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Prey adopt various antipredator responses to minimize the risk of predation, and the fitness costs of antipredator responses can have emergent effects on the population dynamics of prey species. While the trade‐off between food acquisition and predation avoidance has long been recognized in predicting antipredator responses, less attention has been paid to the dynamics of the food‐safety trade‐off driven by temporal variation in multiple risk factors under changing seasonal conditions. Here, we monitored foraging and vigilance behaviour of a central prey species, snowshoe hares (Lepus americanus), at fine temporal scales over the winter with various types of predation risk, while also experimentally manipulating predation risk by attracting predators to foraging patches. Hares increased foraging and decreased vigilance over the winter, but hares under chronic risk decreased their antipredator efforts to a lesser degree, indicating that those individuals prioritized risk avoidance over food acquisition. Hares also decreased foraging and increased antipredator efforts in response to the temporal activity of predators and environmental cues of predation risk. However, the magnitude of the responses to the environmental cues was mediated by time of winter. While we did not detect a reactive response of hares to acute risk, we did find that hares exhibiting camouflage mismatch proactively increased vigilance. Overall, our results highlight the importance of species‐specific traits and changing seasonal conditions in addition to temporal variation in multiple risk factors in predicting antipredator responses and the context dependence of risk effects. [ABSTRACT FROM AUTHOR]

Battery pack provides the backup power supply for DC system of power substations. In the event of an AC power outage or other accidents, it is an important guarantee for the reliable operation of power substation. To prevent possible failures, batteries usually require careful maintenance. Common methods are online monitoring, condition assessments, and health management. Among these, model-based techniques are widely used for battery monitoring and prognostics optimization. Data-driven methods are a good alternative solution when no mathematical models are available. As substations develop towards intelligent and unmanned modes, this paper proposes an online battery monitoring and management system based on the "cloud-network-edge-end" Internet of Things (IoT) architecture. Firstly, advanced battery monitoring system based on IoT architecture is reviewed in depth. It provides basis for later designing. Secondly, the battery online monitoring and management system is designed considering functional requirements and data link. Designing functions include ledger management, basic battery information display, real-time display of battery monitoring data, and the visualization of battery alarm information. It can implement online monitoring and intelligent maintenance management for battery operating status. Finally, the designed and developed system is applied in a 110 kV offshore substation, mainly providing battery maintenance suggestions and fault alarm prompts. Typical results of ledger information management, key parameter monitoring and alarm prompt are presented. This verifies the effectiveness and convenience of IoT-based system for the monitoring and management of batteries. [ABSTRACT FROM AUTHOR]

The main goal of most static analyses is to prove the absence of bugs: if the analysis reports no alarms, then the program will not exhibit any unwanted behaviours. For this reason, they are designed to over-approximate program behaviours and, consequently, they can report some false alarms. O'Hearn's recent work on incorrectness has renewed the interest in the use of under-approximations for bug finding, because they only report true alarms. In principle, Abstract Interpretation techniques can handle under-approximations as well as over-approximations, but, in practice, few attempts were developed for the former, notwithstanding the much wider literature on the latter. In this article, we investigate the possibility of exploiting under-approximation abstract domains for bug-finding analyses. First we restrict to consider concrete powerset domains and highlight some intuitive asymmetries between over- and under-approximations. Then, we prove that the effectiveness of abstract domains defined by under-approximation Galois connection is limited because the analysis is likely to return trivial results whenever common transfer functions are encoded in the program. To this aim, we introduce the original concepts of non-emptying functions and highly surjective function family, and we prove the nonexistence of abstract domains able to under-approximate such functions in a non-trivial way. We show many examples of finite and infinite numerical domains, as well as other generic domains. In all such cases, we prove the impossibility of performing non-trivial analyses via under-approximation Galois connections. [ABSTRACT FROM AUTHOR]

A fire in the cargo compartment has a major impact on civil aviation flight safety, and according to the airworthiness clause of the CCAR-25, the detector must sound an alarm within 1 min of a fire in the cargo compartment. As for the cargo compartment of large transport aircrafts, the internal space is high and open, and the smoke movement speed becomes slower with significant cooling in the process of diffusion. Hysteresis can occur in smoke detectors because of their internal labyrinth structure, which causes the detector's internal and external response signals to be out of sync. This research employs a numerical simulation to examine the detector response parameters under an ambient wind speed of 0.1–0.2 m/s and fits a Cleary two-stage hysteresis model, where τ1= 0.09u−1.43 and τ2= 0.67u−1.59. Finally, multiple full-scale cargo cabin experiments were conducted to validate the prediction model. The results show that the model's predicted alarm range is 43.1 s to 49.0 s, and the actual alarm time obtained by the experiment falls within this interval, confirming the model's accuracy and providing theoretical support for the structural design and layout of the aircraft cargo cabin smoke detector. [ABSTRACT FROM AUTHOR]

Predator–prey dynamics have led to a strong selection of prey's ability to detect and respond to information about the risk environment. Further, intrinsic factors, such as sex, may cause prey to perceive and respond to information differently. Chemical alarm cues from injured conspecifics are a classic example of how prey have evolved to use publicly available information to shape their behavior and enhance fitness, yet sex‐specific alarm reactions are rarely considered. The purpose of our study was to compare how males and females respond to conspecific chemical alarm cues in the livebearing fish species, Brachyrhaphis rhabdophora. Furthermore, we tested males and females from populations with a high‐ or low‐predation environment. Brachyrhaphis rhabdophora showed strong alarm reactions, but contrary to our predictions, showed limited variation due to sex or predation environment. We found that males and females from both populations displayed lower activity levels and increased their swimming depth when exposed to an alarm cue, despite variable and consistent baseline behaviors among individuals. These data further contribute to our understanding of what factors shape the evolution of behavioral responses to chemical alarm cues in fishes. [ABSTRACT FROM AUTHOR]

