Sounding the alarm:Functionally referential signaling in Azure-winged Magpie

Functionally referential signals are a complex form of communication that conveys information about the external environment.Such signals have been found in a range of mammal and bird species and have helped us understand the complexities of animal communication.Corvids are well known for their extraordinary cognitive abilities,but relatively little attention has been paid to their vocal function.Here,we investigated the functionally referential signals of a cooperatively breeding corvid species,Azure-winged Magpie(Cyanopica cyanus).Through field observations,we suggest that Azure-winged Magpie uses referential alarm calls to distinguish two types of threats:’rasp’ calls for terrestrial threats and ’chatter’ calls for aerial threats.A playback experiment revealed that Azure-winged Magpies responded to the two call types with qualitatively different behaviors.They sought cover by flying into the bushes in response to the ’chatter’ calls,and flew to or stayed at higher positions in response to ’rasp’ calls,displaying a shorter response time to ’chatter’ calls.Significant differences in acoustic structure were found between the two types of calls.Given the extensive cognitive abilities of corvids and the fact that referential signals were once thought to be unique to primates,these findings are important for expanding our understanding of social communication and language evolution.

Design of AI-Enhanced and Hardware-Supported Multimodal E-Skin for Environmental Object Recognition and Wireless Toxic Gas Alarm

Post-earthquake rescue missions are full of challenges due to the unstable structure of ruins and successive aftershocks.Most of the current rescue robots lack the ability to interact with environments,leading to low rescue efficiency.The multimodal electronic skin(e-skin)proposed not only reproduces the pressure,temperature,and humidity sensing capabilities of natural skin but also develops sensing functions beyond it—perceiving object proximity and NO2 gas.Its multilayer stacked structure based on Ecoflex and organohydrogel endows the e-skin with mechanical properties similar to natural skin.Rescue robots integrated with multimodal e-skin and artificial intelligence(AI)algorithms show strong environmental perception capabilities and can accurately distinguish objects and identify human limbs through grasping,laying the foundation for automated post-earthquake rescue.Besides,the combination of e-skin and NO2 wireless alarm circuits allows robots to sense toxic gases in the environment in real time,thereby adopting appropriate measures to protect trapped people from the toxic environment.Multimodal e-skin powered by AI algorithms and hardware circuits exhibits powerful environmental perception and information processing capabilities,which,as an interface for interaction with the physical world,dramatically expands intelligent robots’application scenarios.

A lightweight false alarm suppression method in heterogeneous change detection

Overlooking the issue of false alarm suppression in heterogeneous change detection leads to inferior detection per-formance.This paper proposes a method to handle false alarms in heterogeneous change detection.A lightweight network of two channels is bulit based on the combination of convolutional neural network(CNN)and graph convolutional network(GCN).CNNs learn feature difference maps of multitemporal images,and attention modules adaptively fuse CNN-based and graph-based features for different scales.GCNs with a new kernel filter adaptively distinguish between nodes with the same and those with different labels,generating change maps.Experimental evaluation on two datasets validates the efficacy of the pro-posed method in addressing false alarms.

Planning of Urban Public Disaster-prevention Green Spaces: A Case Study of Living Water Park in Chengdu City

Based on disaster-prevention functions of urban public green spaces, necessity and significance of emergency shelters in a city were analyzed. Taking Living Water Park in Chengdu for example, planning of urban public disaster-prevention green spaces was analyzed to further explore planning of urban disaster-prevention green space system and refugee spaces, and provide instructions for the planning and construction of urban disaster-prevention green spaces in China.

Pattern Matching of Industrial Alarm Floods Using Word Embedding and Dynamic Time Warping

Dear Editor,This letter proposes a new pattern matching method based on word embedding and dynamic time warping(DTW)to identify groups of similar alarm floods.First,alarm messages are transformed into numeric values that represent alarms and also reflect the relationships between alarm occurrences.Then,similarities between numerically encoded alarm flood sequences are calculated by DTW and groups of similar floods are identified via clustering.The effectiveness of the proposed method is demonstrated by a case study with alarm&event data obtained from a public industrial simulation model.

