14 research outputs found

    A Zoning Earthquake Casualty Prediction Model Based on Machine Learning

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    The evaluation of mortality in earthquake-stricken areas is vital for the emergency response during rescue operations. Hence, an effective and universal approach for accurately predicting the number of casualties due to an earthquake is needed. To obtain a precise casualty prediction method that can be applied to regions with different geographical environments, a spatial division method based on regional differences and a zoning casualty prediction method based on support vector regression (SVR) are proposed in this study. This study comprises three parts: (1) evaluating the importance of influential features on seismic fatality based on random forest to select indicators for the prediction model; (2) dividing the study area into different grades of risk zones with a strata fault line dataset and WorldPop population dataset; and (3) developing a zoning support vector regression model (Z-SVR) with optimal parameters that is suitable for different risk areas. We selected 30 historical earthquakes that occurred in China’s mainland from 1950 to 2017 to examine the prediction performance of Z-SVR and compared its performance with those of other widely used machine learning methods. The results show that Z-SVR outperformed the other machine learning methods and can further enhance the accuracy of casualty prediction

    Mapping Population Distribution with High Spatiotemporal Resolution in Beijing Using Baidu Heat Map Data

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    Population distribution data with high spatiotemporal resolution are of significant value and fundamental to many application areas, such as public health, urban planning, environmental change, and disaster management. However, such data are still not widely available due to the limited knowledge of complex human activity patterns. The emergence of location-based service big data provides additional opportunities to solve this problem. In this study, we integrated ambient population data, nighttime light data, and building volume data; innovatively proposed a spatial downscaling framework for Baidu heat map data during work time and sleep time; and mapped the population distribution with high spatiotemporal resolution (i.e., hourly, 100 m) in Beijing. Finally, we validated the generated population distribution maps with high spatiotemporal resolution using the highest-quality validation data (i.e., mobile signaling data). The relevant results indicate that our proposed spatial downscaling framework for both work time and sleep time has high accuracy, that the distribution of the population in Beijing on a regular weekday shows “centripetal centralization at daytime, centrifugal dispersion at night” spatiotemporal variation characteristics, that the interaction between the purpose of residents’ activities and the spatial functional differences leads to the spatiotemporal evolution of the population distribution, and that China’s “surgical control and dynamic zero COVID-19” epidemic policy was strongly implemented. In addition, our proposed spatial downscaling framework can be transferred to other regions, which is of value for governmental emergency measures and for studies about human risks to environmental issues

    A Zoning Earthquake Casualty Prediction Model Based on Machine Learning

    No full text
    The evaluation of mortality in earthquake-stricken areas is vital for the emergency response during rescue operations. Hence, an effective and universal approach for accurately predicting the number of casualties due to an earthquake is needed. To obtain a precise casualty prediction method that can be applied to regions with different geographical environments, a spatial division method based on regional differences and a zoning casualty prediction method based on support vector regression (SVR) are proposed in this study. This study comprises three parts: (1) evaluating the importance of influential features on seismic fatality based on random forest to select indicators for the prediction model; (2) dividing the study area into different grades of risk zones with a strata fault line dataset and WorldPop population dataset; and (3) developing a zoning support vector regression model (Z-SVR) with optimal parameters that is suitable for different risk areas. We selected 30 historical earthquakes that occurred in China’s mainland from 1950 to 2017 to examine the prediction performance of Z-SVR and compared its performance with those of other widely used machine learning methods. The results show that Z-SVR outperformed the other machine learning methods and can further enhance the accuracy of casualty prediction

    Changes in health behaviors and conditions during COVID-19 pandemic strict campus lockdown among Chinese university students

