Real-Time Warning System of Regional Landslides Supported by WEGISB and Its Application in Typhoon Rananim,Zhejiang Province,China

As one of the provinces of highest economic growth in coastal China,Zhejiang Province is experiencing serious geological disasters during the past development of economy.The main kinds of geo-hazards include landslides,rock falls and debris-flows in Zhejiang Province,which are mainly induced by intensive rainfall during typhoon season or by long-term rainfall from May to June every year.Thus,

Probabilistic earthquake early warning times in Fujian Province

Earthquake early warning （EEW） systems are a new and effective way to mitigate the damage associated with earthquakes. A prototype EEW system is currently being constructed in the Fujian Province, a region along the Southeast coast of China. It is anticipated that the system will be completed in time to be tested at the end of this year （2013）. In order to evaluate how much advanced warning the EEW system will be able to provide different cities in Fujian, we established an EEW information release scheme based on the seismic monitoring stations distributed in the region. Based on this scheme, we selected 71 historical earthquakes. We then obtained the delineation of the region＇s potential seismic source data in order to estimate the highest potential seismic intensities for each city as well as the EEW system warning times. For most of the Fujian Province, EEW alarms would sound several seconds prior to the arrival of the destructive wave. This window of time gives city inhabitants the opportunity to take protective measures before the full intensity of the earthquake strikes.

A Study on the Early Warning Time of Strong Earthquakes in Taiwan

The paper collects the records by the Fujian Digital Seismic Network of 40 shallow earthquakes in Taiwan with M_S≥5.0 from 1999 to 2013,analyzes the seismic phase(Pn,Sn phase)characteristics and travel-time rules,determines travel-time models and develops a seismic phase travel-time equation based on the two-step fitting algorithm.With the deduction of processing time and network delay time,this method can provide an accurate estimation of early warning time of Taiwan earthquakes for the Fujian region,and has been officially employed in the earthquake early warning system of Fujian Province.

A Method for Detecting Adhesive Related-Factors of Streptococcus suis Serotype 2 by Real-time PCR

[Objective] This study aimed to establish a method for quantitative detection of mRNA transcriptional level of SS2 adhesive related-factors of Streptococcus suis serotype 2 （SS2） by fluorescent quantitative PCR. []Vlethod] The gene fragments en- coding SS2 adhesive related-factors MRP, FBPS and CPS2J and a housekeeping gene aroA were amplified by reverse transcription PCR from the total RNA of SS2, cloned, and sequenced. The recombinant plasmids containing the target genes were constructed, and used as templates in Real-time PCR. [Result] Dynamic curves, stan- dard curves and melting curves of the adhesive related-factors and aroA were ob- tained by the optimized Real-time PCR system. The standard curves showed a good linear relationship between template copy number and circulation number, and the correlation coefficients （FF） of the standard curves were over 0.995. Also, these as- says were highly specific a^d there was single specific melting peak for every gene. Moreover, the assays were highly sensitive and had a detection limit of 1.0×102 copies in 1 μl of initial templates. Finally, it was highly repeatable and had a coeffi- cient of variation less than 2% for intra-assay. [Conclusion] This study will provide a way to reveal the adhesion mechanism of SS2 to different host cells at molecular level.

Feasibility Study of Community Earthquake Warning System Proposed for Mbeya City and Surrounding Regions

The Mbeya city and its surrounding regions (Rukwa, Katavi and Njombe) in south western Tanzania, fall among the fast growing economical regions in Tanzania. It is also unfortunately under potential for seismic risk due to its proximity to the western and eastern junction of the East African Rift (EAR) Valley. Construction of Community Earthquake Early Warning (CEEW) system model based on community owned MEMS accelerometer sensors in being proposed for the region. To optimize the warning time function and as the process of planning and designing the community hosted seismic network, the paper presents the simulation of warning times that can be realized in this region based on the distribution of sensor stations in relation to the historical strong earthquakes and target sites. The distribution of sensor stations determines the detection and reporting time of the event, while location of earthquakes and position of target site determine the available warning time for the target to be protected. Testing the various sensor station configura- tions (regional, On-site and Hybrid) models of CEEWS by simulation of scenario earthquakes, the hybrid configuration that distributes sensor stations closer to the source and on target sites, was able to provide at least 5 seconds of warning times to various targets. This time has been demon- strated to be enough for shutting down hazardous industrial processes and for people to take cover at safer locations to reduce injuries.

