Search Results (626)

This study presents the validation of the Player Personality and Dynamics Scale (PPDS), designed to identify player profiles in educational gamification contexts with narrative elements. Through a sample of 635 participants, a questionnaire was developed and applied, covering sociodemographic data, lifestyle habits, gaming practices, and a classification system of 40 items on a six-point Likert scale. The results of the factorial analysis confirm a structure of five factors: Toxic Profile, Joker Profile, Tryhard Profile, Aesthetic Profile, and Coacher Profile, with high fit and reliability indices (RMSEA = 0.06; CFI = 0.95; TLI = 0.91). The resulting classification enables the design of personalized gamified experiences that enhance learning and interaction in the classroom, highlighting the importance of understanding players’ motivations to better adapt educational dynamics. Applying this scale fosters meaningful learning through the creation of narratives tailored to students’ individual preferences. Full article

Accurately simulating the asymmetric spectral profiles of blue LEDs is crucial for photobiological research, yet it remains a challenge for traditional symmetric models. This study proposes a novel spectral simulation model that effectively captures these asymmetries. The proposed model structure is partly motivated by known broadening and dispersion mechanisms observed in real LED spectra; it employs a ‘base model + correction’ framework, where an Exponentially Modified Gaussian (EMG) function captures the primary spectral shape and falling edge and an Asymmetric Pseudo-Voigt (APV) function corrects the deviations on the rising edge. Requiring only the central wavelength and bandwidth as user inputs, the simulation results exhibit a high degree of agreement with the experimental data spectra. The model provides a rapid and robust tool for pre-evaluating light sources against regulatory criteria (e.g., >99% of the spectral intensity is in the 400–500 nm band), thereby enhancing the efficiency of experimental design in blue light protection studies. Full article

This study aims to develop an optimal underwriting strategy for affordable (H1 and M1) and premium (L1 and M2) electric vehicles (EVs), balancing financial risk and sustainability commitments. The research is motivated by regulatory pressures, risk management needs, and sustainability goals, necessitating an adaptation of traditional underwriting models. The study employs a modified Delphi method with industry experts to identify key risk factors, including accident risk, repair costs, battery safety, driver behavior, and PCAF carbon impact. A sensitivity analysis was conducted to examine premium adjustments under different risk scenarios, categorizing EVs into four risk segments: Low-Risk, Low-Carbon (L1); Medium-Risk, Low-Carbon (M1); Medium-Risk, High-Carbon (M2); and High-Risk, High-Carbon (H1). Findings indicate that premium EVs (L1 and M2) exhibit lower volatility in underwriting costs, benefiting from advanced safety features, lower accident rates, and reduced carbon attribution penalties. Conversely, budget EVs (H1 and M1) experience higher premium fluctuations due to greater accident risks, costly repairs, and higher carbon costs under PCAF implementation. The worst-case scenario showed a 14.5% premium increase, while the best-case scenario led to a 10.5% premium reduction. The study recommends prioritizing premium EVs for insurance coverage due to their lower underwriting risks and carbon efficiency. For budget EVs, insurers should implement selective underwriting based on safety features, driver risk profiling, and energy efficiency. Additionally, incentive-based pricing such as telematics discounts, green repair incentives, and low-carbon charging rewards can mitigate financial risks and align with net-zero insurance commitments. This research provides a structured framework for insurers to optimize EV underwriting while ensuring long-term profitability and regulatory compliance. Full article

Beach Sand Mycobiome is currently among the most important health challenges for viticulture in the world. Remarkably, the study of fungal communities in coastal beach sand and recreational waters remains underexplored despite their potential implications for human health. This research aimed to assess the prevalence of fungal species and the antifungal susceptibility profiles of fungi recovered from the beaches of the Persian Gulf and the Sea of Oman. Sand and seawater samples from 39 stations distributed within 13 beaches along the coastline were collected between May and July 2023. The grown isolates were identified at the species level based on morphological characteristics and DNA sequencing. Antifungal susceptibility testing was performed according to the Clinical Laboratory Standards Institute guidelines. Of 222 recovered isolates, 206 (92.8%) filamentous fungi and 16 (7.2%) yeast strains were identified. Sand-recovered fungi comprised 82.9%, while water-originated fungi accounted for 17.1%. The DNA sequencing technique categorized 191 isolates into 13 genera and 26 species. The most recovered genus was Aspergillus (68.9%), and Aspergillus terreus sensu stricto was the commonly identified species (26.14%). Voriconazole was the most effective antifungal drug against Aspergillus species. Research on fungal contamination levels at these locations could provide a foundation for establishing regulatory frameworks to diminish fungal risks, thereby enhancing public health protection. The ecological significance of fungal communities in sandy beaches to human infections remains to be explored, and earlier reports in the literature may motivate researchers to focus on detecting this mycobiome in natural environments where further investigation is warranted. Ultimately, our discovery serves as a reminder that much remains to be learned about pathogenic fungi and underscores the need for vigilance in areas where emerging pathogens have not yet been identified. Full article