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    Objective: To explore how a stringent campus lockdown affects the physical activity (PA), sleep and mental health of Chinese university students living in student dormitories during the COVID-19 pandemic. Methods: Data on PA, sleep and mental health were collected between 24 March and 4 April 2022 from 2084 university students (mean age = 22.4 years, 61.1% male students) via an online questionnaire distributed by the students’ advisers of each dormitory. The Chinese short version of the International Physical Activity Questionnaire (IPAQ-C), Athens Insomnia Scale (CAIS) and General Health Questionnaire 12-item (GHQ-12) were applied. The Mann–Whitney test and Kruskal-Wallis tests were used to evaluate the PA profile differences between genders, before and during the lockdown period and between students’ living environments. Chi-squared (χ2) or Fisher’s exact test was used to assess changes in health behaviors by gender and students’ living environment compared to before the lockdown. A mediation model was used to examine whether sleep disorder mediated the relationship between PA and mental health in different students’ living environments. Results: Participants reported a significant decrease in weekly total PA levels (63.9%). Mean daily sedentary time increased by 21.4% and daily lying time increased by 10.7% compared to before lockdown. Among the participants, 21.2% had experienced insomnia, and 39.0% reported having high mental distress. Female students reported 10% higher rates of sleep disorders than male students (p < 0.001), and also experienced a higher incidence of mental disorders (p < 0.001). Students living with three roommates had a larger decrease in frequencies and durations of participation in light PA than other students (p < 0.001). PA was negatively associated with sleep and mental health, and sleep disorder was a mediating factor between PA and mental health in the students living with two and three roommates. Conclusion: This study showed that strict lockdowns within university dormitories during the COVID-19 pandemic had a negative effect on the health of university students by changing their health behaviors, physical activity and sleep. Our findings indicate a need for strategies to promote an active lifestyle for students in space-limited dormitories in order to maintain health during a prolonged lockdown.peerReviewe

    An Object-Oriented Method for Extracting Single-Object Aquaculture Ponds from 10 m Resolution Sentinel-2 Images on Google Earth Engine

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    Aquaculture plays a key role in achieving Sustainable Development Goals (SDGs), while it is difficult to accurately extract single-object aquaculture ponds (SOAPs) from medium-resolution remote sensing images (Mr-RSIs). Due to the limited spatial resolutions of Mr-RSIs, most studies have aimed to obtain aquaculture areas rather than SOAPs. This study proposed an object-oriented method for extracting SOAPs. We developed an iterative algorithm combining grayscale morphology and edge detection to segment water bodies and proposed a segmentation degree detection approach to select and edit potential SOAPs. Then a classification decision tree combining aquaculture knowledge about morphological, spectral, and spatial characteristics of SOAPs was constructed for object filter. We selected a 707.26 km2 study region in Sri Lanka and realized our method on Google Earth Engine (GEE). A 25.11 km2 plot was chosen for verification, where 433 SOAPs were manually labeled from 0.5 m high-resolution RSIs. The results showed that our method could extract SOAPs with high accuracy. The relative error of total areas between extracted result and the labeled dataset was 1.13%. The MIoU of the proposed method was 0.6965, representing an improvement of between 0.1925 and 0.3268 over the comparative segmentation algorithms provided by GEE. The proposed method provides an available solution for extracting SOAPs over a large region and shows high spatiotemporal transferability and potential for identifying other objects

    Modeling power flow in the induction cavity with a two dimensional circuit simulation

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    We have proposed a two dimensional (2D) circuit model of induction cavity. The oil elbow and azimuthal transmission line are modeled with one dimensional transmission line elements, while 2D transmission line elements are employed to represent the regions inward the azimuthal transmission line. The voltage waveforms obtained by 2D circuit simulation and transient electromagnetic simulation are compared, which shows satisfactory agreement. The influence of impedance mismatch on the power flow condition in the induction cavity is investigated with this 2D circuit model. The simulation results indicate that the peak value of load voltage approaches the maximum if the azimuthal transmission line roughly matches the pulse forming section. The amplitude of output transmission line voltage is strongly influenced by its impedance, but the peak value of load voltage is insensitive to the actual output transmission line impedance. When the load impedance raises, the voltage across the dummy load increases, and the pulse duration at the oil elbow inlet and insulator stack regions also slightly increase

    An Earth Observation Framework in Service of the Sendai Framework for Disaster Risk Reduction 2015–2030