Intensive Care Unit (ICU) nurses are burdened by excessive number of false and irrelevant alarms generated by patient monitoring systems. Nurses rely on these patient monitoring systems for timely and relevant medical information concerning patients. However, the systems currently in place are not sensitive to the perceptual and cognitive abilities of nurses and thus fail to communicate information efficiently. An efficient communication and an effective collaboration between patient monitoring systems and ICU nurses is only possible by designing systems sensitive to the abilities and preferences of nurses. In order to design these sensitive systems, we need to gain in-depth understanding of the user group through revealing their latent individual characteristics. To this end, we conducted a survey on individual characteristics involving nurses from two IC units. Our results shed light on the personality and other characteristics of ICU nurses. Subsequently, we performed hierarchical cluster analysis to develop data-driven nurse profiles. We suggest design recommendations tailored to four distinct user profiles to address their unique needs. By optimizing the system interactions to match the natural tendencies of nurses, we aspire to alleviate the cognitive burden induced by system use to ensure that healthcare providers receive relevant information, ultimately improving patient safety. [ABSTRACT FROM AUTHOR]

Summary: In this article, the problem of intermittent fault (IF) detection is investigated for nonstationary processes in the multivariate statistics framework. By combining the moving window technique with maximum likelihood estimation (MLE), the moving window Wald‐based control chart is proposed to realize the detection of IFs in nonstationary processes. The computational efficiency and the convergence properties are discussed for the designed iterative algorithm of MLE. Then, necessary and sufficient conditions are presented to guarantee the detectability of IFs with the consideration of window lengths. Moreover, the alarm delays are analyzed for the appearance and disappearance of IFs. In virtue of the above analysis, the optimal window length is derived by minimizing the supremum of alarm delays. In order to estimate the time of IFs' appearance and disappearance, an algorithm is designed with the inspiration of simulated annealing strategy. Finally, a simulation on rotary steerable drilling tool system is provided to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The Bhopal Gas Tragedy, considered the worst industrial disaster in history, occurred in 1984 at a chemical plant in Bhopal, India. Water entered a storage tank containing methyl isocyanate (MIC), causing a release of toxic gas that resulted in numerous deaths and injuries. The incident led to the establishment of the Center for Chemical Process Safety (CCPS) to promote process safety management. The article emphasizes the importance of process safety culture in preventing chemical accidents and discusses the hazards of MIC and the need for refrigeration systems. It also highlights the failure of the high-temperature alarm system and the importance of addressing equipment reliability and process safety culture. The article concludes by suggesting that organizations prioritize equipment performance and avoid accepting equipment failures. [Extracted from the article]

Analysis of interventions by fire protection units in road traffic incidents alerted by the eCall system between 2016 and 2022. Data from the State Fire Service Decision Support System (SFS DSS), provided by the Operational Planning Office, were used. Events from January 1, 2016, 00:00 to December 31, 2022, 23:59 were analyzed. Quantitative data were described using mean (Mean) and standard deviation (SD). Correlations and differences at a significance level of p < 0.05 were considered statistically significant. The analysis is anonymous for both victims and officers involved in the interventions. Between 2016 and 2022, firefighters were alerted by the e-call system 896 times. The shortest average intervention time was 47 ± 37 min and was recorded in 2021. In the comparative analysis of intervention time and factors conditioning the use of the eCall system, it was shown that this time was significantly statistically dependent on the number of cars involved in the incident (p < 0.001), the number of injured persons (p < 0.001), the type of intervention (p < 0.001),), and the occurrence of fuel leakage (p < 0.001). ECall is a relevant system for reporting accidents and collisions on the road. While it proves reliable in road incidents, a significantly high number of false alarms initiated from eCall requires system refinement to avoid accidental alarms and user education about the possibility of unintentionally sending an alarm signal. The authors predict that as the number of vehicles with the eCall system introduced to the roads increases, so will the number of notifications from this system. Data from the analysis of false reports suggest that mechanics and electricians in facilities performing repairs and maintenance of vehicles with the eCall system may lack the necessary knowledge of the need to deactivate the system before starting work. The number of injured people had no impact on the intervention time, which may prove that the rescue services were properly prepared. [ABSTRACT FROM AUTHOR]

The alarm calls of vervet monkeys (Chlorocebus pygerythrus) have been the subject of considerable focus by researchers, owing primarily to the purported referential qualities of different alarm call types. With this focus on reference, acoustic variation among calls elicited by the same range of predators has typically been overlooked. Specifically, at least one type of alarm call—the terrestrial alarm—was described over 50 years ago as being acoustically distinct between males and females—a description that has largely eluded more systematic scrutiny. Here, we provide a quantitative acoustic analysis and comparison of terrestrial alarm calls produced by adult male and female vervet monkeys. We use a random forest model to determine which acoustic variables best distinguish between the calls of males and females, and use an unsupervised clustering technique to objectively determine whether alarms produced by each sex fall into discrete types. We found that the calls of males and females differed most in frequency‐based parameters, with male alarms containing more energy at lower frequencies relative to females. Calls produced by males were also of longer duration, and consisted of longer individual call elements relative to female calls. While calls generally fell into clusters associated with either male or female alarms, we found that some fell into atypical clusters given the caller's sex, and that the clusters themselves showed evidence of intergradation. We discuss these results in terms of potential differences in the function of, and motivation for, calling by males and females. We emphasize the need for a more holistic approach to the classification of vocal signals that considers contextual, functional, and structural variation. [ABSTRACT FROM AUTHOR]

In this study, we describe the acoustic structure of ultrasonic alarm calls of Mongolian gerbils Meriones unguiculatus in the wild and verify these calls as belonging to Mongolian gerbils by comparison of their acoustic parameters with alarm calls recorded in captivity. Both in captivity and in the wild, the alarm calls of Mongolian gerbils represented prolonged calls with an average duration of 118 ms and a flat contour and an average maximum fundamental frequency of 26.84 kHz. We found that alarm calls of captive Mongolian gerbils were shorter and higher in fundamental frequency and followed in a quicker succession than in the wild. Although the dataset size is not sufficient to determine significant acoustic variation between the populations, we discuss the potential reasons of the acoustic differences between the ultrasonic alarm calls produced in the wild and in captivity in our study and between the alarm calls reported in literature for different captive populations. We propose a method for non-invasive estimation of occupancy of the burrows by Mongolian gerbils in fragmented colonies at very low population density, by presence of the ultrasonic alarm calls. [ABSTRACT FROM AUTHOR]

