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IntroductionChina is facing the challenge of “deep aging”, and promoting healthy aging has become a key research topic. Both medical care and physical exercise are important for health, but while medical services focus on treating illness, physical fitness activities focus on prevention, making them a more effective approach for promoting healthy aging.MethodsThis study uses data from the China Health and Retirement Longitudinal Study (CHARLS), focusing on individuals aged 60 to 80, to investigate the relationship between physical fitness activities and medical expenditures. A Tobit model was employed to analyze the data.Results(1) Active participation in physical fitness activities significantly improves the health of older adults, making physical activity an essential pathway to achieve healthy aging. (2) Participation in sports and fitness activities leads to a crowding-out effect on medical expenditures, significantly reducing healthcare costs for participants. (3) High-intensity physical activities are most suitable for individuals aged 60-65, moderate-intensity activities for those aged 66-70, and low-intensity activities for those aged 71–80.Discussion(1) Policies should focus on raising awareness of physical fitness benefits among older adults, encouraging regular physical activity to improve health and reduce medical costs. (2) A shift from treatment to prevention in health management is needed, promoting exercise as a cost-effective way to reduce healthcare spending. (3) Age-specific fitness guidelines should be developed to provide tailored exercise recommendations for different older aldult age groups.

The electromagnetic transient overvoltage in the substation is an important factor that affects the life of insulation equipment. For evaluate the economy and reliability of the insulation design of substation equipment, it is necessary to study and analyze the actual measured overvoltage waveforms in the substation. In this paper, firstly, the 3018 pieces of overvoltage data monitored in the 220 kV substation from 2018 to 2019 are preprocessed to obtain the required overvoltage waveform. Next, by analyzing and classifying the overvoltage data, four typical overvoltage waveforms suffered by the insulation equipment in the substation are extracted. Combined with the IEC60060 standard, the time parameters of the overvoltage waveform are selected and defined: wave-front time (Tf), half-value time (Thalf), main oscillation frequency (f), and attenuation constant (α). Further, statistical analysis is performed on the parameters of each waveform. The distribution law, distribution characteristics and distribution range of the parameters are obtained. Finally, the parameters of four typical overvoltage waveforms are summarized and compared, the cumulative distribution functions are obtained. This results can guide and optimize the insulation design of the insulation equipment in the substation, reduce design errors. It provide data support for the economy and reliability of insulation design.

SF6 is widely developed and applied in gas insulated electrical equipment due to its excellent arc extinguishing performance and insulation performance. However, SF6 is very destructive to the ozone layer and has a strong cumulative effect on the greenhouse effect of global warming. Therefore, the inevitable trend of the development of power grid equipment in the future is to reduce or even prohibit the use of SF6 gas in the power system, power industry and electrical equipment, or to find insulation media that can replace SF6. As a result, the research on environment-friendly insulating gas has become popular. In this paper, power frequency partial discharge platform and GC–MS​ detection platform are used to systematically study the partial discharge and decomposition characteristics of C5F10O under N2 and CO2 background gases. The partial discharge extinction voltage (PDIV) and partial discharge statistical characteristics of C5F10O under two different background gases, as well as the variety and concentration change of mixed gas decomposition products are theoretically analyzed and compared with SF6. It is found that, similar to SF6, the PDIV of C5F10O under two kinds of background gases increases linearly with the increase of air pressure. The PDIV under N2 background is smaller than that under CO2 background under the same conditions, and its insulation performance is slightly better than that under CO2 background. The partial discharge decomposition of C5F10O mixed gas mainly produces fluorocarbons such as CF4, C2F4, C2F6, C3F8, C3F6, and a small amount of CO, COF2, C3HF7, and CHF3. The increase of air pressure inhibits the partial discharge decomposition of mixed gas.

This paper based on density functional theory (DFT) investigate the interaction of C5-PFK molecule with Cu (110) and (100) surfaces, in order to analyze the chemical compatibility of Cu metal in the environment of C5F10O (C5-PFK) insulation gas. The frontier molecular orbital theory implies that the C = O group in the C5-PFK molecule can interact strongly with the Cu surfaces. The results show that both Cu (110) and (100) surfaces have strong interactions with the C5-PFK molecule, showing chemisorption of the molecule, with Ead of -1.17 and -1.03 eV, respectively. Meanwhile, the C5-PFK molecule performs strong electron-accepting behavior in the interactions, which captures 0.262 and 0.204 e from the Cu (110) and (100) surface, respectively. Even though, the geometric and electronic properties of Cu surface are not largely impacted, as supported by the basically unchanged morphologies and DOS curves of Cu surfaces before and after gas adsorption. These findings manifest the favorable stability and compatibility of Cu metal in the C5-PFK environment. Our work uncovers the safe operation of C5-PFK immersed insulation devices with Cu-based generatrix and shell in a long running, which is of great significance to guarantee the safe operation of the power system.

