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The paper introduces a new FireAndSmoke open dataset comprising over 22,000 images and 93,000 distinct instances compiled from 1200 YouTube videos and public Internet resources. The scenes include separate and combined fire and smoke scenarios and a curated set of difficult cases representing real-life circumstances when specific image patches may be erroneously detected as fire/smoke presence. The dataset has been constructed using both static pictures and video sequences, covering day/night, indoor/outdoor, urban/industrial/forest, low/high resolution, and single/multiple instance cases. A rigorous selection, preprocessing, and labeling procedure has been applied, adhering to the findability, accessibility, interoperability, and reusability specifications described in the literature. The performances of the YOLO-type family of object detectors have been compared in terms of class-wise Precision, Recall, Mean Average Precision (mAP), and speed. Experimental results indicate the recently introduced YOLO10 model as the top performer, with 89% accuracy and a mAP@50 larger than 91%. [ABSTRACT FROM AUTHOR]

Fire is a significant cause of fatalities and property loss. In tall spaces, early smoke dispersion is hindered by thermal barriers, and initial flames with limited smoke production may be obscured by ground-level structures. Consequently, smoke, temperature, and other fire sensor signals are weakened, leading to delays in fire detection by sensor networks. This paper proposes a multi-height and heterogeneous fusion discriminant model with a multilayered LSTM structure for the robust detection of weak fire signals in such challenging situations. The model employs three LSTM structures with cross inputs in the first layer and an input-weighted LSTM structure in the second layer to capture the temporal and cross-correlation features of smoke concentration, temperature, and plume velocity sensor data. The third LSTM layer further aggregates these features to extract the spatial correlation patterns among different heights. The experimental results demonstrate that the proposed algorithm can effectively expedite alarm response during sparse smoke conditions and mitigate false alarms caused by weak signals. [ABSTRACT FROM AUTHOR]

Innovation in nanotechnology should be accompanied by advances in the monitoring of nanomaterials, especially the concentration of nanoparticles in the air, because of the risk to human health that their use may pose. Current solutions for determining nanoparticle concentrations are mainly used in research laboratories. Firstly, purchase and secondly conservation, maintenance and servicing of these devices are high costs that companies cannot afford on a larger scale. There is therefore a growing demand for monitoring devices suitable for larger-scale use. The aim of the research was to verify the proposed detector's suitability as a stand-alone measuring device for the determination of the nanoparticles' number concentration. An electronic circuit has been developed that is responsible for reading the measured voltage, the necessary conversions and controlling the operation of the detector. A simple menu was developed for the user to configure basic measurement options. Detector tests were done in two stages: with and without aerosol neutralisation. They indicate that the ionisation nanoparticle detector measurements strongly correlate with those obtained with the reference device (r > 0.90 and r > 0.85, respectively). Statistical analysis confirmed the capability of the investigated ionisation nanoparticle detector to estimate the number concentration of nanoparticles (strong correlations) by following concentration changes (non-normal distribution of measured voltage, Shapiro-Wilk test, p < 0.05 for both stages). However, the aerosol neutralisation affected the measurement data obtained from this device (Mann-Whitney U test, p = 4.519 × 10−7), while the reference device did not (Mann-Whitney U test, p = 7.182 × 10−2). [ABSTRACT FROM AUTHOR]

