MIDDLE-AGED FEMALE DEPRESSION IN PERIMENOPAUSAL PERIOD AND SQUARE DANCE INTERVENTION

Background: Depression is one of the most common psychiatric illnesses among perimenopausal women. Currently, drug treatments for the disorder tend to have higher risks than other forms of treatment. On the contrary, aerobic exercise can effectively relieve menopausal syndrome among perimenopausal women. Square dance, a kind of aerobic exercise favored by middle-aged women in China, could be a beneficial intervention for perimenopausal depression. Subjects and methods: A total of 321 women in perimenopause were chosen from Nangang Community, DaoLi Community, and Daowai Community in Harbin, Heilongjiang Province, from September 2015 through April 2016. Of the women with depressive symptoms, 60 did not participate in square dance. The subjects were randomly assigned to the intervention group (n=26) and control group (n=24). Intervention group patients participated in guided square dance exercise 60-90 min at least 5 times per week at a regular time for 3 months. The women in the control group received no intervention. Results: Of the screened subjects, 72 women (22.4%) suffered mild to severe depression; younger, working married women who square danced regularly presented a low depression rate (p<0.05). The depression index score of the intervention group after three months was 0.43±0.09, a statistically significant decrease (t=5.658, p<0.001). The difference in the depression index changes of the intervention and control group was also significant (t=5.407, p<0.001). Conclusion: The depression rate among women in perimenopause is high. Some intervention measures, such as promoting female employment, organizing collective activities for retired or unemployed women, and stabilizing family ties can prevent or improve the depression of women in perimenopause. Square dance can effectively reduce the depression levels of women in perimenopause

NeRF: Neural Radiance Field in 3D Vision, A Comprehensive Review

Neural Radiance Field (NeRF), a new novel view synthesis with implicit scene representation has taken the field of Computer Vision by storm. As a novel view synthesis and 3D reconstruction method, NeRF models find applications in robotics, urban mapping, autonomous navigation, virtual reality/augmented reality, and more. Since the original paper by Mildenhall et al., more than 250 preprints were published, with more than 100 eventually being accepted in tier one Computer Vision Conferences. Given NeRF popularity and the current interest in this research area, we believe it necessary to compile a comprehensive survey of NeRF papers from the past two years, which we organized into both architecture, and application based taxonomies. We also provide an introduction to the theory of NeRF based novel view synthesis, and a benchmark comparison of the performance and speed of key NeRF models. By creating this survey, we hope to introduce new researchers to NeRF, provide a helpful reference for influential works in this field, as well as motivate future research directions with our discussion section

Changes in wind speed under heat waves enhance urban heat islands in the Beijing Metropolitan Area

The interaction between urban heat islands (UHIs) and heat waves (HWs) is studied using measurements collected at two towers in the Beijing, China, metropolitan area and an analytical model. Measurements show that 1) the positive interaction between UHIs and HWs not only exists at the surface but also persists to higher levels (up to ~70 m) and 2) the urban wind speed is enhanced by HWs during daytime but reduced during nighttime as compared with its rural counterpart. A steady-state advection–diffusion model coupled to the surface energy balance equation is then employed to understand the implication of changes in wind speed on UHIs, which reveals that the observed changes in wind speed positively contribute to the interaction between UHIs and HWs in both daytime and nighttime. The vertical structure of the positive interaction between UHIs and HWs is thus likely an outcome resulting from a combination of changes in the surface energy balance and wind profile

3DPCT: 3D Point Cloud Transformer with Dual Self-attention

Transformers have resulted in remarkable achievements in the field of image processing. Inspired by this great success, the application of Transformers to 3D point cloud processing has drawn more and more attention. This paper presents a novel point cloud representational learning network, 3D Point Cloud Transformer with Dual Self-attention (3DPCT) and an encoder-decoder structure. Specifically, 3DPCT has a hierarchical encoder, which contains two local-global dual-attention modules for the classification task (three modules for the segmentation task), with each module consisting of a Local Feature Aggregation (LFA) block and a Global Feature Learning (GFL) block. The GFL block is dual self-attention, with both point-wise and channel-wise self-attention to improve feature extraction. Moreover, in LFA, to better leverage the local information extracted, a novel point-wise self-attention model, named as Point-Patch Self-Attention (PPSA), is designed. The performance is evaluated on both classification and segmentation datasets, containing both synthetic and real-world data. Extensive experiments demonstrate that the proposed method achieved state-of-the-art results on both classification and segmentation tasks.Comment: 10 pages, 5 figures, 4 table

An Empirical Model for Estimating Soil Thermal Conductivity from Soil Water Content and Porosity

