Alarm Level Research Articles

Gamma Dose Rate Measurements in Northern Spain: Influence of Local Meteorological Scenarios on Radiological “False Alarms” in a Real-Time Radiological Monitoring Network

The present study characterizes gamma dose rate (GDR) concentrations in Bilbao, located in the northern Iberian Peninsula, utilizing a comprehensive 10-min interval database spanning from 2009 to 2018. This station belongs to the radiological environmental monitoring of the Basque Country network. The daily average GDR was found to be 0.07624 ± 0.00004 µSv/h, with the daily 95th percentile averaging 0.08026 ± 0.00007 µSv/h throughout the entire period. Our analysis specifically addresses the impact of precipitation on GDR, revealing a higher correlation coefficient for daily 95th percentile values compared to daily averages. Additionally, the influence of the Galerna (GL) event, a natural meteorological phenomenon in this region, on GDR was investigated, noting that it can develop both with and without precipitation. Understanding the interaction between GDR and this meteorological scenario is vital for the development of more reliable radiological monitoring strategies and for safeguarding public health. For this purpose, 40 GL events were analyzed. The present results indicate that GDR values frequently exceed alarm levels when a GL is formed. These GDR peaks should be considered natural radiological events, necessitating the classification of such GDR peaks as false alarms within the radiological monitoring network. To explain them in detail, 10-min time series of precipitation and radon outdoor concentrations were analyzed. The results demonstrate that the GL event with precipitation is a meteorological scenario that can be associated with false alarms. The present analysis provides a distinct contrast in radon behavior under the same meteorological event in case of being developed with precipitation or without precipitation. The findings from this analysis are crucial for avoiding and understanding false radiological alarms triggered in the monitoring network, thereby enhancing the accuracy of radiological data interpretation and improving public safety measures.

General Purpose Alarm Device: A programmable annunciator

The General Purpose Alarm Device (GPAD) shines lights and makes loud noises to draw the attention of a human being to a problem. It provides a programmable, 80 character display to provide textual information. As an inexpensive modular annunciator, it is intended to decrease the cost of any system that requires complex monitoring and rapid human intervention. Fundamentally, it is designed to act as a peripheral to a controlling computer or microcontroller. The controller may communicate over a USB (COM) connection or a 5V SPI connection via an RJ12 cable. The GPAD is intended to be as general purpose as possible, so that it can be used to provide alarm functionality for many engineering and scientific projects, hobby machines, instruments, and various situations. The original driving use case is to provide medical alarm capability to the PolyVent open-source mechanical medical ventilator. The GPAD supports 5 alarm levels above “silent” of increasing urgency in terms of light, rhythm, and frequency. It has a mute button. It is based on the Arduino Uno Atmega328 design and is potentially extensible through headers and shields like an Uno. The GPAD includes a printed wiring assembly, firmware for the GPAD peripheral, a simple documented API and a 3D printable enclosure. The repo includes instructions for using a second GPAD as a controller as an example for programming.

Children’s Hospital of Philadelphia score to predict Type 1 retinopathy of prematurity in a resource-limited country

Background: Retinopathy of prematurity (ROP) leads to permanent vision loss unless treated timely. National screening guidelines for ROP are sensitive but lack specificity. The Children’s Hospital of Philadelphia ROP (CHOP ROP) model is highly specific for detecting severe ROP. Aim: The aim of the study was to evaluate the diagnostic performance of the CHOP ROP model in an Indian setting. Setting: In this prospective observational study, preterm neonates admitted to the neonatal intensive care unit were included. Materials and Methods: The screening was done as per the conventional screening guidelines. The CHOP ROP algorithm was applied to all neonates undergoing ROP screening, and the risk score was calculated from 2nd week onward. Statistics: The sensitivity, specificity, and Pearson coefficient of the CHOP algorithm in detecting Type 1 ROP were calculated. Results: Keeping the alarm level at 0.014, 143 (87.2%) babies fall into the low-risk group and 21 (12.8%) in the high-risk group. The sensitivity and specificity of the CHOP algorithm in detecting Type 1 ROP were 33% and 88%, respectively. Keeping the alarm level at 0.001, 118 (72%) babies were categorized as high risk, including 8 Type 1 ROP. Conclusion: The CHOP ROP model is unsuitable as an adjunct to the conventional screening guidelines to reduce the screening burden. This may be because the original nomogram was developed based on the screening criteria of Western countries. In India, where larger and older preterm babies tend to develop ROP, the nomogram may need to be updated.