We investigate the acoustic structure of alarm calls in a highly social rodent, the Harting's vole (Microtus hartingi) and describe the phenomenon of collective shouting of bursts of alarm calls which could be produced in synchronized series. The alarm calls of Harting's voles were recorded using an automatic device from 10 different family groups, containing from 4 to 15 potential callers, released to outdoor enclosures. Natural predators and humans served as call-eliciting stimuli. We reveal acoustic differences between urgent alarm calls, evoked by close-vicinity predators and identified from recordings by their calls and by rustle noise from the caller's escape to burrow, and other alarm calls. We also reveal acoustic parameters between alarm calls produced in bursts and other alarm calls (produced in individual series or by a few non-synchronized callers). We discuss why the alarm calls of Harting's voles are unusually high-frequency (about 17 kHz on average in the maximum fundamental frequency) among other vole species producing high-frequency alarms. Adaptive significance of producing the collective synchronized bursts of alarm calls by Harting's voles remains unclear without direct observations of vocal vigilance in this species under natural conditions. [ABSTRACT FROM AUTHOR]

Prey naiveté has been implicated in the global decline and reintroduction failure of many threatened species. A number of tools have been developed to combat prey naiveté including in situ predator exposure using live predators. However, determining the effectiveness and persistence of these interventions can be difficult, and requires comparisons of individual responses from predator-naïve and predator-trained populations. Even when this occurs, interpreting behavioural responses can be challenging because of the constraints of experimental design in large scale population manipulations. We used a common environment experiment and placed burrowing bettongs (Bettongia lesueur) from predator-free and predator-exposed populations together in an 8 ha fenced paddock. The predator-exposed population had been previously exposed to feral cats (Felis catus) under controlled wild conditions for up to six years and initial work suggested this exposure had led to physical and behavioural changes. Within our common environment experiment we compared a range of anti-predator behaviours including trap and feeding behaviour and response to predator scent and models. We found evidence for persistent trait changes up to 7 months, the latest time behaviours were tested, after bettongs were moved to the predator-free common environment and isolated from predators. These behaviours include reduced alarm rates, preference for open areas, and responses to predator scent and model stimuli. Additionally, high risk predator cues were more likely to elicit anti-predator responses than passive measures of anti-predator behaviour. Our design shows that predator exposure leads to persistent shifts in some, but not all, anti-predator behaviours. Identifying the proximate basis of anti-predator traits is important to develop successful tools to combat prey naiveté. Significant statement: Controlled exposure to live predators can lead to improved and persistent changes in some anti-predator responses in naïve mammals. Traits were retained for several months suggesting exposing naïve mammals to low densities of predators prior to reintroduction could assist with improving their ability to co-exist with predators in the longer term. Using high risk predator cues to test anti-predator responses was more successful that passive cues suggesting that practitioners should prioritise the use of high-risk cues when testing anti-predator responses. [ABSTRACT FROM AUTHOR]

Commercial buildings are considered as high‐risk units in fire due to the existence of expensive equipment and large presence of people with different ages. Therefore, the present study aimed to assess the fire risk of commercial and shopping centers in Kashan using the fire risk assessment method for engineering (FRAME). This cross‐sectional study was conducted using FRAME in 15 commercial and shopping centers in Kashan, Iran. The risk assessment data and FRAME checklist were collected by observation and interviews. Data analysis was conducted using Excel and Geographic Information System (GIS) software. The results of 46 checklists showed that the mean score of fire risk R for property was equal to 3.12, for people 3.81 and for activities 1.86. Also, the lowest risk for the property, people and activities belongs to Golestan Center with a score of 1.40, Valiasr with a score of 1.25 and Ataee with a score of 1.15, and the highest risk for the property, people and activities belongs to Mir Seyfi Center with a score of 5.80, Shafagh with a score of 9.96 and Sabz was 2.62, respectively. The present study showed the lack of proper ventilation system, lack of emergency exits, and lack of automatic fire alarm and extinguishing system were the factors affecting fire risk. Therefore, in order to prevent disasters and uncontrollable critical conditions, it is necessary to review the equipment of these buildings and to design and implement fire detection and alarm equipment and different ways of fire control in these centers. [ABSTRACT FROM AUTHOR]

Natural gas is widely used in many homes for cooking, but a lack of gas leak detection has led to large fires and accidents. This article presents the design and implementation of a natural gas detection and extraction system for domestic kitchens in Lima, Peru. The ESP32 microcontroller allowed remote circuit control, resulting in a more convenient setup than the Arduino UNO microcontroller. After calibration of the sensors and their corresponding programming, three actions were established in response to different gas levels: alarm activation, space ventilation and gas extraction, and thermal shutdown. Strategic sensor placement and improved physical presentation of the system were performed to ensure accurate readings and effective deployment. The results demonstrate the proper functioning of the circuit and its ability to prevent accidents related to gas leaks. The designed system offers the advantage of remote monitoring, providing access to the user from any location. In conclusion, this project offers a comprehensive solution to prevent accidents caused by gas leaks in home kitchens. With satisfactory results in terms of operation and rapid response, the project demonstrates its effectiveness in accident prevention. This design offers a practical and accessible solution, improving security and bringing peace of mind to homes. [ABSTRACT FROM AUTHOR]

The emergence of the Internet of Things (IoT) has revolutionized healthcare by enabling remote patient monitoring systems. This system utilizes wearable sensors to collect vital signs such as heart rate, blood glucose level and body temperature. If the patient's temperature increases above 100 degrees Celsius then the alarm will be generated. The collected data is transmitted wirelessly to a secure cloud platform for real-time analysis and visualization. This allows Doctors to remotely monitor patients' health conditions, intervene promptly in case of emergencies, and improve overall care quality. [ABSTRACT FROM AUTHOR]