Enhancing Feature Discretization in Alarm and Fire Detection Systems Using Probabilistic Inference Models

Sensors for fire alarms require a high level of predictive variables to ensure accurate detection, injury prevention, and loss prevention. Bayesian networks can aid in enhancing early fire detection capabilities and reducing the frequency of erroneous fire alerts, thereby enhancing the effectiveness of numerous safety monitoring systems. This research explores the development of optimized probabilistic graphic models for the discretization thresholds of alarm system predictor variables. The study presents a statistical model framework that increases the efficacy of fire detection by predicting the discretization thresholds of alarm system predictor variable fluctuations used to detect the onset of fire. The work applies the Bayesian networks and probabilistic visual models to reveal the specific characteristics required to cope with fire detection strategies and patterns. The adopted methodology utilizes a combination of prior knowledge and statistical data to draw conclusions from observations. Utilizing domain knowledge to compute conditional dependencies between network variables enabled predictions to be made through the application of specialized analytical and simulation techniques.

Machine Learning-Based Alarms Classification and Correlation in an SDH/WDM Optical Network to Improve Network Maintenance

The evolution of telecommunications has allowed the development of broadband services based mainly on fiber optic backbone networks. The operation and maintenance of these optical networks is made possible by using supervision platforms that generate alarms that can be archived in the form of log files. But analyzing the alarms in the log files is a laborious and difficult task for the engineers who need a degree of expertise. Identifying failures and their root cause can be time consuming and impact the quality of service, network availability and service level agreements signed between the operator and its customers. Therefore, it is more than important to study the different possibilities of alarms classification and to use machine learning algorithms for alarms correlation in order to quickly determine the root causes of problems faster. We conducted a research case study on one of the operators in Cameroon who held an optical backbone based on SDH and WDM technologies with data collected from 2016-03-28 to “2022-09-01” with 7201 rows and 18. In this paper, we will classify alarms according to different criteria and use 02 unsupervised learning algorithms namely the K-Means algorithm and the DBSCAN to establish correlations between alarms in order to identify root causes of problems and reduce the time to troubleshoot. To achieve this objective, log files were exploited in order to obtain the root causes of the alarms, and then K-Means algorithm and the DBSCAN were used firstly to evaluate their performance and their capability to identify the root cause of alarms in optical network.

SCADA Data-Based Support Vector Machine for False Alarm Identification for Wind Turbine Management

Maintenance operations have a critical influence on power gen-eration by wind turbines(WT).Advanced algorithms must analyze large volume of data from condition monitoring systems(CMS)to determine the actual working conditions and avoid false alarms.This paper proposes different support vector machine(SVM)algorithms for the prediction and detection of false alarms.K-Fold cross-validation(CV)is applied to evaluate the classification reliability of these algorithms.Supervisory Control and Data Acquisition(SCADA)data from an operating WT are applied to test the proposed approach.The results from the quadratic SVM showed an accuracy rate of 98.6%.Misclassifications from the confusion matrix,alarm log and maintenance records are analyzed to obtain quantitative information and determine if it is a false alarm.The classifier reduces the number of false alarms called misclassifications by 25%.These results demonstrate that the proposed approach presents high reliability and accuracy in false alarm identification.

Glass-compatible and self-powered temperature alarm system by temperature-responsive organic manganese halides via backward energy transfer process

A pioneering glass-compatible transparent temperature alarm system self-powered by luminescent solar concentrators(LSCs) is reported.Single green-emitted organic manganese halides(OMHs) of PEA_(2)MnBr_(2)I_(2),which has a unique temperature-dependent backward energy transfer process from selftrapped state to^(4)T_(1)energy level of Mn,is used for triggering the temperature alarm.The LSC with redemitted CsPbI_(3)perovskite-polymer composite films on the glass substrate is used for power supply.The spectrally separated nature between the green-emitted OMHs for temperature alarm and red-emitted CsPbI3in LSC for power supply allows for probing the signal light of temperature-responsive OMHs without the interference of LSCs,making it possible to calibrate the temperature visually just by a self-powered brightness detection circuit with LED indicators.Taking advantage of LSC without hot spot effects plaguing the solar cells,as-prepared temperature alarm system can operate well on both sunny and cloudy day.

