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Neuroplasticity research supports the idea that varied practice and new environments promote cognitive engagement and enhance learning. Expanding on a meta-analysis of the effect of physical activity interventions on cognition and academic outcomes, we reviewed and quantified the impact of task and environmental factors that foster creative physical activity. Interventions were considered as fostering creative physical activity to a greater extent if (1) they were varied, (2) relied less on technical acquisition, instruction or demonstration, (3) involved open spaces, props, or open-ended instructions, and (4) involved interactions with peers. A wide range of physical activities were considered, from dance to aerobic exercise across 92 studies in 5–12-year-old children. Creativity ratings of physical activity interventions were varied but did not associate with greater beneficial effects on executive functions ( k  = 45), academic achievement ( k  = 47), or fluid intelligence ( k  = 8). Studies assessing on-task behaviour ( k  = 5) tended not to foster creativity, while reversely studies assessing creativity tended to foster creative physical activities ( k  = 5). As a group, three studies that fostered more creative PA showed a small significant negative summary effect on cognitive flexibility. Considering qualitative differences in the physical activities performed in schools will improve our understanding of their mechanisms of impact. Future research should consider using more varied measures, including more proximal outcomes that involve body movements (e.g., a Simon Says task to measure inhibitory control).

The prevailing trend in industrial equipment development is integration, with pipelines as the lifeline connecting system components. Given the often harsh conditions of these industrial equipment pipelines, leakage is a common occurrence that can disrupt normal operations and, in severe cases, lead to safety accidents. Early detection of even minor drips at the onset of leakage can enable timely maintenance measures, preventing more significant leaks and halting the escalation of pipeline failures. In light of this, our study investigates a method for monitoring pipe drips in industrial equipment using machine vision technology. We propose a machine vision model specifically designed for pipe drip detection, aiming to facilitate monitoring of pipe system drips. The system designed to collect the image of the droplet side cross-section with a Charge charge-coupled device (CCD) industrial camera, is aided by the computer image processing system used to analyze and process the collected images. Image enhancement technology is applied to improve the visibility of the image and image filtering technology is applied to remove the noise of the image. With the help of image segmentation technology, target droplet identification and division are achieved. Morphological reconstruction and region-filling techniques are used to remove the noise caused by shooting in the side cross-section image, such as hollow, reflection, and irregular droplet edge, to upgrade the quality of the solution droplet edge. The mathematical model is established for boundary position points extracted from the droplet side cross-section image. Then, the fitting droplet image is drawn. The droplet volume is obtained by calculating the volume of the rotating body. The two-dimensional image of the target droplet is obtained dynamically through the camera capture technology. The droplet boundary extraction algorithm is proposed, and the three-dimensional model of the target droplet is established, so the volume calculation problem of the droplet is solved, which provides a way of thinking for drip leakage detection of the pipeline.

The prevailing trend in industrial equipment development is integration, with pipelines as the lifeline connecting system components. Given the often harsh conditions of these industrial equipment pipelines, leakage is a common occurrence that can disrupt normal operations and, in severe cases, lead to safety accidents. Early detection of even minor drips at the onset of leakage can enable timely maintenance measures, preventing more significant leaks and halting the escalation of pipeline failures. In light of this, our study investigates a method for monitoring pipe drips in industrial equipment using machine vision technology. We propose a machine vision model specifically designed for pipe drip detection, aiming to facilitate monitoring of pipe system drips. The system designed to collect the image of the droplet side cross-section with a Charge charge-coupled device (CCD) industrial camera, is aided by the computer image processing system used to analyze and process the collected images. Image enhancement technology is applied to improve the visibility of the image and image filtering technology is applied to remove the noise of the image. With the help of image segmentation technology, target droplet identification and division are achieved. Morphological reconstruction and region-filling techniques are used to remove the noise caused by shooting in the side cross-section image, such as hollow, reflection, and irregular droplet edge, to upgrade the quality of the solution droplet edge. The mathematical model is established for boundary position points extracted from the droplet side cross-section image. Then, the fitting droplet image is drawn. The droplet volume is obtained by calculating the volume of the rotating body. The two-dimensional image of the target droplet is obtained dynamically through the camera capture technology. The droplet boundary extraction algorithm is proposed, and the three-dimensional model of the target droplet is established, so the volume calculation problem of the droplet is solved, which provides a way of thinking for drip leakage detection of the pipeline.