An alarm or an alarm system is a device that warns or detects any violation of privacy in different places by making noise, sound, or warning the person in a variety of ways, including via email, SMS, phone call, or sounding. Alarms that are currently available in the market have different shapes and types. Generally, they have a common structure and their main purpose is similar to each other. There is another type of burglar alarm in the market, which is used today in certain places to protect property and assets, and in certain organizations such as banks and car alarms. This article reviews and presents a different alarm, whose main purpose is to provide possible security in public and private places, especially military places. [ABSTRACT FROM AUTHOR]

In this study, we first propose the r-k class estimator in COM-Poisson regression, and then we introduce the Shewhart control chart using the deviance residuals obtained from the r-k class estimator. The performance analysis of the new control chart over maximum likelihood, principal components regression, and ridge-based Shewhart control charts is evaluated via a numerical example and a simulation study by using average run length, the standard deviation of run length, the average alarm rate and percentile of run length criteria when different types of shifts are present in data. [ABSTRACT FROM AUTHOR]

AbstractSituational awareness and decision support tools such as procedures and alarm systems are vital for effective interaction among control room operators, especially in safety-critical situations. In safety-critical environments such as process plants, there remains a gap in evaluating specific tools during actual operations, or ”work-as-done.” Additionally, the underlying factors that might impact operators’ cognitive states and performance concerning safety have not been thoroughly explored. The need for such an evaluation is further bolstered by current interaction configurations where operators are more passive than active, thus reducing their cognitive performance. Therefore, this experimental study addresses the highlighted evaluation gap by introducing and comparing three human system interfaces/decision support tools in four human-in-the-loop configurations. The supports include two alarm design formats (prioritized vs. non-prioritized) and three procedure representation formats (paper, screen-based digitized, and an AI-based support system built with an integrated Bayesian network and reinforcement learning model). Ninety-two people (n = 92) participated voluntarily in the test. They were divided equally into four groups. Each group tested three safety-related events in a simulated formaldehyde production facility. Individuals belonging to the group with prioritized alarms and utilized paper procedures rated procedural support slightly higher on average than others in different groups. Unlike the other groups, their assessment of alarm prioritization support remained consistent across all scenarios. Further analysis of the impact of the setup on cognitive states and actual performance will be performed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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This study evaluated the use of endemic enteric coronaviruses polymerase chain reaction (PCR)-negative testing results as an alternative approach to detect the emergence of animal health threats with similar clinical diseases presentation. This retrospective study, conducted in the United States, used PCR-negative testing results from porcine samples tested at six veterinary diagnostic laboratories. As a proof of concept, the database was first searched for transmissible gastroenteritis virus (TGEV) negative submissions between January 1st, 2010, through April 29th, 2013, when the first porcine epidemic diarrhea virus (PEDV) case was diagnosed. Secondly, TGEV- and PEDV-negative submissions were used to detect the porcine delta coronavirus (PDCoV) emergence in 2014. Lastly, encountered best detection algorithms were implemented to prospectively monitor the 2023 enteric coronavirus-negative submissions. Time series (weekly TGEV-negative counts) and Seasonal Autoregressive-Integrated Moving-Average (SARIMA) were used to control for outliers, trends, and seasonality. The SARIMA's fitted and residuals were then subjected to anomaly detection algorithms (EARS, EWMA, CUSUM, Farrington) to identify alarms, defined as weeks of higher TGEV-negativity than what was predicted by models preceding the PEDV emergence. The best-performing detection algorithms had the lowest false alarms (number of alarms detected during the baseline) and highest time to detect (number of weeks between the first alarm and PEDV emergence). The best-performing detection algorithms were CUSUM, EWMA, and Farrington flexible using SARIMA fitted values, having a lower false alarm rate and identified alarms 4 to 17 weeks before PEDV and PDCoV emergences. No alarms were identified in the 2023 enteric negative testing results. The negative-based monitoring system functioned in the case of PEDV propagating epidemic and in the presence of a concurrent propagating epidemic with the PDCoV emergence. It demonstrated its applicability as an additional tool for diagnostic data monitoring of emergent pathogens having similar clinical disease as the monitored endemic pathogens. [ABSTRACT FROM AUTHOR]

The time for smoke gas to reach a smoke detector would depend on its structural characteristics. However, previous studies have neglected to explore this issue. In this paper, three structural variables of a cylindrical smoke detector with a cover are chosen, and the response surface method is used to carry out the experimental design. Furthermore, the incidence point velocity and center point velocity are calculated using the computational fluid dynamics simulation, and the alarm time is obtained using a homemade device. The three parameters that influence the outcomes are then examined to obtain empirical formulas and the best solutions. Comparatively, we observe that the smoke ingress effect is most significantly influenced by the top cover height. After optimization, the incidence point velocity is increased by 115.94 %, the center point velocity is increased by 119.64 %, the alarm time is reduced by 68.57 %, and the gas flowability performance is noticeably improved. [ABSTRACT FROM AUTHOR]

This paper reviews alarm processing methods in electrical power systems, focusing on evolving strategies beyond traditional fault analysis to accommodate modern grid complexities. Historically, alarm processing has predominantly aimed at fault analysis, increasingly merging with technological advances in communication and computing. However, it still needs to fully meet the challenges posed by the dynamic characteristics of modern power systems. This review points out certain inadequacies in current practices, notably their limited adaptation to new grid conditions. The authors propose a novel generation of alarm processing methodologies designed for future grids, emphasizing managing rare events and enhancing operator decision-making through advanced anomaly detection and explainable artificial intelligence. This synthesis presents a prospective direction for future research and applications in alarm processing, advocating for methodologies better suited to supporting system operators amidst technological advancements. [ABSTRACT FROM AUTHOR]

