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Synthetic carbon nanobionic interfaces for enhancement of plant photosynthetic efficiency
Full text linkBoosting the photosynthetic efficiency remains a critical challenge for sustainable crop productivity. In this study, the design, synthesis, and functional evaluation of two carbon-based nanomaterials are reported: a fluorescent carbon nanoassembly (Compound 1) derived from citric acid and urea, and a cerium-incorporated variant (Compound 2) incorporating redox-active cerium oxide domains. Comprehensive characterization confirmed the formation of nanostructured materials with tunable optical properties, surface functionalities, and crystalline features. Both compounds exhibited strong UV-A absorbance and blue photoluminescence, with Compound 2 showing enhanced emission and additional red-shifted features due to cerium integration. Greenhouse trials usingRaphanus sativus as a model plant revealed significant improvements in biomass, pigment concentration, and ascorbic acid levels in the treated plants. Confocal microscopy and inductively coupled plasma mass spectrometry confirmed nanoparticle uptake and translocation, while flow cytometry revealed altered chloroplast fluorescence, supporting functional interaction at the organelle level. Together, these results establish carbon-based nanobionics as a promising platform for photonic enhancement of photosynthesis, offering new opportunities for advanced bioagricultural applications
The influence of the pre-delivery stride on subsequent fast bowling technique in elite male cricketers
Full text linkResearch in cricket fast bowling has primarily investigated technique from back foot contact onwards. This study aims to explore the effect of the pre-delivery stride (step pre-back foot contact) on technique characteristics previously linked with performance and injury risk. Six pre-delivery stride, and 16 performance or lumbar bone stress injury-related technique characteristics were determined for 29 elite male fast bowlers. Fifteen significant correlations were observed between the pre-delivery stride and the performance and injury-risk characteristics (p < 0.05). From a performance perspective, greater pre-delivery run-up velocity and lower take-off angles were associated with increased front leg plant angles at front foot contact and knee flexion at ball release. From an injury perspective, greater anterior pelvic tilt at take-off, jump height, and landing vertical velocity at back foot contact were associated with more flexed rear leg kinematics at back foot contact and anterior pelvic tilt at front foot contact. These findings suggest individual-specific pre-delivery stride run-up velocities and take-off angles exists to synchronously optimise technique to enhance performance and reduce injury risk. This knowledge is essential for enhancing the coaching and rehabilitation of fast bowlers, supporting coaches to account for the effect of the pre-delivery stride on subsequent fast bowling technique
A network analysis of lifetime stressor exposure, mental health, well-being, and immune cell mobilisation to acute stressors in young adults
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Climate finance and green growth: the influence of income level, sectoral heterogeneity and industry specification
No full textThe predictive roles of Dark Triad, Big Five personality traits, loneliness, and attachment to caregivers in social media addiction: a cross-national study in Türkiye and Kyrgyzstan
Full text linkSocial media addiction (SMA) is a form of behavioral addiction that can impact individuals' social, mental, and physical well-being. Although many studies have examined the effects of SMA, its individual antecedents have been relatively underexplored from a cross-national perspective. Therefore, the present study aimed to compare the predictors of SMA, including the Dark Triad, Big Five personality traits, loneliness, and attachment styles stemming from early experiences with caregivers (anxious and avoidant attachment). The sample comprised 604 university students (369 from Türkiye and 235 from Kyrgyzstan). Data were collected through standardized self-report scales, and descriptive statistics, correlation analyses, group comparisons, and hierarchical multiple regression analyses were conducted. Hierarchical regression analyses showed that conscientiousness was the strongest negative predictor of social media addiction (SMA) in both samples. Additionally, neuroticism positively predicted SMA in both countries. Anxious attachment emerged as a significant positive predictor in the Turkish sample, whereas loneliness, extraversion, Machiavellianism, and psychopathy were significant predictors of SMA in the Kyrgyz sample. Females reported higher levels of SMA among both samples. Although the antecedents of SMA appear to vary across cultural contexts, specific personality traits (i.e., low conscientiousness and high neuroticism), consistently emerged as stronger risk factors across both samples
Using machine-learning algorithms to predict self-reported problem gambling among a sample of online gamblers
Full text linkStudies suggest that algorithms can effectively be used to predict self-reported problem gambling using player tracking data. The present study analyzed a sample of real-world online gamblers (N = 1,611) who engaged in lottery playing, casino gambling, bingo playing, and sports betting. The data also comprised each player’s actual gambling activity, as well as age and gender, in the 30 days prior to answering the Problem Gambling Severity Index (PGSI). Players who engaged in at least one lottery game 30 days prior to answering the PGSI were less likely to be problem gamblers compared to players who did not play lottery games. For all other game-categories the relationship was reversed. The results also indicated that specific behavioral tracking features—such as the average number of monetary deposits per session, total amount of money bet per day, session length, and casino gambling involvement—were among the most significant predictors of self-reported problem gambling. When evaluating different machine algorithms, logistic regression and random forest emerged as the most effective in predicting self-reported problem gambling. The present study is among the few which predicts self-reported problem gambling using a sample of online lottery players, casino gamblers, bingo players and sports bettors, and provides further empirical evidence supporting the use of machine learning models to identify self-reported problem gamblers based on player tracking data. These findings can inform responsible gambling strategies by enabling operators to identify and intervene before gambling-related problems escalate
