Sensor Locations Research Articles

Enhanced Bearing Fault Diagnosis Through Trees Ensemble Method and Feature Importance Analysis

Abstract Purpose This research introduces a groundbreaking method for bearing defect detection. It leverages ensemble machine learning (ML) models and conducts comprehensive feature importance analysis. The key innovation is the training and benchmarking of three tree ensemble models—Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—on an extensive experimental dataset (QU-DMBF) collected from bearing tests with seeded defects of varying sizes on the inner and outer raceways under different operating conditions. Method The dataset was meticulously prepared with categorical variable encoding and Min–Max data normalization to ensure consistent class distribution and model accuracy. Implementing the ML models involved a grid search method for hyperparameter tuning, focusing on reporting the models’ accuracy. The study also explores applying ensemble methods and using supervised and unsupervised learning algorithms for bearing fault detection. It underscores the value of feature importance analysis in understanding the contributions of specific inputs to the model’s performance. The research compares the ML models to traditional methods and discusses their potential for advanced fault diagnosis in bearing systems. Results and Conclusions The XGBoost model, trained on data from actual bearing tests, outperformed the others, achieving 92% accuracy in detecting bearing health and fault location. However, a deeper analysis of feature importance reveals that the models weigh certain experimental conditions differently—such as sensor location and motor speed. This research’s primary novelties and contributions are comparative evaluation, experimental validation, accuracy benchmarking, and interpretable feature importance analysis. This comprehensive methodology advances the bearing health monitoring field and has significant practical implications for condition-based maintenance, potentially leading to substantial cost savings and improved operational efficiency.

Identification of optimal accelerometer placement on trains for railway switch wear monitoring via multibody simulation

Accelerometers play a crucial role in the railway industry, especially in track monitoring. Traditionally, they are placed on the railway tracks or often on bridges to monitor the health and condition of the infrastructure. Recently, there has been an increased focus on using regular trains to monitor the condition of railway infrastructure. Often, the sensors are placed based on certain assumptions without much scientific evidence or support. This paper utilizes the multibody simulation software GENSYS to identify the optimal placement of accelerometers on a passenger train for monitoring railway switch wear. Switch wear profiles were generated systematically and used as input for the simulations, studying acceleration at a total of 93 locations distributed among the wheelsets, bogies, and carbody. Based on both time and frequency domain analyses, optimal sensor locations were identified, generally close to the first bogie or wheelset at the leading carbody. Accelerations generated by the wheelset passing the switch can also be captured in the carbody, but it is important to note that these are several orders lower in magnitude compared to the acceleration on the wheelset. If accelerometers are to be placed in the carbody, correct sensitivity must be chosen, and a high-pass filter should be applied to capture the acceleration signals associated with switch wear. The study confirms that there is a direct correlation between the depth of switch wear and the magnitude of the acceleration. It remains effective even under various curve radii and train speeds.

Design of an enhanced spatial resolution indoor plantar strain and gait analysis setup for biomedical applications

In the recent years, measurement of plantar strain and gait analysis has gained huge attention and plays a pivotal role in monitoring posture related ailments or Diabetic Foot Ulcer (DFU). Fibre Bragg Grating Arrays (closely spaced FBGs), another category of optical sensors, are employed in this study to understand the strain and gait of human foot. These arrays offer critical advantages such as enhanced sensitivity, data acquisition, multiplexing abilities, and sensor location effect compensation. For experimentation, five arrays were distributed among different regions (Upper, Medial, and Heel) of foot and their strain patterns for six volunteers (three male and three female) were recorded for eighty seconds. The data was analysed in two ways, Combined (data of array sensors were averaged to target the whole area) and Individual (for independent analysis of each sensor in array). Incoherent transitions and strain patterns of FBGs within the same arrays observed in individual analysis, explains enhanced resolution capabilities of the arrays. Strain mapping of sensor behaviour also confirmed the identification of various forms of gait i.e., Heel Strike, Flat Foot, Heel Off, and Zero Contact. The average standard deviation values for the arrays was reported below 0.16.

