Critical care nurses and their clinical reasoning for customizing monitor alarms:a mixed-method study

Objective:To explore the clinical rationale of critical care nurses for personalizing monitor alarms.One of the most crucial jobs assigned to critical care nurses is monitoring patients'physiological indicators and carrying out the necessary associated interventions.Successful use of equipment in the nursing practice environment will be improved by a thorough understanding of the nurse's approach to alarm configuration.Methods:A mixed-method design integrating quantitative and qualitative components was used.The sample of this study recruited a convenience sample of 60 nurses who have worked in critical care areas.This study took place at Lebanese American University Medical Center Rizk Hospital,utilizing a semi-structured interview with participants.Results:The study demonstrated the high incidence of nuisance alarms and the desensitization of critical care nurses to vital ones.According to the nurses,frequent false alarms and a shortage of staff are the 2 main causes of alarm desensitization.Age was significantly associated with the perception of Smart alarms,according to the data(P=0.03).Four interconnected themes and subcategories that reflect the clinical reasoning process for alarm customization were developed as a result of the study's qualitative component:(1)unit alarm environment;(2)nursing style;(3)motivation to customize;and(4)clinical and technological customization.Conclusions:According to this study,nurses believe that alarms are valuable.However,a qualitative analysis of the experiences revealed that customization has been severely limited since the healthcare team depends on nurses to complete these tasks independently.Additionally,a staffing shortage and lack of technical training at the start of placement have also hindered customization.

Systematic rationalization approach for multivariate correlated alarms based on interpretive structural modeling and Likert scale

Alarm flood is one of the main problems in the alarm systems of industrial process. Alarm root-cause analysis and alarm prioritization are good for alarm flood reduction. This paper proposes a systematic rationalization method for multivariate correlated alarms to realize the root cause analysis and alarm prioritization. An information fusion based interpretive structural model is constructed according to the data-driven partial correlation coefficient calculation and process knowledge modification. This hierarchical multi-layer model is helpful in abnormality propagation path identification and root-cause analysis. Revised Likert scale method is adopted to determine the alarm priority and reduce the blindness of alarm handling. As a case study, the Tennessee Eastman process is utilized to show the effectiveness and validity of proposed approach. Alarm system performance comparison shows that our rationalization methodology can reduce the alarm flood to some extent and improve the performance.

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.

Prioritizing Alarms in Real-Time： Intelligent Alarms Advanced System

During the operation of complex process, such as oil production or refming, abnormal situations may occur, leading to an alarm flooding. Alarm flooding is the signalling of a large number of alarms in a few minutes, in such a way that it is impossible for the operator to attend to all alarms. On these occasions, it is usual that the operator leaves the alarm summary list and gets an analysis of the plant through the screens of the DCS （digital control system）, seeking to understand the situation. The alarm summary list ceases to be a useful tool. In such cases, the operator might have the aid of a filter that would present the highest priority alarms and other information associated with them, enabling him to gain a better knowledge of the situation. This paper describes the interface of a system aimed to help the operator to have a more comprehensive knowledge of the process （a better situational awareness） during process upsets that cause alarm flooding, recovering the utility of the alarm layer to the safety of industrial processes.

Dynamic alarm prediction for critical alarms using a probabilistic model

Alarm systems play important roles for the safe and efficient operation of modern industrial plants. Critical alarms are configured with a higher priority and are safety related among many other alarms. If critical alarms can be predicted in advance, the operator will have more time to prevent them from happening. In this paper,we present a dynamic alarm prediction algorithm, which is a probabilistic model that utilizes alarm data from distributed control system, to calculate the occurrence probability of critical alarms. It accounts for the local interdependences among the alarms using the n-gram model, which occur because of the nonlinear relationships between variables. Finally, the dynamic alarm prediction algorithm is applied to an industrial case study.

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.

Design of Signal Lamp Filament Monitoring Alarm Instrument

To improve the reliability of the light emitting diode(LED)signal lamp filament current monitoring alarm instrument for metro systems,a new type of hot standby online monitoring apparatus was developed which is based on synchronous transmission data(STD)bus technology.In this system,a double hot standby mode can be achieved by adopting bus arbitration.In addition,to detect the effective value of alternating current which is from 0 to 200 mA in the signal lamp lighting circuit,a precision rectifier signal conditioning circuit and an isolated acquisition circuit were designed.This new type of alarm instrument has high detection accuracy and could meet the functional requirements for metro signal systems after comparing it with some industry products that were applied on the spot.

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.