An emergent direct‐write approach, aerosol‐jet printing (AJP), is gaining attention for the deployment of rapid and affordable microadditively manufactured energy‐efficient sensors and printed electronics. AJP enables a broad range of ink viscosities (0.001–1 Pa s) for printing diverse materials ranging from ceramics and metals to polymers and biological matter. Reproducible, high‐spatial‐resolution features (≈10 µm), and wide standoff distances (1–11 mm) between the nozzle and the substrate facilitate conformal printing of complex geometrical designs on nonplanar—e.g., stepped or curved—surfaces. This paper aims to provide a comprehensive overview of state‐of‐the‐art AJP‐based sensors (e.g., strain and temperature gauges, biosensors, photosensors, humidity and surface acoustic wave sensors, dielectric elastomer actuators, and motion, smoke, and hazardous gas detectors) and to discuss prospective applications. The drive toward cost‐effective devices that are smaller, lighter, and better‐performing remains a frontier challenge in the field of printed electronics. Consequently, as AJP becomes increasingly utilized in the high‐volume manufacturing of miniaturized active and passive sensors, it opens a pathway for facile large‐scale fabrication of devices for a wide range of consumer and industrial applications, including transportation, agriculture, infrastructure, aerospace, national defense, and healthcare. [ABSTRACT FROM AUTHOR]

Smoke detectors play a vital role in evacuation and safety during fire incidents, as they directly contribute to the reliability and accuracy of firefighting systems. However, if not installed properly, smoke detectors can trigger unwanted fire alarms (UWFAs), particularly in studio-type apartments. Therefore, this study aimed to develop a method for reducing UWFAs by addressing the challenges posed by cooking by-products in such environments. The proposed algorithm was validated through tests, considering relevant literature and standards, and utilizing indoor air quality sensors. Verification tests were conducted to enhance the accuracy of the algorithm. Based on the experimental results, cutoff values of 5 ppm for CO and 7000 μg/m3 for PM10.0 were proposed as criteria for identifying UWFAs caused by cooking by-products. [ABSTRACT FROM AUTHOR]

Attacks that control single-photon detectors in quantum key distribution using tailored bright illumination are capable of eavesdropping the secret key. Here we report an automated testbench that checks the detector's vulnerabilities against these attacks. We illustrate its performance by testing a free-running detector that includes a rudimentary countermeasure measuring an average photocurrent. While our testbench automatically finds the detector to be controllable in a continuous-blinding regime, the countermeasure registers photocurrent significantly exceeding that in a quantum regime, thus revealing the attack. We then perform manually a pulsed blinding attack, which controls the detector intermittently. This attack is missed by the countermeasure in a wide range of blinding pulse durations and powers, still allowing to eavesdrop the key. We make recommendations for improvement of both the testbench and countermeasure. [ABSTRACT FROM AUTHOR]

To realize early fire identification in cotton harvesting operations, a mid-infrared carbon monoxide (CO) sensor system was developed. To match the broadband light source with a 15° divergence angle, a multipass gas cell (MPGC) with an effective path length of 180 cm was designed to improve sensor sensitivity, leading to a limit of detection (LoD) of 0.83 parts-per-million by volume (ppmv). A damping module with springs at the bottom and front/back sides was fabricated, which can effectively reduce the vibration intensity by >80%. The sensor system can operate normally from −40 °C to 85 °C by stabilizing the temperature of the optical module through heating or cooling as well as using automotive electronic components. An adaptive early fire identification algorithm based on a dual-parameter threshold alarming method was proposed to avoid false and missing alarms. Field deployments on a harvester verified the good practicability of the sensor system. [ABSTRACT FROM AUTHOR]

In a myocardial biopsy, the physician inserts the myocardial biopsy forceps into the heart chamber with fluoroscopic guidance and operates the forceps to collect the myocardium. The tip of the forceps has a gripper and a two-degree-of-freedom passive joint. The myocardial wall is difficult to see through a fluoroscopic image, and sampling may be missed due to the weak contact force and the large angle between the forceps and the myocardium. To solve these problems, we propose a contact force and bending angle detection mechanism for the forceps using optical fibers. The proposed mechanism has three built-in optical fibers and is connected to light-intensity-modulation-type optical sensors. Before contact, the light of the fibers leaks outside through the forceps shaft gap. When the forceps tip comes into contact with the myocardial wall, the external force reduces the shaft gap and the leak light amount. The physician can detect the contact force and bending state at the tip from the ratio of the three measured values. We evaluated the proposed mechanism in in vitro and in vivo experiments. We confirmed that the contact force under 1.4 N and bending angle over 30 deg can be detected. [ABSTRACT FROM AUTHOR]

