Alarm Management in Provisional COVID-19 Intensive Care Units: Retrospective Analysis and Recommendations for Future Pandemics.

BACKGROUND: In response to the high patient admission rates during the COVID-19 pandemic, provisional intensive care units (ICUs) were set up, equipped with temporary monitoring and alarm systems. We sought to find out whether the provisional ICU setting led to a higher alarm burden and more staff with alarm fatigue. OBJECTIVE: We aimed to compare alarm situations between provisional COVID-19 ICUs and non-COVID-19 ICUs during the second COVID-19 wave in Berlin, Germany. The study focused on measuring alarms per bed per day, identifying medical devices with higher alarm frequencies in COVID-19 settings, evaluating the median duration of alarms in both types of ICUs, and assessing the level of alarm fatigue experienced by health care staff. METHODS: Our approach involved a comparative analysis of alarm data from 2 provisional COVID-19 ICUs and 2 standard non-COVID-19 ICUs. Through interviews with medical experts, we formulated hypotheses about potential differences in alarm load, alarm duration, alarm types, and staff alarm fatigue between the 2 ICU types. We analyzed alarm log data from the patient monitoring systems of all 4 ICUs to inferentially assess the differences. In addition, we assessed staff alarm fatigue with a questionnaire, aiming to comprehensively understand the impact of the alarm situation on health care personnel. RESULTS: COVID-19 ICUs had significantly more alarms per bed per day than non-COVID-19 ICUs (P<.001), and the majority of the staff lacked experience with the alarm system. The overall median alarm duration was similar in both ICU types. We found no COVID-19-specific alarm patterns. The alarm fatigue questionnaire results suggest that staff in both types of ICUs experienced alarm fatigue. However, physicians and nurses who were working in COVID-19 ICUs reported a significantly higher level of alarm fatigue (P=.04). CONCLUSIONS: Staff in COVID-19 ICUs were exposed to a higher alarm load, and the majority lacked experience with alarm management and the alarm system. We recommend training and educating ICU staff in alarm management, emphasizing the importance of alarm management training as part of the preparations for future pandemics. However, the limitations of our study design and the specific pandemic conditions warrant further studies to confirm these findings and to explore effective alarm management strategies in different ICU settings.

Evaluating the Construct Validity of the Charité Alarm Fatigue Questionnaire using Confirmatory Factor Analysis.

Background: The Charité Alarm Fatigue Questionnaire (CAFQa) is a 9-item questionnaire that aims to standardize how alarm fatigue in nurses and physicians is measured. We previously hypothesized that it has 2 correlated scales, one on the psychosomatic effects of alarm fatigue and the other on staff's coping strategies in working with alarms. Objective: We aimed to validate the hypothesized structure of the CAFQa and thus underpin the instrument's construct validity. Methods: We conducted 2 independent studies with nurses and physicians from intensive care units in Germany (study 1: n=265; study 2: n=1212). Responses to the questionnaire were analyzed using confirmatory factor analysis with the unweighted least-squares algorithm based on polychoric covariances. Convergent validity was assessed by participants' estimation of their own alarm fatigue and exposure to false alarms as a percentage. Results: In both studies, the χ2 test reached statistical significance (study 1: χ226=44.9; P=.01; study 2: χ226=92.4; P<.001). Other fit indices suggested a good model fit (in both studies: root mean square error of approximation <0.05, standardized root mean squared residual <0.08, relative noncentrality index >0.95, Tucker-Lewis index >0.95, and comparative fit index >0.995). Participants' mean scores correlated moderately with self-reported alarm fatigue (study 1: r=0.45; study 2: r=0.53) and weakly with self-perceived exposure to false alarms (study 1: r=0.3; study 2: r=0.33). Conclusions: The questionnaire measures the construct of alarm fatigue as proposed in our previous study. Researchers and clinicians can rely on the CAFQa to measure the alarm fatigue of nurses and physicians.

The Dynamic Change in the Reliability Function Level in a Selected Fire Alarm System during a Fire.