Eavesdropping on heterospecific alarm calls is common in nature, and successful eavesdropping on heterospecific call information can provide benefits to animal survival. Previous studies have shown that free-ranging flocks of domestic chickens can respond to avian heterospecific alarm calls. However, there are limited studies on whether solitary active domestic chickens (Gallus gallus domesticus), especially hens with chicks eavesdrop on heterospecific alarm calls to detect different types of predators and adjust their response depending on whether the predators are a threat to themselves or their young. In the present study, we evaluated the behavioral responses of hens with chick activity to two alarm calls (test playback) and territorial calls (control playback) of wild Japanese tits (Parus minor). The results showed that hens were more vigilant toward the alarm calls than territorial calls and that anti-predator behavior differed significantly between the two alarm calls as predicted: they engage in self-preservation behaviors when the call informs about aerial predators, and in protective behaviors of their chicks when the calls inform about ground predators. In the later case, the number of chicks also has an impact on the behavior of hens (dilution effect). This suggests that hens can eavesdrop on the alarm call of Japanese tits, identify predator information in the alarm call, and adjust the anti-predation strategy based on the type of alarm and the number of chicks. [ABSTRACT FROM AUTHOR]

This study designs an automatic monitoring and alarm system for integrated meteorological observation based on the cloud platform to improve the automatic monitoring ability for integrated meteorological observation and the operation and maintenance management level of meteorological departments. A meteorological data collector was used to collect comprehensive meteorological observation data, transmit the collected comprehensive meteorological observation data to the main control processing module, preprocess the comprehensive meteorological observation data, and finally transmit the pre-processed comprehensive meteorological observation data to the cloud service module through the data transmission module. In the cloud service module, cloud computing and an improved K-means algorithm were used to analyze the comprehensive meteorological observation data, mine abnormal comprehensive meteorological observation data, and send alarm information to the application module. Users can view the comprehensive meteorological observation data in the cloud server on the client in real-time, thus realizing automatic monitoring and alarm for comprehensive meteorological observation. Results show that the average transmission rate of various meteorological comprehensive observation data transmitted by the system is as high as 99.48%, thereby achieving clustering of abnormal meteorological comprehensive observation data. Upon detecting abnormal meteorological data, the system sends alarm information to the mobile phone client to complete the monitoring of abnormal meteorological information. [ABSTRACT FROM AUTHOR]

This study analyzes the extent to which alarm systems impact geographical displacement and/or diffusion of benefits on burglary, which regards as a substitute for the absence of capable guardians. A quasi-experimental design with three nested concentric zones—target, buffer, and control—are utilized by incorporating the WDQ conceptual approach with GIS and a parcel map. The datasets include burglary incidents and alarm permit records. Alarms produce a sizeable impact on burglary reduction. No indication of spatial displacement is observed from protected houses to nearby houses. Alarms create a short geographic ambit and a wider spatial range of diffusion of benefits. A burglar alarm can protect the house without displacing burglary to nearby houses and provides neighboring houses with protection as well. [ABSTRACT FROM AUTHOR]

Patient monitoring systems provide critical information but often produce loud, frequent alarms that worsen patient agitation and stress. This may increase the use of physical and chemical restraints with implications for patient morbidity and autonomy. This study analyzes how augmenting alarm thresholds affects the proportion of alarm-free time and the frequency of medications administered to treat acute agitation. Our emergency department's patient monitoring system was modified on June 28, 2022 to increase the tachycardia alarm threshold from 130 to 150 and to remove alarm sounds for several arrhythmias, including bigeminy and premature ventricular beats. A pre-post study was performed lasting 55 days before and 55 days after this intervention. The primary outcome was change in number of daily patient alarms. The secondary outcomes were alarm-free time per day and median number of antipsychotic and benzodiazepine medications administered per day. The safety outcome was the median number of patients transferred daily to the resuscitation area. We used quantile regression to compare outcomes between the pre- and post-intervention period and linear regression to correlate alarm-free time with the number of sedating medications administered. Between the pre- and post-intervention period, the median number of alarms per day decreased from 1332 to 845 (−37%). This was primarily driven by reduced low-priority arrhythmia alarms from 262 to 21 (−92%), while the median daily census was unchanged (33 vs 32). Median hours per day free from alarms increased from 1.0 to 2.4 (difference 1.4, 95% CI 0.8–2.1). The median number of sedating medications administered per day decreased from 14 to 10 (difference − 4, 95% CI −1 to −7) while the number of escalations in level of care to our resuscitation care area did not change significantly. Multivariable linear regression showed a 60-min increase of alarm-free time per day was associated with 0.8 (95% CI 0.1–1.4) fewer administrations of sedating medication while an additional patient on the behavioral health census was associated with 0.5 (95% CI 0.0–1.1) more administrations of sedating medication. A reasonable change in alarm parameter settings may increase the time patients and healthcare workers spend in the emergency department without alarm noise, which in this study was associated with fewer doses of sedating medications administered. [ABSTRACT FROM AUTHOR]

An evaluation of flood vulnerability is needed to identify flood risk locations and determine mitigation methods. This research introduces an integrated method combining hydro-morphometric modeling and flood susceptibility mapping to assess Padma River Basin's flood risk. Flood zoning, flooding classes, and resource flood risk were explicitly analyzed in this river basin study. Flood risk was calculated using GIS-based hydro-morphometric modeling. Using Horton's and Strahler's methods, drainage density, stream density, and stream order of the Padma River Basin were determined. The Padma River Basin has five sub-basins: A, B, C, D, and E, with stream densities of 0.53 km−2, 0.13 km−2, 0.25 km−2, 0.30 km−2, and 0.28 km−2 and drainage densities of 0.63 km−1, 0.16 km−1, 0.29 km−1, 0.35 km−1, and 0.33 km−1, respectively. Sub-basin A is the most prone to floods due to its high stream and drainage density, whereas B and C are the least susceptible. This study used elevation, TWI, slope, precipitation, NDVI, distance from road, drainage density, distance from river, LU/LC, and soil type to create a flood vulnerability map incorporating GIS and AHP with pair-wise comparison matrix (PCM). The study's flood zoning shows that the northeastern part of this basin is more likely to flood than the southwestern part due to its elevation and high-order streams. Moderate River Flooding, the region's most hazardous flood class, covers 48.19% of the flooding area, including 1078.30 km2 of agricultural land, 94.86 km2 of bare soil, 486.39 km2 of settlements, 586.42 km2 of vegetation cover, and 39.34 km2 of water bodies. The developed hydro-morphometric model, the flood susceptibility map, and the analysis of this data may be utilized to offer long-term advance alarm insight into areas potentially to be invaded by a flood catastrophe, boosting hazard mitigation and planning. [ABSTRACT FROM AUTHOR]

