Search Results (581)

Background and objectives: The role of parents and the family in promoting children’s oral health is increasingly acknowledged in the dental literature. This study aimed to investigate twelve-year changes in pre-schoolers’ oral health and parental involvement in children’s dental care using data from two repeated cross-sectional surveys. The objectives were (1) to assess the temporal changes in children’s dental health status and oral health-related behaviours, (2) to examine temporal changes in parental attitudes toward their children’s oral health care, and (3) to analyze the associations between observed changes. Materials and methods: Two identical cross-sectional surveys were conducted in 2010/2011 (n = 294) and 2023 (n = 304). In both surveys, parents answered questions regarding their children’s oral health care and completed the 38-item ‘Parental Attitudes toward Children’s Oral Health’ (PACOH) scale, while the dental health of their 3–7-year-old children was clinically assessed. Changes in the examined characteristics were analyzed using general linear models. Results: Significant positive changes (p < 0.001) were observed when comparing the surveys: the dmf-t (decayed, missing, filled teeth) score for deciduous teeth decreased from 5.56 to 3.31; the Silness–Löe Plaque Index decreased from 1.45 to 1.15; the percentage of children brushing their teeth regularly increased from 33.0% to 55.3%; the percentage of parents assisting their child with tooth brushing increased from 19.2% to 85.1%; and regular visits to a children’s dentist increased from 36.4% to 85.1%. Moreover, the study revealed better parental attitudes toward their children’s oral health care, as reflected by a change in the total score of the PACOH scale from 112 to 122. Conclusions: It was concluded that changes in parental attitudes played a crucial role in driving positive trends in oral health-related behaviours, which likely contributed to the improvement of dental health in children. Therefore, understanding and influencing parental attitudes can be essential for promoting good oral health skills and good oral health in young children. Full article

The South African beauty and personal care industry has been significantly impacted by digital technologies, with social media influencers (SMIs) playing a pivotal role in shaping consumer behaviour. Despite the significant role of SMIs, there is a notable research gap in optimising their influence to boost customer engagement and drive sales. This study addresses this gap by introducing a comprehensive digital framework integrating artificial intelligence (AI) and digitalisation to enhance influencer marketing (IM) strategies in South Africa’s beauty and personal care industry. The primary objective is to present a novel approach that digitally overlays traditional SMI marketing processes, enhancing engagement and effectiveness. A mixed-method approach was employed, integrating quantitative data from web scraping and sentiment analysis (SA) of Instagram posts with qualitative insights from the existing literature. This approach facilitated data-driven decision-making and optimised IM efficacy through technology-driven feedback analysis. The empirical findings confirmed that incorporating AI into influencer marketing (IM) campaigns significantly improves data collection and sentiment analysis, enhancing consumer engagement and purchase intent. This study underscores the transformative impact of digitalisation on marketing practices and the potential of digital tools to optimise SMI strategies in the beauty and personal care sector. This research also provides a valuable framework for marketers aiming to enhance IM campaigns, emphasising the strategic advantage of an automated response workflow based on feedback analysis. This study contributes to developing more impactful digital marketing strategies, maximising the effect of SMIs, and ensuring market competitiveness. Full article

Electric vehicles are increasingly seen as a viable alternative to conventional combustion-engine vehicles, offering advantages such as lower emissions and enhanced energy efficiency. The critical role of batteries in EVs drives the need for high-performance, cost-effective, and safe solutions, where thermal management is key to ensuring optimal performance and longevity. This study is motivated by the need to address the limitations of current battery thermal management systems (BTMS), particularly the effectiveness of cooling methods in maintaining safe operating temperatures. The hypothesis is that immersion cooling offers superior thermal regulation compared to the widely used indirect liquid cooling approach. Using MATLAB Simulink, this research investigates the dynamic thermal behaviour of three cooling systems, including air cooling, indirect liquid cooling, and immersion cooling, by comparing their performance with an uncooled battery. The results show that immersion cooling outperforms indirect liquid cooling in terms of temperature control and safety, providing a more efficient solution. These findings challenge the existing literature, positioning immersion cooling as the optimal BTMS. The main contribution of this paper lies in its comprehensive evaluation of cooling technologies and its validation of immersion cooling as a superior method for enhancing EV battery performance. Full article