Background Occupational stress is an important risk factor for mental health among occupational population. Exploring related mediators of workers’ mental health are important to improve their health and performance. Our study aims to explore the relationships between work stress, positive psychological resources, burnout and well-being. Methods The study was performed during the period of June and July in 2015. A questionnaire that consisted of the Effort-reward Imbalance Scale, the Maslach Burnout Inventory-General Survey, the Psychological Capital Questionnaire, the Rosenberg Self-esteem Scale, the Flourishing Scale, as well as demographic and working factors. Results Psychological capital mediated the associations of effort-reward imbalance and emotional exhaustion, cynicism, professional efficacy and well-being. Self-esteem mediated the associations of effort-reward imbalance ratio with cynicism, professional efficacy and well-being, and mediated the associations of overcommitment and cynicism, professional efficacy and well-being. Conclusions The findings indicated that enhancing positive psychological resources could be considered in developing intervention strategies for psychological health among manufacturing employees.

The phase transitions in IrTe 2 have been extensively studied but the symmetry at each phase is yet to be settled. Employing second harmonic generation (SHG) measurements over a temperature range of 4 –300 K, we probe the evolution of the symmetry of IrTe 2 . Our results indicate shifts in two distinct transition temperatures (T s1 and T s2 with T s1 > T s2 ) through thermal cycling, providing an explanation for the variations of reported values in literature. The SHG polarimetry measurements identify symmetries in different temperature ranges, confirming the trigonal symmetry above T s1 , the triclinic symmetry between T s1 and T s2 , and the coexistence of multiple stripe phases below T s2 . The most striking feature is the reemergence of a trigonal phase as reflected by six-fold symmetry below ~ 10 K which is likely responsible for phenomena observed at low temperatures.

Background Children’s physical and emotional health are affected by acute cough. Cough severity and influence on quality of life can be measured with specialized questionnaires. This study aimed to present the Chinese version of the 16-item Parent-proxy Children’s Acute Cough-specific Quality of Life Questionnaire (PAC-QoL 16 ) and examine its validity and reliability. Methods Using independent translations and counter translations, the Chinese version of the PAC-QoL 16 was translated, and its suitability was evaluated among the families of five coughing children. Children with acute cough who were hospitalized at the Children’s Hospital Affiliated to Zhengzhou University from October 2022 to June 2023 were included, and the caregivers were invited to complete the questionnaire and two cough-related measures (visual analogue score (VAS) and verbal category descriptive (VCD) score) on the day of admission and on the 5th day of treatment. Additionally, the questionnaire’s internal consistency and concurrent validity were evaluated. Results A total of 148 complete and valid questionnaires were collected. Eighty-one males and 67 females composed the 148 children, whose average age was 4.6 years and ranged from 2 to 12 years. Cronbach’s alpha coefficients for the three domains and the whole score ranged from 0.824 to 0.978. The VAS and VCD scores showed a strong correlation with the overall and domain scores of the PAC-QoL16. After therapy, there were significant decreases in the cough VAS and VCD scores, while the overall and domain scores of the PAC-QoL16 improved ( P  < 0.05). Concurrently, correlations were observed between the difference in VAS and VCD scores and the difference in PAC-QoL16 scores before and after treatment. Conclusion The Chinese version of the PAC-QoL16 has good applicability in Chinese children with acute cough and can reflect the quality of life and disease severity of children with acute cough.

