Search Results (77,702)

Financial institutions are currently undergoing a significant shift from traditional robo-advisors to more advanced generative artificial intelligence (GenAI) technologies. This transformation has motivated us to investigate the factors influencing consumer responses to GenAI-driven financial advice. Despite extensive research on the adoption of robo-advisors, there is a gap in our understanding of the specific contributors to, and differences in, consumer attitudes and reactions to GenAI-based financial guidance. This study aims to address this gap by analyzing the impact of personalized investment suggestions, human-like empathy, and the continuous improvement of GenAI-provided financial advice on its authenticity as perceived by consumers, their utilitarian attitude toward the use of GenAI for financial advice, and their reactions to GenAI-generated financial suggestions. A comprehensive research model was developed based on service-dominant logic (SDL) and Artificial Intelligence Device Use Acceptance (AIDUA) frameworks. The model was subsequently employed in a structural equation modeling (SEM) analysis of survey data from 822 mobile banking users. The findings indicate that personalized investment suggestions, human-like empathy, and the continuous improvement of GenAI’s recommendations positively influence consumers’ perception of its authenticity. Moreover, we discovered a positive correlation between utilitarian attitudes and perceived authenticity, which ultimately influences consumers’ responses to GenAI’s financial advisory solutions. This is manifested as either a willingness to engage or resistance to communication. This study contributes to the research on GenAI-powered financial services and underscores the significance of integrating GenAI financial guidance into the routine operations of financial institutions. Our work builds upon previous research on robo-advisors, offering practical insights for financial institutions seeking to leverage GenAI-driven technologies to enhance their services and customer experiences. Full article

Elevating Indigenous leadership in conservation science is critical for social and ecological wellbeing. However, Indigenous knowledges are frequently undermined by persistent colonial research standards. In response, calls to implement ethical guidelines that advance Indigenous research and data governance are mounting. Despite this growing movement, most environmental studies continue to follow largely colonial, extractive models, presenting a widening gap between ethical guidelines and practical applications across diverse research contexts. To address this gap, our study aims to design and evaluate a wildlife conservation research project based on the Relational Science Model, which outlines guidance for improving research relations with Indigenous Peoples. To achieve this aim, we conducted a post-survey to evaluate the project from the perspectives of the intended beneficiaries of La Bendición in southwestern Guatemala, accompanied by researcher reflections and observations. The results revealed strong agreement between community research partner experience and Relational Science Model outcomes, including relevant and innovative knowledge generation, alongside improved trust in research collaborations. Respondents also outlined several areas of improvement, including a desire for more diverse community engagement, particularly regarding youth. Overall, this study outlines pathways and recommendations for researchers, institutions, and agencies to improve relational accountability in conservation science practice, supporting Indigenous conservation governance and environmental justice. Full article

The Coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has prompted a massive global vaccination campaign, leading to the rapid development and deployment of several vaccines. Various COVID-19 vaccines are under different phases of clinical trials and include the whole virus or its parts like DNA, mRNA, or protein subunits administered directly or through vectors. Beginning in 2020, a few mRNA (Pfizer-BioNTech BNT162b2 and Moderna mRNA-1273) and adenovirus-based (AstraZeneca ChAdOx1-S and the Janssen Ad26.COV2.S) vaccines were recommended by WHO for emergency use before the completion of the phase 3 and 4 trials. These vaccines were mostly administered in two or three doses at a defined frequency between the two doses. While these vaccines, mainly based on viral nucleic acids or protein conferred protection against the progression of SARS-CoV-2 infection into severe COVID-19, and prevented death due to the disease, their use has also been accompanied by a plethora of side effects. Common side effects include localized reactions such as pain at the injection site, as well as systemic reactions like fever, fatigue, and headache. These symptoms are generally mild to moderate and resolve within a few days. However, rare but more serious side effects have been reported, including allergic reactions such as anaphylaxis and, in some cases, myocarditis or pericarditis, particularly in younger males. Ongoing surveillance and research efforts continue to refine the understanding of these adverse effects, providing critical insights into the risk-benefit profile of COVID-19 vaccines. Nonetheless, the overall safety profile supports the continued use of these vaccines in combating the pandemic, with regulatory agencies and health organizations emphasizing the importance of vaccination in preventing COVID-19’s severe outcomes. In this review, we describe different types of COVID-19 vaccines and summarize various adverse effects due to autoimmune and inflammatory response(s) manifesting predominantly as cardiac, hematological, neurological, and psychological dysfunctions. The incidence, clinical presentation, risk factors, diagnosis, and management of different adverse effects and possible mechanisms contributing to these effects are discussed. The review highlights the potential ambivalence of human response post-COVID-19 vaccination and necessitates the need to mitigate the adverse side effects. Full article