Owing to the increasing amount of information presented in the cockpit, the visual and hearing channels are unable to adequately transmit information, which may increase the mental load on pilots. This study explores the benefits of multimodal alarms under high and low residual capacities during take‐off in civil aircrafts in a quasi‐experimental study. The performance of two modes of multimodal (visual and auditory [VA], and visual, auditory, and tactile [VAT]) alarms were tested. The results showed that the VAT alarms were superior to the VA alarms in terms of choice response times (CRTs) when the participants were exposed to low residual capacities of vision and hearing. However, this effect was not observed when the participants had high residual capacities for vision and hearing. Thus, we considered that an additional tactile alarm could play a significant role in the CRTs when VA resources were consumed. There was no significant difference in the number of response errors between the three multimodal alarm modes. This study provides a key comparison of the two modes of multimodal alarms, indicating that VAT alarms are ideal for use in alarm design strategies for next‐generation civil cockpits. [ABSTRACT FROM AUTHOR]

Cross‐site scripting (XSS) attack has been one of the most dangerous attacks in cyberspace security. Traditional methods essentially discover XSS attack by detecting malicious payloads in requests, which is unable to distinguish attacking attempts with the attacking reality. The authors collect responses from a web server and train a bagging‐based PU learning model to determine whether the XSS vulnerability is truly triggered. To validate the authors' proposed framework, experiments are performed on 5 popular web applications with 11 specified CVE recorded vulnerabilities and 32 vulnerable inputs. Results show that the authors' approach outperforms existing research studies, effectively identifies the attacking reality from attacking attempts, and meanwhile reduces the number of worthless security alarms. [ABSTRACT FROM AUTHOR]

Lost circulation, a common risk during the drilling process, significantly impacts drilling safety and efficiency. The presence of data noise and temporal evolution characteristics pose significant challenges to the accurate monitoring of lost circulation. Traditional supervised intelligent monitoring methods rely on large amounts of labeled data, which often do not consider temporal fluctuations in data, leading to insufficient accuracy and transferability. To address these issues, this paper proposes an unsupervised time series autoencoder (BiLSTM-AE) intelligent monitoring model for lost circulation, aiming to overcome the limitations of supervised algorithms. The BiLSTM-AE model employs BiLSTM for both the encoder and decoder, enabling it to comprehensively capture the temporal features and dynamic changes in the data. It learns the patterns of normal data sequences, thereby automatically identifying anomalous risk data points that deviate from the normal patterns during testing. Results show that the proposed model can efficiently identify and monitor lost circulation risks, achieving an accuracy of 92.51%, a missed alarm rate of 6.87%, and a false alarm rate of 7.71% on the test set. Compared to other models, the BiLSTM-AE model has higher accuracy and better timeliness, which is of great significance for improving drilling efficiency and ensuring drilling safety. [ABSTRACT FROM AUTHOR]

Beeps and tones are ubiquitous in the clinical environment. Their use came about because auditory signals are useful when clinicians are away from the patient and/ or attending to other tasks. However, as technology increased, so did the number of alarms, making it possible for alarms to be confused, masked, or otherwise missed. Alarm signals were restricted in quality and variety because the technology used to reproduce the signals was limited in scope. For much, but not all medical technology, this is no longer the case. In this paper, the author describes the process of updating the alarm signals recommended by a global medical device safety standard, IEC 60601-1-8, where the previously confusing tonal alarms have been replaced by meaningful and heavily tested auditory icon alarms, which are superior along several key performance criteria. [ABSTRACT FROM AUTHOR]

Construction safety requires real-time monitoring due to its hazardous nature. Existing vision-based monitoring systems classify each frame to identify safe or unsafe scenes, often triggering false alarms due to object misdetection or false detection, which reduces the overall monitoring system's performance. To overcome this problem, this research introduces a safety monitoring system that leverages a novel temporal-analysis-based algorithm to reduce false alarms. The proposed system comprises three main modules: object detection, rule compliance, and temporal analysis. The system employs a coordination correlation technique to verify personal protective equipment (PPE), even with partially visible workers, overcoming a common monitoring challenge on job sites. The temporal-analysis module is the key component that evaluates multiple frames within a time window, triggering alarms when the hazard threshold is exceeded, thus reducing false alarms. The experimental results demonstrate 95% accuracy and an F1-score in scene classification, with a notable 2.03% average decrease in false alarms during real-time monitoring across five test videos. This study advances knowledge in safety monitoring by introducing and validating a temporal-analysis-based algorithm. This approach not only improves the reliability of safety-rule-compliance checks but also addresses challenges of misdetection and false alarms, thereby enhancing safety management protocols in hazardous environments. [ABSTRACT FROM AUTHOR]

