Multi-channel Data Acquisition Research Articles

Expert consensus on the key technologies for a multi-point trigger intelligent surveillance and early warning system for infectious diseases

An effective infectious disease surveillance and early warning system is a crucial component of public health safety and is essential for preventing and controlling outbreaks of infectious diseases. Enhancing surveillance and early warning capabilities is an urgent priority for advancing high-quality disease prevention and control efforts. Combining the research findings and practical experiences of experts in epidemiology, clinical medicine, disease prevention and control, data science, and computer science, and following multiple rounds of expert discussions, we have developed a consensus on the key technologies for a multi-point trigger intelligent surveillance and early warning system for infectious diseases. This consensus primarily covers the related concepts and definitions of the multi-point trigger intelligent surveillance and early warning system for infectious diseases, the key technical framework, sources, acquisition, and governance of multi-channel warning data, classification of early warning methods, multi-point trigger intelligent surveillance and early warning paths, multi-point trigger warning and comprehensive assessment, response to warning signals, and evaluation of early warning effectiveness. It aims to provide technical references for the construction and application of a multi-point trigger intelligent surveillance and early warning system for infectious diseases.

IoT-enabled advanced monitoring system for tubular batteries: Enhancing efficiency and reliability

Monitoring battery health is crucial for predicting the lifespan of batteries in electric vehicles. Real time voltage, current, and operating temperature are the key factors influencing longevity. An efficient data acquisition (DAQ) system becomes essential for accurately monitoring real-time battery operating parameters. For commercially viable three-wheeled electric vehicles, a cost-effective yet highly accurate DAQ is imperative. Current commercially available lead-acid three-wheeled electric vehicles lack a dedicated DAQ for monitoring battery health. In this investigation, we address this gap by developing a low-cost, multi-channel DAQ tailored for such electric vehicles, integrated with a cloud server for subsequent data analysis and Remaining Useful Life (RUL) prediction. The implemented prototype excels in measuring individual battery terminal voltage and battery pack voltage under operating conditions, employing the node voltage subtraction method. It also features real-time detection of high current discharges up to 30A using a Hall effect-based current sensor. A custom temperature sensor probe was designed to detect battery electrolyte temperatures up to 60 °C, with multiple probes operating in multiplexed mode to reduce circuit complexity. The system is proficient in storing recorded data locally and in the cloud. A mobile device platform has been developed and interfaced with the system for real-time monitoring of battery parameters. After testing multiple prototypes in running vehicles for over 1000h without failure, the system demonstrated a high accuracy level of 98.6 %. Multiple prototypes were designed to validate the measured accuracy.

Investigation of current density and temperature distribution characteristics under transient conditions based on a 108 cm2 segmented proton exchange membrane fuel cell

Visualizing the electrochemical and temperature distribution characteristics inside proton exchange membrane fuel cell (PEMFC) should greatly contribute to the product development and performance optimization. In the study, a customized 27-segment 108 cm2 fuel cell was fabricated based on the printed circuit board methodology to achieve the local current density and local voltage measurement. In addition, 27 thermocouples were embedded in the segmented cathode graphite gas flow plate for local temperature measurement. Detailed information of fuel cell core components, multi-channel data acquisition system, external temperature control system, and uncertainty analysis was presented. According to the test results of 4000 s current rising conditions and 2400 s current decreasing conditions, the measurement deviation of total current between data acquisition system and TOYO test station is 2.4%–3.2%. Meanwhile, the deviation of output voltage ranges from 0.1% to 2.6%. To quantify the degree of distribution non-uniformity, the range and standard deviation of local parameter values as well as contour map are demonstrated. The non-uniformity of local current density and local temperature becomes more significant under higher operating current conditions. Furthermore, the degree of parameter non-uniformity is more noticeable under current rising conditions compared with current decreasing conditions. The transient increase of operating current leads to the sudden overshoot phenomena of local current range, and the change of local current density among all segments is instantaneously finished. However, the range of current demonstrates relatively smooth transition trends when reactant flow rate rises. Higher temperature area appears in the middle of membrane electrode assembly, especially in the position with higher local current density. The parameter distribution and transition characteristics could provide helpful guidance for fuel cell optimization and diagnosis.

A data compression algorithm with the improved SRLE for high-throughput neural signal acquisition device.