Voltage fault diagnosis and prognostic of lithium-ion batteries in electric scooters based on hybrid neural network and multiple thresholds

Voltage fault diagnosis and prognostics of lithium-ion batteries (LIBs) are critical in ensuring their safe and reliable operation, and timely voltage prediction can help detect the oncoming fault of LIBs effectively. To this end, this study develops an efficient voltage fault diagnosis and prognostic method for LIBs based on voltage prediction and multiple thresholds. Firstly, a hybrid neural network integrating convolutional neural network and gated recurrent unit is established to extract both temporal and spatial features from the current operation data and predict voltage. Then, a residual calculator is devised to generate the voltage residuals, and the maximum voltage residuals of the fault-free data during charge and discharge operations are leveraged to constitute the multiple thresholds of fault diagnosis and prognostics strategy. The robustness, reliability and feasibility of voltage prediction are validated under the conditions of different driving scenarios, different dynamic temperature and real-world operation data. Moreover, the validation results on the fault electric scooter reveals that the proposed method can reliably diagnose the battery over-voltage and under-voltage faults and their alarm levels. Additionally, the warning moment by this method is at least 630s earlier than the alarm time in the battery management system, highlighting its efficient prognostic functionality.

Feasibility of Non-contact Noise Cancellation for Medical Device Alarms in Intensive Care Unit: An In Vitro Experiment

Objective: Medical device alarms in intensive care units (ICUs) are essential but their constant noise contributes to sleep disturbance and delirium in patients, jeopardizing their health and recovery. Therefore, improving the sound environment for patients in the ICU is crucial. This fundamental experiment investigated the potential of non-contact noise cancellation to reduce the noise level from medical device alarms in patients’ ears. We tested whether non-contact noise cancellation using a loudspeaker reduced the noise level of medical device alarms. Methods: In Experiment 1, we measured and analyzed the electrocardiogram sounds (volume 2, default setting) from a bedside monitor and the alarm sounds (volume 2, default setting) from a syringe pump. Negative-phase sound waves were subsequently generated for each alarm type. In Experiment 2, we tested whether non-contact noise cancellation using a loudspeaker reduced the noise level of each alarm. Results: The electrocardiogram sound from the bedside monitor was highest around 1kHz, while, the alarm sound from the syringe pump peaked around 4kHz. Non-contact noise cancellation using the loudspeaker reduced the electrocardiogram sounds emitted from the bedside monitor by more than 15decibel (dB) and the alarm sounds from the syringe pump by more than 10dB. Conclusion: A 10dB change doubles or halves the perceived loudness, making the over 10dB reduction achieved for medical device alarms in this study highly significant. The results of this experimental study demonstrate the promising potential of noise-cancelling loudspeakers for improving patient comfort and well-being in ICUs.

Improving the Detectionand Warning Fire System on the Smart Campus Area using ANFIS

The adoption of IoT technology in universities has significantly improved campus security, emergency response, and control systems, particularly through IoT-based surveillance and fire detection systems. Traditional fire detection systems suffer from high false alarm rates, leading to unnecessary resource allocation. This paperproposes an intelligent fire detection system leveraging IoT and adaptive fuzzy systems to enhance accuracy and reduce false alarms.The system uses sensors to collect real-time data, which is analyzed by an Adaptive Neuro-Fuzzy Inference System (ANFIS) to determine alarm levels based on input severity. ANFIS combines fuzzy logic and neural networks, enabling learning and adaptation. Data analyzed by ANFIS is sent to ThingSpeak channels for real-time monitoring. When a fire is confirmed, alerts with fire location, timestamp, and severity are sent via SMS through GSM to the fire management system. The system utilizes cost-effective, small-sized sensors, ensuring repeatable solutions.After training, the ANFIS system achieved 99.59% accuracy, significantly reducing false alarms. It demonstrated faster detection times by reducing the fire detection rate by 28% and maintaining high detection accuracy with fewer sensors, training inputs, and epochs. Utilizing IoT and ANFIS technologies, the system integrates efficiency, speed, and reliability, protecting lives and property and highlighting the importance of safety andtechnology in serving humanity.