This article discusses fundamental issues associated with the functional reliability of selected fire alarm systems (FASs) in operation during building fires. FASs operate under diverse external or internal natural environmental conditions, and the operational process of FAS should take into account the impacts of physical phenomena that occur during fires. Their operation is associated with the constant provision of reliability. FAS designers should also consider the system's reliability when developing fire control matrices, tables, algorithms, or scenarios. All functions arising from an FAS control matrix should be implemented with a permissible reliability level, RDPN(t), prior to, as well as during, a fire. This should be assigned to the controls saved in the fire alarm control unit (FCP). This article presents the process by which high temperatures generated during a fire impact the reliability of FAS functioning. It was developed considering selected critical paths for a specific scenario and the control matrix for an FAS. Such assumptions make it possible to determine the impact of various temperatures generated during a fire on the reliability of an FAS. To this end, the authors reviewed that the waveform of the R(t) function changes for a given FAS over time, Δt, and then determined the fitness paths. The critical paths are located within the fire detection and suppression activation process, using FAS or fixed extinguishing devices (FEDs), and the paths were modeled with acceptable and unacceptable technical states. The last section of this article defines a model and graph for the operational process of a selected FAS, the analysis of which enables conclusions to be drawn that can be employed in the design and implementation stages.

A Positioning Alarm System for Explosive Impact Debris Protective Suit Based on an Accelerometer Array.

Protection suits are vital for firefighters' safety. Traditional protection suits physically protect firemen from burns, but cannot locate the position of bodily injuries caused by impact debris. Herein, we present a wearable impact debris positioning system for firefighter protection suits based on an accelerometer array. Wearable piezoelectric accelerometers are distributed regularly on the suit to detect the vibration on different body parts, which is conducive to determining the position of injured body parts. In addition, the injured parts can be displayed on a dummy body model on the upper computer with a higher localization accuracy of 4 cm. The positioning alarm system has a rapid response time of 0.11 ms, attributed to the smart signal processing method. This work provides a reliable and smart method for locating and assessing the position of bodily injuries caused by impact debris, which is significant because it enables fire commanders to rescue injured firefighters in time.

Identifying Characteristic Fire Properties with Stationary and Non-Stationary Fire Alarm Systems.

The article reviews issues associated with the operation of stationary and non-stationary electronic fire alarm systems (FASs). These systems are employed for the fire protection of selected buildings (stationary) or to monitor vast areas, e.g., forests, airports, logistics hubs, etc. (non-stationary). An FAS is operated under various environmental conditions, indoor and outdoor, favourable or unfavourable to the operation process. Therefore, an FAS has to exhibit a reliable structure in terms of power supply and operation. To this end, the paper discusses a representative FAS monitoring a facility and presents basic tactical and technical assumptions for a non-stationary system. The authors reviewed fire detection methods in terms of fire characteristic values (FCVs) impacting detector sensors. Another part of the article focuses on false alarm causes. Assumptions behind the use of unmanned aerial vehicles (UAVs) with visible-range cameras (e.g., Aviotec) and thermal imaging were presented for non-stationary FASs. The FAS operation process model was defined and a computer simulation related to its operation was conducted. Analysing the FAS operation process in the form of models and graphs, and the conducted computer simulation enabled conclusions to be drawn. They may be applied for the design, ongoing maintenance and operation of an FAS. As part of the paper, the authors conducted a reliability analysis of a selected FAS based on the original performance tests of an actual system in operation. They formulated basic technical and tactical requirements applicable to stationary and mobile FASs detecting the so-called vast fires.

CuO/TiO2/MXene-Based Sensor and SMS-TENG Array Integrated Inspection Robots for Self-Powered Ethanol Detection and Alarm at Room Temperature.

