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This study aims to explore the impact of dual learning on preschool teachers' thriving at work and creative performance, and whether preschool teachers' thriving at work acts as a mediating factor in the relationship between dual learning and teachers' creative work. The participants were preschool teachers from Taiwan. Convenience sampling was used, resulting in a sample size of 388 people. The study results found that preschool teachers' dual learning, which includes both exploitative and exploratory learning, can improve the novelty and usefulness of their work and creativity. Furthermore, the study found that preschool teachers' thriving at work mediates the relationship between dual learning and creativity. This study also discovered that dual learning should combine and complement both exploitative and exploratory learning to achieve balance and the most significant effect. Therefore, it is essential to first help preschool teachers engage in both exploitative and exploratory learning. Schools and kindergartens can also provide relevant courses to help teachers combine these two learning methods. By integrating and complementing their advantages and disadvantages, a balance in learning can be achieved, thereby promoting teachers' creativity in their work.

Intermittent fasting (IF) and caloric restriction (CR) are dietary strategies to prevent and attenuate obesity associated with conditions and aging-related outcomes. This scoping review examined the cardiometabolic, cancer, and neurocognitive outcome differences between IF and CR interventions among adults. We applied a systematic approach to scope published randomized controlled trials (databases: PubMed, CINAHL Plus, PsychInfo, Scopus, and Google Scholar) from inception through August 2023. The initial search provided 389 unique articles which were critically appraised. Thirty articles met the eligibility criteria for inclusion: 12 were IF, 10 were CR, and 8 were combined IF and CR interventions. IF and CR were associated with weight loss; however, IF studies tended to report greater adherence compared with CR. Overall, IF and CR were equivalently effective across cardiometabolic, cancer, and neurocognitive outcomes. Our findings suggest that IF has health benefits in a variety of conditions and may be better accepted and tolerated than CR, but more comparative research is required.

Abstract High-end equipment always operate in low-speed and heavy-load working environments, highlighting the need for cam-lobe hydraulic motors with excellent speed stability (< 1 r/min) and ultrahigh-power rotary output (> 1 MW). The successful operation of cam-lobe hydraulic motors relies on the circulation supply of high- and low-pressure oil. However, the switching between high-/low-pressure oil controlled by the oil distributor inevitably causes an obvious pressure impact and speed pulsation, which directly reduces the speed stability of hydraulic motors. Therefore, an optimization design approach for the oil distributor port is proposed to minimize the speed pulsation of cam-lobe hydraulic motors. In the proposed approach, a simulation model that links the oil distributor port structural parameters with the hydraulic motor speed pulsation was developed to clarify the effect of the oil distributor structural parameters on speed pulsation. Then, an orthogonal analysis method was used to identify the optimized oil distributor port structural parameters while minimizing the hydraulic motor’s speed pulsation as much as possible. Finally, several experiments were conducted to validate the effectiveness and accuracy of the proposed optimization design approach. The experimental results indicate that the pulsation rate of the hydraulic motor equipped with the optimized oil distributor was 62.5% lower than that of the original motor at a working pressure of 25 MPa, which is consistent with the simulation results using the proposed optimization design approach. The findings of this study offer a feasible and effective approach to guide the design optimization of the oil distributor port for low-pulsation hydraulic motors.

Abstract Nowadays, some fracking fluids can enable resourceful extraction of coalbed methane and reduce greenhouse gas emissions. However, their toxicity or corrosiveness will cause harm to downhole workers and pollute groundwater resources. Thus, five kinds of clean composite fracturing fluids were developed in this paper by using starch solution as the matrix and adding various preparations. The change rule of methane adsorption capacity by microstructure changes of coal samples was investigated systematically, and the optimal composite fracturing fluid was determined. The results showed that the new fracturing fluid increased the degree of aromatic ring condensation by 43.3% and the average pore size by 52.1%. Also, the adsorption constants of a value decreased by 11.6% and b value decreased by 23.9%, which can remarkably reduce the methane adsorption. The experimental results provide theoretical support for the clean production of coalbed methane.