The present study investigated the multi-scale structure of starch derived from acorn kernels and the effects of the non-starch nutrients on the physicochemical properties and in vitro digestibility of starch. The average polymerization degree of acorn starch was 27.3, and the apparent amylose content was 31.4%. The crystal structure remained as C-type but the relative crystallinity of acorn flour decreased from 26.55% to 25.13%, 25.86% and 26.29% after the treatments of degreasing, deproteinization, and the removal of β-glucan, respectively. After the above treatments, the conclusion temperature of acorn flour decreased and had a significant positive correlation with the decrease in the crystallinity. The aggregation between starch granules, and the interactions between starch granules and both proteins and lipids, reduced significantly after degreasing and deproteinization treatments. The endogenous protein, fat, and β-glucan played key roles in reducing the digestibility of acorn starch relative to other compounds, which was dictated by the ability for these compounds to form complexes with starch and inhibit hydrolysis.

The detection of anomalies in high-dimensional time-series has always played a crucial role in the domain of system security. Recently, with rapid advancements in transformer model and graph neural network (GNN) technologies, spatiotemporal modeling approaches for anomaly detection tasks have been greatly improved. However, most methods focus on optimizing upstream time-series prediction tasks by leveraging joint spatiotemporal features. Through experiments, we found that this modeling approach not only risks the loss of some original anomaly information during data preprocessing, but also focuses on optimizing the performance of the upstream prediction task and does not directly enhance the performance of the downstream detection task. We propose a spatiotemporal anomaly detection model that incorporates an improved attention mechanism in the process of temporal modeling. We adopt a heterogeneous graph contrastive learning approach in spatio modeling to compensate for the representation of anomalous behavioral information, thereby guiding the model through thorough training. Through validation on two widely used real-world datasets, we demonstrate that our model outperforms baseline methods. We also explore the impact of multivariate time-series prediction tasks on the detection task, and visualize the reasons behind the benefits gained by our model.

Low-toxicity, air-stable cesium bismuth iodide Cs3Bi2X9 (X = I, Br, and Cl) perovskites are gaining substantial attention owing to their excellent potential in photoelectric and photovoltaic applications. In this work, the lattice constants, band structures, density of states, and optical properties of the Cs3Bi2X9 under high pressure perovskites are theoretically studied using the density functional theory. The calculated results show that the changes in the bandgap of the zero-dimensional Cs3Bi2I9, one-dimensional Cs3Bi2Cl9, and two-dimensional Cs3Bi2Br9 perovskites are 3.05, 1.95, and 2.39 eV under a pressure change from 0 to 40 GPa, respectively. Furthermore, it was found that the optimal bandgaps of the Shockley–Queisser theory for the Cs3Bi2I9, Cs3Bi2Br9, and Cs3Bi2Cl9 perovskites can be reached at 2–3, 21–26, and 25–29 GPa, respectively. The Cs3Bi2I9 perovskite was found to transform from a semiconductor into a metal at a pressure of 17.3 GPa. The lattice constants, unit-cell volume, and bandgaps of the Cs3Bi2X9 perovskites exhibit a strong dependence on dimension. Additionally, the Cs3Bi2X9 perovskites have large absorption coefficients in the visible region, and their absorption coefficients undergo a redshift with increasing pressure. The theoretical calculation results obtained in this work strengthen the fundamental understanding of the structures and bandgaps of Cs3Bi2X9 perovskites at high pressures, providing a theoretical support for the design of materials under high pressure.

This study investigated whether the interference between two tasks in dual-task processing stems from bottleneck limitations or insufficient cognitive resources due to resource sharing. Experiment 1 used tone discrimination as Task 1 and word or pseudoword classification as Task 2 to evaluate the effect of automatic versus controlled processing on dual-task interference under different SOA conditions. Experiment 2 reversed the task order. The results showed that dual-task interference persisted regardless of task type or order. Neither experiment found evidence that automatic tasks could eliminate interference. This suggests that resource limitations, rather than bottlenecks, may better explain dual-task costs. Specifically, when tasks compete for limited resources, the processing efficiency of both tasks is significantly reduced. Future research should explore how cognitive resources are dynamically allocated between tasks to better account for dual-task interference effects.