A Physics-Based Model-Data-Driven Method for Spindle Health Diagnosis—Part III: Model Training and Fault Detection

Abstract The primary goal of the paper is to monitor the health of the spindle in machine tools to ensure optimal performance and reduce costly downtimes. Spindle health monitoring is essential to detect wear and cracks in spindle bearings, which can be challenging due to their gradual development and hidden locations. The proposed approach combines physics-based modeling and data-driven techniques to monitor spindle health effectively. In Part I and Part II of the paper, mathematical models of bearing faults and spindle imbalance are integrated into the digital model of the spindle. This allows for simulating the operation of the spindle both with and without faults. The integration of fault models enables the generation of vibrations at sensor locations along the spindle shaft. The generated vibration data from the physics-based model are used to train a recurrent neural network-based (RNN) fault detection algorithm. The RNN learns from the labeled vibration spectra to identify different fault conditions. Bayesian optimization is used to automatically tune the hyperparameters governing the accuracy and efficiency of the learning models during the training process. The RNN classifiers are further fine-tuned using a small set of experimentally collected data for the generalization of the model on real-world data. Once the RNN classifier is trained, it can distinguish between different types of damage and identify their specific locations on the spindle assembly. The proposed algorithms achieved an accuracy of 98.43% on experimental data sets that were not used in training the network.

PSIII-3 Evaluation of remote detection of heart rate, respiratory rate, and body temperature for improved detection of bovine respiratory disease in high-risk receiving calves

Abstract The objective of this study was to evaluate the effectiveness of the VetGuardian remote monitor to create data-driven detection of bovine respiratory disease (BRD) by identifying vital sign abnormalities for early disease detection. Male calves (n = 20, initial body weight = 213 ± 3 kg) were purchased from regional auction markets and shipped to the University of Arkansas Stocker Receiving Unit. Upon arrival, calves were weighed, branded, received an identification tag, vaccinated, dewormed, and bulls were castrated. On d 1, the remote sensor was used to monitor all calves while restrained in the chute system detecting their body temperatures, and heart and respiratory rates. The remote sensor was placed in 1 of 2 locations for recordings: 1) in front of chute to capture the head, or 2) on the side of chute capturing the torso. Each calf was assigned to a scan location (10 calves/scan location). For all calves, rectal temperatures were also recorded manually, and heart and respiratory rates were determined by thoracic auscultation. Before processing and for the following 28 d, cattle were observed daily for clinical BRD. If presenting symptoms of BRD and if rectal temperature was ≥ 40°C, cattle were deemed morbid and treated with an antibiotic according to a standard preplanned protocol. For any cattle that were examined for BRD the remote sensor was again used for a minimum of 5 min to compare with manual recordings. Statistical analyses were performed using the CORR, MIXED, and MEANS procedures of SAS 9.4 with sex as a random effect and individual calf specified as the subject. There was a positive correlation between manual recordings of rectal temperatures and remote recordings from the head (0.67; P = 0.04) and the side (0.88; P = 0.0008). Remote recordings did not correlate (P ≥ 0.21) with manual recordings for heart rates. However, there was a positive correlation (0.79; P = 0.01) between manual respiratory rates and the side scans, but no relationship (P = 0.16) between manually collected respiratory rates with head scans. LSMeans for temperature data collected manually or by the remote sensor at either location did not differ (P ≥ 0.34). LSMeans for heart rate differed between manual and remote measurements from either location (P ≤ 0.0008). Manual measurements for respiratory rate did not differ (P = 0.26) from remote recordings from the side; however, differed (P < 0.0001) from recordings from the head. In conclusion, the VetGuardian remote sensor varied from manual recordings for heart rates, regardless of sensor location. Scan location had an influence on accuracy of respiratory rate recordings, and body temperature recordings from either location did not differ from manual measurements. Preliminary data should be used for further investigation of this remote sensor.