An analytical model of a pulse signal reflected from a small-sized object located in the covering layer

Formulation of the problem. Existing methods and devices implementing them do not provide the required probability of correct detection and a low level of false alarms when detecting and recognizing anti-personnel and anti-tank mines in the ground. Based on this, there is currently an objective need to solve the urgent scientific task of improving the scientific and methodological apparatus for detecting and recognizing small-sized objects located in the covering layer. Purpose. Improving the scientific and methodological apparatus for detecting and recognizing subsurface targets by refining the analytical model of a pulse signal reflected from a small-sized object located in the covering layer. Results. A refined analytical model of a pulse signal reflected from a small-sized object located in the covering layer is proposed. The results of mathematical modeling and assessment of the adequacy of the obtained model are presented. Practical significance. The results obtained can be used in the synthesis of algorithms for the search, detection and recognition of small objects located in the covering layer.

Analysis of the possibility of increasing the level of reliability of fire alarms systems through integration with video-based flame detection system

Classic Fire Alarm Systems (FAS) are characterized by a high level of false alarms in relation to the number of confirmed reports. To increase the level of reliability of operation of this type of system, it was proposed to integrate it with a Video-Based Flame Detection System (VBFDS). For this purpose, a video-based fire detection algorithm was designed. In addition, methods popular in the literature for increasing the reliability of such systems, such as color filtering, the Tracking Growth Object (TGO) factor, and the use of the Naive-Bayes (NB) classifier, were tested. The purpose of the article is to analyze these methods in comparison with the basic version of the algorithm, as well as the possibility of integrating Video-Based Flame Detection System (VBFDS) with classic FAS.

Inferring the confidence level of BGP-based distributed intrusion detection systems alarms

Inferring the confidence level of BGP-based distributed intrusion detection systems alarms

Applying the British Association of Oral and Maxillofacial Surgeons quality outcomes metrics to a regional Australian oncology and reconstructive service: benchmarking the data to audit clinical outcomes in emerging, regional, and small-volume centres

The traditional model of centralisation of care, whilst having many advantages, also requires adaptation and upscaling to meet the requirements of both regional areas and the increasing urban sprawl. However, to ensure comparable outcomes with current major centres, this transition, when required, must be delivered in a safe and effective manner. Our project, which utilised the British Association of Oral and Maxillofacial Surgeons (BAOMS) recently published outcome data from the Quality and Outcomes in Oral and Maxillofacial Surgery (QOMS) project to benchmark data prospectively collected from a small-volume, emerging centre in Northern Queensland, was the first of its kind in terms of validation studies. As expected, the small volume of our centre impacted the ability to derive powerful statistical models and comparators, an intrinsic limitation for small-volume centres whilst they are developing services. However, during this evolution project, the use of comparison metrics allowed for the detection of alert and alarm levels, which are invaluable to ensure patient safety and quality of outcome.Our paper demonstrated that, irrespective of size or volume, the utilisation of quality assurance metrics (national or international) provides for the safe and transparent upscaling of head and neck services in emerging, regional, and small-volume centres.

DeepFogAQ: A fog-assisted decentralized air quality prediction and event detection system

Air pollution is a pervasive environmental and public health concern, prompting the imperative development of predictive systems to facilitate proactive interventions. The complexity of predicting air pollution arises from intricate factors, including the accumulation of pollutants, traffic dynamics, and industrial emissions. Traditional methodologies, reliant on historical or real-time data analysis, often encounter limitations in providing comprehensive and accurate solutions to this multifaceted problem. To address the existing problem, we propose a novel fog-assisted decentralized air quality prediction and event detection system (DeepFogAQ) for managing air pollution of future cities. We integrate Deep Learning (DL), Fog Computing (FC), Complex Event Processing (CEP), and virtualization technologies within the architecture of DeepFogAQ. Specifically, for predicting pollutant concentrations, we employ Transformers, CNN-LSTM, GRU, and RFR models. Additionally, we construct Fog and Cloud layers based on container-based virtualization technology. To demonstrate the feasibility of the system, the developed ML/DL models were run on DeepFogAQ and alarm levels for future air quality were derived. In this way, both the success of the prediction models and the validity of the architecture were ensured. Experimental results showed that Transformers is the most successful model in air quality prediction and event detection. As a result, the proposed DeepFogAQ architecture has the potential to offer a powerful alternative to decision-makers to solve the air pollution problem with its decentralized, scalable, and fault-tolerant structure.