Multi-channel acquisition systems of brain neural signals can provide a powerful tool with a wide range of information for the clinical application of brain computer interfaces. High-throughput implantable systems are limited by size and power consumption, posing challenges to system design. To acquire more comprehensive neural signals and wirelessly transmit high-throughput brain neural signals, a FPGA-based acquisition system for multi-channel brain nerve signals has been developed. And the Bluetooth transmission with low-power technology are utilized. To wirelessly transmit large amount of data with limited Bluetooth bandwidth and improve the accuracy of neural signal decoding, an improved sharing run length encoding (SRLE) is proposed to compress the spike data of brain neural signal to improve the transmission efficiency of the system. The functional prototype has been developed, which consists of multi-channel data acquisition chips, FPGA main control module with the improved SRLE, a wireless data transmitter, a wireless data receiver and an upper computer. And the developed functional prototype was tested for spike detection of brain neural signal by animal experiments. From the animal experiments, it shows that the system can successfully collect and transmit brain nerve signals. And the improved SRLE algorithm has an excellent compression effect with the average compression rate of 5.94%, compared to the double run-length encoding, the FDR encoding, and the traditional run-length encoding. The developed system, incorporating the improved SRLE algorithm, is capable of wirelessly capturing spike signals with 1024 channels, thereby realizing the implantable systems of High-throughput brain neural signals.

A SoC-FPGA based readout platform for the free-running AMBER data acquisition system

Owing to a change in the scope of physics research, the AMBER spectrometer at CERN is undergoing an update on its instrumentation and trigger strategy for the data acquisition system. One of the key updates is the adoption of a free-running and trigger-less operation, which differs from the predecessor of AMBER, COMPASS, by working in a continuous mode but in an event-based triggered way.In this article, we present a multichannel data-acquisition platform developed in line with the next generation of trigger-less and free-running data acquisition systems. The platform is based on a pre-existing Mezzanine Sampling ADC board for the analog-to-digital conversion and a Xilinx Zynq Ultrascale+ System on Module for online real-time data processing. We also present the evaluation of the system operating in continuous mode, taking data from a 25-element electromagnetic calorimeter prototype with a muon beam, during the first AMBER pilot run. The acquired data were used for noise and pulse shape studies required for the design of the algorithms for lossless compression and data feature extraction needed for trigger-less operation.

Performance evaluation of Yonsei Single-photon Emission Computed Tomography (YSECT) for partial-defect inspection within PWR-type spent nuclear fuel

As the amount of power generated by nuclear power plants increases, the importance of managing Spent Nuclear Fuel (SNF) to prevent proliferation increases as well. In a previous study, we optimally designed the gamma emission tomography instrument named Yonsei Single-photon Emission Computed Tomography version 2 (YSECT.v.2) based on Monte Carlo (MC) simulation for inspection and detection of defects in SNF. The objective of the current study was to fabricate a prototype YSECT.v.2 instrument and to evaluate its performance. The YSECT.v.2 instrument consists of scintillator-based detection modules, a multi-channel data acquisition (DAQ) module, a heat reduction module, and a waterproof housing. Based on the experimental results for a mock-up of fresh nuclear fuel rods, the spatial resolution of the prototype YSECT.v.2 instrument was determined to be 10.8 mm. In the experiment with 137Cs test sources, a high-quality image could be obtained with the exclusion of the pixel value below the threshold. For a 6 × 6 array, the spatial resolution for 137Cs was analyzed to be 7.2 mm. Considering these results, we expected that source distribution could be distinguishable using the YSECT.v.2. In the future, further experimentation will validate the performance of the fabricated instrument for a mock-up of SNF rods in water.

Research and Development of an Embedded Multi-Channel Sensor Data Acquisition Device for Reservoir Dams

