A112: Effects of Healthy Physical Education Curriculum Model of China on Student Physical Fitness: A Meta-Analysis

Purpose: The development of Healthy Physical Education Curriculum Model of China (HPE-C) was aimed for students’ physical and mental health. Some studies have proved that the application of HPE-C can effectively improve Chinese primary and secondary school students\u27 physical fitness. However, the results of these studies were not uniform due to the differences in intervention methods, experimental periods, and regions. Consequently, the purpose of this study was to specifically synthesize the evidence for HPE-C for Chinese primary and secondary school students’ physical fitness. Methods: An extensive online search, utilizing keywords such as \u27Healthy Physical Education Curriculum Model of China,\u27 \u27Primary school,\u27 \u27Middle school,\u27 \u27High school,\u27 \u27Application effect,\u27 and \u27Experimental study,\u27 yielded over 80 published studies within the past 7 years. The databases employed for the search included CNKI, CQVIP, and the WanFang database. The eligibility criteria required that the research: (a) had full-text availability, (b) involved primary and secondary students aged 7-18 years in China, (c) conducted a randomized controlled trial or quasi-experiment, and (d) reported essential parameters such as sample size, mean difference, and standard deviation. Since the outcome measures were continuous variables, mean difference or standardized mean difference was utilized. Effect sizes, along with 95% confidence intervals, were calculated using Review Manager 5.4 software, and the heterogeneity among effect sizes was assessed through I² statistics. Results: A total of 81 studies and articles were considered for inclusion and 11 studies qualified for this review. Seven studies used 50-meter running to test the speed quality of students, had an effect size of -0.24, 95%CI[-0.30,-0.18], Z=7.73 (p \u3c 0.00001). Five studies used sit and reach to test the flexibility quality of students, had an effect size (mean difference) of 0.59, 95%CI[-0.03,1.20]，Z=1.87(p=0.06). Four studies used stand long jump to test the strength quality of students with an effect size (mean difference) of 4.66, 95%CI[2.16,7.17], Z=3.65(p=0.0003). Five studies tested the student’s endurance quality with 1000-meter running for male students and 800-meter running for female students, had an effect standardized mean difference of -12.56, 95%CI[-20.47,-4.65], Z=3.11(p=0.002). Conclusions: This meta-analysis shows that the application of HPE-C had an significant effect on the speed, strength and endurance of Chinese primary and secondary school students, but the effects on flexibility is not significant. Teachers should attach importance to the exercise of students\u27 flexibility in order to promote the all-round development of students\u27 physical fitness when applying HPE-C

Learned, Uncertainty-driven Adaptive Acquisition for Photon-Efficient Multiphoton Microscopy

Multiphoton microscopy (MPM) is a powerful imaging tool that has been a critical enabler for live tissue imaging. However, since most multiphoton microscopy platforms rely on point scanning, there is an inherent trade-off between acquisition time, field of view (FOV), phototoxicity, and image quality, often resulting in noisy measurements when fast, large FOV, and/or gentle imaging is needed. Deep learning could be used to denoise multiphoton microscopy measurements, but these algorithms can be prone to hallucination, which can be disastrous for medical and scientific applications. We propose a method to simultaneously denoise and predict pixel-wise uncertainty for multiphoton imaging measurements, improving algorithm trustworthiness and providing statistical guarantees for the deep learning predictions. Furthermore, we propose to leverage this learned, pixel-wise uncertainty to drive an adaptive acquisition technique that rescans only the most uncertain regions of a sample. We demonstrate our method on experimental noisy MPM measurements of human endometrium tissues, showing that we can maintain fine features and outperform other denoising methods while predicting uncertainty at each pixel. Finally, with our adaptive acquisition technique, we demonstrate a 120X reduction in acquisition time and total light dose while successfully recovering fine features in the sample. We are the first to demonstrate distribution-free uncertainty quantification for a denoising task with real experimental data and the first to propose adaptive acquisition based on reconstruction uncertaint

Case report: A case of high grade serous carcinoma of peritoneal origin

This case report describes an 80-year-old female patient admitted to the emergency department due to abdominal distension, abdominal pain, and hematemesis persisting for three days. Subsequent postoperative pathological examination confirmed the diagnosis of peritoneal cancer. The occurrence, diagnosis, treatment, and prognosis of primary peritoneal cancer (PPC) are presented in detail. PPC is a type of cancer originating from the primary peritoneal mesothelium organization, causing diffuse malignant tumors in the abdominal and pelvic regions. Due to the lack of specific clinical manifestations for this disease, the importance of early diagnosis and treatment is hereby emphasized. The article also mentions the histological source of this type of cancer and the advantages of preoperative intraperitoneal chemotherapy in improving the efficacy of PPC treatment. Finally, the importance of a comprehensive treatment approach and proficient use of targeted therapy techniques are highlighted to enhance the treatment outcomes of PPC