Recognizing Early Warning Signs (EWS) in Patients Is Critically Important

Introduction: Monitoring vital signs is a basic indicator of a patient’s health status and allows prompt detection of delayed recovery or adverse effects and early intervention. Patients with adverse events during hospitalization often display clinical decline for several hours before the event is observed. Non-critical care Nurses’ inconsistent recognition and response to patient deterioration lead to an increase in the length of hospital stay, unexpected admissions to the ICU, and increased morbidity and mortality. Aim: The study aimed to assess the factors that facilitate or impede the detection of early warning signs among adult patients hospitalized in tertiary care settings. Training should be provided to improve nurses’ knowledge, practice and attitude toward early warning signs of deteriorating patients leading to enhanced clinical judgment, skills and decision-making in addressing alerts. Methodology: A literature search was carried out in various databases;these were Cumulative Index to Nursing and Allied Health Literature (CINHAL), Google Scholar, PubMed, Science Direct, and Sage. The search area was narrowed from 2017 to 2022. The keywords used were “prevalence” AND “unplanned ICU admission”, “the importance of early warning signs” “outcome failure in rescue” “patient deterioration, communication” “improvement in early detection” AND “patient outcome admission” AND “early warning signs” AND “Pakistan”. After the analysis process, around 33 articles that met the inclusion criteria and were most relevant to the scope and context of the current study were considered. Conclusion: Most of the studies had reviewed literature in a qualitative retrospective observational study, content analysis, mixed method, and quasi-experimental study. The literature review identified that long hours of shift, nurse staffing levels, missed vital signs, lack of nursing training and education, and communication impact nurses’ ability to recognize and respond to early warning signs.

Real Time Data Analysis of West Rapti River Basin of Nepal

Floods are the most widespread climate-related hazards in the world, and they impact more people globally than any other type of natural disasters. It causes over one third of the total economic loss from natural catastrophes and is responsible for two thirds of people affected by natural disasters. On the other hand, studies and analysis have shown that damage reductions due to forecasts improvements can range from a few percentage points to as much as 35% of annual flood damages. About 300 people lose their lives each year due to floods and landslides in Nepal with property damage exceeding 626 million NPR on average. The West Rapti River basin is one of the most flood prone river basins in Nepal. The real-time flood early warning system together with the development of water management and flood protection schemes plays a crucial role in reducing the loss of lives and properties and in overall development of the basin. The non-structural mitigating measure places people away from flood. This method is designed to reduce the impact of flooding to society and economy. This paper presents an overview of flood problems in the West Rapti River basin, causes and consequences of recent floods and the applicability and effectiveness of the real time data to flood early warning in Nepal.

近地小行星材料特性对激光烧蚀驱动效率的影响试验分析

基于激光驱动效果和小行星材料特性密切相关的问题,针对脉冲激光对近地小行星物质的烧蚀驱动效率开展了试验研究,选用不同材质、孔隙率的类小行星试验样品,开展了冲量耦合规律测量试验。试验结果表明:在脉冲激光烧蚀的作用下,C型小行星比S型小行星具有更高的冲量耦合效率;随着小行星孔隙率的升高,冲量耦合效率降低,激光烧蚀驱动的效率下降。此外,还对激光烧蚀类小行星物质质量变化的规律进行试验,试验结果表明存在使得小行星质量烧蚀效率最佳的激光功率密度。对于需长期预警时间的威胁小行星,在烧蚀驱动偏转其轨道的同时,以最快的速率减少其质量,可进一步提升防御效率,实现防御的目的。

