Search Results (235)

This study assessed the effectiveness of stress-based cognitive-behavioral training compared to standard training in firefighters, emphasizing their ability to distribute extinguishing water and cool environments evenly during enclosure fires. Experiments took place at the Zbiroh training facility with two firefighter teams (Team A with stress-based training and Team B with standard training) under realistic conditions. Using 58 thermocouples and 4 radiometers, temperature distribution and radiant heat flux were measured to evaluate water distribution efficiency and cooling performance during interventions. Team A consistently achieved temperature reductions of approximately 320 °C in the upper layers and 250–400 °C in the middle layers, maintaining stable conditions, whereas Team B only achieved partial cooling, with upper-layer temperatures remaining at 750–800 °C. Additionally, Team A recorded lower radiant heat flux densities (e.g., 20.74 kW/m² at 0°) compared to Team B (21.81 kW/m²), indicating more effective water application and adaptability. The findings confirm that stress-based training enhances firefighters’ operational readiness and their ability to distribute water effectively during interventions. This skill is essential for safer and effective management of indoor fires under extreme conditions. This study supports the inclusion of stress-based and scenario-based training in firefighter education to enhance safety and operational performance. Full article

Wearable sensors are central to health-monitoring systems, but the limited capacity of compact batteries poses a challenge for long-term and maintenance-free operation. In this study, we investigated ambient electromagnetic wave (AEMW) energy harvesting using a human body antenna (HBA) as a means to supply power to wearable sensors. The power density and frequency distribution of AEMWs were measured in diverse indoor, outdoor, and basement environments. We designed and fabricated a flexible HBA–circuit interface electrode, optimized for broadband impedance matching when worn on the body. Experimental comparisons using a simulated AEMW source demonstrated that the HBA outperformed a conventional small whip antenna, particularly at frequencies below 300 MHz. Furthermore, the outdoor measurements indicated that the power harvested by the HBA was estimated to be −31.9 dBm (0.64 μW), which is sufficient for the intermittent operation of low-power wearable sensors and Bluetooth Low Energy modules. The electromagnetic safety was also evaluated through numerical analysis, and the specific absorption rate was confirmed to be well below the international safety limits. These findings indicate that HBA-based AEMW energy harvesting provides a practical and promising approach to achieving battery-maintenance-free wearable devices. Full article

The use of aerial robots for inspection and maintenance in industrial settings demands high maneuverability, precise control, and reliable measurements. This study explores the development of a fully customized unmanned aerial manipulator (UAM), composed of a tilting drone and an articulated robotic arm, designed to perform non-destructive in-contact inspections of iron structures. The system is intended to operate in complex and potentially hazardous environments, where autonomous execution is supported by shared-control strategies that include human supervision. A parallel force–impedance control framework is implemented to enable smooth and repeatable contact between a sensor for ultrasonic testing (UT) and the inspected surface. During interaction, the arm applies a controlled push to create a vacuum seal, allowing accurate thickness measurements. The control strategy is validated through repeated trials in both indoor and outdoor scenarios, demonstrating consistency and robustness. The paper also addresses the mechanical and control integration of the complex robotic system, highlighting the challenges and solutions in achieving a responsive and reliable aerial platform. The combination of semi-autonomous control and human-in-the-loop operation significantly improves the effectiveness of inspection tasks in hard-to-reach environments, enhancing both human safety and task performance. Full article

Despite recent advances in deep learning for fire detection, much of the current research prioritizes model-centric metrics over dataset fidelity, particularly from a fire safety engineering perspective. Commonly used datasets are often dominated by fully developed flames, mislabel smoke-only frames as non-fire, or lack intra-video diversity due to redundant frames from limited sources. Some works treat smoke detection alone as early-stage detection, even though many fires (e.g., electrical or chemical) begin with visible flames and no smoke. Additionally, attempts to improve model applicability through mixed-context datasets—combining indoor, outdoor, and wildland scenes—often overlook the unique false alarm sources and detection challenges specific to each environment. To address these limitations, we curated a new video dataset comprising 1108 annotated fire and non-fire clips captured via indoor surveillance cameras. Unlike existing datasets, ours emphasizes early-stage fire dynamics (pre-flashover) and includes varied fire sources (e.g., sofa, cupboard, and attic fires), realistic false alarm triggers (e.g., flame-colored objects, artificial lighting), and a wide range of spatial layouts and illumination conditions. This collection enables robust training and benchmarking for early indoor fire detection. Using this dataset, we developed a spatiotemporal fire detection model based on the mixed convolutions ResNets (MC3_18) architecture, augmented with Convolutional Block Attention Modules (CBAM). The proposed model achieved 86.11% accuracy, 88.76% precision, and 84.04% recall, along with low false positive (11.63%) and false negative (15.96%) rates. Compared to its CBAM-free baseline, the model exhibits notable improvements in F1-score and interpretability, as confirmed by Grad-CAM++ visualizations highlighting attention to semantically meaningful fire features. These results demonstrate that effective early fire detection is inseparable from high-quality, context-specific datasets. Our work introduces a scalable, safety-driven approach that advances the development of reliable, interpretable, and deployment-ready fire detection systems for residential environments. Full article