This study compares the mobility behaviour, in a H₂O₂ environment, of three different geometries of hybrid particle made of silica core functionalized by gold (nanoparticles or layer). It is known that the decomposition of H₂O₂ on gold surfaces drives mobility; however, the link between mobility orientation and the organization of gold on silica surfaces is still questionable. While conventional wisdom posits that asymmetric designs are crucial for generating phoretic forces or localized bubble propulsion, recent research suggests that symmetrical particles may also exhibit motility. To address this debate, we developed a robust workflow for synthesizing gold grafted silica nanoparticles with precise control over size and shape, enabling the direct comparison of their motile behaviour by dynamic light scattering and particle tracking velocimetry. Our results indicate, first, that a combination of techniques is necessary to overcome their intrinsic limitation and, second, that the inherent asymmetry generated by isotropic gold nanoparticle deposition onto silica surfaces may enable particle motility. Full article

This real-world investigation aimed to quantify the human–machine interaction between remote drivers of teleoperation systems and the Level 4 automated vehicle in a real-world setting. The primary goal was to investigate the effects of disengagement and distraction on remote driver performance and behaviour. Key findings revealed that mental disengagement, achieved through distraction via a reading task, significantly slowed the remote driver’s reaction time by an average of 5.309 s when the Level 4 automated system required intervention. Similarly, disengagement resulted in a 4.232 s delay in decision-making time for remote drivers when they needed to step in and make critical strategic decisions. Moreover, mental disengagement affected the remote drivers’ attention focus on the road and increased their cognitive workload compared to constant monitoring. Furthermore, when actively controlling the vehicle remotely, drivers experienced a higher cognitive workload than in both “monitoring” and “disengagement” conditions. The findings emphasize the importance of designing teleoperation systems that keep remote drivers actively engaged with their environment, minimise distractions, and reduce disengagement. Such designs are essential for enhancing safety and effectiveness in remote driving scenarios, ultimately supporting the successful deployment of Level 4 automated vehicles in real-world applications. Full article

Social media platforms play a significant role in facilitating business decision making, especially in the context of emerging technologies. Such platforms offer a rich source of data from a global audience, which can provide organisations with insights into market trends, consumer behaviour, and attitudes towards specific technologies, as well as monitoring competitor activity. In the context of social media, such insights are conceptualised as immediate and real-time behavioural responses measured by likes, comments, and shares. To monitor such metrics, social media platforms have introduced tools that allow users to analyse and track the performance of their posts and understand their audience. However, the existing tools often overlook the impact of contextual features such as sentiment, URL inclusion, and specific word use. This paper presents a data-driven framework to identify and quantify the influence of such features on the visibility and impact of technology-related tweets. The quantitative analysis from statistical modelling reveals that certain content-based features, like the number of words and pronouns used, positively correlate with the impressions of tweets, with increases of up to 2.8%. Conversely, features such as the excessive use of hashtags, verbs, and complex sentences were found to decrease impressions significantly, with a notable reduction of 8.6% associated with tweets containing numerous trailing characters. Moreover, the study shows that tweets expressing negative sentiments tend to be more impressionable, likely due to a negativity bias that elicits stronger emotional responses and drives higher engagement and virality. Additionally, the sentiment associated with specific technologies also played a crucial role; positive sentiments linked to beneficial technologies like data science or machine learning significantly boosted impressions, while similar sentiments towards negatively viewed technologies like cyber threats reduced them. The inclusion of URLs in tweets also had a mixed impact on impressions—enhancing engagement for general technology topics, but reducing it for sensitive subjects due to potential concerns over link safety. These findings underscore the importance of a strategic approach to social media content creation, emphasising the need for businesses to align their communication strategies, such as responding to shifts in user behaviours, new demands, and emerging uncertainties, with dynamic user engagement patterns. Full article

The time irreversibility of a dynamical process refers to the phenomenon where its behaviour or statistical properties change when it is observed under a time-reversal operation, i.e., backwards in time and indicates the presence of an “arrow of time”. It is an important feature of both synthetic and real-world systems, as it represents a macroscopic property that describes the mechanisms driving the dynamics at a microscale level and that stems from non-linearities and the presence of non-conservative forces within them. While many alternatives have been proposed in recent decades to assess this feature in experimental time series, the evaluation of their performance is hindered by the lack of benchmark time series of known reversibility. To solve this problem, we here propose and evaluate the use of a geometric Brownian motion model with stochastic resetting. We specifically use synthetic time series generated with this model to evaluate eight irreversibility tests in terms of sensitivity with respect to several characteristics, including their degree of irreversibility and length. We show how tests yield at times contradictory results, including the false detection of irreversible dynamics in time-reversible systems with a frequency higher than expected by chance and how most of them detect a multi-scale irreversibility structure that is not present in the underlying data. Full article

