Phonological awareness and RAN contribute to Chinese reading and arithmetic for different reasons.

The present study investigated how phonological awareness and rapid automatized naming (hereafter, RAN), simultaneously contributed to Chinese reading and arithmetic fluency. Specifically, we proposed a new hypothesized mechanism that processing speed would mediate the relations of RAN with Chinese reading and arithmetic fluency. One hundred and forty-five Chinese children at the fifth grade were administered with a battery of measures, including three phonological processing measures, character reading, and whole number computation, as well as nonverbal IQ, and vocabulary knowledge. Path analyses revealed that phonological awareness and RAN were uniquely related to character reading and arithmetic fluency, while phonological memory was not significantly correlated to either character reading or arithmetic fluency, after controlling for age, nonverbal IQ, and vocabulary knowledge. Further analysis indicated that processing speed demonstrated a mediating effect on the importance of RAN in character reading, rather than in arithmetic fluency. Results underscore the potential importance of phonological awareness and RAN in character reading and arithmetic fluency, and the mediating role of processing speed in RAN to promote Chinese character reading fluency.

A Lightweight Hybrid Model with Location-Preserving ViT for Efficient Food Recognition.

Food-image recognition plays a pivotal role in intelligent nutrition management, and lightweight recognition methods based on deep learning are crucial for enabling mobile deployment. This capability empowers individuals to effectively manage their daily diet and nutrition using devices such as smartphones. In this study, we propose an Efficient Hybrid Food Recognition Net (EHFR-Net), a novel neural network that integrates Convolutional Neural Networks (CNN) and Vision Transformer (ViT). We find that in the context of food-image recognition tasks, while ViT demonstrates superiority in extracting global information, its approach of disregarding the initial spatial information hampers its efficacy. Therefore, we designed a ViT method termed Location-Preserving Vision Transformer (LP-ViT), which retains positional information during the global information extraction process. To ensure the lightweight nature of the model, we employ an inverted residual block on the CNN side to extract local features. Global and local features are seamlessly integrated by directly summing and concatenating the outputs from the convolutional and ViT structures, resulting in the creation of a unified Hybrid Block (HBlock) in a coherent manner. Moreover, we optimize the hierarchical layout of EHFR-Net to accommodate the unique characteristics of HBlock, effectively reducing the model size. Our extensive experiments on three well-known food image-recognition datasets demonstrate the superiority of our approach. For instance, on the ETHZ Food-101 dataset, our method achieves an outstanding recognition accuracy of 90.7%, which is 3.5% higher than the state-of-the-art ViT-based lightweight network MobileViTv2 (87.2%), which has an equivalent number of parameters and calculations.

How and when institutional trust helps deal with group crisis like COVID-19 pandemic for Chinese employees? A social perspective of motivation.

Social factors cannot be neglected in predicting individual psychological health during a group crisis. Grounded in Vroom's expectancy theory of motivation, the present research explores how and when institutional trust influences crisis related worries and griefs. Both the survey study (N = 234) and the experimental study (N = 111) were conducted to examine our hypothesized moderated mediation model. Multiple waves of surveys were delivered in Study 1 and participants were requested to rate their experiences and responses during the COVID-19 pandemic. In Study 2, participants from both the collectivism manipulation group and the control group were requested to rate their psychological responses in an imagined group crisis. Regression analyses showed that institutional trust reduced worries and griefs by improving personal resilience, which is more significant for individuals with high levels of collectivism. These findings point to the importance of understanding individual psychological health in a social perspective of motivation.

Multi-Gas Detection System Based on Non-Dispersive Infrared (NDIR) Spectral Technology.

Automobile exhaust gases, such as carbon dioxide (CO2), carbon monoxide (CO), and propane (C3H8), cause the greenhouse effect, photochemical smog, and haze, threatening the urban atmosphere and human health. In this study, a non-dispersive infrared (NDIR) multi-gas detection system consisting of a single broadband light source, gas cell, and four-channel pyroelectric detector was developed. The system can be used to economically detect gas concentration in the range of 0-5000 ppm for C3H8, 0-14% for CO, and 0-20% for CO2. According to the experimental data, the concentration inversion model was established using the least squares between the voltage ratio and the concentration. Additionally, the interference coefficient between different gases was tested. Therefore, the interference models between the three gases were established by the least square method. The concentration inversion model was experimentally verified, and it was observed that the full-scale error of the sensor changed less than 3.5%, the detection repeatability error was lower than 4.5%, and the detection stability was less than 2.7%. Therefore, the detection system is economical and energy efficient and it is a promising method for the analysis of automobile exhaust gases.