A mobile manipulation robot combines the navigation capability of unmanned ground vehicles and manipulation advantage of robotic arms. However, the development of a mobile manipulation robot is challenging due to the integration requirement of numerous heterogeneous subsystems. In this paper, we propose a multifunctional mobile manipulation robot by integrating perception, mapping, navigation, object detection, and grasping functions into a seamless workflow to conduct search-and-fetch tasks. To realize navigation and collision avoidance in complex environments, a new hierarchical motion planning strategy is proposed by fusing global and local planners. Control Lyapunov Function (CLF) and Control Barrier Function (CBF) are employed to realize path tracking and to guarantee safety during navigation. The convolutional neural network and the gripper’s kinematic constraints are adopted to construct a learning-optimization hybrid grasping algorithm to generate precise grasping poses. The efficiency of the developed mobile manipulation robot is demonstrated by performing indoor fetching experiments, showcasing its promising capabilities in real-world applications. Full article

With increasing global climate change, extreme weather threats to indoor environments are growing. Heatwave events provide essential data for building thermal resilience analysis. However, existing heatwave definition indicators vary widely and lack standardized criteria. To more accurately evaluate indoor overheating risks, this study compared indoor overheating responses under different heatwave definition indicators, considering the temporal disconnect between indoor and outdoor heat conditions. Focusing on Beijing, this study established an indoor–outdoor coupled heatwave evaluation framework using 1951–2021 meteorological data and the heat index as an overheating metric. By analyzing indoor overheating degree and overlap degree to characterize indoor–outdoor correlations, we concluded that different definitions of heatwaves lead to variations in identifications, while multidimensional indicators better capture extreme events. Heatwaves with prolonged duration and high intensity pose greater health risks. Although Beijing’s indoor thermal conditions are generally safe, peak heat indices during summer heatwaves exceed danger thresholds in some buildings, highlighting thermal safety concerns. The metrics for heatwave 6 and heatwave 7 optimally integrate indoor–outdoor characteristics with higher thresholds identifying more extreme events. These findings support the design of building thermal resilience, overheating early warnings, and climate-adaptive electrification strategies. Full article

Time-of-flight sensors have emerged as a viable solution for real-time distance sensing in robotic safety applications due to their compact size, fast response, and contactless operation. This study addresses one of the key challenges with time-of-flight sensors, focusing on how they perceive and evaluate the environment, particularly in the presence of complex geometries and reflective surfaces. Using a Universal Robots UR5e arm in a controlled indoor workspace, two different sensors were tested across eight scenarios involving objects of varying shapes, sizes, materials, and reflectivity. Quantitative metrics including the root mean square error, mean absolute error, area difference, and others were used to evaluate measurement accuracy. Results show that the sensor’s field of view and operating principle significantly affect its spatial resolution and object boundary detection, with narrower fields of view providing more precise measurements and wider fields of view demonstrating greater resilience to specular reflections. These findings offer valuable insights into selecting appropriate ToF sensors for integration into robotic safety systems, particularly in environments with reflective surfaces and complex geometries. Full article

Traffic measurement systems are an essential part of intelligent transportation systems (ITS). These are specialized transport infrastructures where traffic data is collected and analyzed in order to optimize the use of road systems, improve transport safety, and implement future transport plans. The rapid development of transportation systems, urbanization, and industrialization have led to a global problem of air pollution. This has raised the topical issue of measuring and monitoring environmental parameters at traffic monitoring stations in ITS. In this paper, we present a wireless environmental monitoring system, which is a subsystem of a traffic monitoring station. Along with measuring traffic parameters, the station also collects useful meteorological information. A novel hybrid, dual-band IoT system based on LoRa and LoRaWAN for environmental parameters monitoring is presented. The hardware realization of a developed hybrid LoRaWAN end device, together with the sensors used for the measurement of air parameters, is described. Initial results from real test monitoring of environmental parameters on the road in urban environments are presented as a proof of concept. The presented wireless environmental monitoring system can also be used for indoor or outdoor air pollution monitoring, serving as a useful complement to intelligent transport systems. Full article