This study investigated the motivational factors driving energy citizenship through the lens of self-determination theory. Utilizing data from a survey of 749 respondents, we examined the role of intrinsic and extrinsic motivations in predicting energy citizenship. Our findings reveal that intrinsic motivations, such as personal responsibility for climate change, community involvement, and the desire to reduce one’s carbon footprint, significantly predict engagement in energy citizenship. Conversely, extrinsic motivations, including financial incentives and external pressures, were not significant predictors. The study underscores the importance of intrinsic motivations in fostering sustained pro-environmental behaviours, particularly as the complexity of these behaviours increases. These insights suggest that policymakers should focus on enhancing intrinsic motivations through education, community engagement, and autonomy-supportive initiatives to promote active participation in sustainable energy practices. Full article

Background: Anti-N-Methyl-d-aspartate receptor (NMDAR) autoimmune encephalitis (NMDAR AE) is an autoimmune disease characterized by severe psychiatric and neurological symptoms. While the pathogenic role of antibodies (Abs) directed against the GluN1 subunit of NMDAR is well described in this disease, the immune mechanisms involved in the generation of the autoimmune B cell response, especially the role of T helper cells, are poorly understood. Previously, we developed a B-cell-mediated mouse model of NMDAR AE by immunization with a GluN1_359–378 peptide that drives a series of symptoms that recapitulate AE such as anxiety behaviour and spatial memory impairment. Results: In this mouse model, we identified anti-GluN1-specific CD4⁺ but also CD8⁺ T cells in both spleen and meninges. T helper cells have a polyfunctional profile, arguing for a T and B cell crosstalk to generate anti-GluN1 pathogenic Abs. Interestingly, proteomic analysis of AE meninges showed enrichment of differentially expressed proteins in biological processes associated with B cell activation and cytokine signalling pathways. Conclusions: This study identified, for the first time, a potential contribution of T helper cells in the pathology of NMDAR AE and paved the way for the development of future tolerogenic approaches to treat relapses. Full article

Understanding the factors that drive consumers to purchase electric vehicles (EVs) is critical to achieving decarbonization of China’s transportation sector, as well as mitigating global warming. This study aims to construct a research model based on altruistic and self-interested perspectives by integrating the Theory of Planned Behaviour (TPB) and Norm Activation Model (NAM) to predict the psychological factors that influence Chinese consumers’ intention to purchase EVs. Data were collected from 867 participants in China and empirically tested using Structural Equation Modeling (SEM). Self-interested factors, namely subjective norms, attitudes and perceived behavioural control, all had a significant positive effect on EV purchase intention. Additionally, the results showed that personal norms had the greatest effect on EV purchase intention. It was also found that awareness of consequence, ascription of responsibility and subjective norms were positive predictors of personal norms. Awareness of consequence had a positive effect on both the ascription of responsibility and attitudes. The findings contribute to understanding the psychological drivers of Chinese consumers’ intention to purchase EVs and can provide decision-making references for policy makers and manufacturers. Full article

The shift in consumer dietary patterns from meat-based to plant-based food products has become a prominent trend worldwide. This shift is driven by various factors, including concerns about personal health and environmental awareness. Despite the global growth of the plant-based food industry, developing nations tend to be slow with adopting non-meat-based diets. This is mainly due to high levels of food insecurity and meat being the main source of protein, especially in countries like South Africa where food consumption is needs-based, and consumers are unaware of the environmental footprint of meat production. This paper is part of a two-phase study and reports on the quantitative results, which were obtained during the second phase. The main aim of this study was to investigate the factors that influence consumers’ decisions to choose plant-based food products. The factors that were selected were informed by the thematic results from phase 1, which involved focus groups that explored consumers’ opinions and behaviours towards plant-based lifestyles. By means of survey distribution, 426 online questionnaires were distributed among young consumers in South Africa. A conceptual model with six hypotheses was tested and the data were analysed using SmartPLS 4.1.0.8. The findings indicated the product taste and product knowledge are the most important factors that drive young consumers’ decisions to choose plant-based food products. The study further found that product quality, affordability, social influence, and product packaging play a role but to a lesser degree. Food marketers can utilise these findings and implement marketing strategies that can assist with persuading consumers to choose plant-based food products and adopt a healthier, more sustainable lifestyle. Full article