Stain-free histopathology by programmable supercontinuum pulses

The preparation, staining, visualization, and interpretation of histological images of tissue is well-accepted as the gold standard process for the diagnosis of disease. These methods were developed historically, and are used ubiquitously in pathology, despite being highly time and labor intensive. Here we introduce a unique optical imaging platform and methodology for label-free multimodal multiphoton microscopy that uses a novel photonic crystal fiber source to generate tailored chemical contrast based on programmable supercontinuum pulses. We demonstrate collection of optical signatures of the tumor microenvironment, including evidence of mesoscopic biological organization, tumor cell migration, and (lymph-)angiogenesis collected directly from fresh ex vivo mammary tissue. Acquisition of these optical signatures and other cellular or extracellular features, which are largely absent from histologically processed and stained tissue, combined with an adaptable platform for optical alignment-free programmable-contrast imaging, offers the potential to translate stain-free molecular histopathology into routine clinical use

A Collaborative optimization approach for grid equipment overhaul/retirement strategy considering system effectiveness and risk

In order to improve the science of grid equipment overhaul and decommissioning decisions, a collaborative optimization method of equipment overhaul/decommissioning strategy considering system effectiveness and risk is proposed. The interaction effect between overhaul and decommissioning is analysed from the perspective of system effectiveness, and 2 attributes of integrated cost input and system reliability improvement are proposed, and then a prospect model with cost/benefit attributes is established based on the prospect theory. Then, with the maximum integrated prospect value as the optimization objective, a collaborative optimization model of overhaul/decommissioning strategy is established with the system risk level as the constraint. Finally, the effectiveness of the proposed method is verified by taking the transformer equipment of an urban area distribution network as an example

Two-tier power planning considering capacity tariff compensation

Faced with a high proportion of new energy power systems in the future, thermal power units will become the main peaking power source, and the reasonable recovery of their capacity value will become a focus of attention. To plan the power supply structure while considering the capacity tariff of thermal units and support the development of low-carbon energy, this paper proposes a two-tier power supply planning model that coordinates the scenery-to-fire ratio with the capacity tariff and proposes a capacity compensation mechanism for thermal units based on the effective capacity. With the gradually increasing ratio of scenery to fire, the capacity tariff compensation for thermal power units will be on the rise, providing a reference for the coordinated development of power supply planning and power market under the high proportion of new energy

Research on the Preparation and Application of Synthetic Leather from Coffee Grounds for Sustainable Development

As the market demand for environmentally friendly synthetic leather products has increased, water-based synthetic leather manufacturing technology and product performance have made great progress. Along with the explosive growth of coffee grounds generated by urban consumers in their daily lives, research on the sustainable reuse of coffee grounds has gradually become a trend in the field. This study discusses the method of preparing environmentally friendly water-based synthetic leather that reuses coffee grounds and is assessed by standardized physical tests for friction color fastness, Martindale abrasion resistance, breathability and moisture permeability, softness, and peel strength. The results have indicated that sustainable coffee-ground synthetic leather fully meets the performance of aqueous synthetic leather for apparel and luggage, with even some performance indicators exceeding existing aqueous synthetic leather, which is an innovative and sustainable product that can be applied to the apparel industry in the future. Its development and application in the textile field will provide research ideas with the transformation of environmental problems into new opportunities

Approximated Slack Scaling for Structural Support Vector Machines in Scene Depth Analysis

Based upon the framework of the structural support vector machines, this paper proposes two approaches to the depth restoration towards different scenes, that is, margin rescaling and the slack rescaling. The results show that both approaches achieve high convergence, while the slack approach yields better performance in prediction accuracy. However, due to its nondecomposability nature, the application of the slack approach is limited. This paper therefore introduces a novel approximation slack method to solve this problem, in which we propose a modified way of defining the loss functions to ensure the decomposability of the object function. During the training process, a bundle method is used to improve the computing efficiency. The results on Middlebury datasets show that proposed depth inference method solves the nondecomposability of slack scaling method and achieves relative acceptable accuracy. Our approximation approach can be an alternative for the slack scaling method to ensure efficient computation