Early detection of fire is key to mitigate fire related damages. This paper presents a differential pyro-electric infrared (PIR) sensor and deep neural networks (DNNs) based method to detect fire in real-time. Since the PIR sensor is sensitive to sudden body motions and emits a continuous time-varying signal, experiments are carried out to collect human and fire motions using a PIR sensor. These signals are processed using one-dimensional continuous wavelet transform to perform feature extraction. The corresponding wavelet coefficients are converted into RGB spectrum images that are then used as inputs for a deep convolutional neural network. Various pre-trained DNN architectures are adopted to train and identify the collected data for background (no motion), human motion, and fire categories: small quasi-static and spreading fires. Experimental results show that the ShuffleNet architecture yields the highest prediction accuracy of 87.8%. Experimental results for the real-time strategy which works at a speed of 12 frames-per-second show 95.34% and 92.39% fire and human motion detection accuracy levels respectively. [ABSTRACT FROM AUTHOR]

Explosive developments in modern society bring huge fire loads. Previous fire detections at early stages are basically enabled by recognizing abnormal high‐temperatures, smoke particles, and flame light signals. However, the identification of these characteristic signals is generally accompanied by an open flame or smoke, which makes it difficult to prevent further serious damage. Herein, a latent‐fire‐detecting strategy of trace ammonia (NH3) analysis based on nanohybrid Ti3C2Tx MXene/MoS2 is proposed. Benefiting from nanoscale high‐density Schottky heterojunctions between MoS2 and Ti3C2Tx MXene, ultrafast (3 s @100 ppm), sub‐ppm (200 ppb minimum), and high‐sensitivity (81.7% @100 ppm and 10.2% @200 ppb) detection of NH3 are enabled. An assembled latent‐fire‐detecting olfactory system (LFOS) based on MXene/MoS2 and interdigital electrodes can monitor trace NH3 releases from different materials (wool, leather, foam, and nylon) during thermal decomposition at latent stages. Notably, the LFOS can detect fire threats at least 84 s earlier than commercialized smoke detectors, providing more fire dealing time and an escape period; this offers a promising latent‐fire‐warning approach for eliminating fire treats at an early stage. [ABSTRACT FROM AUTHOR]

The solid-state nuclear track detectors are widely used in a variety of nuclear physics experiments and radiation protection. The paper presents the first results of the newly acquired TASLImage™ neutron dosimetry system at the Slovak University of Technology in Bratislava. A comparison of experimental and calculated responses of a TASTRAK plastic nuclear track detector to neutron spectrum of Pu–Be source is presented. The ambient dose equivalents determined by the TASLImage™ system are compared with the ones measured by the NuDET-ENE neutron detector based on the use of ZnS(Ag) scintillator covered with a thin film of 6LiF thermal neutron converter accommodated inside the HDPE moderator. The first results are concluded, and further research needs are identified. [ABSTRACT FROM AUTHOR]

Indicative air quality sensor technologies have the potential to compliment compliance monitoring networks by providing both increased temporal and spatial coverage with low capital and ongoing operational costs. Indicative sensors can also be deployed faster and in a wider range of environments than traditional continuous equivalence methods. Since mid-2019 the NSW Department of Planning, Industry and Environment (DPIE) have been testing numerous indicative particle sensors including Purple Airs and KOALAs. The KOALA units also measure carbon monoxide. This trial involved co-locating these sensors with equivalence methods at numerous DPIE stations for a period of six months. The aim was to determine not only sensor performance, precision and accuracy, but also to test the infrastructure that it is interfaced with such as physical design, power options, data logging, time stamping and telemetry. During the co-location period daily PM2.5 levels were above the benchmark on 99 days across the NSW DPIE Air Quality Monitoring Network (AQMN) due to intense fires throughout the state During the same period daily PM10 standards were above the benchmark on 140 days due to both regional scale dust events and fire. The data obtained during this co-location demonstrates that indicative sensors perform well compared to equivalence methods for fine particulate matter (PM2.5) and visibility measurements from nephelometers. The sensors did not perform well when detecting PM10, missing the magnitude of most dust events that the equivalence methods detected. All indicative sensors were reliable over the period with no sensor requiring repairs/ replacement and provided near 100% data capture rate. The sensors were also able to be quickly and easily deployed. [ABSTRACT FROM AUTHOR]