This paper investigates the effect of O2 on the partial discharge (PD) and decomposition characteristics of C5F10O/CO2/O2 gas mixture employed as an eco-friendly gas insulating medium. We found that the insulation performance of the C5F10O/CO2/O2 gas mixture is less degraded as the content of O2 is less than 4%. However, PDIV+ is significantly reduced, and the discharge repetition rate increases drastically when the O2 content exceeds 4%. The PD decomposition products of C5F10O/CO2/O2 mainly include CF4, C2F6, C3F8, C3F6, CF2O, C2F6O3, and C3F7H. The addition of 2% O2 has a strong inhibitory effect on the generation of CF4, C2F6, C3F6, C3F8, CF2O, and C2F6O3. This work provides guidance for the application of the C5F10O/CO2/O2 gas mixture.

The on-site measurement of transient voltages is of great significance in analyzing the fault cause of power systems and optimizing the insulation coordination of power equipment. Conventional voltage transformers normally have a narrow bandwidth and are unable to accurately measure various transient voltages in power systems. In this paper, a wideband parallel resistive–capacitive voltage divider is developed, which can be used for online monitoring of transient voltages in a 220 kV power grid. The structures of the high-voltage and low-voltage arms were designed. The internal electric field distribution of the high-voltage arm was analyzed. The influence factors and improvement techniques of the upper frequency limit were studied. The parameters of the elements of the divider were determined. The voltage withstand performances and scale factors under lightning impulses and AC and DC voltages, the temperature stabilities of scale factors and the step response and bandwidth of the developed voltage divider were tested. The results show that the deviations of the scale factors under various voltage waveforms and different temperatures ranging from −20 to 40 °C are within 3%. The withstand voltage meets the relevant requirements specified in IEC60071-1-2011. The step response 10~90% rise time is approximately 29 ns, and the 3 dB bandwidth covers the range of DC to 10 MHz.

Sulfur hexafluoride (SF6) is widely used in the power industry because of its excellent insulation and arc extinguishing performance. However, the high greenhouse effect of this material is being restricted by many countries around the world, thereby discouraging its usage. As a potential alternative to SF6, the compatibility of C5F10O with conductive copper materials used in electrical equipment is of great significance in ensuring the safe and stable operation of environmentally friendly gas-insulated equipment. In this paper, the interaction among C5F10O/N2, C5F10O/air gas mixture, and copper was studied via experiments and simulations. When the C5F10O/N2 (or air) gas mixture comes in contact with copper at the gas–solid interface, a small portion of C5F10O is decomposed to form C3F6 (or C3F6 and C3F6O) at high temperatures. Meanwhile, at low temperatures (120 °C), the C5F10O/air gas mixture becomes more compatible with copper than with the C5F10O/N2 gas mixture. When the experiment temperatures range between 170 °C and 220 °C, the compatibility of the C5F10O/air gas mixture with copper is significantly inferior to its compatibility with copper. Under high temperatures, the C5F10O/air gas mixture shows severe corrosion on the copper surface due to the presence of O2, forms a thick cubic grain, and emits irritating gases. The simulations show that the carbonyl group in C5F10O is chemically active and can be easily adsorbed on the copper surface. An anti-corrosion treatment must be performed on copper materials in manufacturing equipment. The findings provide an important reference for the application of C5F10O gas mixture.

This study using the first-principles theory investigates the geometric and electronic properties of Cu-decorated NiS2 (Cu-NiS2) monolayer, and the adsorption and sensing performance of Cu-NiS2 monolayer upon five C5F10O decomposed species, in order to explore its potential to evaluate the operation status of C5F10O-based insulation devices. It is found that the Cu atom prefers to be trapped on the H1 site of the NiS2 monolayer with the formation energy of -3.09 eV without the cluster issue. The chemisorption is determined in the Cu-NiS2/C2F6O3 with an adsorption energy of -1.05 eV, while physisorption is identified in the C3F6, CF2O, C2F6 and CF4 systems. The analyses of electronic property and recovery property reveal the strong potential of Cu-NiS2 monolayer to be a reusable C3F6 or CF2O sensor at room temperature, to be a high-sensitive and one-shot C2F6O3 sensor instead and is not suitable for C2F6 and CF4 sensing due to the low sensitivity. This work is meaningful in terms of proposing novel sensing material for application in the power system to ensure the good operation of C5F10O-based insulation devices. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most common cancers in the world, especially among women. Breast tumor segmentation is a key step in the identification and localization of the breast tumor region, which has important clinical significance. Inspired by the swin-transformer model with powerful global modeling ability, we propose a semantic segmentation framework named Swin-Net for breast ultrasound images, which combines Transformer and Convolutional Neural Networks (CNNs) to effectively improve the accuracy of breast ultrasound segmentation. Firstly, our model utilizes a swin-transformer encoder with stronger learning ability, which can extract features of images more precisely. In addition, two new modules are introduced in our method, including the feature refinement and enhancement module (RLM) and the hierarchical multi-scale feature fusion module (HFM), given that the influence of ultrasonic image acquisition methods and the characteristics of tumor lesions is difficult to capture. Among them, the RLM module is used to further refine and enhance the feature map learned by the transformer encoder. The HFM module is used to process multi-scale high-level semantic features and low-level details, so as to achieve effective cross-layer feature fusion, suppress noise, and improve model segmentation performance. Experimental results show that Swin-Net performs significantly better than the most advanced methods on the two public benchmark datasets. In particular, it achieves an absolute improvement of 1.4–1.8% on Dice. Additionally, we provide a new dataset of breast ultrasound images on which we test the effect of our model, further demonstrating the validity of our method. In summary, the proposed Swin-Net framework makes significant advancements in breast ultrasound image segmentation, providing valuable exploration for research and applications in this domain.

