, as natural resources, rot fungi align perfectly with soil sustainability and the green growth policies and strategies outlined in this decade by the European Commission (2021) and United Nations (2021). This short piece aims to highlight and encourage efforts that channel into the exploration of this group of organisms as bioinoculants and biofertilizers for agriculture and forestry, as remediators and rehabilitators of soils affected by anthropogenic contamination (e.g., metals, agrochemicals, and plastics), and devastated by phenomena arising from climate change (e.g., forest fires) by briefly presenting the pros and cons of each of these lines of action and how rot fungi characteristics may fill in the current knowledge gap on degraded soil rehabilitation. Full article

Power grid operation occurs in complex, dynamic environments where the timely identification of operator violations is essential for safety. Traditional methods often rely on manual supervision and rule-based detection, leading to inefficiencies. Existing deep learning approaches, while powerful, require fully labeled data and long training times, thereby increasing costs. To address these challenges, we propose an active hard sample learning method specifically for the violation action recognition of operators in power grid operation. We design a hard instance sampling module with multi-strategy fusion based on active learning to improve training efficiency. This module identifies hard samples based on the consistency of models or samples, where we develop uncertainty evaluation and the instance discrimination strategy to assess the contributions of samples effectively. We utilize ResNet50 and ViT architectures with Faster-RCNN for detection and recognition, developed using PyTorch 2.0. The dataset comprises 2000 samples, and 30% and 60% labeled data are employed. Experimental results show significant improvements in model performance and training efficiency, demonstrating the method’s effectiveness in complex power grid environments. Our approach enhances safety monitoring and advances active learning and hard sample techniques in practical applications. Full article

In this study, an action recognition system was developed to identify fundamental basketball movements using a single Inertial Measurement Unit (IMU) sensor embedded in a wearable vest. This study aims to enhance basketball training by providing a high-performance, low-cost solution that minimizes discomfort for athletes. Data were collected from 21 collegiate basketball players, and movements such as dribbling, passing, shooting, layup, and standing still were recorded. The collected IMU data underwent preprocessing and feature extraction, followed by the application of machine learning algorithms including KNN, decision tree, Random Forest, AdaBoost, and XGBoost. Among these, the XGBoost algorithm with a window size of 250 and a 75% overlap yielded the highest accuracy of 96.6%. The system demonstrated superior performance compared to other single-sensor systems, achieving an overall classification accuracy of 96.9%. This research contributes to the field by presenting a new dataset of basketball movements, comparing the effectiveness of various feature extraction and machine learning methods, and offering a scalable, efficient, and accurate action recognition system for basketball. Full article

Botulinum toxin (BoNT), the most potent substance known to humans, likely evolved not to kill but to serve other biological purposes. While its use in cosmetic applications is well known, its medical utility has become increasingly significant due to the intricacies of its structure and function. The toxin’s structural complexity enables it to target specific cellular processes with remarkable precision, making it an invaluable tool in both basic and applied biomedical research. BoNT’s potency stems from its unique structural features, which include domains responsible for receptor recognition, membrane binding, internalization, and enzymatic cleavage. This division of labor within the toxin’s structure allows it to specifically recognize and interact with synaptic proteins, leading to precise cleavage at targeted sites within neurons. The toxin’s mechanism of action involves a multi-step process: recognition, binding, and catalysis, ultimately blocking neurotransmitter release by cleaving proteins like SNAP-25, VAMP, and syntaxin. This disruption in synaptic vesicle fusion causes paralysis, typically in peripheral neurons. However, emerging evidence suggests that BoNT also affects the central nervous system (CNS), influencing presynaptic functions and distant neuronal systems. The evolutionary history of BoNT reveals that its neurotoxic properties likely provided a selective advantage in certain ecological contexts. Interestingly, the very features that make BoNT a potent toxin also enable its therapeutic applications, offering precision in treating neurological disorders like dystonia, spasticity, and chronic pain. In this review, we highlight the toxin’s structural, functional, and evolutionary aspects, explore its clinical uses, and identify key research gaps, such as BoNT’s central effects and its long-term cellular impact. A clear understanding of these aspects could facilitate the representation of BoNT as a unique scientific paradigm for studying neuronal processes and developing targeted therapeutic strategies. Full article