费森4008B血透机Flow alarm故障维修

1故障现象 机器运行过程中电导度不稳定,上下漂移且出现Flow alarm报警。

SoundAlarm纯软件智能语音报警系统在火电厂的应用

部分火电厂计算机控制系统中的自动报警系统在出现报警信息时发出单调的警铃或蜂鸣器声音报警,CRT画面上故障点闪亮。由于火电厂计算机控制系统的CRT画面较多且机组大部分报警光字牌已取消,因此操作人员难以及时查明故障点并迅速处理机组缺陷。纯软件智能语音报警系统SoundAlarm通过给计算机控制系统扩展真人语音报警功能,很好地弥补了计算机控制系统实现自动报警时的不足。该智能语音报警系统还具有不影响数据源系统的安全、对硬件要求低、配置灵活方便、无需进行专门维护等特点。SoundAlarm系统目前已成功与MCSmaxDNA和欧陆公司的NETWORK6000DCS实现了无缝集成,在电厂的自动报警中获得了成功应用。

省级地球物理站网仪器运行监控系统设计与实现

顺应地震监测业务改革方向,为提升地球物理站网仪器运维效率,设计了一套地球物理站网仪器运行监控系统。系统包括基于B/S架构设计的监控软件平台、手机监控App软件和基于C/S架构设计的调试工具,通过实时获取地球物理仪器状态流和数据流,对仪器时钟准确性、连通性,数据完整性、有效性等进行自动化分析,实现辖区范围内地球物理站网仪器运行状态的实时监控。该系统可对仪器运行状态异常、产出数据不符合规范要求等情况进行异常告警,提示运维人员第一时间排查故障,从而提高了地球物理站网仪器运行率和数据质量;系统对获取的数据进行备份,提高了地球物理台网数据完整性,并为今后实现数据质量实时监控等数据服务提供了技术路线。

Multitask Data Processing in a Wireless Alarm System

Aim To achieve multitask data procssing in a wireless alarm system by computer. Methods The alarm system was composed of hardware and software. The hardware was composed of a master master computer and slave transmitters. On urgent ugent occasion, one or more of the transmitters transmitted alarm signals and the master computer received the signals; interruption, residence, graph and word processing were utilized in software to achieve multitiask data processing . Results The main computer can conduct precise and quick multitask data procesing in any condition so long as alarm signals are received. The processing speed is higher than ordinary alarm System. Conclusion The master computer can conduct safe and quick multitask data processing by way of reliable design of software and hardware , so there is no need of special processor.

OMCI协议栈Alarm Control机制的实现

GPON设备在家庭中的使用越来越广泛,而Alarm报告机制在GPON ONU设备中广泛存在.因此,对Alarm Control机制的实现可以有效的实现OLT对ONU运行状态的管理和控制.从G.988出发,论述Alarm控制实现的基本原理,随后根据OMCI协议栈的1种可能实现论述Alarm Control的实现方法.

地下综合管廊天然气泄漏扩散及爆炸研究综述

该文针对城市地下综合管廊燃气舱内的天然气泄漏、扩散以及火灾爆炸风险等问题开展综述分析。关于燃气舱内天然气管线的泄漏研究,必须充分考虑泄漏孔的位置、尺寸、管道工作压力即管线内压强变化、通风和气体探测器报警时限等影响,当舱室天然气大量泄漏后,仅增加通风次数无法有效防止天然气积聚,必须同时启用舱内防火门进行截断隔离措施。分析受限空间内气体爆炸已有的研究进展,结果表明,针对燃气舱天然气泄漏爆炸的模拟研究,应充分考虑管廊长径比及舱内物体的影响;且爆炸冲击波强度较高,需采用相应的防爆抑爆措施。本文为城市地下综合管廊中天然气管道运输的安全性研究提供了方向和参考。

Function of note strings in Japanese Tit alarm calls to the Common Cuckoo:a playback experiment

Background: Birds produce alarm calls to convey information about threats. Some Passerine alarm calls consist of several note strings, but few studies have examined their function. Previous studies have shown that Japanese Tits(Parus minor) can alter the calling rate and number and combination of notes in response to predators. We previously found the combinations of note types in Japanese Tit alarm calls to be significantly different in response to the Sparrowhawk(Accipiter nisus) and Common Cuckoo(Cuculus canorus).Methods: Through playback experiments, we tested whether the note strings in Japanese Tit alarm calls to the Common Cuckoo have different functions in conveying information. The note strings of selected alarm calls were divided into the categories of C and D, and different calls were then constructed separately based on the two note string categories. Original alarm calls(C–D), C calls and D calls were played back to male Japanese Tits during the incubation period.Results: Male Japanese Tits had a significantly stronger response to C calls than to C–D calls, and they showed a significantly stronger response to both C and C–D calls than to D calls, suggesting that Japanese Tits discriminated between the C and D calls.Conclusions: Our study demonstrated that the C-and D-category note strings of Japanese Tit alarm calls to the Common Cuckoo have different functions, which supports the previous finding that different note strings in an alarm call can provide different information to receivers. However, the exact meanings of these note strings are not yet known, and further investigation is therefore required.

Research on Micro-vibration Generator for Powering Tire Pressure Alarm System

In order to meet the self-powered requirement by microelectronic products and micro-electrostatic vibration generator and avoid the environmental pollution caused by waste batteries,many countries have started their researches on piezoelectric power generation based on the researches on the recovery device of rotational energy of piezoelectric effect.The piezoelectric power generation has many advantages compared with other generation patterns.The piezoelectric power generation has become a new field of research.In this study,the piezoelectric vibrators were used to convert energy.According to the requirements by tire pressure alarm system,the energy conversion and storage were researched and analyzed by using the passive sensing device used in automotive inspection.The converted energy was then provided to the alarm,achieving intelligent control and utilization of piezoelectric vibrators.