A Study on Scan Trajectory Based Effective Localization Method Using Mobile Anchor in Wireless Sensor Networks

A Wireless Sensor Network (WSN) is a collection of different nodes working together in a networked fashion in an ordered way. For many applications, sensor location in wireless sensor networks (WSN) is crucial. It is expensive to equip each sensor node with a GPS receiver. Numerous methods, including range-based and range-free methods, have been put forth in the past to determine the locations of sensor nodes that are randomly distributed. The majority of them make use of unique nodes known as anchor nodes, which are thought to be aware of their own positions. These anchor nodes supply the information that other sensors use to calculate their locations. Massive numbers of sensor nodes are used in wireless sensor networks (WSNs) to gather data about the immediate environment. However, this data is useless until the precise location of the data collection points is known. Sensor node localization in wireless sensor networks (WSNs) is important for a number of applications. The current research study develops a path planning technique to apply mobile anchors to the localization systems that are currently in use. The two mobile anchors supported by the effective trajectories, or effective SCAN, is the focus of the proposed research project. To localise the sensor node and transmit data, this method involves setting the mobile anchors to move in the opposite direction. The advantages of this dual movable anchor are demonstrated by the research study in terms of decreased localization time, localization error, and other factors.

Sensing spin wave excitations by spin defects in few-layer-thick hexagonal boron nitride.

Optically active spin defects in wide bandgap semiconductors serve as a local sensor of multiple degrees of freedom in a variety of "hard" and "soft" condensed matter systems. Taking advantage of the recent progress on quantum sensing using van der Waals (vdW) quantum materials, here we report direct measurements of spin waves excited in magnetic insulator Y3Fe5O12 (YIG) by boron vacancy [Formula: see text] spin defects contained in few-layer-thick hexagonal boron nitride nanoflakes. We show that the ferromagnetic resonance and parametric spin excitations can be effectively detected by [Formula: see text] spin defects under various experimental conditions through optically detected magnetic resonance measurements. The off-resonant dipole interaction between YIG magnons and [Formula: see text] spin defects is mediated by multi-magnon scattering processes, which may find relevant applications in a range of emerging quantum sensing, computing, and metrology technologies. Our results also highlight the opportunities offered by quantum spin defects in layered two-dimensional vdW materials for investigating local spin dynamic behaviors in magnetic solid-state matters.

Computational Tool for Aircraft Fuel System Analysis

Fuel level gauging in aircraft presents a significant flight mechanics challenge due to the influence of aircraft movements on measurements. Moreover, it constitutes a multidimensional problem where various sensors distributed within the tank must converge to yield a precise and single measurement, independent of the aircraft’s attitude. Furthermore, fuel distribution across multiple tanks of irregular geometries complicates the readings even further. These issues critically impact safety and economy, as gauging errors may compromise flight security and lead to carrying excess weight. In response to these challenges, this research introduces a multi-stage project in aircraft fuel gauging systems, as a continuum of studies, where this first article presents a computational tool designed to simulate aircraft fuel sensor data readings as a function of fuel level, fuel tank geometry, sensor location, and aircraft attitude. Developed in an open-source environment, the tool aims to support the statistical inference required for accurate modeling in which synthetic data generation becomes a crucial component. A discretization procedure accurately maps fuel tank geometries and their mass properties. The tool, then, intersects these geometries with fuel-level planes and calculates each new volume. It integrates descriptive geometry to intersect these fuel planes with representative capacitive level-sensing probes and computes the sensor readings for the simulated flight conditions. The method is validated against geometries with analytical solutions. This process yields detailed fuel measurement responses for each sensor inside the tank, and for different analyzed fuel levels, providing insights into the sensors’ signals’ non-linear behavior at each analyzed aircraft attitude. The non-linear behavior is also influenced by the sensor saturation readings at 0 when above the fuel level and at 1 when submerged. The synthetic fuel sensor readings lay the baseline for a better understanding on how to compute the true fuel level from multiple sensor readings, and ultimately optimizing the amount of used sensors and their placement. The tool’s design offers significant improvements in aircraft fuel gauging accuracy, directly impacting aerostructures and instrumentation, and it is a key aspect of flight safety, fuel management, and navigation in aerospace technology.