The embedded system of intelligent reservoir dam achieves the integration and efficient utilization of water conservancy dam system data through multi-channel data collection and analysis calculated by computer technology, CNC system, and neural network. Compared with traditional data collection and processing methods, both timeliness and accuracy have been greatly improved. This study aims to develop a multi-channel sensor data acquisition device for reservoir dams based on embedded system technology. This device can collect real-time and efficient data from sensors in various parts of the dam, ensuring the safe operation of the reservoir dam. By using advanced embedded system technology, this device has advantages such as low power consumption, high stability, and real-time data transmission. The Analytic Hierarchy Process (AHP) was used to study the embedded multi-channel sensor data acquisition device for reservoir dams in multiple directions and factors. The AHP method provides an effective means for problem decision-making in complex situations. Referring to the AHP method, the factors that affect reservoir dams can be divided into different levels. Compare the importance of two random factors in each level to obtain a specific quantitative expression of the relative important factors on a scale. Then repeat this step to obtain the weight ranking for different levels. At the same time, the device monitors key parameters such as temperature, humidity, displacement, and pressure in various parts of the dam through multiple sensors, providing strong support for early warning and decision-making of reservoir dams. The results of this study have important practical significance and application value for improving the safety and stability of reservoir dams.

Triboelectric nanogenerator for Self-Powered musical instrument sensing based on the Ion-Electricfield-Migration Nylon/Na2SO4 nanofiber film

The advanced sensing technologies have a significantly important influence in promoting the design and research of intelligent musical instruments. Herein, we propose an ion-electricfield-migration enhanced triboelectric nanogenerator (IE-TENG) with the features of low-cost, lightweight and high-sensitive for the sensors of intelligent musical instruments. Compared to pure nylon nanofiber films, the voltage of IE-TENG with the nylon/Na2SO4 nanofiber films has been improved by 4.5 times, which is benefiting from the improved electrostatic induction of anions and cations migration. The fabricated IE-TENG sensor can achieve an excellent sensing performance with a sensitivity of 7.29 V N−1, a signal-to-noise ratio of 68.61 dB and a response-time of 10 ms, which are several times higher than the previous research works. Importantly, combined with the array structure design, multi-channel data acquisition and artificial intelligence digital analysis, an intelligent musical instrument system is constructed to gather and analyze the performer's playing information in real-time and quickly give the corresponding evaluation and improvement suggestions. Our finding not only give a good way for fabricating excellent performance TENG, but also promotes the application of TENG in intelligent musical instruments.

Performance testing of gas-tight portable RPC for muography applications

This paper reports the latest developmental efforts for a position-sensitive glass-based Resistive Plate Chamber (RPC) and a multi-channel Data AcQuisition (DAQ) system tailored for muon tracking in muography applications. The designed setup prioritizes portability, aiming for field applications where both the detector and the DAQ operate effectively in external environmental conditions. Comprehensive discussions on hardware development activities and signal processing techniques are included, incorporating noise filtering to enhance the accurate detection of real muons. A muon absorption measurement has also been carried out to understand the behavior of these detectors from an application perspective.

Unbiased multivariate multiscale sample entropy

The development of multi-channel data acquisition techniques has provided richer prior information for studying the nonlinear dynamic characteristics of complex systems. However, conventional nonlinear feature extraction algorithms prove unsuitable in the context of multi-channel data. Previously, the multivariate multiscale sample entropy (MMSE) algorithm was introduced for multi-channel data analysis. Although the MMSE algorithm generalized the multiscale sample entropy algorithm, presenting a novel method for multidimensional data analysis, it remains deficient in theoretical underpinning and suffers from shortcomings, such as missing cross-channel correlation information and having biased estimation results. In this paper, unbiased multivariate multiscale sample entropy algorithm (UMMSE) is proposed. UMMSE increases the embedding dimension from <i>M</i> to <i>M</i> + <i>p</i>. This increasing strategy facilitates the reconstruction of a deterministic phase space. By virtue of theoretical scrutiny grounded in probability theory and subsequent experimental validation, this paper illustrates the algorithm's effectiveness in extracting inter-channel correlation information through the integration of cross-channel conditional probabilities. The computation of similarities between sample points across different channels is recognized as a potential source of bias and instability in algorithms.Through simulation experiments, this study delineates the parameter selection range for the UMMSE algorithm. Subsequently, diverse simulation signals are employed to showcase the UMMSE algorithm’s efficacy in extracting both within-channel and cross-channel correlation information. Ultimately, this paper demonstrates that the new algorithm has the lowest computational cost compared with traditional MMSE algorithms.

