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Experimental evidence on consumers’ willingness to pay in the sustainable fashion industry
Demographic influences on trust in artificial intelligence across cognitive domains: A statistical perspective
As artificial intelligence (AI) systems become increasingly integrated into decision-making across various sectors, understanding public trust in these systems is more crucial than ever. This study presents a quantitative analysis of survey data from 335 participants to examine how demographic factors, age, gender, familiarity with AI, and frequency of technology use influence trust across a range of cognitive tasks. The findings reveal statistically significant relationships that vary by task type, with distinct patterns emerging in memory recall, complex problem-solving, and medical decision-making. Familiarity with AI and frequent use of technology are strong predictors of trust, suggesting that exposure and experience enhance confidence in AI capabilities. Conversely, age contributes significantly to disparities in responses, especially in high-stakes domains like healthcare, where older participants exhibit greater skepticism. Gender-based differences are also observed, though less pronounced. These results underscore the importance of AI systems that are technically sound and sensitive to user diversity, advocating for personalized and context-aware trust-building strategies to support the ethical and effective integration of AI into human decision-making processes. The SEM model explained 43% of the variance in trust toward AI. Based on the findings, we recommend designing adaptive, user-centered AI systems and enhancing public education to reduce skepticism and increase familiarity.
F2 bulk segregant analysis reveals salt-tolerant QTLs related to the ubiquitination process
Physiological and yield responses of Camelina sativa to modified Biochar under drought stress
Effect of genetic polymorphism of rosuvastatin transporter gene rs2231137 on its clinical efficacy and safety
Unveiling the role of harmonization on clinically significant prostate cancer detection using MRI
Optimizing microgrid operations with consideration of energy conservation and emission reduction benefits in spatial econometrics
Evaluating eye-hand coordination with digital technologies
Biometric analysis of Oncorhynchus mykiss (Walbaum, 1792) and Salmo trutta, Linnaeus, 1758 from the different habitat in Himachal Pradesh
Development of a machine learning-based interface for insulin dependency prediction using clinical data
Adaptive long-range modeling of EEG and ECG with Mamba and dynamic graph learning
Correction: A deep learning model for epidermal growth factor receptor prediction using ensemble residual convolutional neural network
Self selected music during warm up improves anaerobic performance in female handball players across time of day
Influence of silver nanoparticles on the in vitro growth, phenolic profile, antioxidant potential, enzyme inhibition, and essential oil composition of Clinopodium nepeta subsp. spruneri (Boiss.) Bartolucci & F. Conti
Attitudes and behavioral outcomes of Nebraska hunters toward tick-borne disease
Impact of high-energy photon irradiation on early-stage dissolution of EAF slag and brownmillerite
Abstract The influence of external conditions on the dissolution of minerals within inorganic sidestreams, such as steel slags, is a critical factor when considering their utilization pathways. This study addresses the aqueous dissolution characteristics of electric arc furnace slag (EAFS) and one of its main crystal phases – brownmillerite (BM), and delves into the impact of high energy photon irradiation (HEPI). The untreated forms of EAFS and BM were exposed to HEPIs using Cs-137 isotope (0.662 MeV, 250 Gy) and medical linear accelerator (10 MeV, 52 kGy) for 72 h and 16 h, respectively. The impact of HEPIs on dissolution was quantified based on batch dissolution experiments in water under ambient conditions with a solid-to-liquid ratio of 1:100 g/mL. Afterward, a systematic characterization series is conducted to understand structural changes, surface alteration, and solution chemistry in EAFS and BM samples. XRD and FTIR analysis reveal that exposure to different HEPIs caused almost no structural changes in both powders. In contrast, SEM analysis shows that HEPIs led to prominent microcracks on BM’s surface, with slight variations on EAFS. The extent of dissolution for Al and Ca ranges from 5% to 10% and 3% to 5% over time for the untreated BM, respectively, and these values are, at least, doubled when HEPIs is applied. For the case of EAFS, similar enhancements via HEPIs are achieved compared to its untreated form, but with higher Ca and Al extents. The enhancement in dissolution is associated with the micro-cracks, as evidenced by SEM analysis. To conclude, HEPIs can affect the early-stage dissolution properties of EAFS and BM to a certain degree, and more elements can be released if a high-energy photon dose is applied.
Utility of clavicle cortex index as a screening tool for low bone mineral density
Phosphoproteomic profiling highlights CDC42 and CDK2 as key players in the regulation of the TGF-β pathway in ALMS1 and BBS1 knockout models
Computational and molecular docking analysis of a novel azo compound and its nanocomposite biopolymer synthesized via a single-pot method
Abstract The synthesis of an azo molecule from phenol and dapsone was investigated using density functional theory (DFT) and infrared spectroscopy. The electronic structure and molecular interactions of the 4-((4-((4-aminophenyl)sulfonyl)phenyl)diazenyl) phenol (AZO) compound were analyzed at the B3LYP/6-311G(d,p) level. Pharmacokinetic properties were predicted using Swiss ADME, and molecular docking revealed π-alkyl and hydrophobic interactions with the 3H7O protein. Structural analysis via XRD and TEM confirmed the successful intercalation of AZO into Na-BNT, contributing to improved dispersion within the PCL/cornstarch matrix. The resulting nanocomposite demonstrated enhanced thermal stability and mechanical performance, highlighting its potential for biodegradable packaging and advanced material applications.