Synthesis Strategy of Metal Oxide Quantum Wires via a Nanoparticle-Induced Graphene Oxide Rolling Procedure.

The efficient synthesis of quantum materials is becoming a research hotspot as it determines their successful application in the fields of biomedicine, illumination, energy, sensors, information, and communication. Among the quantum materials, it is still a challenge to synthesize quantum wires (QWs) with surfactants due to the inevitable radial growth of QWs in the soft template method. In this paper, amphipathic graphene oxide (GO) was adopted as a macromolecular surfactant to limit the radial growth instead of the commonly used surfactant. GO could roll up under its electrostatic interaction with a cuprous oxide (Cu2O) quantum dot (QD) and then form a tubular template for the growth of the Cu2O QW, which was named herein as the nanoparticle-induced graphene oxide rolling (NIGOR) procedure. The NIGOR procedure was confirmed by the molecular dynamics results by simulating systems consisting of GO and Cu2O nanoparticles. An intermediate with a necklace morphology corresponding to the simulation result was also observed experimentally during the formation of the QW. Meanwhile, the formation mechanism of the QW was demonstrated rationally. Furthermore, increasing the dosage of the reactant, reaction time, and temperature altered the diameter of the QW from 2 to 4 nm and also changed the morphology of the final products from a QD to a QW and then to a bundle of QWs. This was attributed to the aggregation of materials for the lowest surface energy in the system. Additionally, the universality of NIGOR was manifested via the synthesis of other metal oxides as well. The NIGOR strategy provided an alternative, convenient, and mass production method for synthesizing QWs.

The immediate and durable effects of yoga and physical fitness exercises on stress.

OBJECTIVE: This study aimed to examine the effects of yoga and physical fitness exercises on stress and the underlying mechanisms. Participants: Healthy undergraduates from four yoga and four fitness classes participated in Study 1 (n = 191) and Study 2 (n = 143), respectively (in 2017 Fall). Methods: Study 1 evaluated the immediate effect (a 60-minute practice) while Study 2 evaluated the durable effect (a 12-week intervention). Results: Results showed that immediate stress reduction was more salient in the yoga group than that in the fitness group in Study 1. Yoga group had a greater increase in mindfulness, which predicted stress reduction. Similar observations were made in Study 2 showing the durable effect of yoga on stress reduction through mindfulness. Conclusions: Yoga intervention is better than fitness exercises in helping undergraduates cultivate mindfulness and reduce stress. These findings may guide future interventions in stress management in college students.

Comparing the Psychological Effects of Meditation- and Breathing-Focused Yoga Practice in Undergraduate Students.

OBJECTIVES: The present study aimed to compare the psychological effects of meditation- and breathing-focused yoga practice in undergraduate students. METHODS: A 12-weeks yoga intervention was conducted among a group of undergraduate students enrolled in four yoga classes at an academically prestigious university in Beijing, China. Four classes were randomized to meditation-focused yoga or breathing-focused yoga. A total of 86 participants finished surveys before and after the 12-weeks intervention, measuring work intention, mindfulness, and perceived stress. The repeated-measure multivariate analysis of covariance (MANCOVA) followed by univariate analyses were conducted to examine the differences in work intention, mindfulness, and stress between the two yoga intervention groups over the semester, after controlling for age and gender. RESULTS: The repeated-measure MANCOVA revealed significant group differences with a median effect size [Wilks' lambda, Λ = 0.90, F(3, 80) = 3.10, p = 0.031, η2 = 0.104]. Subsequent univariate analyses showed that students in the breathing-focused yoga group had significant higher work intentions [F (1, 82) = 5.22; p = 0.025; η2 p = 0.060] and mindfulness [F (1, 82) = 6.33; p = 0.014; η2 p = 0.072] but marginally lower stress [F (1, 82) = 4.20; p = 0.044; η2 p = 0.049] than students in the meditation-focused yoga group. CONCLUSION: Yoga practice with a focus on breathing is more effective than that with a focus on meditation for undergraduates to retain energy for work, keep attention and awareness, and reduce stress.