Fire is indeed one of the major contributing factors to fatalities, property damage, and economic disruption. A large number of fire incidents across the world cause devastation beyond measure and description every year. To minimalize their impacts, the implementation of innovative and effective fire early warning technologies is essential. Despite the fact that research publications on fire detection technology have addressed the issue to some extent, fire detection technology still confronts hurdles in decreasing false alerts, improving sensitivity and dynamic responsibility, and providing protection for costly and complicated installations. In this review, we aim to provide a comprehensive analysis of the current futuristic practices in the context of fire detection and monitoring strategies, with an emphasis on the methods of detecting fire through the continuous monitoring of variables, such as temperature, flame, gaseous content, and smoke, along with their respective benefits and drawbacks, measuring standards, and parameter measurement spans. Current research directions and challenges related to the technology of fire detection and future perspectives on fabricating advanced fire sensors are also provided. We hope such a review can provide inspiration for fire sensor research dedicated to the development of advanced fire detection techniques. [ABSTRACT FROM AUTHOR]

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Understandings of reinforcement mechanisms of landslide-stabilizing pile system are important for long-term safety of reservoir landslides installed piles. The paper proposes a framework to study deformation response and identify triggering factors for landslide–pile system by using geographic detector technology and uncertainty of monitoring data. Majiagou landslide, a representative reservoir landslide installed stabilizing and test piles, is selected as the case study. Firstly, monitoring data of monthly rainfall, variations of reservoir water level, deformation of landslide surface and piles' head were preprocessed. The random deformation data were generated considering uncertainty of deformation monitoring data. Meanwhile, the deformation response of landslide–pile system is analyzed through studying the monitoring deformation data and random deformation data using the clustering algorithm. Finally, geographic detector technique was used to identify main triggering factors of landslide surface deformation and explore interaction types of any two factors. The uncertainty of monitoring data was used to identify the most important triggering factor. Influences of different error and uncertainty of monitoring data on the influence degrees of each factor were further discussed. The comparison with improved Apriori algorithm shows that the presented framework can intuitively measure influence degrees of each factor and analyze interaction types of any two factors. The main conclusions by studying Majiagou landslide indicate that the anti-slide performance and action range of piles gradually decrease with the increase of hydraulic cycle; the deterioration of geomaterials' properties are the most important triggering factor leading to the deformation of landslide–pile system and the degradation of piles' performance, supported by most random deformation groups. [ABSTRACT FROM AUTHOR]

In most cases, fixed temperature heat detectors are used to detect fire in vehicles. The response parameter of such detectors is constant. The time of fire detection by a fire detector, as well as the probability of its false operation, are affected by heat flux from an internal combustion engine. This paper reports the development and investigation of an operational algorithm of the fixed-dynamic heat detectors with variable response parameters. Depending on the temperature influence exerted by engine operation modes, a given algorithm automatically changes a value of the minimal static response temperature of a detector, as well as value of the rate of rise in the temperature of its response. The experimental results showed that in the initial period of engine operation, the temperature change rate in the engine compartment fluctuates and is the largest. It can exceed 290 °С/min. However, regardless of the type of vehicle and the type of engine, when the temperature reaches technological, the temperature change rate would vary within small limits, approximately 30÷50 °С/min. The study results from the Simulink software package (USA) in the MATLAB programming environment (USA) confirm the effectiveness of the programmed operational algorithm of a thermal fire detector. The developed algorithm of a fire detector's operation makes it possible to detect the fire at an early stage and reduce the cases of the device's false response. The fire detector responded to both the maximal and dynamic components. As regards the maximal component, the proposed detector is triggered about 2.3 times faster than the classic maximal thermal fire detector. Detection of fire at an early stage makes it possible to quickly use the fire extinguishing system. [ABSTRACT FROM AUTHOR]