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A major concern for Industry 4.0 is security issues because of several new cyber-security risks. In recent eras, various Deep Learning methods have been applied for intrusion Detection. On the other hand, these methods have to send the data to the centralized unit. This may alarm various problems associated with efficiency, privacy, and response duration. Furthermore, the data generated by the Internet of Things (IoT) gadgets are more and it is challenging to receive the labeled data as labeling the data is more time-consuming and expensive. This Masquerade several issues to the Deep Learning methods where labeled data is required. To prevail over these challenges a new mechanism has to be acquired. This paper proposes a new federated transfer semisupervised learning approach that takes both labeled and unlabelled data cooperatively. In the first phase, the data is preprocessed, and normalized, and optimal features are selected using Walrus optimization. In the second phase, to learn the representative and less dimensional features an autoencoder(AE) is trained on every gadget using private or local unlabeled data. Then the centralized cloud server aggregates the local model into a global autoencoder by applying Federated Transfer Learning (FTL). In the end, the cloud server consists of a semisupervised neural network with fully connected network layers to the global encoder which in turn trains the model with the readily available tagged data. Experiments were carried out with real-world industrial datasets and the proposed model ensures more privacy without sharing the local data, improved classification performance even with less tagged data, and less overhead in communication. [ABSTRACT FROM AUTHOR]

This article investigates the trade‐off between privacy and security in cyber‐physical systems, with the goal of designing a privacy‐preserving mechanism based on information theory. Considering the unreliability of the communication channel, we assume that the private data is vulnerable to eavesdropping and bias injection attacks. To maintain privacy, the system is equipped with a privacy‐preserving mechanism achieved by injecting Gaussian‐type privacy noise into transmitted data, which inevitably leads to degraded detecting performance. Therefore, we investigate the trade‐off between privacy level and detection performance, where the privacy level and the detection performance are measured by mutual information and Kullback–Leibler divergence, respectively. Then, the optimal privacy noise is obtained by solving a convex optimization problem for maximizing the privacy degree and constraining a bound on detection performance degradation. Furthermore, to optimize the detection performance, another convex optimization problem is proposed to minimize both the false alarm rate and the missed alarm rate while guaranteeing a level of the privacy. Finally, a numerical example of the vehicle tracking problem is adopted to illustrate the effectiveness of the designed framework. [ABSTRACT FROM AUTHOR]

Landslides are a significant geological phenomenon with the potential for catastrophic consequences. There are numerous potential causes of landslides, including both natural and man-made ones. Their growing frequency raises alarm due to the often catastrophic consequences of these events. The monitoring of regions prone to landslides is a multifaceted undertaking that involves the use of several technologies and approaches, such as geospatial technology and modeling. The primary objective of this work is to construct a spatial analysis model for a region in north-western Romania that is susceptible to landslides. A case study was conducted to estimate the susceptibility of landslides in a monitored region located near Sighetu Marmației, Romania. To achieve this objective, a combination of several technologies and approaches were used, such as remote sensing, land surveying, aerial photogrammetry, geographic information systems, and spatial modeling. The research region underwent landslide susceptibility evaluation by processing geo-information data using Geographic Information Systems (GIS) and developing a prediction model based on the MaxEnt (Maximum Entropy) approach. The research's distinctive contribution is integrating geo-information data acquired from the hazardous site utilizing geomatics methods into a novel geo-monitoring system. The research offers valuable insights into the monitoring of landslides, adding to the current body of literature by introducing an expanded paradigm. The study's theoretical and practical implications might be valuable for professionals, local authorities, emergency response agencies, and other parties involved in mitigating the danger of landslides. [ABSTRACT FROM AUTHOR]

(E)-β-Farnesene (EBF) serves as the primary component of the alarm pheromone used by most aphid pest species. Pyrethrum (Tanacetum cinerariifolium) exhibits tissue-specific regulation of EBF accumulation and release, effectively mimicking the aphid alarm signal, deterring aphid attacks while attracting aphid predators. However, cultivated chrysanthemum (Chrysanthemum morifolium), a popular and economically significant flower, is highly vulnerable to aphid infestations. In this study, we investigated the high expression of the pyrethrum EBF synthase (TcEbFS) gene promoter in the flower head and stem, particularly in the parenchyma cells. Subsequently, we introduced the TcEbFS gene, under the control of its native promoter, into cultivated chrysanthemum. This genetic modification led to increased EBF accumulation in the flower stem and young flower bud, which are the most susceptible tissues to aphid attacks. Analysis revealed that aphids feeding on transgenic chrysanthemum exhibited prolonged probing times and extended salivation durations during the phloem phase, indicating that EBF in the cortex cells hindered their host-location behavior. Interestingly, the heightened emission of EBF was only observed in transgenic chrysanthemum flowers after mechanical damage. Furthermore, we explored the potential of this transgenic chrysanthemum for aphid resistance by comparing the spatial distribution and storage of terpene volatiles in different organs and tissues of pyrethrum and chrysanthemum. This study provides valuable insights into future trials aiming for a more accurate replication of alarm pheromone release in plants. It highlights the complexities of utilizing EBF for aphid resistance in cultivated chrysanthemum and calls for further investigations to enhance our understanding of this defense mechanism. [ABSTRACT FROM AUTHOR]

Forage fish species provide essential linkages for energy transfer within pelagic marine food webs. Capelin (Mallotus villosus), the focal forage fish in coastal Newfoundland, Canada, suffered a stock collapse in 1991 and has not recovered. Despite this collapse, capelin continue to provide locally abundant prey aggregations. Here, I synthesize the lessons learned from a long-term capelin-predator research program (2004–2022) on the northeast Newfoundland coast during the postcollapse period. I highlight the importance of simultaneously estimating forage fish biomass and predator responses in a multispecies and multiyear context. High interannual variation in capelin spawning timing and biomass was observed. Lower capelin biomass consistently resulted in predator species- and assemblage-level dietary shifts toward a higher diversity of lower trophic level, alternative prey. Energetic foraging costs of seabirds also increased under lower capelin biomass, but responses differed among species. Summer capelin consumption by dominant seabirds (9389 tonnes) and whales (778 tonnes) indicated predator energetic requirements and revealed higher natural mortality relative to fishery-based (1289 tonnes) mortality. Overall, this case study illustrated that, despite high observed behavioural plasticity, varying species-specific predator responses to changing capelin biomass integrated to increase potential competitive interactions under low capelin biomass, providing a basis for ecosystem-level change. [ABSTRACT FROM AUTHOR]