In this study, a high-precision CuO/TiO2/MXene ethanol sensor operating at room temperature was prepared. The sensor exhibits excellent response value (95% @1 ppm ethanol), extremely low detection limit (0.3 ppm), fast response/recovery time (16/13 s), and remarkable long-term stability for trace detection of ethanol gas at room temperature, attributed to the p-n heterojunction formed by CuO and TiO2, as well as the rich functional groups and large specific surface area of MXene. Furthermore, a high-performance triboelectric nanogenerator (SMS-TENG) was developed through the introduction of the silicone/Mxene@silicone dual dielectric layer as the triboelectric layer, which improves the charge storage capacity of the dielectric layer and greatly enhances the output performance of the TENG. At the optimal doping ratio, the open-circuit voltage of the SMS-TENG can reach 1160 V, which is sufficient to light 720 LEDs. By combining the sensor and SMS-TENG, the resistive response of ethanol sensing is converted to a voltage response, which amplifies the response value up to 15.8 times. Finally, the designed SMS-TENGs are expected to be arrayed on an inspection robot as energy supply and combined with the CuO/TiO2/Mxene ethanol sensor to build a self-powered ethanol detection alarm system, endowing the inspection robot with the capability of self-powered ethanol detection at ppb level. This work provides an effective pathway for the intelligence of ethanol detection.

Design of wireless battery management system monitoring and automated alarm system based on improved long short-term memory neural network.

The battery management system (BMS) can intelligently manage and maintain each battery unit while monitoring its status, thereby preventing any possible overcharge or over-discharge of the battery. In BMS research, battery state parameter collection and analysis are essential. However, traditional data collection methods require personnel to be present at the scene, leading to offline data acquisition. Therefore, this study aimed to develop a wireless BMS monitoring and alarm system based on socket connection that would enable researchers to observe the operating parameters and problem details of the battery pack from a distance. A device like this effectively raises the battery's level of cognitive control. In the study, the researchers first designed the overall scheme of the BMS remote monitoring system, followed by building a wireless BMS monitoring and alarm system. Performance evaluations of the system were then conducted to confirm its effectiveness. A Long Short-Term Memory (LSTM) network enhanced by the Batch Normalization (BN) technique was applied to the time series data of battery parameters to solve the large accuracy inaccuracy in battery state of charge estimate. Furthermore, the Denoise Auto Encoder (DAE) algorithm was utilized to denoise the data and reduce the model's parameter dependence. The accuracy and robustness of the estimation are improved, and the model error is gradually stabilized within 5%.

Domesticating Social Alarm Systems in Nursing Homes: Qualitative Study of Differences in the Perspectives of Assistant Nurses.

BACKGROUND: New social alarm solutions are viewed as a promising approach to alleviate the global challenge of an aging population and a shortage of care staff. However, the uptake of social alarm systems in nursing homes has proven both complex and difficult. Current studies have recognized the benefits of involving actors such as assistant nurses in advancing these implementations, but the dynamics by which implementations are created and shaped in their daily practices and relations have received less attention. OBJECTIVE: Based on domestication theory, this paper aims to identify the differences in the perspectives of assistant nurses when integrating a social alarm system into daily practices. METHODS: We interviewed assistant nurses (n=23) working in nursing homes to understand their perceptions and practices during the uptake of social alarm systems. RESULTS: During the four domestication phases, assistant nurses were facing different challenges including (1) system conceptualization; (2) spatial employment of social alarm devices; (3) treatment of unexpected issues; and (4) evaluation of inconsistent competence in technology use. Our findings elaborate on how assistant nurses have distinct goals, focus on different facets, and developed diverse coping strategies to facilitate the system domestication in different phases. CONCLUSIONS: Our findings reveal a divide among assistant nurses in terms of domesticating social alarm systems and stress the potential of learning from each other to facilitate the whole process. Further studies could focus on the role of collective practices during different domestication phases to enhance the understanding of technology implementation in the contexts of complex interactions within a group.

A First-Out Alarm Detection Method via Association Rule Mining and Correlation Analysis.

