58 research outputs found
Fluorescent sensing of mercury(II) based on formation of catalytic gold nanoparticles
A fluorescence assay for the highly sensitive and selective detection of Hg2+ using a gold nanoparticle (AuNP)-based probewas proposed. The assay was based on the formation of Hg-Au alloys, which accelerated the oxidization of o-phenylenediamine by dissolved oxygen to produce 2,3-diaminophenazine, a fluorescent product.;A fluorescence assay for the highly sensitive and selective detection of Hg2+ using a gold nanoparticle (AuNP)-based probewas proposed. The assay was based on the formation of Hg-Au alloys, which accelerated the oxidization of o-phenylenediamine by dissolved oxygen to produce 2,3-diaminophenazine, a fluorescent product
ReliCD: A Reliable Cognitive Diagnosis Framework with Confidence Awareness
During the past few decades, cognitive diagnostics modeling has attracted
increasing attention in computational education communities, which is capable
of quantifying the learning status and knowledge mastery levels of students.
Indeed, the recent advances in neural networks have greatly enhanced the
performance of traditional cognitive diagnosis models through learning the deep
representations of students and exercises. Nevertheless, existing approaches
often suffer from the issue of overconfidence in predicting students' mastery
levels, which is primarily caused by the unavoidable noise and sparsity in
realistic student-exercise interaction data, severely hindering the educational
application of diagnostic feedback. To address this, in this paper, we propose
a novel Reliable Cognitive Diagnosis(ReliCD) framework, which can quantify the
confidence of the diagnosis feedback and is flexible for different cognitive
diagnostic functions. Specifically, we first propose a Bayesian method to
explicitly estimate the state uncertainty of different knowledge concepts for
students, which enables the confidence quantification of diagnostic feedback.
In particular, to account for potential differences, we suggest modeling
individual prior distributions for the latent variables of different ability
concepts using a pre-trained model. Additionally, we introduce a logical
hypothesis for ranking confidence levels. Along this line, we design a novel
calibration loss to optimize the confidence parameters by modeling the process
of student performance prediction. Finally, extensive experiments on four
real-world datasets clearly demonstrate the effectiveness of our ReliCD
framework
A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics
In today's competitive and fast-evolving business environment, it is a
critical time for organizations to rethink how to make talent-related decisions
in a quantitative manner. Indeed, the recent development of Big Data and
Artificial Intelligence (AI) techniques have revolutionized human resource
management. The availability of large-scale talent and management-related data
provides unparalleled opportunities for business leaders to comprehend
organizational behaviors and gain tangible knowledge from a data science
perspective, which in turn delivers intelligence for real-time decision-making
and effective talent management at work for their organizations. In the last
decade, talent analytics has emerged as a promising field in applied data
science for human resource management, garnering significant attention from AI
communities and inspiring numerous research efforts. To this end, we present an
up-to-date and comprehensive survey on AI technologies used for talent
analytics in the field of human resource management. Specifically, we first
provide the background knowledge of talent analytics and categorize various
pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant
research efforts, categorized based on three distinct application-driven
scenarios: talent management, organization management, and labor market
analysis. In conclusion, we summarize the open challenges and potential
prospects for future research directions in the domain of AI-driven talent
analytics.Comment: 30 pages, 15 figure
Mucin corona delays intracellular trafficking and alleviates cytotoxicity of nanoplastic-benzopyrene combined contaminant
Nanoplastics have recently become a worldwide concern as newly emerging airborne pollutants, which can associate with polycyclic aromatic hydrocarbons (PAHs) and form combined contaminant nanoparticles (CCNPs). After being inhaled in the respiratory system, the CCNPs would first encounter the mucous gel layer being rich in mucin. Herein, polystyrene-benzopyrene (PS@Bap) NPs were prepared as CCNPs model and their interaction with mucin and the resultant biological responses were studied. It was observed that mucin corona stably attached to the CCNPs surface, which significantly altered the fate of the CCNPs in lung epithelial cells (A 549 cell line). The mucin corona would 1) stably adsorbed on PS@Bap at the early stages of endocytosis until degraded during the lysosomal transport and maturation process, 2) delay intracellular trafficking of PS@Bap and the progress of Bap detached from PS, 3) enhance uptake of PS@Bap but reduce the cytotoxicity elicited by PS@Bap, as indicated by cell viability, generation of reactive oxygen species, impairment on mitochondrial function, and further cell apoptosis. In addition, in vivo study also verified the enhanced effect of PS on the development of an acute lung inflammatory response induced by Bap. This study highlights the significance of incorporating the effects of mucin for precisely assessing the respiratory system toxicity of nanoplastics based CCNPs in atmospheric environments
Contribution a la modelisation numerique des phenomenes electromagnetiques
SIGLECNRS T Bordereau / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc
A facile synthesis of perforated reduced graphene oxide for high performance electrochemical sensors
Highly active perforated reduced graphene oxide (P-rGO) was synthesized by a facile methodology based on co-deposition of graphene oxide with sacrificial Prussian blue. Electrode surface properties were characterized by SEM and EDS. The GC/P-rGO electrode exhibited a larger specific surface area than that of GCE. These findings highlighted that the signal was enhanced for both dopamine detection and selenium detection by using P-rGO as a relevant supporting substrate. The result indicated that the large number of perforated structures formed numerous electrically conductive channels in the structure, improving the electrocatalytic properties
Ping pong : an exergame for cognitive inhibition training
Cognitive inhibition, a key constituent of healthy cognition, has been shown to be susceptible to age-related cognitive declines. Research has shown that cognitive rehabilitation training can facilitate older adults to maintain healthy cognitive functions. Compared to cognitive rehabilitation alone, the combination of physical and cognitive exercises is more effective to train older adults’ cognitive functions. Focusing on the training of older adults’ cognitive inhibition, we design the Ping Pong exergame in this work, which incorporates the traditional cognitive task with physical exercises in the game environment to improve older adults’ cognitive inhibition. A longitudinal study was conducted to evaluate the usability of Ping Pong exergame and its effectiveness on training older adults’ cognitive inhibition. The results show that the Ping Pong exergame received a good usability score and players presented significantly better performance in cognitive tasks after playing the exergame.AI SingaporeMinistry of Health (MOH)National Research Foundation (NRF)Accepted versionThis research is supported, in part, by the Singapore Ministry of Health under its National Innovation Challenge on Active and Confident Ageing (NIC Project No. MOH/NIC/COG04/2017), and also by the National Research Foundation, Singapore, under its AI Singapore Programme (AISG Award No: AISG–GC–2019–003)
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