Tellurene Nanoflake-Based NO2 Sensors with Superior Sensitivity and a Sub-Parts-per-Billion Detection Limit.

Industrial production, environmental monitoring, and clinical medicine put forward urgent demands for high-performance gas sensors. Two-dimensional (2D) materials are regarded as promising gas-sensing materials owing to their large surface-to-volume ratio, high surface activity, and abundant surface-active sites. However, it is still challenging to achieve facilely prepared materials with high sensitivity, fast response, full recovery, and robustness in harsh environments for gas sensing. Here, a combination of experiments and density functional theory (DFT) calculations is performed to explore the application of tellurene in gas sensors. The prepared tellurene nanoflakes via facile liquid-phase exfoliation show an excellent response to NO2 (25 ppb, 201.8% and 150 ppb, 264.3%) and an ultralow theory detection limit (DL) of 0.214 ppb at room temperature, which is excellent compared to that of most reported 2D materials. Furthermore, tellurene sensors present a fast response (25 ppb, 83 s and 100 ppb, 26 s) and recovery (25 ppb, 458 s and 100 ppb, 290 s). The DFT calculations further clarify the reasons for enhanced electrical conductivity after NO2 adsorption because of the interfacial electron transfer from tellurene to NO2, revealing an underlying explanation for tellurene-based gas sensors. These results indicate that tellurene is eminently promising for detecting NO2 with superior sensitivity, favorable selectivity, an ultralow DL, fast response-recovery, and high stability.

Room-Temperature and Humidity-Resistant Trace Nitrogen Dioxide Sensing of Few-Layer Black Phosphorus Nanosheet by Incorporating Zinc Oxide Nanowire.

Surmounting the issues of high-sensitivity and room-temperature detection toward trace NO2 gas is of paramount importance in the fields of human health and ultralow emission. Recently, black phosphorus (BP), a novel two-dimensional material, has gained considerable interest to achieve this goal. However, related work is far from satisfactory due to sluggish response, insufficient recovery, and fragile stability. In this scenario, we report on an inspiring NO2 sensor featuring composite film of few-layer BP nanosheets and zinc oxide (ZnO) nanowires serving as the sensing layer. Compared with BP-only counterpart, BP-ZnO sensor exhibited enhanced performance including boosted response (74% vs. 37.7% toward 50 ppb, which was among the best performances of BP involved NO2 sensors), accelerated response speed, better long-term stability, and strengthened humidity-repelling properties. In addition, excellent selectivity toward trace NO2 gas was revealed. These improvements could be ascribed into porous film, abundant sorption sites, numerous p-n heterojunctions, and passivation effect of ZnO nanowires on BP nanosheets. Furthermore, the proposed basic-solution assisted BP exfoliation favored film deposition, and enabled versatile composition design involving BP nanosheets in the future. In brief, the as-prepared BP-ZnO NO2 sensors paved the avenue for further BP applications and enriched its underlying transduction mechanism in gas sensing.

Nitrogen dioxide sensing based on multiple-morphology cuprous oxide mixed structures anchored on reduced graphene oxide nanosheets at room temperature.

Sensitive detection of trace nitrogen dioxide (NO2) gas at room temperature is of urgent necessity in the fields of healthcare and environment monitoring. To achieve this goal, we report on a porous composite film featuring reduced graphene oxide (rGO) nanosheets as the template platform of nanostructured cuprous oxide (Cu2O) nanowires and nanoparticles via a hydrothermal method. The sensor performance was investigated in terms of sensing response, optimal operation temperature, repeatability, long-term stability, selectivity and humidity effect on NO2 sensing. The sensor response achieved 0.66 towards 50 ppb NO2 gas with a full recovery at room temperature (25 °C ± 2 °C), which was among the best cases of Cu2O-related NO2 detection concerning sensor response and operation temperature. Moreover, a modest repeatability, stability, selectivity as well as a negligible humidity effect on NO2 sensing were exhibited. A mass of interspaces existing within nanostructured composites as well as the synergistic effect between rGO and Cu2O materials endowed the sensing layer with favorable gas accessibility and sufficient gas-solid interaction. Simultaneously, highly conductive rGO nanosheets facilitated an effective electron transfer and collection. In brief, the as-prepared rGO/Cu2O sensors showed a competitive room temperature detection capability for ppb-level NO2 gas, providing a vast potential in the future applications such as the real-time monitoring of ultralow emission.