Alarm systems are commonly deployed in complex industries to monitor the operation status of the production process in real time. Actual alarm systems generally have alarm overloading problems. One of the major factors leading to excessive alarms is the presence of many correlated or redundant alarms. Analyzing alarm correlations will not only be beneficial to the detection of and reduction in redundant alarm configurations, but also help to track the propagation of abnormalities among alarm variables. As a special problem in correlated alarm detection, the research on first-out alarm detection is very scarce. A first-out alarm is known as the first alarm that occurs in a series of alarms. Detection of first-out alarms aims at identifying the first alarm occurrence from a large number of alarms, thus ignoring the subsequent correlated alarms to effectively reduce the number of alarms and prevent alarm overloading. Accordingly, this paper proposes a new first-out alarm detection method based on association rule mining and correlation analysis. The contributions lie in the following aspects: (1) An association rule mining approach is presented to extract alarm association rules from historical sequences based on the FP-Growth algorithm and J-Measure; (2) a first-out alarm determination strategy is proposed to determine the first-out alarms and subsequent alarms through correlation analysis in the form of a hypothesis test on conditional probability; and (3) first-out rule screening criteria are proposed to judge whether the rules are redundant or not and then consolidated results of first-out rules are obtained. The effectiveness of the proposed method is tested based on the alarm data generated by a public simulation platform.

Development of a Contact Glass-Break Detector for the Highest Security Level.

The main object of this research was to develop a security system to evaluate the intrusion into an object through a glass pane. More specifically, this study deals with sensing and evaluating signals from a contact glass-break detector, which is part of an intruder alarm system. Each alarm detector in an alarm system must accomplish certain security level requirements that strictly describe the requirements for the area of use and the detector's reliability. To date, no contact glass-break detector has been developed and fully tested to meet the stringent requirements of the highest security level. A contact glass-break detector was developed whose main part is an accelerometer that transmits signals from the glass pane. These signals were evaluated according to the developed methodology. It was verified that the proposed system can distinguish at the highest security level between false alarms and situations where the building has been intruded.

A Scoring Framework and Apparatus for Epilepsy Seizure Detection Using a Wearable Belt.

To develop a wearable device that can detect epilepsy seizures. In particular, due to their prevalence, attention is focused on detecting the generalized tonic-clonic seizure (GTCS) type. When a seizure is detected, an alert phone call is initiated and an alarm SMS sent to the nearest health-care provider (and/or a predesignated family member), including the patient's location as global positioning system (GPS) coordinates. A wearable belt is developed including an Arduino processor that constantly acquires data from four different sensing modalities and monitors the acquired signal patterns for abnormalities. The sensors are a heart rate sensor, electromyography sensor, blood oxygen level (oxygen saturation) sensor, and an accelerometer to detect sudden falls. Higher-than-normal threshold levels are established for each sensor's signal. If two or more signal measurements exceed the corresponding threshold value for a predetermined time interval, then the seizure alarm is triggered. Clinical trials were not pursued in this study as this is the initial phase of system development (phase 0). Instead, the instrumented belt seizure detection prototype was tested on nine healthy individuals mimicking, to some degree, seizure symptoms. A total of eighteen trials took place of which half had <2 sensor thresholds exceeded and no alarm, whereas the other half resulted in activating the alarm when two or more sensor thresholds were exceeded for at least the predetermined time interval corresponding to each of the higher-than-normal sensor readings. For each trial that triggered the alarm when a seizure was detected, the on-board GPS and global system for mobile communication (GSM) units successfully initiated an alert phone call to a predesignated number in addition to sending an SMS message, including GPS location coordinates. Continuous real-time monitoring of signals from the four different sensors allows the developed wearable belt to detect GTCS while reducing false alarms. The proposed device produces an important alarm that may save a patient's life.

An Artificial Intelligence-Based Alarm Strategy Facilitates Management of Acute Myocardial Infarction.