Background: Emerging evidence suggests that cell deaths are involved in tumorigenesis and progression, which may be treated as a novel direction of cancers. Recently, a novel type of programmed cell death, disulfidptosis, was discovered. However, the detailed biological and clinical impact of disulfidptosis and related regulators remains largely unknown. Methods: In this work, we first enrolled pancancer datasets and performed multi-omics analysis, including gene expression, DNA methylation, copy number variation and single nucleic variation profiles. Then we deciphered the biological implication of disulfidptosis in clear cell renal cell carcinoma (ccRCC) by machine learning. Finally, a novel agent targeting at disulfidptosis in ccRCC was identified and verified. Results: We found that disulfidptosis regulators were dysregulated among cancers, which could be explained by aberrant DNA methylation and genomic mutation events. Disulfidptosis scores were depressed among cancers and negatively correlated with epithelial mesenchymal transition. Disulfidptosis regulators could satisfactorily stratify risk subgroups in ccRCC, and a novel subtype, DCS3, owning with disulfidptosis depression, insensitivity to immune therapy and aberrant genome instability were identified and verified. Moreover, treating DCS3 with NU1025 could significantly inhibit ccRCC malignancy. Conclusion: This work provided a better understanding of disulfidptosis in cancers and new insights into individual management based on disulfidptosis.

Introduction: Atherosclerosis, a major contributor to cardiovascular disease, remains a significant health concern worldwide. While previous research has shown that acid-sensing ion channel 1 (ASIC1) impedes macrophage cholesterol efflux, its precise role in atherogenesis and the underlying mechanisms have remained elusive. Objectives: This study aimed to investigate the role of ASIC1 in atherosclerosis and its underlying mechanisms. Methods: First, data from a single-cell RNA sequencing (scRNA-seq) database were used to explore the relationships between ASIC1 differential expression and lipophagy in human atherosclerotic lesions. Finally, we validated the role of ASIC1/RIP1 signaling in lipophagy in vivo (human and mice) and in vitro (RAW264.7 and HTP-1 cells). Result: Our results demonstrated a significant increase in ASIC1 protein levels within CD68+ macrophages in both human aortic lesions and AopE-/- mouse lesion areas compared to nonlesion regions. Concurrently, there was a notable decrease in lipophagy, a crucial process for lipid metabolism. In vitro assays further elucidated that ASIC1 interaction with RIP1 (receptor-interacting protein 1) promoted the phosphorylation of RIP1 at serine 166 and transcription factor EB (TFEB) at serine 142, leading to disrupted lipophagy and increased lipid accumulation. Intriguingly, all these events were reversed upon ASIC1 deficiency and RIP1 inhibition. Furthermore, in ApoE-/- mouse models of atherosclerosis, silencing ASIC1 expression or inhibiting RIP1 activation not only significantly attenuated atherogenesis but also restored TFEB-mediated lipophagy in aortic tissues. This was evidenced by reduced TFEB Ser-142 phosphorylation, decreased LC3II and LAMP1 protein expression, increased numbers of lipophagosomes, and a decrease in lipid droplets. Conclusion: Our findings unveil the critical role of macrophage ASIC1 in interacting with RIP1 to inhibit lipophagy, thereby promoting atherogenesis. Targeting ASIC1 represents a promising therapeutic avenue for the treatment of atherosclerosis.

To address the challenges of fracture recognition in the CT scanning images of coal or rock, particularly the interference of gangue and the recognition of fractures at different scales, proposed and implemented a network model for coal-rock fracture extraction based on deep learning (MCSN). According to the U-Net architecture, the model utilized its encoder-decoder structure and skip connections to segment the fracture structure images from complex coal-rock body images. Firstly, the fracture structures in the CT scanning images were annotated manually using the internal scan images captured by a coal industrial CT scanning system. And the annotated original data was augmented to create a coal-rock CT fracture dataset. Subsequently, to make the extraction network of main features have a stronger extraction capability of fracture structure features, the weights of a pre-trained VGG16 model were transferred to the U-Net encoder through a transfer learning technique. Simultaneously, the decoder part of the U-Net model was improved using the deep separable dilated convolutional modules (DCAC) and residual modules to effectively boost the recognition capability of fracture structures in the CT images, demonstrating a superior segmentation accuracy and robustness. To validate the effectiveness of the coal-rock fracture extraction network model proposed, the results obtained by the MCSN were compared with those of classical convolutional neural networks and threshold segmentation methods. Experimental comparisons revealed a significant advantage of the model proposed in both qualitative and quantitative analyses. The proposed model, employing a multi-scale fusion strategy, demonstrated the capability to effectively extract fractures in complex coal-rock images, thereby enhancing the efficiency and accuracy of fracture identification. The model was applied to the identification of fractures in roadway surrounding rock based on borehole imaging. Fracture extraction was performed through the analysis of borehole videos and planar unfolded images collected by a borehole imaging instrument. The results from both sources were cross-validated to obtain an accurate distribution of fractures in roadway surrounding rock. Furthermore, the model provides guidelines for the injection and sealing of boreholes, increasing volume fraction of coal seam gas extraction.