Predictive value of alarm features in diagnosing upper gastrointestinal malignancies among dyspeptic patients:A cross-sectional study in Ethiopia

Objective:The study aimed to determine the overall predictive value of alarm features in diagnosing upper Gastrointestinal(GI)malignancies and significant endoscopic findings among patients undergoing elective Esophagogastroduodenoscopy(EGD)at Tikur Anbessa Special Hospital(TASH)and Adera Medical Centre(AMC).Methods:It was an institution-based cross-sectional study conducted on patients undergoing elective endoscopy for an upper GI complaint from July to September 2022.Data was collected from patient charts,and biopsies were taken for histologic confirmation.The study assessed the association of alarm symptoms and signs with significant upper gastrointestinal(UGI)endoscopic findings and malignancies.Results:142 patients were selected,with an average age of 48.35 and 52.1% being male.Epigastric pain was the most common reason for an endoscopy.62% of patients had at least one alarm feature,the most common being unexplained weight loss and UGI bleeding.The study found a strong association between the presence of alarm features,significant endoscopic findings,and UGI malignancies.The pooled sensitivity and specificity of any alarm feature for any significant finding were 79% and 64.9%,respectively,and for malignancy,100% and 39.7%,respectively.The presence of the alarm feature was associated with an increase of 6.801 in the odds of developing SEF and an increase of 4.199 in the odds of developing malignancy.Conclusions:UGI alarm symptoms and signs like an abdominal mass,persistent vomiting,dysphagia,and UGI bleeding are predictive of significant endoscopic findings and malignancies.Hence,EGD should be done and suspicious lesions should be biopsied early,regardless of gender,age,or duration of symptoms.

基于STM32单片机的家庭安防系统设计

为了更好地对居家环境进行安全监测与防范,以STM32F103单片机为处理核心设计了一套家庭安防系统。系统采用各种传感器进行环境监测,并将监测信息传至单片机,进行存储、显示与报警等处理,同时通过ESP8266无线传输模块将监测信息传至互联网,实现远程监测与报警。试验结果表明,传感器与报警系统工作正常,数据传输准确,验证了系统设计的可靠性。

4种血培养瓶4种临床常用抗菌药物吸附能力分析

目的分析4种血培养瓶对亚胺培南、哌拉西林-他唑巴坦、万古霉素和卡泊芬净4种临床常用抗菌药物的吸附能力,为提高血培养病原体检出率提供参考。方法选择亚胺培南、哌拉西林-他唑巴坦、万古霉素和卡泊芬净敏感标准菌株进行配对,将抗菌药物和配对菌株分别加入4种临床常用的血培养瓶(FA plus、FNplus、Aerobic、Anaerobic)进行培养,根据培养瓶报阳情况和阳性报警时长,评估不同血培养瓶对4种抗菌药物的吸附能力。结果对照瓶(无抗菌药物吸附措施)培养5 d均未报阳。FA plus、FN plus、Aerobic、Anaerobic阳性报警率分别为75.0%(15/20)、100.0%(10/10)、70.0%(14/20)、0.0%(0/10)。亚胺培南/大肠埃希菌组,FN plus均能报阳(5/5),阳性报警时长为(12.4±0.3)h,FA plus、Aerobic和Anaerobic5d均未报阳;哌拉西林-他唑巴坦/铜绿假单胞菌组,FAplus和Aerobic能报阳(5/5),阳性报警时长分别为(16.2±0.3)和(18.1±0.4)h,2种血培养瓶阳性报警时长差异有统计学意义(P<0.05);万古霉素/金黄色葡萄球菌组,FA plus、FN plus和Aerobic能报阳,阳性报警时长分别为(16.7±0.4)、(24.1±0.6)和(31.3±3.9)h,3种血培养瓶阳性报警时长差异有统计学意义(P<0.05),Anaerobic培养5 d未报阳。卡泊芬净/白念珠菌组,FA plus和Aerobic能报阳,阳性报警时长分别为(47.1±3.3)和(42.9±2.0)h,2种血培养瓶阳性报警时长差异有统计学意义(P<0.05)。结论FN plus具有吸附亚胺培南、万古霉素的能力,FA plus和Aerobic具有吸附万古霉素、哌拉西林-他唑巴坦和卡泊芬净的能力;对采集血液样本前使用了抗菌药物的患者,不建议使用无吸附抗菌药物措施的血培养瓶,应根据临床抗菌药物使用习惯选择合适的培养瓶。