(1) Background: While an artificial intelligence (AI)-based, cardiologist-level, deep-learning model for detecting acute myocardial infarction (AMI), based on a 12-lead electrocardiogram (ECG), has been established to have extraordinary capabilities, its real-world performance and clinical applications are currently unknown. (2) Methods and Results: To set up an artificial intelligence-based alarm strategy (AI-S) for detecting AMI, we assembled a strategy development cohort including 25,002 visits from August 2019 to April 2020 and a prospective validation cohort including 14,296 visits from May to August 2020 at an emergency department. The components of AI-S consisted of chest pain symptoms, a 12-lead ECG, and high-sensitivity troponin I. The primary endpoint was to assess the performance of AI-S in the prospective validation cohort by evaluating F-measure, precision, and recall. The secondary endpoint was to evaluate the impact on door-to-balloon (DtoB) time before and after AI-S implementation in STEMI patients treated with primary percutaneous coronary intervention (PPCI). Patients with STEMI were alerted precisely by AI-S (F-measure = 0.932, precision of 93.2%, recall of 93.2%). Strikingly, in comparison with pre-AI-S (N = 57) and post-AI-S (N = 32) implantation in STEMI protocol, the median ECG-to-cardiac catheterization laboratory activation (EtoCCLA) time was significantly reduced from 6.0 (IQR, 5.0-8.0 min) to 4.0 min (IQR, 3.0-5.0 min) (p < 0.01). The median DtoB time was shortened from 69 (IQR, 61.0-82.0 min) to 61 min (IQR, 56.8-73.2 min) (p = 0.037). (3) Conclusions: AI-S offers front-line physicians a timely and reliable diagnostic decision-support system, thereby significantly reducing EtoCCLA and DtoB time, and facilitating the PPCI process. Nevertheless, large-scale, multi-institute, prospective, or randomized control studies are necessary to further confirm its real-world performance.

[Design and Realization of Remote Infusion Monitoring System].

In order to reduce the working intensity of medical staff in inspecting patients during traditional infusion, a remote monitoring system for intravenous infusion is designed for solving the problem of delay in handling treatment during infusion process and to reduce the incidence of medical accidents. The system uses Visual Basic.NET language to develop the upper computer platform for infusion monitoring. It uses the Arduino control board and infrared photoelectric sensor to form a monitoring device to detect relevant information. At the same time, it uses Zigbee wireless sensing technology to transmit data and upload it to the software platform. The results show that the system can receive data from multiple monitoring terminal devices in the upper computer platform application interface at the same time. It can display the data in the nurse station in a graphical way, and perform alarm warning and information storage during the infusion process. The infusion monitoring system can observe the monitoring situation in real time, reduce the workload of medical staff, and further improve the operating efficiency and safety of the hospital.

Unconscious processing of subliminal stimuli in panic disorder: A systematic review and meta-analysis.

Attentional biases to threat exist in panic disorder (PD), probably related to altered subliminal processing. We systematically reviewed studies investigating subliminal processing in PD. Studies were retrieved from MEDLINE and Scopus®. We meta-analytically compared PD (n = 167) and healthy controls (HC, n = 165) for processing of masked panic-related and neutral words. We also compared subliminal and supraliminal presentations of panic-related words relative to neutral words within PD subjects and HC. We found a significantly enhanced Stroop interference to masked panic-related words in PD vs HC (Hedges' g = 0.60, p = 0.03; Q = 14.83, I2 = 66.3 %, p = 0.01). While both PD subjects and HC tended to be slower to respond to supraliminal threat words than to neutral words, PD subjects only showed a marginally significant slower response to subliminal panic-related words vs neutral words. Findings remain inconclusive regarding comparison to other mental disorders, neural correlates, and the effect of psychotherapy. Even if possibly flawed by methodological weaknesses, our findings support the existence of a sensitivity to subliminal threat cues in PD, which could be targeted to improve treatment.

Patient Monitoring Alarms in an Intensive Care Unit: Observational Study With Do-It-Yourself Instructions.

