Analysis of Temporal Distribution, Research Power and Hot Topics of Physical Education Curriculum Research in Chinese Colleges and Universities

With the help of Cite Space software and data statistical analysis tools, the research employed bibliometric method and co-word analysis method to analyze the research on physical education curriculum in Chinese colleges and universities, which was included in CNKI from January 1, 1984 to December 31, 2022, in order to grasp the characteristics of its temporal distribution, research power, and hot topics. The findings showed that the research on physical education curriculum in Chinese colleges and universities had gone through a slow formation period, an initial growth period, a rapid growth period, and is currently in a mature and stable period in terms of temporal distribution. However, in terms of research power, there were lack of high-impact research institutions and researchers in China. Furthermore, the hot topics mainly focus on the reform of the physical education curriculum, teaching reform, ideological and political education, and resources in the physical education curriculum of the colleges and universities. Therefore, the research suggests that the further research is required to expand the ideas, methods, breadth, and depth, in order to promote the comprehensive and in-depth reformation and development of physical education curriculum practice in Chinese colleges and universities

Fiber-Based High-Power Supercontinuum and Frequency Comb Generation

Ultrafast optics has been a rich research field, and picosecond/femtosecond pulsed laser sources seek many applications in both the areas of fundamental research and industrial life. Much attention has been attached to fiber lasers in recent decades as they offering various superiorities over their solid-state counterparts with compact size, low cost, and great stability due to the inherent stability and safety of the waveguide structures as well as high photoelectric conversion efficiency. Fiber-based sources of ultrashort and high-peak/high-average optical pulses have become extremely important for high-precision laser processing while sources whose carrier-envelop offset and repetition rate are stabilized can serve as laser combs with applications covering many research areas, such as precision spectroscopy, optical clock, and optical frequency metrology. For the application as laser combs, four parts as fiber laser, broadband supercontinuum, nonlinear power amplification, and repetition rate stabilization must be concerned. This chapter is intended to give a brief introduction about the achievement of the four technologies mentioned above with different experimental setups, recently developed such as divided-pulse amplification (DPA) in emphasize. Moreover, detailed descriptions of the experimental constructions as well as theoretical analyses about the phenomena they produced are also involved

Simultaneous Identification of Linear Building Dynamic Model and Disturbance Using Sparsity-Promoting Optimization

A dynamic model of a building\u27s temperature is necessary for model-based control of building HVAC (Heating Ventilation and Air Conditioning) systems. Due to the complexity of thermal dynamics, system identification from data is considered advantageous, from which a particular challenge may arise: temperature is affected by large, unknown disturbances, especially the cooling load induced by the occupants. Many system identification methods ignore these disturbances, which can produce highly erroneous results, or use a specialized test building to measure the occupant induced load. In this paper we propose a method that simultaneously identifies a dynamic model of a building\u27s temperature in the presence of large, unmeasured disturbances, and a transformed version of the unmeasured disturbance from easily measurable input-output data. The proposed method, which we call SPDIR (Simultaneous Plant and Disturbance Identication through Regularization) is based on solving a l1-regularized least-squares problem, where l1 penalty encourages the identified transformed disturbance to be sparse. The motivation for this is that the disturbance, which consists mostly of internal load due to occupants, is often piecewise-constant. We show that this makes the transformed disturbances an approximately sparse signal, motivating the use of l1 regularization. The selection of regularization parameter can be crucial to the identification accuracy of the l1 regularized-least-square problems. There are a number of heuristics for the classic Lasso (least absolute shrinkage and selection operator). However, the conditions required for their application do not hold in our case. We therefore propose a distinct heuristic to choose λ. The method proposed here can enforce properties of the system that are known from the physics of the thermal processes, including stability and signs of DC gains for certain input-output pairs. Such properties are interpreted into convex constraints and augmented to the proposed problem. Thus the our method consists of solving a convex optimization problem, which we prove to be feasible and regular. Our method uses data collected during regular operation of a building and does not need data collected when the building is empty. Even when data from unoccupied periods is available, assuming the disturbance to be zero during that time is not desirable since doing so will prevent the disturbance from absorbing model mismatch. We test our method via simulations, and results indicate that the method can accurately estimate the thermal dynamic model and transformed disturbance with both open-loop and closed-loop data, even when the disturbance does not satisfy the piecewise constant property. The main advantages of the method is posing the estimation problem as a convex optimization problem with constraints from physical insights about the system and the disturbances, without requiring specially collected data. Previous methods lacked both convexity and/or physically meaningful constraints. The main limitation is that the identified disturbance is a linear transformation of the true disturbance with unknown coefficients

Trends in the prevalence of social isolation among middle and older adults in China from 2011 to 2018: the China Health and Retirement Longitudinal Study