In a complex background, insulator fault is the main factor behind transmission accidents. With the wide application of unmanned aerial vehicle (UAV) photography, digital image recognition technology has been further developed to detect the position and fault of insulators. There are two mainstream methods based on deep learning: the first is the "two-stage" example for a region convolutional neural network and the second is the "one-stage" example such as a single-shot multibox detector (SSD), both of which pose many difficulties and challenges. However, due to the complex background and various types of insulators, few researchers apply the "two-stage" method for the detection of insulator faults in aerial images. Moreover, the detection performance of "one-stage" methods is poor for small targets because of the smaller scope of vision and lower accuracy in target detection. In this article, the authors propose an accurate and real-time method for small object detection, an example for insulator location, and its fault inspection based on a mixed-grouped fire single-shot multibox detector (MGFSSD). Based on SSD and deconvolutional single-shot detector (DSSD) networks, the MGFSSD algorithm solves the problems of inaccurate recognition in small objects of the SSD and complex structure and long running time of the DSSD. To resolve the problems of some target repeated detection and small-target missing detection of the original SSD, the authors describe how to design an effective and lightweight feature fusion module to improve the performance of traditional SSDs so that the classifier network can take full advantage of the relationship between the pyramid layer features without changing the base network closest to the input data. The data processing results show that the method can effectively detect insulator faults. The average detection accuracy of insulator faults is 92.4% and the average recall rate is 91.2%. [ABSTRACT FROM AUTHOR]

Fire has been giving rise to enormous loss of life and property worldwide annually. Early fire warning represents an active and effective means to avoid potential fire hazards before huge losses occur. Despite encouraging advances in early fire warning systems, to date there remains an urgent lack of the design of a durable, flexible, and universal early fire warning sensor for large‐area practical applications. Herein, facile fabrication of a durable, flexible, large‐scale early fire‐warning sensor is demonstrated through constructing a hierarchical flame retardant nanocoating, composed of graphene oxide, poly(dimethylaminoethyl methacrylate), and hexagonal boron nitride, on cotton fabric in combination with the parallelly patterned conductive ink as built‐in electrodes. As‐designed large‐scale sensor (>33 cm and extendable) exhibits a short alarming time of <3 s in response to external abnormal high temperature, heat, or fire. In addition to high washability, flexibility, resistance to abrasion and wear, this hierarchical nanocoating can self‐extinguish, thus enabling the sensor to continue warning during fire. This work offers an inventive concept to develop a universal and large‐scale very early fire‐monitoring platform, which opens up new opportunities for their practical applications in effectively reducing fire‐related casualties and economic losses. [ABSTRACT FROM AUTHOR]

Spontaneous ignition caused by material discharge inside a silo causes considerable economic damage. To prevent this, we developed a silo hotspot detector that can be installed inside the silo to monitor the temperature according to the depth of the silo. However, if the silo hotspot detector located inside the silo is destroyed because of the pressure and load generated during material discharge, it could lead to a larger accident. Therefore, the structural safety of the silo hotspot detector should be evaluated based on material discharge; currently, there is no particular method to achieve this. Therefore, in this study, the theoretical formula is obtained through Eurocode, and the pressure and tensile force acting on the silo hotspot detector are predicted through the finite element method (FEM) using the Coupled Eulerian–Lagrangian(CEL) method. These result were verified by comparing the load measurement data acting on the silo hotspot detector when the silo material was discharged. It was confirmed that simulation using the CEL method can sufficiently simulate the behavior of the silo according to material discharge. Additionally, we confirmed that the structural safety of the silo hotspot detector inside the silo can be evaluated through FEM. [ABSTRACT FROM AUTHOR]