BACKGROUND: As one of the most essential technical components of the intensive care unit (ICU), continuous monitoring of patients' vital parameters has significantly improved patient safety by alerting staff through an alarm when a parameter deviates from the normal range. However, the vast number of alarms regularly overwhelms staff and may induce alarm fatigue, a condition recently exacerbated by COVID-19 and potentially endangering patients. OBJECTIVE: This study focused on providing a complete and repeatable analysis of the alarm data of an ICU's patient monitoring system. We aimed to develop do-it-yourself (DIY) instructions for technically versed ICU staff to analyze their monitoring data themselves, which is an essential element for developing efficient and effective alarm optimization strategies. METHODS: This observational study was conducted using alarm log data extracted from the patient monitoring system of a 21-bed surgical ICU in 2019. DIY instructions were iteratively developed in informal interdisciplinary team meetings. The data analysis was grounded in a framework consisting of 5 dimensions, each with specific metrics: alarm load (eg, alarms per bed per day, alarm flood conditions, alarm per device and per criticality), avoidable alarms, (eg, the number of technical alarms), responsiveness and alarm handling (eg alarm duration), sensing (eg, usage of the alarm pause function), and exposure (eg, alarms per room type). Results were visualized using the R package ggplot2 to provide detailed insights into the ICU's alarm situation. RESULTS: We developed 6 DIY instructions that should be followed iteratively step by step. Alarm load metrics should be (re)defined before alarm log data are collected and analyzed. Intuitive visualizations of the alarm metrics should be created next and presented to staff in order to help identify patterns in the alarm data for designing and implementing effective alarm management interventions. We provide the script we used for the data preparation and an R-Markdown file to create comprehensive alarm reports. The alarm load in the respective ICU was quantified by 152.5 (SD 42.2) alarms per bed per day on average and alarm flood conditions with, on average, 69.55 (SD 31.12) per day that both occurred mostly in the morning shifts. Most alarms were issued by the ventilator, invasive blood pressure device, and electrocardiogram (ie, high and low blood pressure, high respiratory rate, low heart rate). The exposure to alarms per bed per day was higher in single rooms (26%, mean 172.9/137.2 alarms per day per bed). CONCLUSIONS: Analyzing ICU alarm log data provides valuable insights into the current alarm situation. Our results call for alarm management interventions that effectively reduce the number of alarms in order to ensure patient safety and ICU staff's work satisfaction. We hope our DIY instructions encourage others to follow suit in analyzing and publishing their ICU alarm data.

Smart Wearable Sensors Based on Triboelectric Nanogenerator for Personal Healthcare Monitoring.

Accurate monitoring of motion and sleep states is critical for human health assessment, especially for a healthy life, early diagnosis of diseases, and medical care. In this work, a smart wearable sensor (SWS) based on a dual-channel triboelectric nanogenerator was presented for a real-time health monitoring system. The SWS can be worn on wrists, ankles, shoes, or other parts of the body and cloth, converting mechanical triggers into electrical output. By analyzing these signals, the SWS can precisely and constantly monitor and distinguish various motion states, including stepping, walking, running, and jumping. Based on the SWS, a fall-down alarm system and a sleep quality assessment system were constructed to provide personal healthcare monitoring and alert family members or doctors via communication devices. It is important for the healthy growth of the young and special patient groups, as well as for the health monitoring and medical care of the elderly and recovered patients. This work aimed to broaden the paths for remote biological movement status analysis and provide diversified perspectives for true-time and long-term health monitoring, simultaneously.

Design and Realization of Remote Infusion Monitoring System / 中国医疗器械杂志

In order to reduce the working intensity of medical staff in inspecting patients during traditional infusion, a remote monitoring system for intravenous infusion is designed for solving the problem of delay in handling treatment during infusion process and to reduce the incidence of medical accidents. The system uses Visual Basic.NET language to develop the upper computer platform for infusion monitoring. It uses the Arduino control board and infrared photoelectric sensor to form a monitoring device to detect relevant information. At the same time, it uses Zigbee wireless sensing technology to transmit data and upload it to the software platform. The results show that the system can receive data from multiple monitoring terminal devices in the upper computer platform application interface at the same time. It can display the data in the nurse station in a graphical way, and perform alarm warning and information storage during the infusion process. The infusion monitoring system can observe the monitoring situation in real time, reduce the workload of medical staff, and further improve the operating efficiency and safety of the hospital.

Estimating the net return of a remote calving alarm system in a dairy farm.