Background: Epidemiological studies have shown that social isolation, which is prevalent in older adults, is associated with a range of adverse health outcomes, but the prevalence of and trends in regard to social isolation remain ambiguous in China. The aim of this study was to elucidate the trends regarding the prevalence of social isolation among middle-aged and older adults in China from 2011 to 2018 and to further identify associated risk factors. Methods: A repeated cross-sectional study, The data were derived from panel sample data of four waves conducted from May 2011 to August 2018 in the nationally representative China Health and Retirement Longitudinal Study (CHARLS) using multistage probability sampling. Social isolation was ascertained by the five item Steptoe Social Isolation Index. The potential covariates were demographic characteristics, lifestyle factors, and health status. Linear-by-linear association was used to assess the trends in regard to social isolation over time under the influence of the potential covariates. Linear-by-linear association and an age-period-cohort analysis were used to explore the trends, and two-level (time, individual) generalized estimating equation models (GEE) linked multivariate binary logistic regression were performed to identify risk factors. Results: A high prevalence of social isolation and a moderate upward trend from 2013 to 2018 were observed among a U-shaped trend prevalence of social isolation from 2011 to 2018 across China, with rates of 38.09% (95% CI = 36.73–39.45) in 2011, 33.66% (32.32–35.00) in 2013, 39.13% (37.59–40.67) in 2015, and 39.95% (38.59–41.31) in 2018 (p < 0.001). The prevalence of social isolation increased with age and educational attainment. Females had a higher prevalence than males. The prevalence of social isolation was found to be significantly lower in pensioners than in non-pensioners between 2011 and 2018 (p < 0.001). The prevalence of social isolation was 38.9%, 34.9%, 38.5%, and 44.08% about three times higher among those who doid not use the Internet and 13.44%, 11.64%, 12.93%, and 16.73% than among those who doid in 2011, 2013, 2015 and 2018 respectively. The participants with short (0–5 h) and long sleep (9 or more hours), and poor self-rated health had a higher prevalence of social isolation than the others. Older age, lower educational attainment, living in a rural region, lack of medical insurance or pension, lack of internet use and poor health were risk factors (p < 0.05). Conclusions: We found a U-shaped prevalence of social isolation trends from 2011 to 2018 and revealed increasing trends from 2013 to 2018 among middle-aged and older adults in China. The findings of the study highlight the urgent need for interventions to reduce social isolation including improving sleep quality and internet skills. Disadvantaged groups in terms of age, economic status, and health status should be the focus of such interventions, especially in the era of COVID-19

Developing an Indicator System for a Healthy City: Taking an Urban Area as a Pilot

Purpose: The Healthy Cities Project is an important strategy for global health. This study aimed to develop a scientific and appropriate indicator system for the evaluation of a Healthy City in Chongqing, China. Methods: Data were collected via a review of government documents, focus group discussions, and in-depth interviews. A total of 34 government documents were reviewed to build the indicator database based on our previous studies. The first round of focus group discussions, which involved eight health-related experts, was conducted to form the indicator system framework. In-depth interviews with 15 experts from government departments were conducted to design the improved indicator system. The second round of focus group discussions, which featured four experts, was conducted to obtain the final recommended list of indicators. A thematic framework was used to analyze the detailed interview notes. Results: The indicator system for the Healthy City consisted of 5 first-level indicators, 21 second-level indicators (e.g., health literacy), 73 third-level indicators (e.g., incidence of myopia), and three characteristic indicators. This indicator system spanned the scope of the environment, society, health services, healthy people, and health behaviors. Conclusion: This indicator system was based on the current status of the construction of the Healthy City in the pilot district. The indicator system could be dynamically adjusted according to the development of the Healthy City in the pilot district. Government departments play an important decision-making role in the development process of this indicator system

VDIP-TGV: Blind Image Deconvolution via Variational Deep Image Prior Empowered by Total Generalized Variation

Recovering clear images from blurry ones with an unknown blur kernel is a challenging problem. Deep image prior (DIP) proposes to use the deep network as a regularizer for a single image rather than as a supervised model, which achieves encouraging results in the nonblind deblurring problem. However, since the relationship between images and the network architectures is unclear, it is hard to find a suitable architecture to provide sufficient constraints on the estimated blur kernels and clean images. Also, DIP uses the sparse maximum a posteriori (MAP), which is insufficient to enforce the selection of the recovery image. Recently, variational deep image prior (VDIP) was proposed to impose constraints on both blur kernels and recovery images and take the standard deviation of the image into account during the optimization process by the variational principle. However, we empirically find that VDIP struggles with processing image details and tends to generate suboptimal results when the blur kernel is large. Therefore, we combine total generalized variational (TGV) regularization with VDIP in this paper to overcome these shortcomings of VDIP. TGV is a flexible regularization that utilizes the characteristics of partial derivatives of varying orders to regularize images at different scales, reducing oil painting artifacts while maintaining sharp edges. The proposed VDIP-TGV effectively recovers image edges and details by supplementing extra gradient information through TGV. Additionally, this model is solved by the alternating direction method of multipliers (ADMM), which effectively combines traditional algorithms and deep learning methods. Experiments show that our proposed VDIP-TGV surpasses various state-of-the-art models quantitatively and qualitatively.Comment: 13 pages, 5 figure

On the Differential Spectrum of a Differentially 33-Uniform Power Function

In this paper, we investigate the cardinality, denoted by $(j_1,j_2,j_3,j_4)_2$, of the intersection of $(\mathcal{C}^{(2)}_{j_1}-1)\cap(\mathcal{C}^{(2)}_{j_2}-2)\cap(\mathcal{C}^{(2)}_{j_3}-3) \cap(\mathcal{C}^{(2)}_{j_4}-4)$ for $j_1,j_2,j_3,j_4\in\{0,1\}$, where $\mathcal{C}^{(2)}_0, \mathcal{C}^{(2)}_1$ are the cyclotomic classes of order two over the finite field $\mathbb{F}_{p^n}$, $p$ is an odd prime and $n$ is a positive integer. By making most use of the results on cyclotomic classes of orders two and four as well as the cardinality of the intersection $(\mathcal{C}^{(2)}_{i_1}-1)\cap(\mathcal{C}^{(2)}_{i_2}-2)\cap(\mathcal{C}^{(2)}_{i_3}-3)$, we compute the values of $(j_1,j_2,j_3,j_4)_2$ in the case of $p=5$, where $i_1,i_2,i_3\in\{0,1\}$. As a consequence, the power function $x^{\frac{5^n-1}{2}+2}$ over $\mathbb{F}_{5^n}$ is shown to be differentially $3$-uniform and its differential spectrum is also completely determined