基于LSTM网络的单台仪器地震烈度预测模型

烈度是地震预警系统的关键产出.如何实现快速预测目标场址的地震烈度是地震预警方法技术研究中的核心问题.本文提出了一种基于长短时记忆神经网络(Long Short-Term Memory,LSTM)的单台仪器地震烈度的预测模型(LSTM-Ⅰ).该模型以一个台站观测到地震动参数的时间序列特征为输入,实现动态预测该台站可能遭受的最大烈度.选取了日本K-NET台网记录的102次地震的5103条强震加速度记录训练了神经网络,利用89次地震的3781条数据检验了模型的泛化能力.利用准确率、漏报率以及误报率三个评价指标评价了LSTM-Ⅰ模型的性能.结果表明,当采用P波触发后3 s的序列进行预测时,模型出现漏报的概率为46.78%,出现误报的概率为1.25%;当采用P波触发后10 s的序列进行预测时,模型出现漏报的概率大幅降低到17.6%,出现误报的概率降低到1.14%.结果表明LSTM-Ⅰ模型很好把握住了时间序列中蕴含的特征.进一步基于LSTM-Ⅰ模型评估了Ⅵ度下台站所能提供的预警时间.本文模型能够提供的预警时间与P-S波到时差接近,说明LSTM-Ⅰ模型具有较高的时效性.

基于分形维数的高速铁路地震预警系统地震P波震相识别方法

为更好保障高速铁路的运营安全,高速铁路地震预警系统的实施十分必要。5级以下的小震事件占据地震事件的95%以上,提高高速铁路预警系统在小震震相识别的速度与精度能有效保障高铁安全运营。提出一种基于分形维数的地震震相识别算法,优化传统分形维数的计算方法,提高分形维数的计算速度。引入分形斜率曲线并划分为4个阶段,分析临界点的分形斜率特征,通过识别分形斜率极值时刻判断地震波精准到达时刻。提出的算法平均误差达到0.006 3 s,标准差达到0.043 8 s,满足高速铁路地震预警系统的时效性和准确度要求。

基于LSTM神经网络的现地烈度实时估算模型——以JMA烈度为例

如何快速并且准确估计目标场点烈度是地震预警中的关键问题。常用基于衰减关系的场点烈度估计和基于P波信息的现地烈度估计往往存在大震烈度低估的问题。本文提出了一种基于长短时记忆神经网络(logn short-term memery,LSTM)的现地JMA烈度持续估计模型。该模型以现地观测地震动的能量、能量增长率、地震动卓越周期和震源距作为输入,以该点的最大仪器地震烈度为预测目标。选取了日本K-NET台网记录101次地震数据作为训练集,94次地震数据作为测试集,训练了现地烈度估算LSTM神经网络模型。结果表明:在采用3 s时窗长度的序列进行预测时,高估的比例为1.51%,低估的比例为4.00%;并且,随着时窗长度的增加,高估和低估的比例也在不断降低。模型对高烈度(大于等于4.5度)样本的预测时效性随震源距的增加而增加,对大震远场高烈度区域能提供20 s以上的预警时间。

考虑椭圆模型M_W≥5.5地震震源参数时域计算方法

为提高时效性,地震预警系统对震源的处理一般采用“点源”假定,不考虑震源尺度、破裂方向和震源区应力降,对震源参数简化处理使得预测地震动参数分布过于粗略,特别是对破坏性大震.本文应用日本地震(M_W≥5.5)强震动数据,考虑椭圆震源模型,提出了一种适用于中、大地震矩震级、拐角频率和应力降等震源参数时域计算方法,并分析了场地效应、地震动传播衰减、震源效应和滤波频带等因素对时域方法的影响.结果表明,该方法能利用P波信息快速获取震源能量释放过程,量化随破裂传播震源谱拐角频率、应力降和破裂面积的演化过程,为地震动预测提供更多重要的震源参数.