Under a distributed information system, the scale of various operational components such as applications, operating systems, databases, servers, and networks is immense, with intricate access relationships. The silo effect of each professional is prominent, and the linkage mechanism is insufficient, making it difficult to locate the infrastructure components that cause exceptions under a particular application. Current research only plays a role in local scenarios, and its accuracy and generalization are still very limited. This paper proposes a novel fault location method based on dynamic operation maps and alarm common point analysis. During the fault period, various alarm entities are associated with dynamic operation maps, and alarm common points are obtained based on graph search addressing methods, covering deployment relationship common points, connection common points (physical and logical), and access flow common points. This method, compared with knowledge graph approaches, eliminates the complex process of knowledge graph construction, making it more concise and efficient. Furthermore, in contrast to indicator correlation analysis methods, this approach supplements with configuration correlation information, resulting in more precise positioning. Through practical validation, its fault hit rate exceeds 82%, which is significantly better than the existing main methods. [ABSTRACT FROM AUTHOR]

Early fire identification is critical to saving lives and property. One viable strategy for improving fire detection systems' efficacy and precision is the use of Internet of Things (IoT) technology. This article details research into the conception and development of an IoT, ESP32-based, Arduino MEGA-connected smart pre-alert detection system for fires. Because of its flexible nature, the Arduino microcontroller can easily exchange data with various sensors for real-time monitoring and analysis. This study surveys the existing literature to provide insight into many approaches to early fire detection (forecasting), such as sensor fusion methods and the use of the intelligence concept as a pre-predictor. The research highlights the need to set up sophisticated fire monitoring and alarm systems and to develop predictive models based on sensor data. This study illustrates how embedded systems via IoT technology and the ESP32 architecture may improve the efficiency and dependability of very early fire detection systems. This research adds to the expanding corpus of information that may be used to improve fire safety in contemporary settings. The proposal's sensors include an Arduino MEGA, an ESP32, a MAX6675 module, an MQ-Smoke, and an MQ-Natural Gas for collecting data, which are then processed using Cloud Firebase and a sleek mobile interface that can be reached overseas at any time. [ABSTRACT FROM AUTHOR]

Navigating risk of predation is a major driver of behavioral decision-making in small fishes. Fish use personal information from olfactory and visual indicators of risk, and also rely upon social cues to inform behavioral trade-offs between risk avoidance and fitness-positive activities such as foraging. Here, fathead minnows (Pimephales promelas), were captured, clipped and released at 48 field sites chemically labeled with either fathead minnow alarm cue (high risk) or water (low risk). We removed the chemical label after 2 h, then monitored area use by clipped and non-clipped fish. In addition, a shoal was placed in traps in half of the risky and half of the safe locations as a visual social cue of safety. We caught 2919 fish in the first sample, of which 594 were fathead minnows. These were clipped and released. The second sample caught 1500 fish, of which 164 were fathead minnows including 11 bearing marks from the first sample. Non-clipped fathead minnows and northern redbelly dace in the general community, which lacked personal information about risk status associated with trap sites, avoided areas previously labeled with alarm cues for at least 2 h after the source of alarm cue was removed, unless an experimental shoal was present at the risky site, in which case they joined the shoal in the trap. Clipped fathead minnows with direct personal knowledge of risk showed a significant shift away from areas labeled with conspecific alarm cue and a significant attraction toward sites seeded with a shoal. Moreover, unlike non-clipped fish in the general community, clipped fathead minnows were not influenced by experimental shoals at sites previously labeled as risky. These data indicate that the influence of social cues of safety depend on whether individual minnows have access to recent personal information about risk. Significance statement: Animals use information for making decisions about when and where it is safe. Information comes from direct personal experience and/or from observing the behavior of others (social cues). In this study minnows with different levels of personal knowledge about risk responded differently to social cues about safety. Naïve minnows relied on social cues while minnows with personal knowledge of risk associated with an area ignored social cues. This study, conducted on free-living fish in a natural population, show how fish use information about risk and safety when the risk of predation is highly variable in space and time. [ABSTRACT FROM AUTHOR]

Anomaly detection in deformation is important for structural health monitoring and safety evaluation of dams. In this paper, an anomaly detection model for the deformation of arch dams is presented. It combines the long short-term memory network (LSTM)-based behavior model for dam deformation prediction and the small probability method for control limits determination, and thus is called an LSTM-based anomaly detection model. To demonstrate the advantages of the LSTM-based anomaly detection model, the traditional hydrostatic-seasonal-time behavior model and the confidence interval method are considered for comparison. The 178 m-high Longyangxia Arch Dam is taken as a case study. The results show that the LSTM-based model has sufficiently high accuracy for dam deformation prediction, especially can accurately predict displacement peaks and troughs. The LSTM-based anomaly detection model can significantly avoid false warnings and missing alarms and is able to send alarms in time when the occurrence of adverse conditions causes abnormal deformation of the dam. [ABSTRACT FROM AUTHOR]

In order to ensure the safe operation of the ultra-supercritical thermal power units (USCTPUs), this paper proposes an intelligent alarm method to enhance the performance of the alarm system. Firstly, addressing the issues of slow response and high missed alarm rate (MAR) in traditional alarm systems, a threshold optimization method is proposed by integrating kernel density estimation (KDE) and convolution optimization algorithm (COA). Based on the traditional approach, the expected detection delay (EDD) indicator is introduced to better evaluate the response speed of the alarm system. By considering the false alarm rate (FAR), and EDD, a threshold optimization objective function is constructed, and the COA is employed to obtain the optimal alarm threshold. Secondly, to address the problem of excessive nuisance alarms, this paper reduces the number of nuisance alarms by introducing an adaptive delay factor into the existing system. Finally, simulation results demonstrate that the proposed method significantly reduces the MAR and EDD, improves the response speed and performance of the alarm system, and effectively reduces the number of nuisance alarms, thereby enhancing the quality of the alarms. [ABSTRACT FROM AUTHOR]