As a fundamental technology in environmental perception, sound source localization (SSL) plays a critical role in public safety, marine exploration, and smart home systems. However, traditional methods such as beamforming and time-delay estimation rely on manually designed physical models and idealized assumptions, which struggle to meet practical demands in dynamic and complex scenarios. Recent advancements in deep learning have revolutionized SSL by leveraging its end-to-end feature adaptability, cross-scenario generalization capabilities, and data-driven modeling, significantly enhancing localization robustness and accuracy in challenging environments. This review systematically examines the progress of deep learning-based SSL across three critical domains: marine environments, indoor reverberant spaces, and unmanned aerial vehicle (UAV) monitoring. In marine scenarios, complex-valued convolutional networks combined with adversarial transfer learning mitigate environmental mismatch and multipath interference through phase information fusion and domain adaptation strategies. For indoor high-reverberation conditions, attention mechanisms and multimodal fusion architectures achieve precise localization under low signal-to-noise ratios by adaptively weighting critical acoustic features. In UAV surveillance, lightweight models integrated with spatiotemporal Transformers address dynamic modeling of non-stationary noise spectra and edge computing efficiency constraints. Despite these advancements, current approaches face three core challenges: the insufficient integration of physical principles, prohibitive data annotation costs, and the trade-off between real-time performance and accuracy. Future research should prioritize physics-informed modeling to embed acoustic propagation mechanisms, unsupervised domain adaptation to reduce reliance on labeled data, and sensor-algorithm co-design to optimize hardware-software synergy. These directions aim to propel SSL toward intelligent systems characterized by high precision, strong robustness, and low power consumption. This work provides both theoretical foundations and technical references for algorithm selection and practical implementation in complex real-world scenarios. Full article

The data on the volumetric radon activity of the Akchatau territory were systematized in the context of radioecological safety. Radon (Rn²²² and Rn²²⁰) and indoor radon (isotopes Po, Pb, and Bi) make a significant contribution to radon radiation in residential and industrial premises. Increased radon concentration in a number of areas is associated with the Akchatau tungsten–molybdenum mine. The source of radon in geological terms is acid leucocratic granites in the northwestern and southeastern parts of the studied territory. Seasonal assessment of radon radiation was carried out using modern devices “Alfarad Plus” and “Ramon-Radon”. Frequency analysis of the average annual equivalent equilibrium concentration (EEC) in 181 premises showed that only in 47.5% of the premises does the volumetric radon activity not exceed the current standards (200 Bq/m³). Differentiated values of radon concentration were obtained in cases where daily and seasonal observations were carried out. In 43.1% of premises, the effective dose varies from 6.6 mSv/year to 33 mSv/year, and for 9.4% of premises, from 33 mSv/year to 680 mSv/year. The increased radon concentration is caused by high exhalation from the soil surface, the radioactivity of building materials, and low air exchange in the surveyed premises. In the northwestern part of Akchatau, anomalous zones were found where the exposure dose rate of gamma radiation exceeds 0.6 mkSv/hour. An objective assessment of radon largely depends on a number of factors that take into account the geological, technical, atmospheric, and climatic conditions of the region. Therefore, when planning an optimal radon rehabilitation strategy, it is necessary to take the following factors into account: the design features of residential premises and socio-economic conditions. Practical recommendations are given for radiation-ecological and hygienic monitoring of radon safety levels in the environment to reduce effective doses on the population. Full article

Background: Patients with spinal cord injuries, in addition to rehabilitation in specialized facilities, often continue physical therapy at home. At that time, they become highly exposed to indoor pollutants, which can affect the effectiveness of the recovery program and human health. Methods: Thus, the present study presents the monitoring of indoor air quality in a residential facility where a patient with spinal cord injuries undergoes post-traumatic recuperative physical activity. Such a study is useful for ensuring good air quality for the optimal development of a rehabilitation program with the possibility of screening the indoor air quality of the home by the physiotherapist and even by the patient themselves, in the simplest way possible using low-cost equipment. Thus, 11 indoor air quality parameters were monitored for a period of 18 weeks, using low-cost equipment. An air purifier was put into operation for a period of one week to identify differences in the safety of the indoor environment for physical activities. Results: The results indicate an environment with frequent exceedances of the international standards in force for several indicators. After installing the purifier, the air quality stabilized and a much safer and more efficient environment for carrying out the recovery activities was established. Conclusions: Thus, the process of monitoring and optimizing indoor air quality stands as a fundamental requirement for home rehabilitation because it establishes a secure controlled environment that supports recovery in any residential setting. Full article