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    The Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR) proposed seven targets comprising 38 quantified indicators and various sub-indicators to monitor the progress of disaster risk and loss reduction efforts. However, challenges persist regarding the availability of disaster-related data and the required resources to address data gaps. A promising way to address this issue is the utilization of Earth observation (EO). In this study, we proposed an EO-based disaster evaluation framework in service of the SFDRR and applied it to the context of tropical cyclones (TCs). We first investigated the potential of EO in supporting the SFDRR indicators, and we then decoupled those EO-supported indicators into essential variables (EVs) based on regional disaster system theory (RDST) and the TC disaster chain. We established a mapping relationship between the measurement requirements of EVs and the capabilities of EO on Google Earth Engine (GEE). An end-to-end framework that utilizes EO to evaluate the SFDRR indicators was finally established. The results showed that the SFDRR contains 75 indicators, among which 18.7% and 20.0% of those indicators can be directly and indirectly supported by EO, respectively, indicating the significant role of EO for the SFDRR. We provided four EV classes with nine EVs derived from the EO-supported indicators in the proposed framework, along with available EO data and methods. Our proposed framework demonstrates that EO has an important contribution to supporting the implementation of the SFDRR, and that it provides effective evaluation solutions

    Astaxanthin Prevents Tuberculosis-Associated Inflammatory Injury by Inhibiting the Caspase 4/11-Gasdermin-Pyroptosis Pathway

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    Pyroptosis is a programmed cell death caused by inflammation. Multiple studies have suggested that Mycobacterium tuberculosis infection causes tissue pyroptosis. However, there are currently no protective drugs against the inflammatory damage caused by pyroptosis. In this study, anti-pyroptotic effects of the natural compound astaxanthin (ASTA) were explored in a simulated pulmonary tuberculosis-associated inflammatory environment. The results showed that ASTA maintained the stability of MLE-12 lung epithelial cell numbers in the inflammatory environment established by lipopolysaccharide. The reason is not to promote cell proliferation but to inhibit lipopolysaccharide-induced pyroptosis. The results showed that ASTA significantly inhibited the expression of key proteins in the caspase 4/11-gasdermin D pathway and the release of pyroptosis-related inflammatory mediators. Therefore, ASTA inhibits inflammation-induced pyroptosis by inhibiting the caspase 4/11-gasdermin D pathway and has the potential to protect lung tissue from tuberculosis-related inflammatory injury. ASTA, a functional food component, is a promising candidate for protection against tuberculosis-associated inflammatory lung injury

    Evaluating the analgesic effect and advantage of transcutaneous electrical acupoint stimulation combined with opioid drugs for moderate to severe cancer-related pain: a study protocol for a randomized controlled trial

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    Abstract Background Transcutaneous electrical acupoint stimulation (TEAS), which is also known as acupuncture-like transcutaneous electrical nerve stimulation (TENS), has been widely used in acute or chronic pain. However, previous research has not demonstrated that TEAS is effective for cancer-related pain. Opioid drugs are strongly recommended for treating cancer-related pain, but opioid-induced immunosuppression is still the most intractable drug-induced medical problem. Evaluating the efficacy and potential advantage of TEAS combined with opioid drugs in moderate and severe cancer-related pain in China is important because such studies are lacking. Methods/Design This trial is a multicenter, prospective randomized controlled clinical trial. In total, 160 patients who were enrolled from two hospitals in the Zhejiang Province (China) will be randomly allocated into two groups: a TEAS group and sham TEAS group without acupoint electrical stimulation. Both groups will receive a 21-day interval of chemotherapy and conventional cancer pain therapy. Fifteen treatment sessions will be performed over a three-week period. The primary outcomes will be measured by changes in the Numerical Rating Scale (NRS) scores and equivalent dosage of morphine at baseline, three weeks of treatment and one two-week follow-up. The secondary outcome measures include cellular immunity function, life quality assessment, opioids side effects assessment, and safety and compliance evaluation. Discussion This trial is expected to clarify whether TEAS is effective for cancer-related pain. These results demonstrate the advantage of TEAS combined with opioid drugs on improving immune function and decreasing opioid induced side effects. Trial registration Chinese Clinical Trial Registry, ChiCTR-13003803. Registered on 27 August 2013
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