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.

Feature extraction and damage alarming using time series analysis

Aiming at the problem of on-line damage diagnosis in structural health monitoring （SHM）, an algorithm of feature extraction and damage alarming based on auto-regressive moving-average （ARMA） time series analysis is presented. The monitoring data were first modeled as ARMA models, while a principalcomponent matrix derived from the AR coefficients of these models was utilized to establish the Mahalanobisdistance criterion functions. Then, a new damage-sensitive feature index DDSF is proposed. A hypothesis test involving the t-test method is further applied to obtain a decision of damage alarming as the mean value of DDSF had significantly changed after damage. The numerical results of a three-span-girder model shows that the defined index is sensitive to subtle structural damage, and the proposed algorithm can be applied to the on-line damage alarming in SHM.

Theoretical research on structural damage alarming of long-span bridges using wavelet packet analysis

The state equation and observation equation of the structural dynamic systems under various analysis scales are derived based on wavelet packet analysis. The time-frequency properties of structural dynamic response under various scales are further formulated. The theoretical analysis results reveal that the wavelet packet energy spectrum （WPES） obtained from wavelet packet decomposition of structural dynamic response will detect the presence of structural damage. The sensitivity analysis of the WPES to structural damage and measurement noise is also performed. The transfer properties of the structural system matrix and the observation noise under various analysis scales are formulated, which verify the damage alarming reliability using the proposed WPES with preferable damage sensitivity and noise robusticity.

An ECG Monitoring and Alarming System Based On Android Smart Phone

ECG monitoring in daily life is an important means of treating heart disease. To make it easier for the medical to monitor the ECG of their patients outside the hospital, we designed and developed an ECG monitoring and alarming system based on Android smart phone. In our system, an ECG device collects the ECG signal and transmits it to an Android phone. The Android phone detects alarms which come from the ECG devices. When alarms occur, Android phone will capture the ECG images and the details about the alarms, and sends them to the cloud Alarm Server (AS). Once received, AS push the messages to doctors’ phone, so the doctors could see the ECG images and alarm details on their mobile phone. In our system, high resolution ECG pictures are transmitted to doctors’ phone in a user-friendly way, which can help doctors keep track of their patient’s condition easily.

Damage alarming for bridge expansion joints using novelty detection technique based on long-term monitoring data

Damage alarming and safety evaluation using long-term monitoring data is an area of significant research activity for long-span bridges. In order to extend the research in this field, the damage alarming technique for bridge expansion joints based on long-term monitoring data was developed. The effects of environmental factors on the expansion joint displacement were analyzed. Multiple linear regression models were obtained to describe the correlation between displacements and the dominant environmental factors. The damage alarming index was defined based on the multiple regression models. At last, the X-bar control chart was utilized to detect the abnormal change of the displacements. Analysis results reveal that temperature and traffic condition are the dominant environmental factors to influence the displacement. When the confidence level of X-bar control chart is set to be 0.003, the false-positive indications of damage can be avoided. The damage sensitivity analysis shows that the proper X-bar control chart can detect 0.1 cm damage-induced change of the expansion joint displacement. It is reasonably believed that the proposed technique is robust against false-positive indication of damage and suitable to alarm the possible future damage of the expansion joints.

Damage alarming of long-span suspension bridge based on GPS-RTK monitoring

Structure damage identification and alarming of long-span bridge were conducted with three-dimensional dynamic displacement data collected by GPS subsystem of health monitoring system on Runyang Suspension Bridge.First,the effects of temperature on the main girder spatial position coordinates were analyzed from the transverse,longitudinal and vertical directions of bridge,and the correlation regression models were built between temperature and the position coordinates of main girder in the longitudinal and vertical directions;then the alarming indices of coordinate residuals were conducted,and the mean-value control chart was applied to making statistical pattern identification for abnormal changes of girder dynamic coordinates;and finally,the structural damage alarming method of main girder was established.Analysis results show that temperature has remarkable correlation with position coordinates in the longitudinal and vertical directions of bridge,and has weak correlation with the transverse coordinates.The 3%abnormal change of the longitudinal coordinates and 5%abnormal change of the vertical ones caused by structural damage are respectively identified by the mean-value control chart method based on GPS dynamic monitoring data and hence the structural abnormalities state identification and damage alarming for main girder of long-span suspension bridge can be realized in multiple directions.

Solution to reduce call drop based on alarming information in WCDMA networks

Deep analysis of call detail log of the alarming information indicates that reasons except user equipment(UE)issue are usually ignored.To effectively reduce call drop rate in wideband code division multiple access(WCDMA)mobile communication system,a novel method is presented.First,the reliable trace information is extracted to observe the distribution of all the exceptions from radio network controller(RNC)daily report.Then,the antenna angle of generalized clustering network in 024 base station’s c sector(GCN024C)for environmental test(ET)which is obtained from the radio frequency(RF)engineer is manually changed from 4° to 6°.Experimental results show that the proposed method can guarantee the key performance indicators(KPI)of WCDMA and have obvious effect on reducing call drop rate.

An Automatic Monitoring and Alarming Method for the Power Supply of Weather Radars

As weather radar stations require headroom environment to operate,they were mostly built on highlands which are usually unattended.The mains supply is relatively poor,and the risk of radar stoppages due to power outage is therefore ever-present.As such,the radar construction program is used to build a complementary security video monitoring system.By collecting monitoring images of the regulated power supply in real-time from power supply auto transfer systems in distribution rooms and radar transceiver rooms,using Spearman’s rank correlation coefficient to analyse pixel variation trends,and supplementing statistical analysis of pixel characteristics difference to eliminate misjudgments resulting from low image contrast in special scenarios,a software can be developed through C#.It has the function of automatically monitoring mains supply and alerting staff on duty to handle the power outage in a timely manner via text message so that any potential risk is neutralised before it can cause damage.This monitoring and auto-alerting approach is generally applicable to unattended rooms with large amounts of electronical equipment.

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。