13 research outputs found

    Tuning Glass Transition in Polymer Nanocomposites with Functionalized Cellulose Nanocrystals through Nanoconfinement

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    Cellulose nanocrystals (CNCs) exhibit impressive interfacial and mechanical properties that make them promising candidates to be used as fillers within nanocomposites. While glass-transition temperature (<i>T</i><sub>g</sub>) is a common metric for describing thermomechanical properties, its prediction is extremely difficult as it depends on filler surface chemistry, volume fraction, and size. Here, taking CNC-reinforced poly­(methyl-methacrylate) (PMMA) nanocomposites as a relevant model system, we present a multiscale analysis that combines atomistic molecular dynamics (MD) surface energy calculations with coarse-grained (CG) simulations of relaxation dynamics near filler–polymer interfaces to predict composite properties. We discover that increasing the volume fraction of CNCs results in nanoconfinement effects that lead to an appreciation of the composite <i>T</i><sub>g</sub> provided that strong interfacial interactions are achieved, as in the case of TEMPO-mediated surface modifications that promote hydrogen bonding. The upper and lower bounds of shifts in <i>T</i><sub>g</sub> are predicted by fully accounting for nanoconfinement and interfacial properties, providing new insight into tuning these aspects in nanocomposite design. Our multiscale, materials-by-design framework is validated by recent experiments and breaks new ground in predicting, without any empirical parameters, key structure–property relationships for nanocomposites

    The moderating effect of AI ability.

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    Artificial intelligence (AI) has fundamentally changed the way people live and has largely reshaped organizational decision-making processes. Particularly, AI decision making has become involved in almost every aspect of human resource management, including recruiting, selecting, motivating, and retaining employees. However, existing research only considers single-stage decision-making processes and overlooks more common multistage decision-making processes. Drawing upon person-environment fit theory and the algorithm reductionism perceptive, we explore how and when the order of decision makers (i.e., AI-human order vs. human-AI order) affects procedural justice in a multistage decision-making process involving AI and humans. We propose and found that individuals perceived a decision-making process arranged in human-AI order as having less AI ability-power fit (i.e., the fit between the abilities of AI and the power it is granted) than when the process was arranged in AI-human order, which led to less procedural justice. Furthermore, perceived AI ability buffered the indirect effect of the order of decision makers (i.e., AI-human order vs. human-AI order) on procedural justice via AI ability-power fit. Together, our findings suggest that the position of AI in collaborations with humans has profound impacts on individuals’ justice perceptions regarding their decision making.</div

    The main and interactive effects of the order of decision makers and AI ability on procedural justice in Study 2.

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    The main and interactive effects of the order of decision makers and AI ability on procedural justice in Study 2.</p

    Theoretical model of the current research.

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    Artificial intelligence (AI) has fundamentally changed the way people live and has largely reshaped organizational decision-making processes. Particularly, AI decision making has become involved in almost every aspect of human resource management, including recruiting, selecting, motivating, and retaining employees. However, existing research only considers single-stage decision-making processes and overlooks more common multistage decision-making processes. Drawing upon person-environment fit theory and the algorithm reductionism perceptive, we explore how and when the order of decision makers (i.e., AI-human order vs. human-AI order) affects procedural justice in a multistage decision-making process involving AI and humans. We propose and found that individuals perceived a decision-making process arranged in human-AI order as having less AI ability-power fit (i.e., the fit between the abilities of AI and the power it is granted) than when the process was arranged in AI-human order, which led to less procedural justice. Furthermore, perceived AI ability buffered the indirect effect of the order of decision makers (i.e., AI-human order vs. human-AI order) on procedural justice via AI ability-power fit. Together, our findings suggest that the position of AI in collaborations with humans has profound impacts on individuals’ justice perceptions regarding their decision making.</div

    Scale items used in Studies 1 and 2.

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    Artificial intelligence (AI) has fundamentally changed the way people live and has largely reshaped organizational decision-making processes. Particularly, AI decision making has become involved in almost every aspect of human resource management, including recruiting, selecting, motivating, and retaining employees. However, existing research only considers single-stage decision-making processes and overlooks more common multistage decision-making processes. Drawing upon person-environment fit theory and the algorithm reductionism perceptive, we explore how and when the order of decision makers (i.e., AI-human order vs. human-AI order) affects procedural justice in a multistage decision-making process involving AI and humans. We propose and found that individuals perceived a decision-making process arranged in human-AI order as having less AI ability-power fit (i.e., the fit between the abilities of AI and the power it is granted) than when the process was arranged in AI-human order, which led to less procedural justice. Furthermore, perceived AI ability buffered the indirect effect of the order of decision makers (i.e., AI-human order vs. human-AI order) on procedural justice via AI ability-power fit. Together, our findings suggest that the position of AI in collaborations with humans has profound impacts on individuals’ justice perceptions regarding their decision making.</div

    DataSheet_1_A nomogram based on the preoperative neutrophil-to-lymphocyte ratio to distinguish sarcomatoid renal cell carcinoma from clear cell renal cell carcinoma.docx

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    ObjectiveOur study aimed to assess the predictive value of the preoperative neutrophil-to-lymphocyte ratio(NLR) in distinguishing sarcomatoid renal cell carcinoma (SRCC) from clear cell renal cell carcinoma(CCRCC) and to developing a nomogram based on the preoperative NLR and other factors to distinguish SRCC from CCRCC.Materials and methodsThe database involved 280 patients, including 46 SRCC and 234 CCRCC. logistic analysis was conducted to select the variables associated with identifying SRCC preoperatively, and subgroup analysis was used to further validate the ability of NLR with preoperative identification of SRCC.In addition, The data were randomly separated into a training cohort(n=195) and a validation cohort(n=85). And an NLR-based nomogram was plotted based on the logistic analysis results. The nomogram was evaluated according to its discrimination, consistency, and clinical benefits.ResultsMultivariate analysis indicated that NLR, flank pain, tumor size, and total cholesterol(TC) were independent risk factors for identifying SRCC. The results of subgroup analysis showed that higher NLR was associated with a higher probability of SRCC in most subgroups. The area under the curve(AUC) of the training and validation cohorts were 0.801 and 0.738, respectively. The results of the calibration curve show high consistency between predicted and actual results. Decision Curve Analysis(DCA) showed clinical intervention based on the model was beneficial over most of the threshold risk range.ConclusionNLR is a potential indicator for preoperative differentiation of SRCC and CCRCC, and the predictive model constructed based on NLR has a good predictive ability. The new model could provide suggestions for the early identification of SRCC.</p
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