Soldering is a common engineering practice that releases airborne particulate matter (PM), contributing to significant long-term respiratory risk. The health impact of this exposure is significant, with up to 22% of soldering workers worldwide being diagnosed with conditions such as occupational asthma, restrictive lung disease, and bronchial obstruction. Studies have reported that soldering can produce PM_2.5 concentrations up to 10 times higher than the U.S. Environmental Protection Agency’s (EPA) 24 h exposure limit of 35.0 μg/m³—posing significant respiratory and cognitive health risks under chronic exposure. These hazards remain underappreciated by novice engineers in academic and entry-level industrial environments, where safety practices are often informal or inconsistently applied. Air purification systems offer a mitigation approach; however, performance varies significantly with model and placement, and independent validation is limited. This study uses an indoor air quality monitoring system consisting of six AeroSpec sensors to measure PM_2.5–10 concentrations during soldering sessions conducted with and without commercial air purifiers. Tests were conducted with and without a selection of commercial air purifiers, and measurements were recorded under consistent spatial and temporal conditions. Datasets were analyzed to evaluate purifier effectiveness and the influence of placement on pollutant distribution. The findings provide independent validation of air purifier capabilities and offer evidence-based suggestions for minimizing particulate exposure and improving safety in laboratory soldering environments. Full article

Particulate matter (PM) has been associated with various health issues. However, the most hazardous constituents of fine particles remain unclear, particularly in Asia where the chemical compositions are highly diverse and understudied. This study investigated the concentration and health risks of particulate-bound polycyclic aromatic hydrocarbons (PAHs) and heavy metals in the indoor air of religious spaces in Bangkok, Thailand. Air samples were collected from four religious sites during periods of high activity using a six-stage NanoSampler to capture particle sizes ranging from <0.1 to >10 µm. Chemical analyses were conducted using gas chromatography-mass spectrometry (GC-MS/MS) for PAHs and inductively coupled plasma-mass spectrometry (ICP-MS) for heavy metals. The results revealed significantly elevated concentrations of PM2.5, PAHs (notably benzo[a]anthracene (BaA), chrysene (CHR), and fluoranthene (FLU)), and heavy metals (particularly Mn, Ni, and Cu). Health risk assessments indicated that both the incremental lifetime cancer risk (ILCR) and hazard quotient (HQ) values for several pollutants exceeded the U.S. EPA safety thresholds, suggesting serious cancer and non-cancer health risks for workers exposed to these environments over prolonged periods. This study highlights incense burning as a dominant source of toxic indoor air pollutants and underscores the urgent need for mitigation strategies to reduce occupational exposure in religious buildings. Full article

Accurate information on trail difficulty is essential for ensuring safety and enhancing the effectiveness of forest-based health and recreational activities. This study examined the physiological responses of middle-aged adults to varying trail difficulty levels across both controlled indoor and natural outdoor walking environments. A total of ten healthy individuals aged 40–50 years participated in walking tasks across three designated trail difficulty levels: Moderate, Difficult, and Very Difficult. Physiological indicators assessed included step speed (SS), step count (SC), rate of perceived exertion (RPE), heart rate (HR), oxygen saturation (OS), energy expenditure (EE), metabolic equivalents (MET), and oxygen consumption (VO₂). As trail difficulty increased, HR, RPE, VO₂, EE, and MET consistently showed upward trends, whereas SS and SC demonstrated significant decreases. Additionally, the outdoor setting imposed generally greater physiological demands compared to the indoor condition, suggesting that terrain complexity and elevation changes amplify physical exertion during real-world trail use. The findings contribute valuable empirical evidence for the design of individualized exercise programs, improved trail difficulty classifications, and the advancement of forest-based health promotion policies. Full article

Bioaerosol particles contribute to the reduced indoor air quality and cause various health issues, thus their concentration must be managed. Air cleaning is one of the most viable technological options for reducing quantities of indoor air contaminants. This study assesses the effectiveness of a prototype multi-stage air cleaner in reducing bioaerosol particle viability and concentrations. The single-pass type unit consisted of non-thermal plasma (NTP), ultraviolet-C (UV-C) irradiation, bipolar ionization (BI), and electrostatic precipitation (ESP) stages. The device was tested under controlled laboratory conditions using Escherichia coli (Gram-negative) and Lactobacillus casei (Gram-positive) bacteria aerosol at varying airflow rates (50–600 m³/h). The device achieved over 99% inactivation efficiency for both bacterial strains at the lowest airflow rate (50 m³/h). Efficiency declined with increasing airflow rates but remained above 94% at the highest flow rate (600 m³/h). Among the individual stages, NTP demonstrated the highest standalone inactivation efficiency, followed by UV-C and BI. The ESP stage effectively captured inactivated bioaerosol particles, preventing re-emission, while an integrated ozone decomposition unit maintained ozone concentrations below safety thresholds. These findings show the potential of multi-stage air cleaning technology for reducing bioaerosol contamination in indoor environments, with applications in healthcare, public spaces, and residential settings. Full article