The aim of the study was to evaluate the net return of the implementation of a remote calving monitoring system for obstetrical and neonatal assistance on the herd economy in a dairy farm model. A total of 680 parturitions over a 7-yr period were evaluated. Age at first calving was restricted from 23 to 27 mo for primiparous cows to be included. Among groups of cows that were ready to calve in a 15-d interval, primiparous and multiparous were randomly assigned to the experimental group and monitored through a calving alarm system, whereas the others accounted for controls. Final parturition groups were as follows: control primiparous (CPP, n = 218), control multiparous (CM, n = 345), monitored primiparous (MPP, n = 56), and monitored multiparous (MM, n = 61). Monitored groups received prompt calving assistance and first neonatal care, whereas the presence of farm personnel was discontinuous for controls. A biological model was built considering significant differences in calf loss, early culling, milk production, and days open between groups. Then, a partial budget model was used to estimate costs and net return on a simulated herd of 100 lactating cows. Incidence of calf death was greater in control groups (11.06% and 10.73% in CPP and CM, respectively) compared with monitored cows (0.00% and 1.69% in MPP and MM, respectively). Multiparous cows with calf loss had increased relative risk (relative risk = 3.487) for early culling compared with multiparous counterparts with no neonatal loss. Daily milk production in the first 2 mo was 3.79 kg greater in multiparous cows with no dead calf, compared with their counterparts. A significant difference in median days open was found in MPP and CPP (118 and 148 d, respectively). In the final economic model, different simulations were analyzed. They were created assuming different prices or hypothesizing calving monitoring only in primiparous animals. The model estimated different, but always positive, net return. In conclusion, implementing a calving alarm system led to a net return from €37 to 90 per cow per year (€1 = US$1.15 at the time of the study). However, the device alone is not sufficient: it must be supported by qualified calving monitoring and assistance. Optimized personnel presence in the calving area at the right time leads to prompt calving and neonatal calf assistance and colostrum feeding within the first hours of life, thus reducing calf death and days open, and increasing milk production.

A Fully Self-Powered Vibration Monitoring System Driven by Dual-Mode Triboelectric Nanogenerators.

Vibration sensor is very necessary for monitoring the structural health of constructions. However, it is still a major challenge to meet simultaneously real-time monitoring, continuous assessment, and early incident warning in a simple device without a complicated power and analysis system. Here, we report a self-powered vibration sensor system to achieve real-time and continuous detection of the vibration characteristics from a dual-mode triboelectric nanogenerator (AC/DC-TENG), which can produce either alternating current (AC) or direct current (DC) within different operation zones. Within the vibration-safe region, the AC/DC-TENG with AC output not only can continuously assess the vibration characteristics but also can power the signal transmission. More importantly, once the vibration amplitude crosses the danger threshold, the AC converts immediately to DC, meanwhile triggering the alarm system directly to accurately predict the danger of construction. Our self-powered vibration sensor system can serve as a facile tool for accurately monitoring the structural health of constructions.

Evidence updating with static and dynamical performance analyses for industrial alarm system design.

In the Dempster-Shafer theory (DST) of evidence, the alarm evidence updating-based method can effectively deal with the uncertainty of the monitored process variable so as to significantly reduce the false alarm rates (FAR) and missed alarm rates (MAR) of the industrial alarm system. But the price of the decrease of FAR and MAR is the increase of the averaged alarm delay (AAD). In order to obtain better comprehensive performance, besides the accuracy indices (FAR and MAR), the sensitivity index (AAD) should be considered simultaneously in the alarm system parameter optimization design. In the framework of DST, firstly, this paper defines the static and dynamical performance indices in the alarm evidence space which are compatible with FAR/MAR/AAD in the process variable space. But the former can measure the performance of the DST-based alarm systems more naturally and elaborately than the latter; secondly, a systematic parameter optimization design procedure for the alarm system is investigated by using these new indices and the tradeoff among them. Finally, two typical numerical experiments and an industrial case are provided to illustrate the effectiveness of the static and dynamical indices for improving the comprehensive performance of the DST-based alarm systems.