This work presents the development of a macro X-ray fluorescence (MA-XRF) scanner system for in situ analysis of paintings. The instrument was developed to operate using continuous acquisitions, where the module with the X-ray tube and detector moves at a constant speed, dynamically collecting spectra for each pixel of the artwork. Another possible configuration for the instrument is static acquisitions, where the module with the X-ray tube and detector remains stationary to acquire spectra for each pixel. The work also includes the analytical characterization of the system, which incorporates a 1.00 mm collimator that allows for a resolution of 1.76 mm. Additionally, the study presents the results of the analysis of two Brazilian paintings using this instrument. The elemental maps obtained enabled the characterization of the pigments used in the creation of the artworks and materials used in restoration processes. Full article

This study aimed to forecast trends in swimming world records (WRs) and in the age of record holders. A total of 566 individual freestyle WRs (290 for males and 276 for females) were retrieved from open access websites. The frequency of observations in WRs in each decade and event was computed for males and females. The swimmers’ chronological age was converted into decimal age at the time of breaking the world record. ARIMA forecasting models and exponential smoothing techniques were used to examine historical trends and predict future observations. The WRs improved over time, and there was a nuanced pattern in the age of world record holders. While certain events (50 m and 100 m) showed swimmers achieving records at older ages, others (e.g., 200 m, 400 m, 800 m, and 1500 m) displayed variations. Forecasting shows a continuing improvement in WRs in the upcoming years, with the age of male world record holders stabilizing in shorter events and decreasing in longer distance ones, while for females, general stabilization should be expected for the majority of competitive events. Full article

Carotenoids are vital organic pigments that determine the color of flowers, roots, and fruits in plants, imparting them yellow, orange, and red hues. This study comprehensively analyzes carotenoid accumulation in different tissues of the Brassica rapa mutant “YB1”, which exhibits altered flower and root colors. Integrating physiological and biochemical assessments, transcriptome profiling, and quantitative metabolomics, we examined carotenoid accumulation in the flowers, roots, stems, and seeds of YB1 throughout its growth and development. The results indicated that carotenoids continued to accumulate in the roots and stems of YBI, especially in its cortex, throughout plant growth and development; however, the carotenoid levels in the petals decreased with progression of the flowering stage. In total, 54 carotenoid compounds were identified across tissues, with 30 being unique metabolites. Their levels correlated with the expression pattern of 22 differentially expressed genes related to carotenoid biosynthesis and degradation. Tissue-specific genes, including CCD8 and NCED in flowers and ZEP in the roots and stems, were identified as key regulators of color variations in different plant parts. Additionally, we identified genes in the seeds that regulated the conversion of carotenoids to abscisic acid. In conclusion, this study offers valuable insights into the regulation of carotenoid metabolism in B. rapa, which can guide the selection and breeding of carotenoid-rich varieties. Full article

Work disparities, such as unfairness in pay or unequal distribution of work experienced by nurses in long-term care (LTC), can impact the retention and health of this workforce. Background: Despite the significant impact of disparities on nurses’ health in LTC, a literature review on work disparities of nurses in LTC has not been conducted. Method: This scoping review aimed to explore the nature and extent of research on meso-level work disparities experienced by nurses in LTC and its links with nurse health and well-being. Five databases were searched: MEDLINE (Ovid), EMBASE (Ovid), PsycINFO (Ovid), SCOPUS, and CINAHL (EBSCO host). Results: Of the 5652 articles retrieved, 16 studies (14 quantitative and 2 qualitative) published between 1997 and 2024 met the inclusion criteria. A total of 53 work disparities were identified. Only four articles investigated the association of a work disparity with a variable of health (e.g., physical, mental, or poor general health). Conclusions: The results suggest that more attention to how disparities impact nurses’ health and lived experiences is warranted. Meso-level disparities from this review provide an initial basis to consider possibilities in the workplace, especially in supporting equity and opportunities for health and well-being at work (e.g., through fair access to professional growth opportunities and a more equitable balance of work expectations and demands of nursing staff). Future studies of the intersection of macro- and meso-level factors are needed to inform better workplace practices and social and economic policies to support the well-being, health, and safety of nurses at work in LTC. Full article

