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[Display omitted] • Fast, simple and direct determination of GBL in adulterated beverages. • GBL prediction capability statistically comparable in all samples evaluated. • Suitable methodology for in-situ forensic Analytical Chemistry without solvent and reagents. • Developed procedure in good agreement with the principles of Green Analytical Chemistry. • Simplicity in calibration model required. A green partial least square (PLS) attenuated total reflectance infrared spectroscopy (ATR-FTIR) method has been developed for the prediction of the γ-butyrolactone (GBL) in all type of soft and alcoholic beverages. The method involves the use of a drop (less than 5 µL) of sample and a single calibration model based on aqueous and alcoholic solutions of GBL and beverages free from GBL. A selected wavenumber range from 1225 to 1171 cm−1 and from 999 to 972 cm−1 of the second derivative (SD) spectra of samples and standards were employed. The validation set provided root mean square error of prediction (RMSEP) of the order of 300 µg g−1 GBL with a ratio of standard error of performance to standard deviation value (RPD) of 13.4. The prediction of unknown samples not considered in the calibration nor in the validation sets offered accuracy relative errors between −1.0 and 2.9 % GBL. The proposed PLS-FTIR based method allows a fast and direct determination of GBL in all type of beverages, in less than 2 min and avoiding the use of neither reagents nor solvents, without any sample treatment required, being in agreement with the principles of Green Analytical Chemistry. [ABSTRACT FROM AUTHOR]

Studies on performance impact due to the high-dimensional geometric uncertainties face the "curse of dimensionality" problem, making the cost of uncertainty quantification (UQ) unaffordable. To reduce the number of uncertainties, a sensitivity-based deep dimensionality reduction (SBDDR) method is introduced, verified and applied to the UQ studies of a low-pressure turbine cascade considering geometric variations. The basis modes of geometric variations are extracted from more than one thousand casting turbine blades using principal component analysis (PCA) and ordered by eigenvalues. However, the selection of primary basis mode by PCA neglects the performance impact of geometric variations. In the study, total-order sensitivities of performance parameters to the basis modes are calculated by Sobol analysis, in which fast performance prediction methods are employed. The total-order sensitivities are given in detail, by which the basis modes previously obtained by PCA are reordered. Furthermore, considering different numbers of basis modes ordered by PCA and SBDDR, the statistical means and standard deviations of performance changes are calculated and compared with those obtained by the direct Monte Carlo simulation (MCS). The results show that the SBDDR-based statistics are closer to the MCS ones, especially in the cases considering a limited number of basis modes. [ABSTRACT FROM AUTHOR]

We study the effect of disclosing relative performance information (feedback) about students' performance in high school on subsequent university enrollment and on expected future earnings. We exploit a large-scale natural experiment in which students in some cohorts receive information about their relative performance in some subjects (and not others) within their schools and across the nation. Using unique data, we find an asymmetric response to feedback: High-achieving students improve their final-year performance by 0.15 of a standard deviation, whereas the final-year performance of low-achieving students drops by 0.3 of a standard deviation. Results indicate that females are more discouraged by feedback in all parts of the ability distribution. We also document the longer-term effects of feedback: High-achieving students reduce their repetition rate for the national exams, enroll in university departments that are more selective by 0.11 of a standard deviation, and increase their expected annual earnings by 0.10 of a standard deviation. In contrast, results for low-achieving students are negative. A likely driver of our results is students' potential uncertainty about their own relative ability. [ABSTRACT FROM AUTHOR]

Shallow Geothermal Energy (SGE) systems are emerging as an attractive solution to enhance building energy efficiency and contribute to a low-carbon economy. This research investigates a SGE system comprising 40 boreholes installed in a university building located in Aveiro City under Mediterranean climate conditions. Both the building and the underground soil were monitored during the SGE operation, and EnergyPlus® software was subsequently employed for the integrated dynamic simulation. The building energy model was calibrated using field data, and a good agreement between the measured and the simulated temperatures was archived, with a Coefficient of Variance of the Root Mean Square Error (CVRMSE) below 10 %. The case study model performance was analysed and compared to other alternatives. The impact of the climate on the system performance was investigated by analyzing nine Portuguese cities situated in different climate contexts. A sustainability indicator was proposed, and an initial sustainability assessment was conducted. The results emphasize that the SGE system offered advantages compared to other alternative systems, achieving a 55 % reduction in heating and cooling energy consumption, and mitigating climate change by 405.67 ton of CO 2 per 25-year lifespan. This study can guide further implementation of sustainable geothermal systems in Mediterranean climates. [ABSTRACT FROM AUTHOR]