Impulsivity profile analysis and it’s potential role in the differential diagnostics of adult Attention Deficit Hyperactivity Disorder and Borderline Personality Disorder

IntroductionImpulsivity is a complex construct, having at least three factors: 1) impulsivity as a personality trait, 2) ismpulsive action – waiting and stopping impulsivity and 3) choice impulsivity. Impulsive symptoms are present in Attention Deficit Hyperactivity Disorder (ADHD) and Borderline Personality Disorder (BPD) as well, eventhough impulsvity profile significantly differs.ObjectivesOur aim is to describe the impulsivity profile in adult ADHD (aADHD) and BPD in comparison with the control group, and describe a characteristic pattern, which associates with these disorders.Methods aADHD (n=100) and BPD Patients (n=63) were included, based on DSM-5 diagnostic criteria. Healthy control subjects (n=100) were screened using the Derogatis Symptom Checklist (SCL-90). Comorbid psychiatric disorders were assessed by structured clinical interviews and those who have both aADHD and BPD were excluded from the study. Participants were further investigated with online questionnaires: e.g. Barratt Impulsiveness Scale (BIS-11) Difficulties in Emotion Regulation Scale (DERS) and neuropsychological tests, like CANTAB Rapid Visual Processing, Stop Signal Task, and the Rogers’ decision-making test.Results Based ont the BIS-11 results, significantly higher attentional impulsivity was present in adult ADHD compared to BPD (p<.001) and healthy controls (p<.001). Emotional regulation difficulties, measured by DERS were significantly higher in BPD (p<.001) than aADHD, but the impulse control problems were more pronounced in the aADHD group, compared to BPD (p<.001). Using CANTAB neuropsychological test battery, strategy formulation difficulties (p=0.16) and stopping impulsivity (p<.001) were only present in aADHD compared to HC. BPD patients did not differ significantly from the control group in strategy formulation and in Stop Signal Reaction Time, a measure of stopping impulsivity. The significantly higher level of total false alarms, reflecting on waiting impulsivity were present both in aADHD and BPD.ConclusionsAccording to our results these two disorders have different impulsivity profile characteristics, which can be useful in differentiating these two disorders, and in buiding treatment plans. Stopping impulsivity, measured by SST was found in aADHD, but not in BPD. In BPD impulsive behavior is more likely attached to emotional dysregulation, a trait rooted in childhood traumatization.This study was supported by the National Research, Development and Innovation Office grant K 129195 and K 135437.Disclosure of InterestNone Declared

Network Traffic Monitoring and Real-time Risk Warning based on Static Baseline Algorithm

With the rapid growth of network traffic, in order to monitor network traffic, the author proposes a baseline based traffic inspection method. The main objective is to develop a global system for identifying malicious traffic, rather than a precise method for detecting the types of worms produced by malicious traffic. Although traffic is caused by the causes, network administrators can use this international search technique to detect malicious traffic data. The system based approach mainly includes designing time based on the traditional traffic model, detecting various equipments and network traffic process, and configuring the traffic flow according to each time frame. This method uses Cisco's NetFlow Collector, a NetFlow Collector (NFC), to collect raw NetFlow data transmitted by the device through UDP every 5 minutes. the Then, three-dimensional data such as communication port, communication time, and traffic flow (bytes or packets) is used to filter, remove the different values, calculate the base values, and compare the real-time results with the base values to check the traffic defects in the current network. If there are differences between the monitoring data and the system configuration at the same time, the system will issue an abnormal warning, and as time accumulates, the alarm level will gradually escalate.

METHOD OF MINIMIZATION SIDELOBES LEVEL AUTOCORRELATION FUNCTIONS OF SIGNALS WITH NON-LINEAR FREQUENCY MODULATION

Context. At present, when creating new and upgrading existing radar systems, solid-state generator devices are widely used, which imposes certain restrictions on the peak power of probing signals. To overcome this limitation, longer duration signals with internal pulse modulation are used. The main efforts of the researchers are focused on reducing the maximum level of the side lobes of the autocorrelation function of such signals, which, without taking additional measures, has a significant level, which complicates the work of systems for detecting and stabilizing the level of false alarms. Attention is paid to signals with non-linear frequency modulation, which consist of two and three linearly frequency-modulated fragments. The maximum level of the side lobes of such signals depends significantly on the frequency-time parameters of the fragments, and therefore it is very difficult to obtain its stable value. Searching for signals with minimal side lobe level values by optimizing their time-frequency parameters is a difficult task, because changing the parameters of previous signal fragments leads to changes in the parameters of subsequent fragments
 Objective. The aim of the work is to develop a method for simplifying the search for local minima of the level of side lobes of two- and three-fragment signals with nonlinear frequency modulation by using a modified mathematical model with a whole number of periods of radio oscillations of linear-frequency modulated fragments.
 Method. The developed method is based on the proposed modification of the mathematical model, which corrects the frequencytime parameters of two- and three-fragment signals with non-linear frequency modulation by modifying the values of the frequency modulation speed while providing an integer number of complete periods of radio frequency oscillations for each of the fragments, which simplifies the process of finding local minima of the level of side lobes.
 Results. Modification of the initial mathematical model leads to the expansion of the possible range of values of frequency-time parameters, ratios of durations and frequency deviations of linearly-frequency modulated fragments and ensures stability of the mathematical model with a decrease in the maximum level of side lobes of the autocorrelation function.
 Conclusions. It has been experimentally confirmed that the use of the proposed method of modifying the input frequency-time parameters of signals with non-linear frequency modulation in the vast majority of cases reduces the maximum level of side lobes and simpli-fies the process of finding its local minima. The optimal ratios of durations and deviations of the frequency of the signal frag-ments are determined, subject to these, stable operation of the models is ensured and, in most cases, - less than the value of the maximum level of the side lobes.