This paper investigates the hydrodynamic characteristics of a rectangular otter board in different working postures by using a dynamic model. Dynamic models are mainly based on dynamic mesh techniques. The results of the dynamic model are, compared to the model test, carried out in a flume tank. Furthermore, different rotation speeds of dynamic model were analyzed. The research results are as follows: compared to flume tank results, the maximum error of the dynamic model is 23.77%. Moreover, the influence of rotation speed on the hydrodynamic board is not obvious, and 2 deg./s was chosen as the rotation speed. When the board is tilted slightly (including four working postures), its lift-to-drag ratio first increases slightly and then gradually decreases. Compared with the other three working postures, the pressure center coefficient of the board does not change significantly when it is tilted inward. When studying different working angles (including AOA and tilt angle) of the otter board, the numerical dynamic model significantly reduces repetitive setup work, making simulations more efficient. Its ability to provide continuous curves and a large volume of results offers researchers a more detailed and comprehensive understanding of the board’s hydrodynamics. Additionally, the dynamic model supports innovative fishery equipment development by allowing more accurate and continuous numerical simulations. Full article

God the Father was considered so completely inexpressible and unembodied that his visual appearance in early modern masterpieces has long challenged the theological accuracy of such works. A recent discovery complicates that issue. Albrecht Dürer’s 1500 Self-portrait as Christ is incorrectly considered an isolated example of divine self-representation. It was, in fact, as shown here, part of a long tradition throughout Europe between at least the fifteenth and seventeenth centuries. The praxis, potentially sacrilegious, raises questions about the truth of art at its highest level. To address this conundrum, this article analyzes works by three eminent, but very different, artists: Michelangelo, Raphael, and Dürer. Two current methodologies—visual exegesis and the poetics of making—support the argument. The analysis reveals that there is a fundamental unity to their work, which has not been recognized on account of three popular misconceptions about the nature of art, divinity, and the mind. This article concludes that depictions of God the Father and Christ by these artists are neither heretical nor false because, as the evidence shows, all three were part of a continuous spiritual tradition embedded within their craft. Full article

This article questions the changes brought about in the teaching system of university courses after the COVID-19 pandemic. Online learning, once considered an experimental and emergency tool, is increasingly becoming a standard practice integrated into teaching delivery systems. This article examines the impact that blended learning has on the host city and how it is perceived and experienced by the key stakeholders. Focusing on two degree courses that employ a blended learning model, the article proposes a survey for teachers and students to investigate the problems and advantages of blended courses. In both groups, there is no consensus of opinion or a clear trend on any issue. This fragmentation of responses should be understood as stemming from the individual motivations, unique characteristics, and personal experiences of each participant, intertwined with their university careers. It also reflects the level of adaptation to blended teaching achieved by each individual. It becomes clear that blended teaching encompasses all the challenges of online teaching but also expands access to university education and offers the possibility of exploring the educational potential offered by technology. Blended learning encourages students to be more independent and to develop their capacity for self-directed learning, though not all students are prepared for this shift. For teachers, blended learning also demands a methodological shift, differentiating between online and in-person lessons. However, while challenges remain, the progress made so far appears promising for the future. To ensure continued success, it is essential to focus on improving internet access, strengthening students’ self-learning abilities, and simultaneously enhancing teachers’ competencies in using digital tools and facilitating remote teaching. Full article

Deep learning is becoming the most widely used technology for multi-sensor data fusion. Semantic correspondence has recently emerged as a foundational task, enabling a range of downstream applications, such as style or appearance transfer, robot manipulation, and pose estimation, through its ability to provide robust correspondence in RGB images with semantic information. However, current representations generated by self-supervised learning and generative models are often limited in their ability to capture and understand the geometric structure of objects, which is significant for matching the correct details in applications of semantic correspondence. Furthermore, efficiently fusing these two types of features presents an interesting challenge. Achieving harmonious integration of these features is crucial for improving the expressive power of models in various tasks. To tackle these issues, our key idea is to integrate depth information from depth estimation or depth sensors into feature maps and leverage learnable weights for feature fusion. First, depth information is used to model pixel-wise depth distributions, assigning relative depth weights to feature maps for perceiving an object’s structural information. Then, based on a contrastive learning optimization objective, a series of weights are optimized to leverage feature maps from self-supervised learning and generative models. Depth features are naturally embedded into feature maps, guiding the network to learn geometric structure information about objects and alleviating depth ambiguity issues. Experiments on the SPair-71K and AP-10K datasets show that the proposed method achieves scores of 81.8 and 83.3 on the percentage of correct keypoints (PCK) at the 0.1 level, respectively. Our approach not only demonstrates significant advantages in experimental results but also introduces the depth awareness module and a learnable feature fusion module, which enhances the understanding of object structures through depth information and fully utilizes features from various pre-trained models, offering new possibilities for the application of deep learning in RGB and depth data fusion technologies. We will also continue to focus on accelerating model inference and optimizing model lightweighting, enabling our model to operate at a faster speed. Full article