This study addresses the critical need for innovation in the healthcare sector, particularly in Japan’s functional food industry, amid rising chronic diseases and healthcare costs. It explores the complex relationship between regulatory design and corporate behaviour, focusing on how companies’ compliance strategies influence their research and development (R&D) investments. A mixed-method approach was used, analysing data from 15 major dietary supplement companies and 74 products under Japan’s Foods with Function Claims (FFCs) regulation. The study reveals a correlation between companies’ engagement in the FFCs system and their preference for conducting in-house clinical trials, indicative of higher R&D investments (R = 0.66, p = 0.007), and that between the latter variable and average product sales, which is a measure of returns on regulatory compliance (R = 0.66, p = 0.008). Companies actively complying with FFCs regulations tend to conduct R&D and accumulate knowledge in-house, to innovate and differentiate their products, gaining competitive advantages. The study also highlights the role of a company’s size, market presence, and industry origins in shaping regulatory strategies, with firms from other industries using FFCs regulations to explore new business opportunities. The research underscores the importance of flexible regulatory frameworks that encourage R&D investment, leading to innovation and competitive advantages in the healthcare sector. Full article

The argument made in this study is that sustainability is a system of beliefs that extends beyond policy and the classroom to transform actions and societies into pro-sustainable behaviours. Therefore, it is crucial to equip trainee teachers to embrace their role as catalysts for change in driving Sustainable development goals, sustainable thinking, systems thinking, wise consumption, sustainable competencies, and sustainable action in their daily lives. The lack of practice-led research on incorporating Education for Sustainable Development (ESD) into trainee teachers’ training programmes is noted in the literature. This qualitative practice-led research explored trainee mathematics and technology teachers’ (TMTTs) experiences learning about sustainability. Kolb’s experiential theory framed this study theoretically. The data were collated from twenty purposively selected TMTTs via semi-structured interviews and reflective diaries. All ethical protocols were observed. The analysis highlighted the core experiences TMTTs gained in learning about sustainability, transformative learning, design thinking, wise consumption, agency, and sustainable teaching strategies. Theoretically, the findings emphasise the importance of using an ESD perspective and experiential learning methods to integrate sustainability education into mathematics and technology teaching. The results suggest that when TMTTs are encouraged to actively engage with sustainability issues in their communities, informed decisions are made about their future roles as teachers, the teaching strategies they intend to use, and the type of learning they aim to foster in their learners. Full article

Accurately estimating the modulation parameters of pseudorandom binary code–pulse amplitude modulation (PRBC–PAM) signals damaged by strong noise poses a significant challenge in emitter identification and countermeasure. Traditionally, weak signal detection methods based on chaos theory can handle situations with low signal-to-noise ratio, but most of them are developed for simple sin/cos waveform and cannot face PRBC–PAM signals commonly used in ultra-low altitude performance equipment. To address the issue, this article proposes a novel adaptive detection and estimation method utilizing the in-depth analysis of the Duffing oscillator’s behaviour and output characteristics. Firstly, the short-time Fourier transform (STFT) is used for chaotic state identification and ternary processing. Then, two novel approaches are proposed, including the adjusting zero value (AZV) method and the chaotic state ratio (CSR) method. The proposed weak signal detection system exhibits unique capability to adaptively modify its internal periodic driving force frequency, thus altering the difference frequency to estimate the signal parameters effectively. Furthermore, the accuracy of the proposed method is substantiated in carrier frequency estimation under varying SNR conditions through extensive experiments, demonstrating that the method maintains high precision in carrier frequency estimation and a low bit error rate in both the pseudorandom sequence and carrier frequency, even at an SNR of −30 dB. Full article

This study examines how telematics variables such as annual percentage driven, total miles driven, and driving patterns influence the distributional behaviour of conventional rating factors when incorporated into predictive models for capturing auto insurance risk in rate regulation. To effectively manage the complexity inherent in telematics data, we advocate for the adoption of non-negative sparse principal component analysis (NSPCA) as a structured approach for data dimensionality reduction. By emphasizing sparsity and non-negativity constraints, NSPCA enhances the interpretability and predictive power of models concerning both loss severity and claim counts. This methodological innovation aims to advance statistical analyses within insurance pricing frameworks, ensuring the robustness of predictive models and providing insights crucial for rate regulation strategies specific to the auto insurance sector. Results show that, to enhance auto insurance risk pricing models, it is essential to address data dimension reduction challenges when integrating telematics data variables. Our findings underscore that integrating telematics variables into predictive models maintains the integrity of risk relativity estimates associated with traditional policy variables. Full article