我国经济风险的实时监测:结构性拐点判别与预警信息辨识

实时监测经济风险、判别经济风险演化方向,对于促进高质量发展和高水平安全良性互动具有重要意义。本文运用马尔科夫区制转移偏正态分布模型甄别经济风险的结构变迁路径,并基于驱动结构分解、概率分布矩、概率密度函数以及尾部风险等多维视阈,有效辨析经济和金融状况对于经济风险的预警功能。研究发现,首先,随着经济增长概率分布的风险区制由低到高,经济增长增速预期降低、波动区间扩张以及下行风险升高。其次,经济风险在2012年存在显著的结构性拐点,在突变点后,偏度系数由正转负,经济步入长期的下行风险通道。此外,经济风险在2019年以及2021年前后存在阶段性拐点,短期内经济上行概率较高。然后,经济风险的动态演化路径表现出显著的“事件驱动”特征,在极端事件冲击下,经济下行风险有所增加。最后,经济和金融状况对于经济风险的预警功能存在明显的异质性,其中经济状况无法提供经济风险的预警信息,而金融状况能够灵敏地识别与捕捉经济增长的风险信息,且在经济高风险时期,金融状况的预警功能更为显著。

基于物联网的生猪健康状态监测及预警平台设计

生猪规模化养殖的疾病预防和控制是提升养殖效率和质量的重要保障。为了实现生猪健康状态的即时监测和预警,有效提升生猪养殖管理的数字化、智能化水平,本研究通过总结和借鉴现有研究成果和经验,结合实际需求和条件,设计了基于物联网的生猪健康状态监测及预警平台,实现分类别的用户管理、圈舍管理、状态监测、预警等功能,能够为相关人员提供针对性的防控建议。该平台的推广和应用,为生猪规模化养殖管理提供了一种低成本、高质量、智能化的解决方案。

稠油开采注汽锅炉在线综合预警关键技术

为了对工作在高温高压下注汽锅炉的各项运行参数进行在线准确监测和异常预警,本文在对稠油开采注汽锅炉工况参数进行采集、处理、分析的基础上,提出对注汽锅炉显性故障和隐性故障进行检测的方案。采用长期短期记忆神经网络,利用锅炉的时序数据对系统进行分析和建模,完成锅炉显性故障检测和预警,并通过预测数据的方式缓解锅炉大时滞的特性;利用深度异常检测技术,将无故障判别标准的数据进行隐性故障分析和预警。本文提出的综合预警方案对克拉玛依油田注汽锅炉进行了实验验证,预测误差仅有0.08%,同时异常检测范围也在设定值范围内。

预警护理对急性支气管炎患儿病情改善情况的影响研究

目的 分析预警护理对急性支气管炎患儿病情改善情况的影响。方法 选取2021年1月至2022年12月期间漯河医学高等专科学校第二附属医院收治的86例急性支气管炎患儿,在区间随机法辅助下开展组别划分,对照组(43例),接受常规护理;实验组(43例),在常规护理基础上实施预警护理。比较两组患儿症状消失时间、护理前后肺功能指标[用力肺活量(FVC)、呼气流量峰值(PEF)、一秒用力吸气容积(FEV1)]变化情况、急性支气管炎并发症发生情况及家属对护理服务的满意度。结果 实验组发热消失时间、咳嗽消失时间、喘息消失时间、肺部湿啰音消失时间均短于对照组,差异有统计学意义(P <0.05)。护理前,两组间肺功能指标比较,差异无统计学意义(P> 0.05);护理后,两组FVC、PEF、FEV1均明显上升,且实验组高于对照组,差异有统计学意义(P <0.05)。实验组急性支气管炎并发症发生率(2.33%)低于对照组(16.28%),差异有统计学意义(P <0.05)。实验组满意度评分显著高于对照组,差异有统计学意义(P <0.05)。结论 对急性支气管炎患儿实施预警护理有利于患儿康复与病情改善,在缩短症状消失时间、预防并发症、改善肺功能等方面均有突出体现,值得应用。