智能报警气管导管防滑脱装置的设计及应用

目的:设计智能报警气管导管防滑脱装置,并评价其临床应用效果。方法:选取2022年4月-5月镇江市某三级甲等医院恢复室收治的172例全身麻醉手术病人作为研究对象,将2022年4月的86例全身麻醉气管插管病人作为对照组,将2022年5月的86例全身麻醉气管插管病人作为观察组。对照组按照常规的气管插管方法协助置管及护理,观察组应用智能报警气管导管防滑脱装置协助气管导管固定。对两组病人气管导管移位或脱出情况、病人面部皮肤情况、病人咽喉痛发生情况及护士满意度进行比较。结果:观察组病人气管导管移位或脱出风险小于对照组,面部皮肤并发症发生率、咽喉痛程度低于对照组,护士满意度高于对照组,差异均有统计学意义(均P<0.05)。结论:使用智能报警气管导管防滑脱装置可减少病人气管导管移位或脱出,降低病人皮肤并发症发生率、咽喉痛程度,提高护士满意度。

基于模式物理量参数的云南雷暴大风概率预报技术研究

利用2019—2021年云南125个国家站大风数据和云南省地闪资料,统计云南雷暴大风个例,并挑选对雷暴大风有重要意义的物理量参数,结合NCEP再分析资料确定云南雷暴大风个例的物理量阈值,再基于ECMWF数值模式预报产品及确定的阈值,采用二分法进行云南雷暴大风概率预报。结果表明:在云南三次雷暴大风过程预报检验中,8月4日云南中部及东北部雷暴大风均命中,但在云南西部及西南部出现大范围的虚警,导致此次过程虚警率较高和临界成功指数较低;8月5日云南中部、东北及西北部雷暴大风预报正确,且云南西部及南部虚警范围小,虚警率较8月4日明显降低;7月7日云南自东北向西南出现大范围的雷暴大风天气过程,雷暴大风预报落区与实况基本吻合,呈现命中率高、虚警率较低的特征;三次过程命中率、临界成功指数、虚警率平均为0.873、0.203、0.789。

基于机器学习对呼吸机报警分析

目的 探讨应用机器学习方法对呼吸机在临床使用中的通气类报警进行研究,获得影响报警的重要参数及报警预测模型,识别无效报警并给予临床提示,使临床得以高效应对呼吸机报警,避免造成报警疲劳等消极影响。方法 建立符合标准数据流程的呼吸机数据管理平台,根据单中心的呼吸机报警信息分析特征值,得出重要参数排序;利用超参数调优建模预测报警的真假;用混淆矩阵、受试者工作特征(Receiver Operating Characteristic,ROC)对机器学习模型进行多项指标验证。结果 对测试集5936次通气类报警进行评估,得出无效报警率为88%(召回率为0.88),模型准确度为0.94,精准度为0.78,ROC曲线下面积为0.92,F1得分为0.82。结论 采用机器学习便于临床单中心数据建模,能够及时分析获得呼吸机真实警报的重要参数及报警预测;通过呼吸机数据管理平台可有效提示临床无效报警,从而减少医护人员的压力,提高医疗质量。