• An adaptive decoupled method is proposed to solve robust design optimization of structures. • A novel estimation expression of performance standard deviation is constructed. • Closed-form expressions of output moments with metamodeling uncertainty are established. • An adaptive framework is proposed to ensure high accuracy and efficiency. Robust design optimization (RDO) is a valuable technique in the design of engineering structures as it can provide an optimum design solution that is relatively insensitive to input uncertainties. However, the nested double-loop estimation process required in RDO often results in significant computational costs. To address this issue, we propose an adaptive decoupled RDO method based on the Kriging surrogate model. This method transforms the nested double-loop estimation process into a traditional deterministic optimization procedure, thus reducing computational costs. Furthermore, a novel estimation expression for the performance standard deviation that can simultaneously reflect the uncertainties in both the prediction and the performance mean is established. The closed-form expressions of the performance mean and performance standard deviation under different design parameters are deduced, which are further implemented to the uncertainty propagation during the design optimization. Moreover, an adaptive framework is introduced to improve the computational accuracy of uncertainty propagation as well as optimization procedure to guarantee the estimation accuracy of RDO problems. Several numerical examples along with engineering cases are introduced to illustrate the effectiveness of the established adaptive decoupled adaptive RDO method, and the results demonstrate that the proposed method can effectively optimize the design of structures while reducing computational costs. [ABSTRACT FROM AUTHOR]

• The use of voice message apps causes perceptual-motor and cognitive distraction to young drivers. • The car-following performance is weakened by necessary manual operation to voice message apps. • The difficulty of cognitive processing in voice message apps affects drivers' performance. Hand-free voice message apps are frequently used by young people while driving. Previous studies have identified voice message apps as a common source of driving distraction. To quantitatively evaluate the factors contributing to driving distractions, three simulated driving experiments were designed using a dual-task experimental paradigm. In Experiment 1, participants completed several common tasks related to voice messages in WeChat with or without manual operations (perceptual-motor distraction). Experiments 2 and 3 further took into consideration the cognitive distraction level, measured by task difficulty and task frequency. The results showed that, in comparison with undistracted driving, the perceptual-motor distraction related to voice message app use significantly (p s < 0.05) weakened young drivers' driving performance with respect to the standard deviation of lateral position (SDLP) between two cars (0.24 m), response time (0.21 s) and error rate (0.12) to turning lights, and collision percentage (0.54%), similar to the effects induced by non-voice-based apps. There were also significant differences (p s < 0.05) between driving with secondary tasks with and without continuous manual operations in the SDLP between two cars (0.19 m) and in the response time (0.18 s) and error rate (0.10) to turning lights, which indicates that the distracting effect produced by voice-message apps comes from the related manual operations. The effects of cognitive distraction on driving performance mainly depended on task difficulty level. High-difficulty secondary tasks via a voice message app significantly (p s < 0.05) weakened the driving performance in response time (by 0.13 s and 0.13 s compared to low-difficulty and baseline conditions, respectively) and error rate (by 0.07 and 0.07 compared to low-difficulty and baseline conditions, respectively) to turning lights and collision percentage (by 0.90% and 0.80% compared to low-difficulty and baseline conditions, respectively). The findings provide a theoretical reference for analysing the distracting components of voice messages and suggest that drivers should limit the use of these kinds of apps during driving. [ABSTRACT FROM AUTHOR]

In probabilistic robust optimization problems we generally take as objective function the weighted sum of the expected value and the standard deviation of the performance. Unfortunately, it has been observed that if the weight given to the standard deviation is too high, then the designs obtained may have no physical meaning at all. If this occurs we say that the design obtained is not consistent from the physical point of view. In this work we present a probabilistic robust optimization approach aimed to preserve physical consistency of the problem being addressed. The objective function is written as the p -norm of a vector composed of the expected value and the weighted standard deviation. We then demonstrate that the standard approach is a particular case of this p -norm approach with p = 1. In the proposed approach we take p = 2 and prove that physical consistency is ensured provided the standard deviation is not given more weight than the expected value. A similar proof is not available for the standard approach. This makes statement of the robust optimization problem much easier with the proposed approach. The proposed approach can be adapted into existing computational routines, since the same statistical moments and sensitivities are required. The theoretical results are illustrated in the context of compliance-based topology optimization. In the numerical examples we show that the concept of physical consistency may have a significant impact on the designs obtained. • A discussion on physical consistency of robust optimization is presented. • An approach that is always consistent is proposed. • Theoretical results are illustrated in the context of compliance-based topology optimization. [ABSTRACT FROM AUTHOR]