考虑暂态过电压特性的架空输电线路雷击故障时空预警方法

针对运行环境复杂多变,故障率持续居高不下,雷击故障占比较大,并具有随机性与无法预知性的问题,提出考虑暂态过电压特性的架空输电线路雷击故障时空预警方法。深入解析雷击机理与雷电流,确定雷击(感应雷与直击雷)输电线路暂态过电压计算方式,通过小波多分辨率分解算法处理输电线路暂态信号,评估暂态过电压在信号频段中的能量比,识别累计故障,判断雷击点与故障点是否一致,利用单端检测确定雷击点的位置,完成架空输电线路雷击故障的时空预警。实验数据显示,在不同实验组别背景下,当阈值pΔ取值为9.3时,雷击故障识别精度达到最大值92%,雷击点定位精度高达80%。可以获得精准的雷击故障识别结果与雷击点定位结果,充分证实了提出方法具备较好的雷击故障时空预警效果。

暴发性心肌炎患者急诊标准化急救流程的临床实践

目的探讨暴发性心肌炎患者急诊标准化急救流程的实施效果。方法将急诊科就诊的疑似暴发性心肌炎患者80例,按照患者就诊时间顺序分为对照组40例和观察组40例。对照组按常规急救流程实施救治及护理,观察组按照科室构建的暴发性心肌炎标准化急救流程实施救治及护理。结果对照组31例、观察组40例完成研究。观察组急诊预检分诊准确率显著高于对照组,确诊患者急诊候诊时间及实施前后急诊科停留时间显著短于对照组(均P<0.05);实施标准化急救流程后急诊医护人员安全态度的安全氛围、管理感知维度得分显著高于实施前(均P<0.05)。结论暴发性心肌炎患者急诊标准化急救流程的建立与实施,有利于早期筛查识别暴发性心肌炎患者,缩短急诊候诊时间及停留时间,可提高患者救治效率。

基于道路监控的高速公路作业区碰撞风险预警

为了更及时地掌握高速公路作业区车辆碰撞风险态势,提出基于闭路电视监控的作业区碰撞风险预警方法.采用计算机视觉技术进行车辆检测、坐标转换和车辆3D形态估计,获取作业区交通流和车辆信息.以改进事故时间指数(ITA)为上游过渡段的碰撞风险量化指标,依据警告区起点的交通流特征,实现上游过渡段的碰撞风险预测.通过集成一维卷积神经网络(1D CNN)、长短期记忆网络(LSTM)和注意力机制,构建基于1D CNN+LSTM+Attention (CLA)框架的碰撞风险预测模型.结果表明:所提数据采集方法满足碰撞风险预测的需求.相较其他冲突指标,ITA在风险量化中具有更适宜的敏感度.相较LSTM和1D CNN+LSTM,基于CLA的预警模型准确度更高,其拟合优度确定系数和均方根误差分别为0.805和0.359.所提方法能够提前90 s为作业区提供碰撞风险预警.

基于声音信号的离心泵轴承故障预警研究

为了对离心泵运行状态进行监测,通过采集离心泵运转声音信号,对离心泵声音信号进行时域分析、频域分析,提取时频域轴承故障预警特征。利用变分模态分解方法,筛选合适的本征模态函数进行包络调解,通过包络谱图分析离心泵轴承故障特征信息,提取离心泵轴承故障预警特征。针对离心泵轴承故障预警,从中提取正常运行和轴承故障运行特征参数,建立预警特征,进而实现对离心泵维护和保养的预警,提高离心泵使用寿命,避免离心泵轴承故障。研究结果对离心泵轴承故障预警具有指导意义。