Effect of sensor number and location on accelerometry-based vertical ground reaction force estimation during walking.

Knee osteoarthritis is a major cause of global disability and is a major cost for the healthcare system. Lower extremity loading is a determinant of knee osteoarthritis onset and progression; however, technology that assists rehabilitative clinicians in optimizing key metrics of lower extremity loading is significantly limited. The peak vertical component of the ground reaction force (vGRF) in the first 50% of stance is highly associated with biological and patient-reported outcomes linked to knee osteoarthritis symptoms. Monitoring and maintaining typical vGRF profiles may support healthy gait biomechanics and joint tissue loading to prevent the onset and progression of knee osteoarthritis. Yet, the optimal number of sensors and sensor placements for predicting accurate vGRF from accelerometry remains unknown. Our goals were to: 1) determine how many sensors and what sensor locations yielded the most accurate vGRF loading peak estimates during walking; and 2) characterize how prescribing different loading conditions affected vGRF loading peak estimates. We asked 20 young adult participants to wear 5 accelerometers on their waist, shanks, and feet and walk on a force-instrumented treadmill during control and targeted biofeedback conditions prompting 5% underloading and overloading vGRFs. We trained and tested machine learning models to estimate vGRF from the various sensor accelerometer inputs and identified which combinations were most accurate. We found that a neural network using one accelerometer at the waist yielded the most accurate loading peak vGRF estimates during walking, with average errors of 4.4% body weight. The waist-only configuration was able to distinguish between control and overloading conditions prescribed using biofeedback, matching measured vGRF outcomes. Including foot or shank acceleration signals in the model reduced accuracy, particularly for the overloading condition. Our results suggest that a system designed to monitor changes in walking vGRF or to deploy targeted biofeedback may only need a single accelerometer located at the waist for healthy participants.

Focal Adhesion and Actin Orientation Regulated by Cellular Geometry Determine Stem Cell Differentiation via Mechanotransduction

Tuning cell adhesion geometry can affect cytoskeleton organization and the distribution of cytoskeleton forces, which play critical roles in controlling cell functions. To elucidate the geometrical relationship with cytoskeleton force distribution, it is necessary to control cell morphology. In this study, a series of dextral vortex micropatterns were prepared to precisely control cell morphology for investigating the influence of the curvature degree of adhesion curves on intracellular force distribution and stem cell differentiation at a sub-cellular level. Peripherial actin filaments of micropatterned cells were assembled along the adhesion curves and showed different orientations, filament thicknesses and densities. Focal adhesion and cytoskeleton force distribution were dependent on the curvature degree. Intracellular force distribution was also regulated by adhesion curves. The cytoskeleton and force distribution affected the osteogenic differentiation of mesenchymal stem cells through a YAP/TAZ-mediated mechanotransduction process. Thus, regulation of cell adhesion curvature, especially at cytoskeletal filament level, is critical for cell function manipulation. Statement of SignificanceIn this study, a series of dextral micro-vortexes were prepared and used for the culture of human mesenchymal stem cells (hMSCs) to precisely control adhesive curvatures (0°, 30°, 60°, and 90°). The single MSCs on the micropatterns had the same size and shape but showed distinct focal adhesion (FA) and cytoskeleton orientations. Cellular nanomechanics were observed to be correlated with the curvature degrees, subsequently influencing nuclear morphological features. As a consequence, the localization of the mechanotransduction sensor and activator-YAP/TAZ was affected, influencing osteogenic differentiation. The results revealed the pivotal role of adhesive curvatures in the manipulation of stem cell differentiation via the machanotransduction process, which has rarely been investigated.