This article aims to answer the question of whether identity-based movements are free from tendencies in political economy. By analyzing the actions and orientations of the militant Islamic group from the New Order to the Reform era, we show that social movements based on cultural identities are far from representing the demands of groups of recognition. Rather, these movements are leveraged as political tools for the executive group in determining dominant issues among the public to increase voter preferences and bring economic benefits to militant Islamic groups. This is insisted upon through a shift in political trends from religious populism to authoritarian populism. We argue that a possible solution could be prioritizing democratic values that lead to performance and integrity, not sectoral interests that can create fragmentation in society. Full article

The COVID-19 pandemic has meant a serious risk to the economic viability of companies and the sustainability of employment in the restaurant sector, a high-impact activity for the economy and employment in Mexico and Spain. This paper analyzes the causes of the prolonged and intense damage to companies and employees via multilevel analysis techniques and a qualitative, inductive methodology drawing on multiple sources. Research propositions posit that the sectoral structure, management practices, and institutional actions during and after the pandemic are predictors of recovery or continued losses. The balanced result of these three levels of analysis, in a severe crisis situation, such as the global pandemic, reveals that the combination of low institutional protection at the macro level, a hostile industry structure at the meso level, and a focus solely on economic sustainability as the primary business objective resulted in widespread resignation and put survival at risk, particularly for smaller companies and entrepreneurs. Analysis of the firms’ and stakeholders’ actions also shed light on the inter-relations, such as the negative effect of macro general policies on a fragmented, asymmetric meso level. Inter-relations among customers and firms’ behavior gave insights that could increase resilience before general critical events. Finally, the balanced results recommend a simultaneous effort from firms and policy makers to make possible a profound change while addressing the sector’s shortcomings. Firms’ effort in managing key assets, such as human capital, to acquire the capacity for the flexibility, adaptability, and innovation essential for change and renewal, must be endorsed by institutional support and customer recognition of the contributions of this singular service and cultural industry. Full article

Background: Thymoglobulin is used to prevent allograft rejection and is being explored at low doses as intervention immunotherapy in type 1 diabetes. Thymoglobulin consists of a diverse pool of rabbit antibodies directed against many different targets on human thymocytes that can also be expressed by other leukocytes. Since Thymoglobulin is generated by injecting rabbits with human thymocytes, this conceivably leads to differences between Thymoglobulin batches. Methods: We compared different batches for antibody composition and variation between individuals in binding to PBMC and T cell subsets, and induction of cytokines. Four different batches of Thymoglobulin were directly conjugated with Alexa-Fluor 647. Blood was collected from five healthy donors, and PBMCs were isolated and stained with Thymoglobulin followed or preceded by a panel of fluorescent antibodies to identify PBMC and T cell subsets. In addition, whole blood was incubated with unlabeled Thymoglobulin to measure cytokine induction. Results: Cluster analysis of flow cytometry data shows that Thymoglobulin bound to all PBMC subpopulations including regulatory T cells. However, Thymoglobulin binding was highly variable between donors and to a lesser extent between batches. Cytokines related to cytokine release syndrome were highly, but variably, increased by all Thymoglobulin batches, with strong differences between donors and moderate differences between batches. Discussion: The variation in Thymoglobulin binding and action between donors regarding PBMC recognition and cytokine response may underlie the different clinical responses to Thymoglobulin therapy and require personalized dose adjustment to maximize efficacy and minimize adverse side effects. Full article