Design of a Multi-channel Data Acquisition and Storage Device Based on FPGA

Design of a Multi-channel Data Acquisition and Storage Device Based on FPGA

Self-powered retractable reel sensor for crack monitoring and warning in civil infrastructures

Crack occurrence and propagation are among the critical factors that affect the performance and lifespan of civil infrastructure such as bridges, buildings, etc. Therefore, numerous crack detection and characterization techniques have been developed over the past decades. In this study, we proposed the self-powered with the merits of cost-affordable, high resolution, and wide monitoring range to monitor small cracks in infrastructure. The self-powered retractable reel sensor includes two triboelectric nanogenerator (TENG) sensing units for low and high speed fracture monitoring, respectively. By using the principle of retractable reel structure, the linear motion of the crack is converted into the rotation of the TENG. Through peak extraction and pulse counting, the testing of crack velocity and displacement was realized simultaneously. A real resolution of 0.222 mm are achieved through the structural design and electrical performance characterization. With the help of the LabVIEW platform and multi-channel data acquisition program, its application in crack monitoring and early warning is successfully demonstrated. This work presents a novel self-powered sensor for crack monitoring and early warning, such a strategy is potentially available for distributed sensor construction towards structural health monitoring (SHM) of the infrastructure.

Underwater Single-Photon Lidar Equipped with High-Sampling-Rate Multi-Channel Data Acquisition System

Lidar has emerged as an important technology for the high-precision three-dimensional remote sensing of the ocean. While oceanic lidar has been widely deployed on various platforms, its underwater deployment is relatively limited, despite its significance in deep-sea exploration and obstacle avoidance for underwater platforms. Underwater lidar systems must meet stringent requirements for high performance, miniaturization, and high integration. Single-photon lidar, by elevating the detection sensitivity to the single-photon level, enables high-performance detection under the condition of a low-pulse-energy laser and a small-aperture telescope, making it a stronger candidate for underwater lidar applications. However, this imposes demanding requirements for the data acquisition system utilized in single-photon lidar systems. In this work, a self-developed multi-channel acquisition system (MCAS) with a high-resolution and real-time histogram statistics capability was developed. By utilizing field-programmable gate array (FPGA) technology, a method that combines coarse counters with multi-phase clock interpolation achieved an impressive resolution of 0.5 ns and enabled a time of flight duration of 1.5 μs. To address counting instability, a dual-counter structure was adopted in the coarse counter, and real-time histogram statistics were achieved in the data acquisition system through a state machine. Furthermore, the non-uniform phase shift of the clock was analyzed, and a correction algorithm based on code density statistics was proposed to mitigate the periodic modulation of the backscattered signal, with the effectiveness of the algorithm demonstrated through experimental results. The robustness and stability of the MCAS were validated through an underwater experiment. Ultimately, the development of this compact acquisition system enables the implementation of underwater single-photon lidar systems, which will play a crucial role in underwater target imaging, obstacle avoidance in underwater platforms, and deep-sea marine environment monitoring.

Wearable real-time multi-health parameter monitoring system

PurposeThe purpose of this study is to design a wearable medical device as a human care platform and to introduce the design details, key technologies and practical implementation methods of the system.Design/methodology/approachA multi-channel data acquisition scheme based on PCI-E (rapid interconnection of peripheral components) was proposed. The flexible biosensor is integrated with the flexible data acquisition card with monitoring capability, and the embedded (device that can operate independently) chip STM32F103VET6 is used to realize the simultaneous processing of multi-channel human health parameters. The human health parameters were transferred to the upper computer LabVIEW by intelligent clothing through USB or wireless Bluetooth to complete the transmission and processing of clinical data, which facilitates the analysis of medical data.FindingsThe smart clothing provides a mobile medical cloud platform for wearable medical through cloud computing, which can continuously monitor the body's wrist movement, body temperature and perspiration for 24 h. The result shows that each channel is completely accurate to the top computer display, which can meet the expected requirements, and the wearable instant care system can be applied to healthcare.Originality/valueThe smart clothing in this study is based on the monitoring and diagnosis of textiles, and the electronic communication devices can cooperate and interact to form a wearable textile system that provides medical monitoring and prevention services to individuals in the fastest and most accurate way. Each channel of the system is precisely matched to the display screen of the host computer and meets the expected requirements. As a real-time human health protection platform technology, continuous monitoring of human vital signs can complete the application of human motion detection, medical health monitoring and human–computer interaction. Ultimately, such an intelligent garment will become an integral part of our everyday clothing.