Organised loneliness and its discontents

AbstractThis review paper offers a critique of the discourse of loneliness both in the popular and academic imagination. It questions the stance and approach of much loneliness research and the headlines that have been extracted from it. These position loneliness as an epidemic, framing it as a global public health problem, its aetiology and management located in the individual. The paper draws attention to overlooked alternative framings of loneliness as well as to the risks of maintaining our current levels of alarm regarding it. Finally, the work of Hannah Arendt is turned to, as part of a wider academic reappreciation of her work on loneliness. The paper ends by suggesting what can be learned by loneliness researchers in the medical humanities from such political analyses.

A comparison of constant false alarm rate object detection algorithms for iceberg identification in L- and C-band SAR imagery of the Labrador Sea

Abstract. In this study, we pursue two objectives: first, we compare six different “constant false alarm rate” (CFAR) algorithms for iceberg detection in SAR images, and second, we investigate the effect of radar frequency by comparing the detection performance at C- and L-band. The SAR images were acquired over the Labrador Sea under melting conditions. In an overlapping optical Sentinel-2 image, 492 icebergs were identified in the area. They were used for an assessment of the algorithms' capabilities to accurately detect them in the SAR images and for the determination of the number of false alarms and missed detections. By testing the detectors at varying probability of false alarm (PFA) levels, the optimum PFA for each detector was found. Additionally, we considered the effect of iceberg sizes in relation to image resolution. The results showed that the overall highest accuracy was achieved by applying a log-normal CFAR detector to the L-band image (F score of 70.4 %), however, only for a narrow range of PFA values. Three of the tested detectors provided high F scores above 60 % over a wider range of PFA values both at L- and C-band. Low F scores were mainly caused by missed detections of small-sized (<60 m) and medium-sized (60–120 m) icebergs, with approximately 20 %–40 % of the medium icebergs and 85 %–90 % of small icebergs being missed by all detectors. The iDPolRAD detector, which is sensitive to volume scattering, is less suitable under melting conditions.

Do the pollution related to ozone pose a real health threat to the population in Upper Silesia?

Abstract Background Air pollution is a leading environmental factor, that influences human health. Although Ozone plays crucial role in the screening Earth's surface and lower atmosphere layers from the ultraviolet radiation, the troposphere ozone is proved to have negative effect on human body. Long-term exposure to a higher ozone concentration is related to a decrease in life expectancy. Objectives The objective of this study was the measurement-based assessment for determining whether the concentrations of ozone is within admissible limits or exceeded in Silesia province (5 million agglomeration) and therefore pose a threat to the local population together with the levels of PM2.5 and PM10 significantly exceeded at the same time. Material and methods The data provided by the Voivodship Inspectorate for Environmental Protection in Katowice for period 2015-2022 was used in the statistical analysis. The received data constitutes the result of 8-hour concentrations of ozone in selected air monitoring stations of Silesian province. The locations of 3 monitoring stations were found useful for the aim of this research; one site is situated in rural background area, another one is located in medium - sized city and the Katowice station is representative for urban background situation. Results The alarm level hasn't been exceeded in indicated measurements stations in Silesian Voivodship in period 2015-2022 (averaging time 1 hour: 240 µg/m3 four 3 hours). The target level has been exceeded in 2015-2017, in all three measurements stations and in the following years in one station (in Zloty Potok - 2018 and in Katowice -2019). Each year the largest number of exceedances occurred in August. Conclusions The results clearly indicate lack of hazards for general population health in terms of increased concentrations of ozone in the city centres and outside in Upper Silesia agglomeration and the situation even improved in last years. We identified the main sources of ozone. Key messages • The troposphere ozone is proved to have negative effect on human body, long-term exposure to a higher ozone concentration is related to a decrease in life expectancy. • The results clearly indicate lack of hazards for general population health in terms of increased concentrations of ozone in the city centres and outside in Upper Silesia agglomeration.