The objective of this research was to determine what technologies could be used to improve current forest fire detection methodology. Although human observation provides a wealth of information on the presence and size of fires, remote sensing technologies can provide increasingly more detailed and accurate information. Thus, the cost efficiency, thermal accuracy, spatial accuracy, range and resolution of three different thermal sensors mounted on aircraft or UAV were analyzed. Our results demonstrate that thermal infrared remote sensing technologies can be used in early fire detection. Each of the sensors tested demonstrated limitations in different contexts and could be applied in forest fire detection in various ways depending on environmental conditions. [ABSTRACT FROM AUTHOR]

Interface detector is an enhanced negative selection algorithm with online adaptive learning under small training samples for anomaly detection. It has better detection performance when it has an appropriate self-radius. Otherwise, overfitting or underfitting would occur. In the present paper, an improved interface detector, which is based on vaccination strategy, is proposed. During the testing stage, negative vaccine can overcome overfitting to improve the detection rate and positive vaccine can overcome underfitting to reduce the false alarm rate. The experimental results show that under the same dataset, self-radius, and training samples condition, the detection rate of the interface detector with negative vaccine is much higher than that of interface detector, SVM, and BP neural network. Moreover, the false alarm rate of the interface detector with positive vaccine is much lower than that of the interface detector and PSA. [ABSTRACT FROM AUTHOR]

Fire alarm and signaling systems are a networked system of fire detectors, fire control units, automated fire extinguishers, and fire notification appliances. Malfunction of these safety-critical cyber-physical systems may lead to chaotic evacuations, property damage, and even loss of human life. Therefore, reliability is one of the most crucial factors for fire detectors. Indeed, even a single report of a fire cannot be ignored, considering the importance of early fire detection and suppression. In this article, we showthat wide-area smoke detectors, which are globally installed in critical infrastructures such as airports, sports facilities, and auditoriums, have significant vulnerabilities in terms of reliability; one can remotely and stealthily induce false fire alarms and suppress real fire alarms with a minimal attacker capability using simple equipment. The practicality and generalizability of these vulnerabilities has been assessed based on the demonstration of two types of sensor attacks on two commercial off-the-shelf optical beam smoke detectors from different manufacturers. Further, the practical considerations of building stealthy attack equipment has been analyzed, and an extensive survey of almost all optical beam smoke detectors on the market has been conducted. In addition, we show that the current standards of the fire alarm network connecting the detector and a control unit exacerbate the problem, making it impossible or very difficult to mitigate the threats we found. Finally, we discuss hardware- and software-based possible countermeasures for both wide-area smoke detectors and the fire alarm network; the effectiveness of one of the countermeasures is experimentally evaluated. [ABSTRACT FROM AUTHOR]

• Consideration of detection failure outperforms standard models in real scenarios. • Solution approach is computationally tractable and achieves global optimality. • Formulation is successfully tested on two petrochemical facility layouts. Flame detectors provide an important layer of protection for personnel in petrochemical plants, but effective placement can be challenging. A mixed-integer nonlinear programming formulation is proposed for optimal placement of flame detectors while considering non-uniform probabilities of detection failure. We show that this approach allows for the placement of fire detectors using a fixed sensor budget and outperforms models that do not account for imperfect detection. We develop a linear relaxation to the formulation and an efficient solution algorithm that achieves global optimality with reasonable computational effort. We integrate this problem formulation into the Python package, Chama, and demonstrate the effectiveness of this formulation on a small test case and on two real-world case studies using the fire and gas mapping software, Kenexis Effigy. [ABSTRACT FROM AUTHOR]