Adaptive soft sensor modeling of chemical processes based on an improved just‐in‐time learning and random mapping partial least squares

AbstractThe just‐in‐time learning‐based partial least squares (JIT‐PLS) has been extensively applied to adaptive soft sensor modeling of complex nonlinear processes. However, it still has the problems of unreasonable relevant samples selection and unsatisfactory local modeling. Aiming at these problems, this paper proposes an improved just‐in‐time learning‐based random mapping partial least squares (IJIT‐RMPLS), including an improved relevant samples selection strategy and a random mapping PLS (RMPLS) model. On the one hand, considering the different correlation degrees between input variables and output variable, this method applies mutual information to evaluate the importance of each input variable and designs a variable‐weighted Euclidean distance to select relevant samples for local modeling. On the other hand, in order to prompt the prediction precision of local soft sensor models, this method combines the idea of nonlinear random mapping in extreme learning machines with PLS and builds a RMPLS with multiple activation functions. Applications on a numerical example and a real chemical process show that the proposed IJIT‐RMPLS has smaller prediction error compared with traditional JIT‐PLS.

A novel thermal turbulence reconstruction method using proper orthogonal decomposition and compressed sensing coupled based on improved particle swarm optimization for sensor arrangement

With the development of offshore wind turbine single power toward levels beyond 10 MW, the increase in heat loss of components in the nacelle leads to a high local temperature in the nacelle, which seriously affects the performance of the components. Accurate reconstruction and control of thermal turbulence in the nacelle can alleviate this problem. However, the physical environment of thermal turbulence in the nacelle is very complex. Due to the intermittent and fluctuating nature of turbulence, the turbulent thermal environment is highly nonlinear when coupled with the temperature field. This leads to large reconstruction errors in existing reconstruction methods. Therefore, we improve the sparse reconstruction method for compressed sensing (CS) based on the concept of virtual time using proper orthogonal decomposition (POD). The POD-CS method links the turbulent thermal environment reconstruction with matrix decomposition to ensure computational accuracy and computational efficiency. The improved particle swarm optimization (PSO) is used to optimize the sensor arrangement to ensure stability of the reconstruction and to save sensor resources. We apply this novel and improved PSO-POD-CS coupled reconstruction method to the thermal turbulence reconstruction in the nacelle. The effects of different basis vector dimensions and different sensor location arrangements (boundary and interior) on the reconstruction errors are also evaluated separately, and finally, the desired reconstruction accuracy is obtained. The method is of research value for the reconstruction of conjugate heat transfer problems with high turbulence intensity.

Development, Simulation and Visualization of Autonomous Farm Vehicles

Abstract: A Research in Autonomous Agricultural Vehicle Technology is presented. With the coming of the Smart Farming Revolution the development of more complicated machines has begun. Agricultural machines are made with the capability of driving themselves, using GPS maps and electronic sensors, an approach that will lead to the development of futuristic Precise Autonomous Farming Systems. The basic issues to be addressed are autonomous path planning, obstacle detection and avoidance for which various sensors will be employed such as GPS, laser-based sensors, ultrasonic sensors, IMU sensors and Camera sensors whose data has to be constantly monitored using Dead Reckoning algorithm and constant Pose Estimation. In these sensors, Camera, Ultrasonic, Laser-Based Sensors are used as Detection sensors while the IMU and GPS sensors are used as Localization sensors. Global Avoidance Subsystem which examines the whole environment of an autonomous vehicle mission-level path planner that pre-plans paths around all known obstacles. While Local Avoidance Subsystem/Reactive planner consists of an obstacle filter and an obstacle avoidance algorithm and re-plans a short way for the vehicle to navigate. With the ROS framework and all sensors data, a vehicle can guide itself through an unknown environment. This will help the farmer to undercut the labour shortage and improve the precision in farming which in turn improves the output to meet the growing demands

Early detection of bovine respiratory disease in pre-weaned dairy calves using sensor based feeding, movement, and social behavioural data