Deep Learning/Machine Learning Techniques for Vibration Condition Monitoring of Major Facilities in Automobile Assembly/Painting Plants

We have been expanding the vibration monitoring system to prevent malfunctions of rotating equipment in Hyundai/Kia Motors' global factories. In this paper, a secondary analysis model was explored using an existing legacy program containing vibration trend and spectrum data. In the existing case, it goes through the steps of setting the alarm level - raising the vibration - reaching the alarm - alarm - recognizing - analyzing the vibration - drawing the result. An automation program was applied to reduce the steps to vibration increase - derivation of abnormal equipment - result analysis. In addition, we will also cover essential system components for the operation of additional development programs.

Whole Watershed-based Estimation of FEWS Installation Site Using UAV Photogrammetry

For small rivers and reservoirs, a flood early warning system (FEWS) is generally used to prepare for sudden flood events whose impact is augmented from homonized environment and climate change. Currently, a FEWS requires the installation of a water gauge, typically on a bridge. This is a limitation of the FEWS owing to the possibility of existence of areas that are more vulnerable than the bridge. Therefore, to analyze the flood risk over the entire target basin, a procedure to install a FEWS was proposed. First, unmanned aerial vehicle (UAV)-based photogrammetry information is analyzed to preselect flood-prone areas. Second, further investigation including the ground surveying is followed for the preselected areas. Finally, the flood amount is estimated and the UAV photogrammetry information for issuing the alarm levels of the FEWS. For this purpose, Malgol Reservoir, a reservoir with high-risk level from floods, containing residential areas was selected as the current study area. Eleven flood-prone points were identified by analyzing the UAV photogrammetry information. Then, the flooding point with the lowest flood volume among the 11 points was selected as the most vulnerable point; this section was located at 464 m from the Malgol Reservoir spillway in the downstream direction. The warning alarm for the basin-wide FEWS could be issued according to the embankment of the most vulnerable section. This ensured the safety over the entire basin including the reservoir and its downstream area. The proposed procedure can overcome the limitations of the current bridge water level-based analysis by employing UAV-photogrammetry information of the entire basin and considering the entire flood-prone cross-sections.

Relationship between noise levels and intensive care patients' clinical complexity: An observational simulation study.

Noise pollution in intensive care units is a relevant problem, associated with psychological and physiological consequences for patients and healthcare staff. Sources of noise pollution include medical equipment, alarms, communication tools, staff activities, and conversations. To explore the cumulative effects of noise caused by an increasing number and type of medical devices in an intensive care setting on simulated patients with increasing clinical complexity. Secondly, to measure medical device alarms and nursing activities' sound levels, evaluating their role as potentially disruptive noises. Observational simulation study (reported according to the STROBE checklist). Using an electronic sound meter, the sound levels of an intensive care room in seven simulated clinical scenarios were measured on a single day (09 March 2022), each featuring increasing numbers of devices, hypothetically corresponding to augmented patients' clinical complexity. Secondly, noise levels of medical device alarms and specific nursing activities performed at a distance of three meters from the sound meter were analysed. The empty room's mean baseline noise level was 37.8 (±0.7) dBA; among the simulated scenarios, noise ranged between 45.3 (±1.0) and 53.5 (±1.5) dBA. Alarms ranged between 76.4 and 81.3 dBA, while nursing tasks (closing a drawer, opening a saline bag overwrap, or sterile packages) and speaking were all over 80 dBA. The noisiest activity was opening a sterile package (98 dBA). An increased number of medical devices, an expression of patients' higher clinical complexity, is not a significant cause of increased noise. Some specific nursing activities and conversations produce higher noise levels than medical devices and alarms. This study's findings suggest further research to assess the relationships between these factors and to encourageadequate noise reduction strategies. Excessive noise level in the intensive care unit is a clinical issue that negatively affects patients' and healthcare providers' well-being. The increase in baseline room noise from medical devices is generally limited. Typical nursing tasks and conversations produce higher noise levels than medical devices and alarms. These findings could be helpful to raise awareness among healthcare professionals to recognize noise sources. The noisiest components of the environment can be modified by staff behaviour, promoting noise reduction strategies and improving the critical care environment.