Nowadays, great effort has been devoted to designing biomass-derived nanoscale carbon fibers with controllable fibrous morphology, high conductivity, big specific surface area and multifunctional characteristics. Herein, a green and renewable strategy is performed to prepare the biomass-based nanoscale carbon fibers for fire warning sensor, supercapacitor and moist-electric generator. This preparation strategy thoroughly gets over the dependence of petroleum-based polymeride, and effectually improves the energy storage capacity, sensing sensitivity, humidity power generation efficiency of the obtaining biomass-based carbon nanofibers. Without the introduction of any active components or pseudocapacitive materials, the specific capacitance and energy density for biomass-based nanoscale carbon fibers achieve 143.58 F/g and 19.9 Wh/kg, severally. The biomass-based fire sensor displays excellent fire resistance, stability, and flame sensitivity with a response time of 2 s. Furthermore, the biomass-based moist-electric generator shows high power generation efficiency. The output voltage and current of five series connected and parallel-connected biomass-based moist-electric generators reaches 4.30 V and 43 μA, respectively. Notably, as the number of biomass-based moist-electric generators in series or parallel increases, the overall output voltage and current of the device system have a linear relationship. This work proposes a self-powered fire prediction system based on nanoscale carbon fibers that integrates sensing, power generation, and energy storage functions. [ABSTRACT FROM AUTHOR]

Early fire detection is an efficient method to mitigate disastrous fire loss. However, developing smart low-temperature fire-warning sensors that better diminish fire hazards, especially those caused by household appliances, is still challenging. Herein, a salts-modified chitosan (salts-modified CS) based sensor with integrated fire-warning and humidity-monitoring capability is proposed using an easy assembling method. This sensor can respond to temperatures as low as 50 °C and a flame within 2 s quickly and detect relative humidity (RH) range above 50 % at 50 °C and 75 °C sensitively. This system can be reusable for multiple ignitions and works in high-humidity environments (>50 %). Furthermore, the comparison between different salts-modified CS films is carried out to elucidate the mechanism of the formation of electric current under the joint driven by temperature and humidity. Moreover, real-time temperature and RH monitoring can be achieved with a wireless transmission section. This design shows a promising approach for multifunctional CS-based sensors and paves a path to developing a new generation of smart fire-warning detectors. [Display omitted] • The functional chitosan-based film is used as a fire-warning and humidity sensor. • The sensor responds to a low temperature of 50 °C and a high-humidity environment. • This sensor presents reusable fire warnings under multiple fire attacks. • The real-time message of temperature and humidity is transmitted via wireless. [ABSTRACT FROM AUTHOR]

Audibility of fire alarm systems at mandated levels is critical to ensure a warning is heard by building occupants. To accomplish this, designers and installers utilize on-scene measurements as well as design calculations to ensure that the audibility of the fire alarm system is as designed as required. Despite this, there is a dearth of information on the error and variability rates associated with these experiments and calculations. To fill in this gap, a three-room and connected corridor test setup was utilized and multiple repeat audibility tests were conducted across 12 configurations of different receiving rooms and door positions. Calculations were then conducted utilizing two oft-used and referenced methods. Ultimately, the experimental measurements had a coefficient of variation of 3.0% to 10.9% depending on the configuration. The calculation methods had differences from the experimental average of − 6.8% to 8.0% combined. It is shown that designers and installers should consider making multiple measurements to capture the experimental variability or at least assume it is 10% and if the measurement errors are randomized, could be as high as 20% or more. Meanwhile, a 10% factor of safety should be considered with the calculation methods to ensure the design implementation exceeds the intended audibility. Some additional observations from the experiments and calculations are also discussed including that the tests and calculations provide justification for the code requirements that a notification appliance be present in each bedroom or room that may have a door closed and, if it is a smoke alarm, that it be interconnected to other alarms to ensure audibility. [ABSTRACT FROM AUTHOR]