Previous research shows that feeding and activity behaviours in combination with machine learning algorithms has the potential to predict the onset of bovine respiratory disease (BRD). This study used 229 novel and previously researched feeding, movement, and social behavioural features with machine learning classification algorithms to predict BRD events in pre-weaned calves. Data for 172 group housed calves were collected using automatic milk feeding machines and ultrawideband location sensors. Health assessments were carried out twice weekly using a modified Wisconsin scoring system and calves were classified as sick if they had a Wisconsin score of five or above and/or a rectal temperature of 39.5 °C or higher. A gradient boosting machine classification algorithm produced moderate to high performance: accuracy (0.773), precision (0.776), sensitivity (0.625), specificity (0.872), and F1-score (0.689). The most important 30 features were 40% feeding, 50% movement, and 10% social behavioural features. Movement behaviours, specifically the distance walked per day, were most important for model prediction, whereas feeding and social features aided in the model’s prediction minimally. These results highlighting the predictive potential in this area but the need for further improvement before behavioural changes can be used to reliably predict the onset of BRD in pre-weaned calves.

A Performance Analysis of Security Protocols for Distributed Measurement Systems Based on Internet of Things with Constrained Hardware and Open Source Infrastructures.

The widespread adoption of Internet of Things (IoT) devices in home, industrial, and business environments has made available the deployment of innovative distributed measurement systems (DMS). This paper takes into account constrained hardware and a security-oriented virtual local area network (VLAN) approach that utilizes local message queuing telemetry transport (MQTT) brokers, transport layer security (TLS) tunnels for local sensor data, and secure socket layer (SSL) tunnels to transmit TLS-encrypted data to a cloud-based central broker. On the other hand, the recent literature has shown a correlated exponential increase in cyber attacks, mainly devoted to destroying critical infrastructure and creating hazards or retrieving sensitive data about individuals, industrial or business companies, and many other entities. Much progress has been made to develop security protocols and guarantee quality of service (QoS), but they are prone to reducing the network throughput. From a measurement science perspective, lower throughput can lead to a reduced frequency with which the phenomena can be observed, generating, again, misevaluation. This paper does not give a new approach to protect measurement data but tests the network performance of the typically used ones that can run on constrained hardware. This is a more general scenario typical for IoT-based DMS. The proposal takes into account a security-oriented VLAN approach for hardware-constrained solutions. Since it is a worst-case scenario, this permits the generalization of the achieved results. In particular, in the paper, all OpenSSL cipher suites are considered for compatibility with the Mosquitto server. The most used key metrics are evaluated for each cipher suite and QoS level, such as the total ratio, total runtime, average runtime, message time, average bandwidth, and total bandwidth. Numerical and experimental results confirm the proposal's effectiveness in foreseeing the minimum network throughput concerning the selected QoS and security. Operating systems yield diverse performance metric values based on various configurations. The primary objective is identifying algorithms to ensure suitable data transmission and encryption ratios. Another aim is to explore algorithms that ensure wider compatibility with existing infrastructures supporting MQTT technology, facilitating secure connections for geographically dispersed DMS IoT networks, particularly in challenging environments like suburban or rural areas. Additionally, leveraging open firmware on constrained devices compatible with various MQTT protocols enables the customization of the software components, a crucial necessity for DMS.

Event-Triggered confidentiality fusion estimation against eavesdroppers in cyber-physical systems

System state plays an important role in cyber-physical systems (CPSs). Ensuring the security of the CPSs is a key issue that can be widely applied. The confidentiality of system state is a fundamental feature of the CPSs security. This paper studies the distributed fusion estimation problem in the presence of eavesdropper, where local sensors send their estimates to a remote fusion center (FC). To prevent eavesdropping, the event triggered scheduling strategy was adopted on each sensor. Some sufficient conditions on the triggers’ threshold were derived to make the eavesdropping expected covariance unbounded while the expected error covariance for the user remains bounded. Moreover, the distributed confidentiality fusion estimation algorithm is provided to achieve perfect expected secrecy. Finally, simulations of different trigger levels for two local systems are employed to show the effectiveness of the proposed methods.