Soil thermal conductivity l is a vital parameter for soil temperature and soil heat flux forecasting in hydrological models. In this study, an empirical model is developed to relate l only to soil volumetric water content u and soil porosity us. Measured l values for eight soils are used to establish the empirical model, and data from four other soils are used to evaluate the model. The new model is also evaluated by its performance in the Simple Biosphere Model 2 (SiB2). Results show that the root-mean-square errors (RMSEs; ranging from 0.097 to 0.266 W m21 K21 ) of the new model estimates of l are lower than those (ranging from 0.416 to 1.006 W m21 K21 ) for an empirical model of similar complexity reported in the literature earlier. Further, with simple inputs and equations, the new model almost has the accuracy of other more complex models (RMSE of l ranging from 0.040 to 0.354 W m21 K21 ) that require additional detailed soil information. The new model can be readily incorporated in large-scale models because of its simplicity as compared to the more complex models. The new model is tested for its effectiveness by incorporating it into SiB2. Compared to the original SiB2 l model, the new l model provides better estimates of surface effective radiative temperature and soil wetness. Owing to the newly presented empirical model’s requirement for simple, available inputs and its accuracy, its usage is recommended within large-scale models for applications where detailed information about soil composition is lacking

Study on Leading Vehicle Detection at Night Based on Multisensor and Image Enhancement Method

Low visibility is one of the reasons for rear accident at night. In this paper, we propose a method to detect the leading vehicle based on multisensor to decrease rear accidents at night. Then, we use image enhancement algorithm to improve the human vision. First, by millimeter wave radar to get the world coordinate of the preceding vehicles and establish the transformation of the relationship between the world coordinate and image pixels coordinate, we can convert the world coordinates of the radar target to image coordinate in order to form the region of interesting image. And then, by using the image processing method, we can reduce interference from the outside environment. Depending on D-S evidence theory, we can achieve a general value of reliability to test vehicles of interest. The experimental results show that the method can effectively eliminate the influence of illumination condition at night, accurately detect leading vehicles, and determine their location and accurate positioning. In order to improve nighttime driving, the driver shortage vision, reduce rear-end accident. Enhancing nighttime color image by three algorithms, a comparative study and evaluation by three algorithms are presented. The evaluation demonstrates that results after image enhancement satisfy the human visual habits

A transducer positioning method for transcranial focused ultrasound treatment of brain tumors

PurposeAs a non-invasive method for brain diseases, transcranial focused ultrasound (tFUS) offers higher spatial precision and regulation depth. Due to the altered path and intensity of sonication penetrating the skull, the focus and intensity in the skull are difficult to determine, making the use of ultrasound therapy for cancer treatment experimental and not widely available. The deficiency can be effectively addressed by numerical simulation methods, which enable the optimization of sonication modulation parameters and the determination of precise transducer positioning.MethodsA 3D skull model was established using binarized brain CT images. The selection of the transducer matrix was performed using the radius positioning (RP) method after identifying the intracranial target region. Simulations were performed, encompassing acoustic pressure (AP), acoustic field, and temperature field, in order to provide compelling evidence of the safety of tFUS in sonication-induced thermal effects.ResultsIt was found that the angle of sonication path to the coronal plane obtained at all precision and frequency models did not exceed 10° and 15° to the transverse plane. The results of thermal effects illustrated that the peak temperatures of tFUS were 43.73°C, which did not reach the point of tissue degeneration. Once positioned, tFUS effectively delivers a Full Width at Half Maximum (FWHM) stimulation that targets tumors with diameters of up to 3.72 mm in a one-off. The original precision model showed an attenuation of 24.47 ± 6.13 mm in length and 2.40 ± 1.42 mm in width for the FWHM of sonication after penetrating the skull.ConclusionThe vector angles of the sonication path in each direction were determined based on the transducer positioning results. It has been suggested that when time is limited for precise transducer positioning, fixing the transducer on the horizontal surface of the target region can also yield positive results for stimulation. This framework used a new transducer localization method to offer a reliable basis for further research and offered new methods for the use of tFUS in brain tumor-related research

An Analytical Solution to the One-Dimensional Heat Conduction–Convection Equation in Soil

Soil heat transfer occurs by conduction and convection. Soil temperatures below infiltrating water can provide a signal for water flux. In earlier work, analysis of field measurements with a sine wave model indicated that convection heat transfer made significant contributions to the subsurface temperature oscillations. In this work, we used a Fourier series to describe soil surface temperature variations with time. The conduction and convection heat transfer equation with a multi-sinusoidal wave boundary condition was solved analytically using a Fourier transformation. Soil temperature values calculated by the single sine wave model and by the Fourier series model were compared with field soil temperature values measured at depths of 0.1 and 0.3 m below an infiltrating ponded surface. The Fourier series model provided better estimates of observed field temperatures than the sine wave model. The new model provides a general way to describe soil temperature under an infiltrating water source