Study on the Force Model of Squeezed Branch Piles Based on Surface Potential Characteristics

Squeezed branch piles, which boast the advantages of great bearing capacity, small settlement, and good stability, are an important infrastructure in the foundation of buildings, and their safety state is related to the safety of the entire structure. As a non-destructive testing method, surface potential can be used to effectively evaluate the damaged state of a pile foundation without destroying its stability. On this basis, in this study, the characteristics of surface potential change during settlement and deformation of squeezed branch piles under graded loading were tested and analyzed with the aid of a self-made loading system of reaction beams and an LB-IV multi-channel potential data acquisition system. The results show that: Under graded loading, squeezed branch piles can produce surface potential signals whose intensity can well reflect the settlement and local failure characteristics of the pile foundation; The potential signals change in advance of load; and they fluctuate violently before local fracturing of squeezed branch piles. The unstable fluctuation of the potential signal can be regarded as a precursor to the fracturing of squeezed branch piles. The research results have positive theoretical significance and important application value for assessing the stability of both branch piles and their stress states on site and monitoring and forecasting the disaster of pile foundation instability.

Investigating the variability in pressure-volume relationships during hemorrhage and aortic occlusion.

The pressure-volume (P-V) relationships of the left ventricle are the classical benchmark for studying cardiac mechanics and pumping function. Perturbations in the P-V relationship (or P-V loop) can be informative and guide the management of heart failure, hypovolemia, and aortic occlusion. Traditionally, P-V loop analyses have been limited to a single-beat P-V loop or an average of consecutive P-V loops (e.g., 10 cardiac cycles). While there are several algorithms to obtain single-beat estimations of the end-systolic and end-diastolic pressure-volume relations (i.e., ESPVR and EDPVR, respectively), there remains a need to better evaluate the variations in P-V relationships longitudinally over time. This is particularly important when studying acute and transient hemodynamic and cardiac events, such as active hemorrhage or aortic occlusion. In this study, we aim to investigate the variability in P-V relationships during hemorrhagic shock and aortic occlusion, by leveraging on a previously published porcine hemorrhage model. Briefly, swine were instrumented with a P-V catheter in the left ventricle of the heart and underwent a 25% total blood volume hemorrhage over 30 min, followed by either Zone 1 complete aortic occlusion (i.e., REBOA), Zone 1 endovascular variable aortic control (EVAC), or no occlusion as a control, for 45 min. Preload-independent metrics of cardiac performance were obtained at predetermined time points by performing inferior vena cava occlusion during a ventilatory pause. Continuous P-V loop data and other hemodynamic flow and pressure measurements were collected in real-time using a multi-channel data acquisition system. We developed a custom algorithm to quantify the time-dependent variance in both load-dependent and independent cardiac parameters from each P-V loop. As expected, all pigs displayed a significant decrease in the end-systolic pressures and volumes (i.e., ESP, ESV) after hemorrhage. The variability in response to hemorrhage was consistent across all three groups. However, upon introduction of REBOA, we observed significantly high levels of variability in both load-dependent and independent cardiac metrics such as ESP, ESV, and the slope of ESPVR (Ees). For instance, pigs receiving REBOA experienced a 342% increase in ESP from hemorrhage, while pigs receiving EVAC experienced only a 188% increase. The level of variability within the EVAC group was consistently less than that of the REBOA group, which suggests that the EVAC group may be more supportive of maintaining healthier cardiac performance than complete occlusion with REBOA. In conclusion, we successfully developed a novel algorithm to reliably quantify the single-beat and longitudinal P-V relations during hemorrhage and aortic occlusion. As expected, hemorrhage resulted in smaller P-V loops, reflective of decreased preload and afterload conditions; however, the cardiac output and heart rate were preserved. The use of REBOA and EVAC for 44 min resulted in the restoration of baseline afterload and preload conditions, but often REBOA exceeded baseline pressure conditions to an alarming level. The level of variability in response to REBOA was significant and could be potentially associated to cardiac injury. By quantifying each P-V loop, we were able to capture the variability in all P-V loops, including those that were irregular in shape and believe that this can help us identify critical time points associated with declining cardiac performance during hemorrhage and REBOA use.