Potential for juvenile freshwater mussels to settle onto riverbeds from field investigation

Freshwater mussel populations have been declining at an alarming rate around the world. Herein, we investigate whether changing flow conditions, as they affect juvenile freshwater mussel settling, could be a potential causative factor for this decline in the Dan River, North Carolina, USA. We deployed two uplooking velocity sensors on the riverbed between May and November 2019: one where mussels reside and another where they do not. From this data, we calculated shear velocity, which is a measure of the turbulence that acts to lift particles into suspension and acts against particle settling. We determined that a shear velocity less than 0.66 cm/s would be required to settle relatively large and dense juvenile mussels onto the riverbed; however, the lowest shear velocity we measured was 0.9 cm/s. Additionally, we estimated that juvenile freshwater mussels as large as 280-510 µm could always be suspended and not be able to settle onto the riverbed at these two locations. Therefore, the flow during May-November 2019 was high enough to prohibit recruitment of juvenile freshwater mussels at the sensor locations. Furthermore, we have identified that the magnitude of the lowest flows has increased since 2000, which may be exacerbating the decline in freshwater mussels.

A detailed review of wireless sensor network, jammer, the types, location, detection and countermeasures of jammers

A detailed review of wireless sensor network, jammer, the types, location, detection and countermeasures of jammers

A long-term global and clustering analysis of the COVID-19 pandemic effects on the sound environment of Milan, Italy: Road traffic and other noise sources

The different measures taken by the competent authorities to tackle the effects of the COVID-19 pandemic affected the acoustic environment worldwide, resulting in significant reductions of noise levels. However, most of the investigations mainly focused on the lockdown during spring 2020, in which all non-essential industrial and commercial activities were prohibited, being people requested to stay at home. The analysis of the data collected and processed by the wireless acoustic sensor network deployed in Milan, Italy, by the DYNAMAP project showed a significant drop of sound levels, mainly due to road traffic as well as other noise sources, denoted as anomalous noise events (ANEs). This work extends that research through a long-term global analysis of the COVID-19 pandemic effects from January 2020 to August 2021, including data clustering to identify local patterns of the sound environment of Milan. As a result of the first hard and subsequent second soft lockdown imposed by the Italian authorities, a double V-shape reduction pattern of the mean sound levels Lden (with and without ANEs) is observed, with maximum drops of around 6 dB and 3 dB, respectively, surrounded with similar progressive reductions in both pre- and post-lockdown periods, showing statistically significant differences. A complementary pattern is obtained regarding the detection of ANEs. The clustering of Lden indicators yields three different sound patterns in the second lockdown, both in terms of noise levels and ANEs, depending on sensor locations and traffic flow variations, unlike the first lockdown with a homogeneous behaviour throughout the city.

Fire detection and suppression in rooms with different geometries

The paper presents experimental research findings on the integral characteristics of physicochemical processes in the area around a fire at different stages of its development and suppression. The purpose of this study was to determine the characteristics of the operation of fire detectors in rooms of various geometries. The temperatures in different cross sections, as well as the composition of gaseo us products of thermal decomposition and flame combustion of materials were investigated. The study dealt with class A fires involving wood. Mock-ups of rooms with distinctly different geometries were used: a parallelepiped, a rectangular truncated pyramid and a bow-roof (arched) structure. Efficient arrangement of technical equipment (light and gas analysis sensors, heat and smoke detectors, nozzles) was determined to provide early detection of a fire, prompt initiation of suppression and completion of combustion and smoldering. Minimum volumes of a firefighting liquid (water in this case) and spraying times were identified that are necessary and sufficient to suppress combustion. Guidelines were proposed on the arrangement of technical equipment for early fire detection and efficient containment of fire in the shortest time. A mathematical apparatus has been developed that allows predicting the performance of fire detectors and sensors. The research results can be used to evaluate the most effective locations for fire sensors in the design of buildings and structures.