Clinical and animal studies suggest that multiple brain systems are involved in mediating reward-motivated and related emotional behavior including the consumption of commonly used drugs and palatable food, and there is evidence that the repeated ingestion of or exposure to these rewarding substances may in turn stimulate these brain systems to produce an overconsumption of these substances along with co-occurring emotional disturbances. To understand this positive feedback loop, this review focuses on a specific population of hypothalamic peptide neurons expressing melanin-concentrating hormone (MCH), which are positively related to dopamine reward and project to forebrain areas that mediate this behavior. It also examines neurons expressing the peptide hypocretin/orexin (HCRT) that are anatomically and functionally linked to MCH neurons and the molecular systems within these peptide neurons that stimulate their development and ultimately affect behavior. This report first describes evidence in animals that exposure in adults and during adolescence to rewarding substances, such as the drugs alcohol, nicotine and cocaine and palatable fat-rich food, stimulates the expression of MCH as well as HCRT and their intracellular molecular systems. It also increases reward-seeking and emotional behavior, leading to excess consumption and abuse of these substances and neurological conditions, completing this positive feedback loop. Next, this review focuses on the model involving embryonic exposure to these rewarding substances. In addition to revealing a similar positive feedback circuit, this model greatly advances our understanding of the diverse changes that occur in these neuropeptide/molecular systems in the embryo and how they relate, perhaps causally, to the disturbances in behavior early in life that predict a later increased risk of developing substance use disorders. Studies using this model demonstrate in animals that embryonic exposure to these rewarding substances, in addition to stimulating the expression of peptide neurons, increases the intracellular molecular systems in neuroprogenitor cells that promote their development. It also alters the morphology, migration, location and neurochemical profile of the peptide neurons and causes them to develop aberrant neuronal projections to forebrain structures. Moreover, it produces disturbances in behavior at a young age, which are sex-dependent and occur in females more than in males, that can be directly linked to the neuropeptide/molecular changes in the embryo and predict the development of behavioral disorders later in life. These results supporting the close relationship between the brain and behavior are consistent with clinical studies, showing females to be more vulnerable than males to developing substance use disorders with co-occurring emotional conditions and female offspring to respond more adversely than male offspring to prenatal exposure to rewarding substances. It is concluded that the continued consumption of or exposure to rewarding substances at any stage of life can, through such peptide brain systems, significantly increase an individual’s vulnerability to developing neurological disorders such as substance use disorders, anxiety, depression, or cognitive impairments. Full article

In today’s increasingly data-driven educational landscape, teachers are expected to use data to inform instructional decisions. However, effective data use depends not only on statistical competence but also on motivation, attitudes, and academic self-concept. This study examines how these factors influence student teachers’ readiness to engage with standardized assessment data. A survey of 164 Flemish primary education student teachers assessed their motivation, attitudes toward data use, and academic self-concept. Cluster analysis identified four distinct profiles, ranging from highly competent yet disengaged users to low-performing but externally motivated individuals, highlighting significant variability in data engagement. A pre- and post-test study design involving an e-course on basic statistical concepts demonstrated that targeted instruction can enhance perceived competence, particularly in areas such as box plot interpretation. Findings suggest that technical training alone is insufficient to promote sustained data use; fostering intrinsic motivation, positive attitudes, and a strong academic self-concept is essential for long-term engagement with data. Full article

In recent decades, European countries have seen a substantial increase in home cultivation of cannabis. In France, the prevalence of cannabis use continues to increase despite its possession, sale, and cultivation being strictly illegal. The present study aimed to describe the profile and motivations of people in France who cultivate cannabis at home. We separately analyzed data from two convenience samples of people who use cannabis daily in France, based on two online cross-sectional surveys. In the first analysis (N = 3840), we used a multivariable logistic regression model to assess factors associated with home cultivation as the main source of cannabis supply. In the second analysis (N = 574), we described participants’ motivations for home cultivation and their cultivation patterns. In the two samples, 11% and 16% reported home cultivation as their main source of supply, respectively. Age, male gender, stable housing, living with a partner, consuming cannabis in herbal form, smoking joints with little or no tobacco, smoking cannabis from a bong or pipe, non-smoking modes of cannabis administration, and using cannabis exclusively for therapeutic reasons were all positively associated with home cultivation, while urban area of residence and at-risk alcohol use were negatively associated. The main reason reported for home cultivation was to manage quality. Few reported selling some of their crop, and most were self-sufficient. Finally, we interpret this practice as a personal response to cannabis prohibition and the unregulated market. Accordingly, possible harm reduction strategies are discussed. Full article