Radio Frequency-Based Diagnostics for Superconducting Magnets

Operation of high-field superconducting magnets relies on diagnostic instrumentation for measuring strain, temperature, and magnetic field variations, detecting quenching, and identifying performance problems. Discrete sensor implementation is costly, requires multi-channel data acquisition systems, and sensor density is often insufficient to resolve problematic locations spatially. Distributed sensing is a viable alternative approach providing location-specific diagnostic information over single terminal output. In particular, it can be the key to quench protection of high-temperature superconductor (HTS)-based magnets where hot spots are known to form and persist before a quench. Fiber-optic technology offers distributed sensing solution for magnets but suffers from drawbacks such as fiber fragility, high costs of optical interrogators, and difficulty in differentiating between physical quantities such as temperature and strain. We propose an alternative, robust, and easily integrable way of implementing distributed sensing in magnets using radio-frequency (RF) technologies. RF Time Domain Reflectometry (TDR) technique has been around for nearly 60 years, and it is an essential diagnostic tool used in multiple areas of technology and applied research. We discuss operational principles and practical implementation of RF TDR sensors capable of detecting local variations of strain, temperature, and magnetic field through changes in RF impedance and wave propagation time. Results of cryogenic testing of our TDR sensors with HTS tape conductors are presented. The perspective of enabling a new diagnostics paradigm for high-energy physics and fusion energy applications based on distributed RF sensing is discussed.

Development of simultaneous multi-channel data acquisition system for large-area Compton camera (LACC)

The large-area Compton camera (LACC), featuring significantly high detection sensitivity, was developed for high-speed localization of gamma-ray sources. Due to the high gamma-ray interaction event rate induced by the high sensitivity, however, the multiplexer-based data acquisition system (DAQ) rapidly saturated, leading to deteriorated energy and imaging resolution at event rates higher than 4.7 × 103 s−1. In the present study, a new simultaneous multi-channel DAQ was developed to improve the energy and imaging resolution of the LACC even under high event rate conditions (104–106 s−1). The performance of the DAQ was evaluated with several point sources under different event rate conditions. The results indicated that the new DAQ offers significantly better performance than the existing DAQ over the entire energy and event rate ranges. Especially, the new DAQ showed high energy resolution under very high event rate conditions, i.e., 6.9% and 8.6% (for 662 keV) at 1.3 × 105 and 1.2 × 106 s−1, respectively. Furthermore, the new DAQ successfully acquired Compton images under those event rates, i.e., imaging resolutions of 13.8° and 19.3° at 8.7 × 104 and 106 s−1, which correspond to 1.8 and 73 μSv/hr or about 18 and 730 times the background level, respectively.

Smart Insoles for Gait Analysis Based on Meshless Conductive Rubber Sensors and Neural Networks

In recent years, advances in wearable medical devices have been driven by the development of flexible sensors and wireless IoT technologies, with research and applications in this field rapidly increasing. Plantar pressure analysis is a gait method that can be used to analyze foot pressure during human movement to identify potential health problems, but most of the devices based on this method are currently single-functional, poorly portable, and expensive. This work proposes a smart insole using meshless conductive rubber sensors, as well as a multi-channel data acquisition and transmission system based on these sensors, deep learning networks, and Bluetooth technology. The smart insole sensing unit and circuit board were made of flexible materials and powered by rechargeable lithium batteries. The system can be used to collect plantar pressure data from the sensors in real time, analyze the pressure in different regions of the foot through a neural network, and infer the gait cycle and abnormal gait of the user. The effective communication range can be up to 70 meters under barrier-free conditions. This smart insole has a bright future thanks to the pressure detection range, fast response time, and good durability.

Research and Perspectives on Fire-Fighting Systems in Tunnels under Strong Piston Wind Action

Guided by the technical requirements for tunnel fire safety, an overview of tunnel piston wind, combustion models, and full-size and small tunnel fire tests is presented. Firstly, the theoretical model and numerical calculation methods for piston wind tunnel fires are presented from the perspective of numerical simulation. Then, full-scale and small-scale test models for tunnel fires are presented, and the advantages and disadvantages of single-row, multi-row, single-fire source, and multi-fire source test methods are described. Finally, key breakthrough directions for future numerical and experimental research on piston winds and tunnel fires are proposed, specifically the mastery of underground tunnel fire development prediction methods. This involves mastering the full-scene elemental fire testing technology for underground tunnel operation systems; developing multi-channel data acquisition technology for fire tests under the effect of multiple disturbances such as high temperature and high humidity; and mastering the smoke flow law during fires in complex tunnel projects.