Retinal vessel extraction plays an important role in the diagnosis of several medical pathologies, such as diabetic retinopathy and glaucoma. In this article, we propose an efficient method based on a B-COSFIRE filter to tackle two challenging problems in fundus vessel segmentation: (i) difficulties in improving segmentation performance and time efficiency together and (ii) difficulties in distinguishing the thin vessel from the vessel-like noise. In the proposed method, first, we used contrast limited adaptive histogram equalization (CLAHE) for contrast enhancement, then excerpted region of interest (ROI) by thresholding the luminosity plane of the CIELab version of the original RGB image. We employed a set of B-COSFIRE filters to detect vessels and morphological filters to remove noise. Binary thresholding was used for vessel segmentation. Finally, a post-processing method based on connected domains was used to eliminate unconnected non-vessel pixels and to obtain the final vessel image. Based on the binary vessel map obtained, we attempt to evaluate the performance of the proposed algorithm on three publicly available databases (DRIVE, STARE, and CHASEDB1) of manually labeled images. The proposed method requires little processing time (around 12 s for each image) and results in the average accuracy, sensitivity, and specificity of 0.9604, 0.7339, and 0.9847 for the DRIVE database, and 0.9558, 0.8003, and 0.9705 for the STARE database, respectively. The results demonstrate that the proposed method has potential for use in computer-aided diagnosis.

Wireless sensing has emerged as an innovative technology that enables many smart applications such as indoor localization, activity recognition, and user tracking. However, achieving reliable and precise results in wireless sensing requires an accurate distinction between line-of-sight and non-line-of-sight transmissions. This paper introduces PEiD, a novel method that utilizes low-cost WiFi devices for transmission path identification, offering real-time measurements with high accuracy through the application of machine-learning-based classifiers. To overcome the deficiencies of commodity WiFi in bandwidth, PEiD explores the peak energy index distribution extracted from the channel impulse responses. Our approach effectively captures the inherent randomness of channel properties and significantly reduces the number of samples required for identification, thus surpassing previous methods. Additionally, to tackle the challenge of mobility, a sliding window technique is also adopted to achieve continuous monitoring of transmission path status. According to our extensive experiments, PEiD can attain a best path identification accuracy of 97.5% for line-of-sight scenarios and 94.3% for non-line-of-sight scenarios, with an average delay of under 300 ms (92% accuracy) even in dynamic environments.

The growing risk of new variants of the influenza A virus is the most significant to public health. The risk imposed from new variants may have been lethal, as witnessed in the year 2009. Even though the improvement in predicting antigenicity of influenza viruses has rapidly progressed, few studies employed deep learning methodologies. The most recent literature mostly relied on classification techniques, while a model that generates the HA protein of the antigenic variant is not developed. However, the antigenic pair of influenza virus A can be determined in a laboratory setup, the process needs a tremendous amount of time and labor. Antigenic shift and drift which are caused by changes in surface protein favored the influenza A virus in evading immunity. The high frequency of the minor changes in the surface protein poses a challenge to identifying the antigenic variant of an emerging virus. These changes slow down vaccine selection and the manufacturing process. In this vein, the proposed model could help save the time and efforts exerted to identify the antigenic pair of the influenza virus. The proposed model utilized an end-to-end learning methodology relying on deep sequence-to-sequence architecture to generate the antigenic variant of a given influenza A virus using surface protein. Employing the BLEU score to evaluate the generated HA protein of the antigenic variant of influenza virus A against the actual variant, the proposed model achieved a mean accuracy of 97.57%.