基于杂波拖尾分布的雷达无人机检测性能分析

固定翼无人机(unmanned aerial vehicle,UAV)给雷达低空监视提出了严峻挑战。分析雷达对固定翼UAV的检测性能,可为雷达UAV检测能力评估和技术升级提供重要参考。本文结合雷达探测低空固定翼UAV外场实测数据,首先分析了低空固定翼UAV雷达接收信号幅度统计分布,采用多项式对杂波拖尾导致的虚警概率进行拟合建模;然后,根据虚警概率分布得到雷达检测门限;进而根据UAV回波+杂波幅度分布理论推导得到雷达检测概率;最后,将理论分析性能与传统性能分析结果、雷达实际检测性能进行对比。结果表明,采用多项式对杂波拖尾导致的虚警概率进行单独建模,由此获得的雷达检测门限精度更高,从而使雷达UAV检测性能分析结果较传统性能分析结果更准确。

一起带有程序误判的液压系统故障分析

某热轧板生产线卸卷车的升降由液压缸驱动,升降高度的检测采用了间接测量方法,通过流量计先测量进出油缸无杆腔的油量,再由PLC进行计算得到位置数据。相对于常规采用的位移传感器直接测量方法,该方式很好地避免了现场高温和水淋对检测元件的损害,提高了元件的可靠度。经过对一起故障的排查、分析、处理,发现了该系统存在的不完善地方,提出了解决办法。

工程训练模式下的火灾自动报警控制系统设计实践与探索

以工程训练中心的基础实践课程为主线,探索OBE模式下的火灾报警控制系统设计,即在基础实践技能篇的基础上,提出一种能够体现学生对电子元件、工具使用具有导向价值的实践项目,同时保持一定的可塑性和拓展性,设计一种基于AT89C51单片机的新型火灾自动报警装置,仿真实现了声光电一体化报警、烟雾浓度测量和温度测量显示等检测功能,进而培养学生的创新实践能力。

压路机驾驶室告警信息优化设计研究

目的为了提高驾驶员在驾驶过程中对告警信息的认知效率,以便更好地做出反馈,进而降低事故的发生,提高安全性。方法通过分析压路机告警信息的呈现方式,并解析其设计要素,再用层次分析法确定设计要素的权重,从中筛选出影响告警信息认知效率的关键设计要素,通过眼动生理实验对关键设计要素因子进行模拟实验,确定认知效率高的压路机驾驶室告警信息设计因子。结论在设计因子的基础上,完成压路机驾驶室告警信息设计方案,并通过眼动仪生理模拟实验确定最优设计方案,完成对告警信息认知效率的提升。

俞募穴速刺治疗儿童单纯性肥胖症的临床研究

目的:探究俞募穴速刺治疗儿童单纯性肥胖症的临床效果。方法:随机选取江阴市中医院儿科2022年1月—2023年3月收治的单纯性肥胖症患儿80例纳入本次研究。采用随机数字表法将其分为对照组和治疗组,每组40例。对照组予以临床常规干预治疗,治疗组在常规干预基础上增加俞募穴速刺治疗。比较两组患儿临床治疗效果、体质量指数(BMI)、腰臀比(WHR)、中医证候积分及治疗安全性评价。结果:治疗组疗效优于对照组(P<0.05)。治疗后,两组中医证候积分均低于治疗前,治疗组低于对照组(P<0.05)。治疗后,两组BMI、WHR水平均低于治疗前,治疗组均低于对照组(P<0.05)。两组安全性比较差异无统计学意义(P>0.05)。结论:对单纯性肥胖症患儿在常规干预治疗基础上增加俞募穴速刺治疗可明显提升患者临床疗效,降低BMI及WHR水平,降低中医证候积分,且临床治疗具有较高安全性,不会明显增加临床不良反应。