Tourism demand is critical for the hospitality industry and is influenced by a set of continuously changing factors. The tourism and hospitality industries play a critical role in many regions and countries, supporting the local economy, providing employment, and fostering economic and social development with effects across multiple industries. This study aims to analyse the nature of tourism and hotel demand through a thematic analysis. By conducting a review of the existing literature published over the period of 2018–2023, this research identifies overarching patterns, trends, and themes characterising the current research landscape. Research results reveal significant insights into market trends and strategic industry shifts. It particularly emphasises areas such as customer demand forecasting, technology integration, and sustainability, which are crucial for understanding demand fluctuations. The findings offer insights into the theoretical foundations of tourism and hotel demand and provide practical implications for industry stakeholders aiming to strategise effectively in a dynamic market. Full article

In recent years, the use of smart in-ear devices (hearables) for health monitoring has gained popularity. Previous research on in-ear breath monitoring with hearables uses signal processing techniques based on peak detection. Such techniques are greatly affected by movement artifacts and other challenging real-world conditions. In this study, we use an existing database of various breathing types captured using an in-ear microphone to classify breathing path and phase. Having a small dataset, we use XGBoost, a simple and fast classifier, to address three different classification challenges. We achieve an accuracy of 86.8% for a binary path classifier, 74.1% for a binary phase classifier, and 67.2% for a four-class path and phase classifier. Our path classifier outperforms existing algorithms in recall and F1, highlighting the reliability of our approach. This work demonstrates the feasibility of the use of hearables in continuous breath monitoring tasks with machine learning. Full article

Globally, the construction industry is facing a severe labor shortage, and attracting and retaining workers has become a pressing challenge. This study examined the effect of rehabilitation exercise on construction workers’ willingness to sustain their careers through a questionnaire survey of 479 construction workers using a quantitative research methodology. It aims to reveal how the self-efficacy, perceived usefulness, and self-regulation of rehabilitation exercise affect construction workers’ occupational sustainability through physical and psychological recovery. The results of the study show that (1) the rehabilitation exercise self-efficacy and perceived usefulness of recovery exercise positively affect construction workers’ career sustainability intentions and (2) psychological recovery and physical recovery play parallel mediating roles in the effects of rehabilitation exercise self-efficacy, the perceived usefulness of recovery exercise, and self-regulation of recovery exercise on construction workers’ career sustainability intentions. The findings suggest that improving the physical and psychological capital of construction workers through recovery exercise can effectively enhance their career commitment and willingness to be sustainable. This study provides a reference for the design of more comprehensive and systematic rehabilitation and health management programs in the future and offers suggestions from the perspective of recovery exercise for the development of sustainable construction workers. Full article

Efficient and affordable plant phenotyping methods are an essential response to global climatic pressures. This study demonstrates the continued potential of consumer-grade photography to capture plant phenotypic traits in turfgrass and derive new calculations. Yet the effects of image corrections on individual calculations are often unreported. Turfgrass lysimeters were photographed over 8 weeks using a custom lightbox and consumer-grade camera. Subsequent imagery was analyzed for area of cover, color metrics, and sensitivity to image corrections. Findings were compared to active spectral reflectance data and previously reported measurements of visual quality, productivity, and water use. Results confirm that Red–Green–Blue imagery effectively measures plant treatment effects. Notable correlations were observed for corrected imagery, including between yellow fractional area with human visual quality ratings (r = −0.89), dark green color index with clipping productivity (r = 0.61), and an index combination term with water use (r = −0.60). The calculation of green fractional area correlated with Normalized Difference Vegetation Index (r = 0.91), and its RED reflectance spectra (r = −0.87). A new chromatic ratio correlated with Normalized Difference Red-Edge index (r = 0.90) and its Red-Edge reflectance spectra (r = −0.74), while a new calculation correlated strongest to Near-Infrared (r = 0.90). Additionally, the combined index term significantly differentiated between the treatment effects of date, mowing height, deficit irrigation, and their interactions (p < 0.001). Sensitivity and statistical analyses of typical image file formats and corrections that included JPEG, TIFF, geometric lens distortion correction, and color correction were conducted. Findings highlight the need for more standardization in image corrections and to determine the biological relevance of the new image data calculations. Full article