Numerical models serve as an essential tool to investigate the causes and effects of Arctic sea ice changes. Evaluating the simulation capabilities of the most recent CMIP6 models in sea ice volume flux provides references for model applications and improvements. Meanwhile, reliable long-term simulation results of the ice volume flux contribute to a deeper understanding of the sea ice response to global climate change. In this study, the sea ice volume flux through six Arctic gateways over the past four decades (1979–2014) were estimated in combination of satellite observations of sea ice concentration (SIC) and sea ice motion (SIM) as well as the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) reanalysis sea ice thickness (SIT) data. The simulation capability of 17 CMIP6 historical models for the volume flux through Fram Strait were quantitatively assessed. Sea ice volume flux simulated from the ensemble mean of 17 CMIP6 models demonstrates better performance than that from the individual model, yet IPSL-CM6A-LR and EC-Earth3-Veg-LR outperform the ensemble mean in the annual volume flux, with Taylor scores of 0.86 and 0.50, respectively. CMIP6 models display relatively robust capability in simulating the seasonal variations of volume flux. Among them, CESM2-WACCM performs the best, with a correlation coefficient of 0.96 and a Taylor score of 0.88. Conversely, NESM3 demonstrates the largest deviation from the observation/reanalysis data, with the lowest Taylor score of 0.16. The variability of sea ice volume flux is primarily influenced by SIM and SIT, followed by SIC. The extreme large sea ice export through Fram Strait is linked to the occurrence of anomalously low air temperatures, which in turn promote increased SIC and SIT in the corresponding region. Moreover, the intensified activity of Arctic cyclones and Arctic dipole anomaly could boost the southward sea ice velocity through Fram Strait, which further enhance the sea ice outflow.

Abstract Cam-lobe radial-piston hydraulic motors are widely used as rotation driving units for various marine machinery owing to their ultrahigh output torque (more than 100 kN m). A multi-row cam roller bearing (MCRB) is the key component that directly determines the fatigue life of a cam-lobe radial-piston hydraulic motor. However, compact geometry and complex loads render MCRB susceptible to fatigue failure, highlighting the need for an optimized MCRB to achieve longer fatigue life and higher reliability. Therefore, this study proposes an innovative geometry optimization method for an MCRB to improve its fatigue life. In this method, a quasi-static model was developed to calculate the load distribution, with the fatigue life of the MCRB calculated using both basic dynamic loading and load distribution. Subsequently, a genetic algorithm was used to obtain the optimized geometry parameters, which significantly improved the fatigue life of the MCRB. Finally, a loading test was conducted on a hydraulic motor installed with both the initial and optimized MCRB to validate the effectiveness of the proposed optimization method. This study provides a theoretical guideline for optimizing the design of MCRB, thereby increasing the fatigue life of hydraulic motors.

Abstract Background Incorporating scientific research into undergraduate medical education is necessary for the quality of future health care. However, providing rigorous research training to a large number of medical students at one institution remains one of the major challenges. The authors studied the impact of a curriculum-based Research Training Program (RTP) for all undergraduate students at Zhejiang University School of Medicine (ZUSM) on research productivity and future research interests. Methods Medical students (n = 2,213) from ZUSM who completed the course of RTP between 2013 and 2020 were studied. The authors measured the academic performance, research publications, and research projects of students across years, and evaluated potential factors that contribute to student research productivity and increased interest in future research. Results Across the years, there was an increase in the number of student publications, a greater proportion of students with publications, and a greater proportion of projects involving three or more students (P

Bully victimization is known to cause adverse psychological outcomes; however, resilience may mitigate the more adverse effects. Little is known regarding the role played by BDNF Val66Met polymorphism in youth resilience against psychological harm caused by bully victimization. In this cross-sectional study, a community sample of 598 participants (M[subscript age] = 20.1 ± 1.4 years, 48.8% males) completed the questionnaire on bully victimization, resilience, and psychological symptoms. Salivary genomic DNA was genotyped for the BDNF Val66Met polymorphism. A path analysis was used to test the mediating role of resilience in the association between bully victimization and psychological symptoms. Furthermore, the BDNF genotype was added to the model to explore its moderating effects on the mediating role of resilience in the path with 5000 bootstrapped samplings using SPSS PROCESS Macro. Results revealed a significant indirect effect via resilience that accounted for 17.2% of the association between bully victimization and psychological symptoms. While the Val66Met polymorphism interacted with bully victimization to predict resilience scores, bully victimization was more strongly associated with poor resilience (F = 4.59, p = 0.03) in subjects with the Met/Met genotype ([beta] = -3.22, p < 0.001), as compared to participants with other genotypes ([beta] = -1.33, p = 0.051). Findings suggest a gene-environment interaction effect on psychological resilience in bully-victimized youth.