Promoting behavioral change in mobility is essential for sustainable urban development. This study evaluates the effectiveness of gamified challenges in fostering sustainable travel behaviors among high school students and teachers within the High School Challenge (HSC) 2024 campaign in Lecco, Italy. Over a 13-week period, participants tracked their commuting habits via gamified mobile application, Play&Go, that awarded points for sustainable mobility choices and introduced weekly challenges. Using behavioral (GPS-based tracking) and self-report data, we assessed the influence of challenge types, player characteristics (HEXAD Player Types, Big Five traits), and user experience evaluations on participation, retention, and behavior change. The results show that challenges, particularly those based on walking distances and framed as intra-team goals, significantly enhanced user engagement and contributed to improved mobility behaviors during participants’ free time. Compared to the 2023 edition without challenges, the 2024 campaign achieved better retention. HEXAD Player Types were more predictive of user appreciation than Personality Traits, though these effects were more evident in subjective evaluations than actual behavior. Overall, findings highlight the importance of tailoring gamified interventions to users’ motivational profiles and structuring challenges around SMART principles. This study contributes to the design of behaviorally informed, scalable solutions for sustainable mobility transitions. Full article

Reinforcement learning (RL), a branch of artificial intelligence (AI), is becoming more popular in a variety of application fields such as games, workplaces, and behavioral analysis, due to its ability to model complex decision-making through interaction and feedback. Traditional systems for personality and behavior assessment often rely on self-reported questionnaires, which are prone to bias and manipulation. RL offers a compelling alternative by generating diverse, objective behavioral data through agent–environment interactions. In this paper, we propose a Reinforcement Learning-based framework in a game environment, where agents simulate personality-driven behavior using context-aware policies and exhibit a wide range of realistic actions. Our method, which is based on the OCEAN Five personality model—openness, conscientiousness, extroversion, agreeableness, and neuroticism—relates psychological profiles to in-game decision-making patterns. The agents are allowed to operate in numerous environments, observe behaviors that were modeled using another simulation system (HiDAC) and develop the skills needed to navigate and complete tasks. As a result, we are able to identify the personality types and team configurations that have the greatest effects on task performance and collaboration effectiveness. Using interaction data derived from self-play, we investigate the relationships between behaviors motivated by the personalities of the agents, communication styles, and team outcomes. The results demonstrate that in addition to having an effect on performance, personality-aware agents provide a solid methodology for producing realistic behavioral data, developing adaptive NPCs, and evaluating team-based scenarios in challenging settings. Full article

The increasing use of Global Positioning System (GPS) tools reshapes nature-based recreational practices. While previous research has examined the role of GPS technologies in outdoor recreation, limited attention has been given to the specific factors driving GPS use in nature-based settings such as natural parks. This case study examines the sociodemographic, behavioural, motivational and experiential factors influencing GPS use among visitors to the Capçaleres del Ter i del Freser Natural Park (Catalonia, Spain). A structured visitor survey (n = 999) was conducted over a one-year period and a hierarchical binary logistic regression model was applied to evaluate the explanatory contribution of four sequential variable blocks. The results showed that the behavioural factors (i.e., physical activity intensity) emerged as the strongest predictor of GPS use. Additionally, the final model demonstrated that visitors who were younger, engaged in higher-intensity physical activities, motivated by health-related goals, undertook longer routes, and reported more positive experiences were significantly more likely to use GPS tools during their visit. These findings highlight the need to adapt communication strategies to diverse visitor profiles and leverage volunteered geographic information (VGI) for improved visitor monitoring, flow management, and adaptive conservation planning. Full article

The purpose of this study is to reveal the characteristics of wine tourists by determining their demographic and psychographic profiles, the effects of their travel motivations and lifestyles on their attitudes towards wine tourism, and the relationships between their demographic characteristics and attitudes. Based on a quantitative approach, study data were collected using a questionnaire. The sample consists of tourists who had visited a winery or participated in the vintage in a wine destination. Wine tourists’ principal motivations were pull factors and wine-related motivations. Their attitudes towards wine consumption had higher mean values than attitudes towards info-seeking, meaning that wine tourists preferred to taste wine and shop from wineries more than learn about wine. Regarding lifestyles, they were mostly innovators and experiencers. For destinations like Türkiye, wine tourism is a significant economic and sociocultural development tool. It is the first study to identify the wine tourists’ profile based on VALS-2, thereby providing a different perspective for the literature. Full article