The safety and efficiency of assembly lines are critical to manufacturing, but human supervisors cannot oversee all activities simultaneously. This study addresses this challenge by performing a comparative study to construct an initial real-time, semi-supervised temporal action recognition setup for monitoring worker actions on assembly lines. Various feature extractors and localization models were benchmarked using a new assembly dataset, with the I3D model achieving an average mAP@IoU=0.1:0.7 of 85% without optical flow or fine-tuning. The comparative study was extended to self-supervised learning via a modified SPOT model, which achieved a mAP@IoU=0.1:0.7 of 65% with just 10% of the data labeled using extractor architectures from the fully-supervised portion. Milestones include high scores for both fully and semi-supervised learning on this dataset and improved SPOT performance on ANet1.3. This study identified the particularities of the problem, which were leveraged and referenced to explain the results observed in semi-supervised scenarios. The findings highlight the potential for developing a scalable solution in the future, providing labour efficiency and safety compliance for manufacturers. Full article

Behavioral computing based on visual cues has become increasingly important, as it can capture and annotate teachers’ and students’ classroom states on a large scale and in real time. However, there is a lack of consensus on the research status and future trends of computer vision-based classroom behavior recognition. The present study conducted a systematic literature review of 80 peer-reviewed journal articles following the Preferred Reporting Items for Systematic Assessment and Meta-Analysis (PRISMA) guidelines. Three research questions were addressed concerning goal orientation, recognition techniques, and research challenges. Results showed that: (1) computer vision-supported classroom behavior recognition focused on four categories: physical action, learning engagement, attention, and emotion. Physical actions and learning engagement have been the primary recognition targets; (2) behavioral categorizations have been defined in various ways and lack connections to instructional content and events; (3) existing studies have focused on college students, especially in a natural classical classroom; (4) deep learning was the main recognition method, and the YOLO series was applicable for multiple behavioral purposes; (5) moreover, we identified challenges in experimental design, recognition methods, practical applications, and pedagogical research in computer vision. This review will not only inform the recognition and application of computer vision to classroom behavior but also provide insights for future research. Full article

The study aims to understand the processes of democracy in the Phutai ethnic group community in Kalasin Province. This would help with defining the complex and critical issues of democracy processes in the Phutai ethnic group community, and then enabling it to reveal the guidelines to strengthen democracy in the Phutai ethnic community and progress towards local community development. Additionally, the research also proposes ways for knowledge exchange and network building regarding democracy development among Phutai ethnic communities in Kalasin and other provinces in Thailand. The research is qualitative, focusing on Phutai ethnic communities with diverse contexts, including urban, semi-urban, rural, and mixed-ethnic communities existing in eight districts of Kalasin Province, where it is one of the main home provinces to Phutai communities in Thailand. Data were gathered through a variety of sources, including academic literature reviews, research reports, in-depth interviews, and focus group discussions. The key informants for in-depth interviews and focus group discussion were recruited by their specific extensive related experience, who are Phutai people. The data collected from these diverse sources were then used for a descriptive analysis to ensure accurate and comprehensive research findings. This study found that the model and process of democracy in ethnic communities in Kalasin Province are a hybrid form, relying on democratic processes rooted in the community to build consensus or approval, which leads to actions that align with government policies and meet the needs of the community. This is achieved through a form of democratic political culture based on ethnonationalism, which contributes to significant democracy within the community. For promoting knowledge exchange and building networks, the research emphasizes the critical need for the precise legal recognition of rights of Phutai ethnic communities and also the need for various methods of information dissemination among all generations of the Phutai group in Kalasin Province and other areas. Full article

To address the challenges of black-box video adversarial attacks, such as excessive query times and suboptimal attack performance due to the lack of result feedback during the attack process, we propose a reinforcement learning-based sparse adversarial attack method called RLVS. This approach leverages reinforcement learning to identify key frames for efficient gradient estimation, significantly reducing the number of queries. First, a self-attention network is integrated into the agent policy network to enable more precise selection of key frames. Second, designed reward functions allow the agent to continuously adapt to the sparse key frames by querying the black-box threat model and receiving feedback on attack outcomes. Lastly, gradient estimation is applied solely to the selected key frames, estimating only the gradient sign rather than the full gradient, further enhancing attack efficiency. We conducted experiments on two video recognition models using three popular action datasets. The experimental results demonstrate that our method outperforms other black-box video attack methods in terms of attack efficiency and effectiveness, achieving higher fooling rates with fewer queries and minimal perturbations. Full article