[Display omitted] • Design and fabricate an all-solid polymeric infrared sensor by mimicking the structure of the IR sensing organ of "fire-beetles". • The sensor is capable of bolometric and photomechanic dual sensing modes implemented through piezoresistive sensing membrane. • Responsivity, response time and detection limit are evaluatedat ∼2 W−1, ∼1 s, and ∼0.1 mW/√Hz. • Finite element modeling identifies the possibility for orders-of-magnitude improvement on the responsivity of the sensor. By mimicking the structure of the infrared-sensing organs of the "fire-beetles" of genus Melanophilaacuminata, we herein designed and fabricated an all-soild "fire-beetles" inspired infrared sensor, the key components of which is composed of a stress-concentrated photothermal expansion dendrite structure made of polydopamine enabled polydimethylsiloxane nanocomposite elastomer and a highly-sensitive piezoresistive membrane processed by direct laser writing carbonization. Our sensor possesses bolometric and photomechanic dual sensing modes, with the optimal responsivity of −1.4 W−1 and 2.2 W−1, the detection limit of 0.03 mW/√Hz and 0.23 mW/√Hz, as well as the response time of 3.8 s and 2.5 s, respectively. The two-step photothermal and solid mechanics FEA modeling was performed to help understanding its sensing behavior in both bolometric and photomechanic operation modes, which indicates the possibility for orders-of-magnitude improvement on the responsiveness of our "fire-beetles" inspired IR sensor when increasing the bonding strength between the photothermal dendrite tip and the piezoresistive sensing membrane. The all-solid nature and the piezoresistive read-out scheme as well as its good performance make our sensor valuable for low-cost IR sensing applications. [ABSTRACT FROM AUTHOR]

In this letter, we report on high responsivity fire detectors based on PbS colloidal quantum dots photoconductors. The devices operate in the near infrared and are equipped with a visible light silicon filter for wavelength selectivity. Devices are fabricated by a simple, low cost, and silicon compatible process based on drop casting of a ligand exchanged solution of PbS nanoparticles. The photodetectors exhibit responsivity as high as 20 A/W at 1-V bias. We exploit the combination of their high responsivity and spectral response for the development of a novel fire detector able to detect a small flame at a distance exceeding 15 m in ambient illumination. [ABSTRACT FROM PUBLISHER]

FeSi2 thermoelectric conversion materials are known for their high resistance to corrosion and heat; deposits of these materials are also plentiful and they have a low environmental impact. For this reason, extensive research has been carried out on FeSi2 thermoelectric conversion materials for power generation. In order to extend its application range, the authors tested the use of a sensor made of FeSi2. It is possible to use FeSi2 in fire sensors because it can be used to detect the voltage produced due to a change in the temperature. The present study examined the thermo-electromotive force of FeSi2 and its thermo-responsiveness in order to assess whether it has functional properties for use in fire sensors. The influence of module dimensions on thermo-electromotive force and thermal responsiveness was also examined. [ABSTRACT FROM AUTHOR]

This paper presents the design and development of a fuzzy logic based multisensor fire detection system and a web-based notification system. Until recently, most consumer grade fire detection systems relied solely on smoke detectors. The protection provided by these has been established to be limited by the type of fire present and the detection technology at use. The problem is further compounded by the lack of adequate alert and notification mechanisms. A typical system relies on the physical presence of a human being to act on the alert. In developing countries, poor planning and addressing negatively affects the fire and rescue crew's response time. To address this problem, a fuzzy logic system was implemented using an Arduino development board with inputs from an MQ2 smoke sensor, a TMP102 temperature sensor, and a DFRobot flame sensor. The output of the detection system is sent over short message service (SMS) using a SIM900 global system for mobile communication (GSM) module to the web-based system and the house owner or caretaker in real-time. With access granted to the web-based system, the fire and rescue crew also get notified in real-time with location information. A comparison between the efficiency of the notification system employed by standard fire detectors and the multisensor remote-based notification approach adopted in this paper showed significant improvements in the form of timely detection, alerting, and response. [ABSTRACT FROM AUTHOR]