Interest in language-learning motivation has been growing recently, particularly in multilingual contexts where individuals acquire additional languages beyond English. Despite increasing the focus on multilingualism within second-language acquisition (SLA) research, less research focuses on the motivational dynamics of multilingual learners in learning languages other than English (LOTE). Addressing this gap, the present study investigates the complex motivational factors influencing multilingual university students in learning French as an additional language and LOTE within the Belgian context. The participants consisted of 121 multilingual university students who were learning French as an additional language and LOTE. Data were collected through questionnaire and semi-structured interviews, and analyzed using a combination of quantitative and qualitative methods to provide a comprehensive understanding of learners’ motivational profile. Findings revealed that multilingual learners’ motivation is multifaceted and dynamic, shaped by a combination of intrinsic interests (e.g., cultural appreciation and personal growth), extrinsic goals (e.g., academic and career aspirations), integrative motives, and prior language-learning experiences. The study also sheds light on the overlapping and evolving nature of motivational patterns and provides nuanced insights into LOTE learning motivation within multilingual settings. Full article

The rapid spread of artificial intelligence (AI) in education means that students need to master both AI literacy and personal agency. This study situates a sample of 425 Slovenian secondary technical students within a three-tier framework that maps psychological empowerment onto AI literacy outcomes within a cultural–historical activity system. The agency competence assessments yielded four profiles of student agency, ranging from fully empowered to largely disempowered. The cluster membership explained significant additional variance in AI literacy scores, supporting the additive empowerment model in an AI-rich vocational education and training context. The predictive modeling revealed that while self-efficacy, mastery-oriented motivations, and metacognitive self-regulation contributed uniquely—though small—to improving AI literacy, an unexpectedly negative relationship was identified for internal locus of control and for behavioral self-regulation focused narrowly on routines, with no significant impact observed for grit-like perseverance. These findings underscore the importance of fostering reflective, mastery-based, and self-evaluative learning dispositions over inflexible or solely routine-driven strategies in the development of AI literacy. Addressing these nuanced determinants may also be vital in narrowing AI literacy gaps observed between diverse disciplinary cohorts, as supported by recent multi-dimensional literacy frameworks and disciplinary pathway analyses. Embedding autonomy-supportive, mastery-oriented, student-centered projects and explicit metacognitive training into AI curricula could shift control inward and benefit students with low skills, helping to forge an agency-driven pathway to higher levels of AI literacy among high school students. The most striking and unexpected finding of this study is that students with a strong sense of competence—manifested as high self-efficacy—can achieve foundational AI literacy levels equivalent to those possessing broader, more holistic agentic profiles, suggesting that competence alone may be sufficient for acquiring essential AI knowledge. This challenges prevailing models that emphasize a multidimensional approach to agency and has significant implications for designing targeted interventions and curricula to rapidly build AI literacy in diverse learner populations. Full article

Communication about moral decision-making involves complex emotional and cognitive processes, especially in critical situations. This study adopted a hyperscanning paradigm to explore neural convergence during moral negotiation. Twenty-six healthy young adults (mean age = 23.59 years; 16 women, 10 men), with no neurological or psychiatric conditions, were paired into 13 same-gender dyads at the Università Cattolica del Sacro Cuore. Each dyad discussed a medical moral dilemma while their electrophysiological (EEG) activity was simultaneously recorded. Participants were first categorized according to their Dominant Reasoning Profile (DRP) (cognitive or affective), and subsequently convergence in DRP within the dyads was established. EEG band dissimilarities within each dyad were analyzed across frontal, temporo-central, and parieto-occipital regions. The results revealed significantly greater dissimilarity in frontal delta-band activity compared to parieto-occipital areas, regardless of the dyad’s DRP. Such results might suggest different emotional and motivational reactions between the two individuals, reflecting a broader gap in how the moral decision-making process was interpreted and internalized by each member, despite their DRP. The EEG hyperscanning paradigm proves useful in the study and understanding of the neural mechanisms involved in social interaction about morally sensitive decisions and provides novel insights into dyadic brain dynamics. Full article

The University of the Balearic Islands is undertaking a significant energy transition toward a zero-emissions model, motivated by escalating energy costs and strong institutional commitments to climate neutrality. This study investigates the technical and operational feasibility of deploying 7.1 MWp of photovoltaic capacity across the campus, integrated with Li-FePO₄ battery systems and thermal energy storage. Through a detailed analysis of hourly energy demand, PV generation profiles, and storage constraints, the research evaluates how these technologies can be optimized to meet campus needs. A linear optimization model is applied to assess system performance under the constraint of a 3 MW grid export limit. Furthermore, the potential of demand-side electrification, implemented via a centralized HVAC plant and a 4th–5th generation district heating and cooling network, is analyzed in terms of its ability to maximize on-site PV self-consumption and reduce reliance on grid electricity during non-generation periods. Full article